Multi-Omic Signatures of Sarcoidosis and Progression in Bronchoalveolar Lavage Cells.

Introduction: Sarcoidosis is a heterogeneous, granulomatous disease that can prove difficult to diagnose, with no accurate biomarkers of disease progression. Therefore, we profiled and integrated the DNA methylome, mRNAs, and microRNAs to identify molecular changes associated with sarcoidosis and disease progression that might illuminate underlying mechanisms of disease and potential genomic biomarkers. Methods: Bronchoalveolar lavage cells from 64 sarcoidosis subjects and 16 healthy controls were used. DNA methylation was profiled on Illumina HumanMethylationEPIC arrays, mRNA by RNA-sequencing, and miRNAs by small RNA-sequencing. Linear models were fit to test for effect of diagnosis and phenotype, adjusting for age, sex, and smoking. We built a supervised multi-omics model using a subset of features from each dataset. Results: We identified 46,812 CpGs, 1,842 mRNAs, and 5 miRNAs associated with sarcoidosis versus controls and 1 mRNA, SEPP1 - a protein that supplies selenium to cells, associated with disease progression. Our integrated model emphasized the prominence of the PI3K/AKT1 pathway in sarcoidosis, which is important in T cell and mTOR function. Novel immune related genes and miRNAs including LYST, RGS14, SLFN12L, and hsa-miR-199b-5p, distinguished sarcoidosis from controls. Our integrated model also demonstrated differential expression/methylation of IL20RB, ABCC11, SFSWAP, AGBL4, miR-146a-3p, and miR-378b between non-progressive and progressive sarcoidosis. Conclusions: Leveraging the DNA methylome, transcriptome, and miRNA-sequencing in sarcoidosis BAL cells, we detected widespread molecular changes associated with disease, many which are involved in immune response. These molecules may serve as diagnostic/prognostic biomarkers and/or drug targets, although future testing will be required for confirmation.

Clinical phenotyping in sarcoidosis using cluster analysis.

BACKGROUND: Most phenotyping paradigms in sarcoidosis are based on expert opinion; however, no paradigm has been widely adopted because of the subjectivity in classification. We hypothesized that cluster analysis could be performed on common clinical variables to define more objective sarcoidosis phenotypes. METHODS: We performed a retrospective cohort study of 554 sarcoidosis cases to identify distinct phenotypes of sarcoidosis based on 29 clinical features. Model-based clustering was performed using the VarSelLCM R package and the Integrated Completed Likelihood (ICL) criteria were used to estimate number of clusters. To identify features associated with cluster membership, features were ranked based on variable importance scores from the VarSelLCM model, and additional univariate tests (Fisher's exact test and one-way ANOVA) were performed using q-values correcting for multiple testing. The Wasfi severity score was also compared between clusters. RESULTS: Cluster analysis resulted in 6 sarcoidosis phenotypes. Salient characteristics for each cluster are as follows: Phenotype (1) supranormal lung function and majority Scadding stage 2/3; phenotype (2) supranormal lung function and majority Scadding stage 0/1; phenotype (3) normal lung function and split Scadding stages between 0/1 and 2/3; phenotype (4) obstructive lung function and majority Scadding stage 2/3; phenotype (5) restrictive lung function and majority Scadding stage 2/3; phenotype (6) mixed obstructive and restrictive lung function and mostly Scadding stage 4. Although there were differences in the percentages, all Scadding stages were encompassed by all of the phenotypes, except for phenotype 1, in which none were Scadding stage 4. Clusters 4, 5, 6 were significantly more likely to have ever been on immunosuppressive treatment and had higher Wasfi disease severity scores. CONCLUSIONS: Cluster analysis produced 6 sarcoidosis phenotypes that demonstrated less severe and severe phenotypes. Phenotypes 1, 2, 3 have less lung function abnormalities, a lower percentage on immunosuppressive treatment and lower Wasfi severity scores. Phenotypes 4, 5, 6 were characterized by lung function abnormalities, more parenchymal abnormalities, an increased percentage on immunosuppressive treatment and higher Wasfi severity scores. These data support using cluster analysis as an objective and clinically useful way to phenotype sarcoidosis subjects and to empower clinicians to identify those with more severe disease versus those who have less severe disease, independent of Scadding stage.

Genomic biomarkers in chronic beryllium disease and sarcoidosis.

Background Previous gene expression studies have identified genes IFNγ, TNFα, RNase 3, CXCL9, and CD55 as potential biomarkers for sarcoidosis and/or chronic beryllium disease (CBD). We hypothesized that differential expression of these genes could function as diagnostic biomarkers for sarcoidosis and CBD, and prognostic biomarkers for sarcoidosis. Study Design/Methods We performed RT-qPCR on whole blood samples from CBD (n = 132), beryllium sensitized (BeS) (n = 109), and sarcoidosis (n = 99) cases and non-diseased controls (n = 97) to determine differential expression of target genes. We then performed logistic regression modeling and generated ROC curves to determine which genes could most accurately differentiate: 1) CBD versus sarcoidosis 2) CBD versus BeS 3) sarcoidosis versus controls 4) non-progressive versus progressive sarcoidosis. Results CD55 and TNFα were significantly upregulated, while CXCL9 was significantly downregulated in CBD compared to sarcoidosis (p < 0.05). The ROC curve from the logistic regression model demonstrated high discriminatory ability of the combination of CD55, TNFα, and CXCL9 to distinguish between CBD and sarcoidosis with an AUC of 0.98. CD55 and TNFα were significantly downregulated in sarcoidosis compared to controls (p < 0.05). The ROC curve from the model showed a reasonable discriminatory ability of CD55 and TNFα to distinguish between sarcoidosis and controls with an AUC of 0.86. There was no combination of genes that could accurately differentiate between CBD and BeS or sarcoidosis phenotypes. Interpretation CD55, TNFα and CXCL9 expression levels can accurately differentiate between CBD and sarcoidosis, while CD55 and TNFα expression levels can accurately differentiate sarcoidosis and controls.