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. 2024 Jul 30;25(1):289.
doi: 10.1186/s12931-024-02919-7.

Multi-omic signatures of sarcoidosis and progression in bronchoalveolar lavage cells

Iain R Konigsberg #  1 , Nancy W Lin #  2   3   4 , Shu-Yi Liao  5   6   7 , Cuining Liu  8 , Kristyn MacPhail  5 , Margaret M Mroz  5 , Elizabeth Davidson  9 , Clara I Restrepo  5 , Sunita Sharma  6 , Li Li  5   6 , Lisa A Maier #  5   6   7 , Ivana V Yang #  9   6
Affiliations

Multi-omic signatures of sarcoidosis and progression in bronchoalveolar lavage cells

Iain R Konigsberg et al. Respir Res. .

Abstract

Background: Sarcoidosis is a heterogeneous granulomatous disease 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 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 sarcoidosis diagnosis and progression phenotype, adjusting for age, sex, smoking, and principal components of the data. We built a supervised multi-omics model using a subset of features from each dataset.

Results: We identified 1,459 CpGs, 64 mRNAs, and five miRNAs associated with sarcoidosis versus controls and four mRNAs associated with disease progression. Our integrated model emphasized the prominence of the PI3K/AKT1 pathway, 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 is required for confirmation.

Keywords: DNA methylation; Epigenetics; Gene expression; Multi-omics; Sarcoidosis; microRNA.

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Conflict of interest statement

L.A.M. has received grant support from the Foundation for Sarcoidosis and Mallinckrodt Pharmaceuticals and serves on the advisory board for the Foundation for Sarcoidosis and the sarcoidosis advisory board for Boehringer Ingelheim. I.V.Y. has received financial support to attend the 2023 World Association for Sarcoidosis and Other Granulomatous Disorders conference and consulting fees from ElevenP15 unrelated to this work. The remaining authors do not have conflicts of interest relating to this work to disclose.

Figures

Fig. 1
Fig. 1
Differentially expressed genes in sarcoidosis. A) Differentially expressed genes in sarcoidosis vs. controls. Significant genes at an FDR-adjusted p-value threshold of 5% are colored in blue. Unadjusted p-values are plotted on the y-axis. B) Differentially expressed genes in progressive vs. non-progressive sarcoidosis. Significant genes at an FDR-adjusted p-value threshold of 5% are colored in blue. Unadjusted p-values are plotted on the y-axis. C) Pathway enrichment of mRNAs. The gray dashed line indicates an FDR-adjusted p-value threshold of 5%. GO: BP: Gene Ontology Biological Process, GO: CC: Gene Ontology Cellular Component
Fig. 2
Fig. 2
Differentially methylated sites in sarcoidosis. A) Differentially methylated sites in sarcoidosis vs. controls. Significant sites at an FDR-adjusted p-value threshold of 5% are colored in blue. Unadjusted p-values are plotted on the y-axis. Sites are labeled by associated gene. B) Boxplots of ANXA2 cg11681321 methylation and expression by sample group. Omics values were residualized by age, sex, and three principal components from the relevant data type. C) Pathway enrichment of hypomethylated DNAm sites. The gray dashed line indicates an FDR-adjusted p-value threshold of 5%. GO: BP: Gene Ontology Biological Process, GO: MF: Gene Ontology Molecular Function
Fig. 3
Fig. 3
Differentially expressed microRNAs in sarcoidosis. A) Differentially expressed miRNAs in sarcoidosis vs. controls. Significant genes at an FDR-adjusted p-value threshold of 5% are colored in blue. Unadjusted p-values are plotted on the y-axis. B) Distribution of significant miRNAs’ expression in cases and controls. C) Sankey plot connecting DE miRNAs to target genes targeted by > 1 DE miRNA. Connection width represents number of sources confirming relationship
Fig. 4
Fig. 4
A sparse multi-omic model of sarcoidosis and progression. A) Projection of samples based on selected feature weights. Points represent samples, colored by disease group, and are plotted for each data type on the top latent variables from the DIABLO multi-omic model. B) Feature importance of selected features for latent variable (1) Bars are colored by strongest sample group loading. C) Feature importance of selected features for latent variable (2) Bars are colored by strongest sample group loading. D) Network constructed from feature correlations on latent variable (1) Features are colored by dataset. Edges are colored by feature correlation, with green representing positive correlation. Features in the network share at least 90% correlation. E) Network constructed from feature correlations on latent variable (2) Features are colored by dataset. Edges are colored by feature correlation, with green representing positive correlation. Features in the network share at least 50% correlation
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