Clinical information of the study subjects is described in our previous analysis[9]. All study subjects are female, with no significant differences in age or subject characteristics (such as BMI, fasting glucose or insulin levels, and inflammatory markers such as C3, C4, CRP and ESR) between groups. Whole blood transcriptomes from the cohort of 11 SLE patients (4 with CMD and 7 without CMD) and 10 age matched healthy controls (HC) were analyzed by RNA-sequencing (RNA-seq).
Principal Component Analysis (PCA) unveiled distinctive expression profiles between HC and SLE (Figure 1A). After data normalization using DEseq2, preprocessing and filtering with the criteria of padj <0.05, We identified 143 differentially expressed (DE) genes when comparing the SLE group and HC group. The DE gene analysis delineated a discernible molecular signature between SLE and HC, as displayed in Figure 1B. Upon further filtering with a |log2FC|>0.5 threshold, we identified 52 upregulated genes and 50 downregulated genes. Among the top 10 upregulated genes were IFI27, IFI44L, RSAD2, IFI44, SIGLEC1, IFIT1, SLC12A1, RPL23P3, CTXN2, ISG15, while the top 10 downregulated genes were RN7SKP227, RN7SL1, NTN4, RN7SL653P, SLC1A7, SGCD, S1PR5, KIR2DL3, LIM2, MMP23B. To gain insights into the functionality of those genes, we conducted a comprehensive Gene Ontology (GO) analysis. As expected, GO analysis results revealed SLE is significantly associated with gene enrichment of functions related to defense response to virus, type I interferon signaling pathway, and response to virus in upregulated DE genes (Figure 1C)[12-15].
Next, to understand if there were differences in gene signatures between patients with or without CMD, we conducted a DE analysis comparing SLE-CMD to SLE-non-CMD. Due to the considerable variability among the samples and coupled with the fact that the dataset size is relatively small, a direct comparison between SLE groups revealed only 14 DE genes at padj < 0.1 (table 1). To comprehensively understand if there were differences within whole blood transcriptomes between SLE-CMD, SLE-non-CMD, and HC groups, we employed the HC as reference and conducted a comparative analysis of commonly up regulated and down regulated genes between SLE-CMD and SLE-non-CMD. This generated datasets that comprised genes commonly upregulated or downregulated between SLE-CMD and SLE-non-CMD versus healthy control and genes uniquely up or down regulated in both patient subgroups (as shown by the Venn diagrams in figure 2A, B). Our investigation unveiled 36 genes that were consistently upregulated and 20 genes that were downregulated across both SLE-CMD and SLE-non-CMD groups when compared to the HC group (overlapping area of figure 2A and B and table 2). Furthermore, we identified 176 genes displaying unique expression patterns between SLE-CMD and HC (left hand area of Venn diagrams in figure 2A and B), along with 144 unique DE genes between SLE-non-CMD and HC (right hand area of Venn diagrams in figure 2A and B). Analyzing the DE genes in common between SLE-CMD and SLE-non-CMD, Gene Ontology (GO) and pathway enrichment analyses indicated that these genes are clearly associated with antiviral immune responses and were primarily IFN stimulated genes (Figure 2C).
We next conducted Ontology (GO) and pathway enrichment analyses for genes unique to SLE-CMD and SLE-non-CMD patients. Sorting by P value, the top GO terms of the biological process (BP), cellular component (CC) and molecular function (MF) categories are shown in Figure 3A (SLE-CMD) and 3B (SLE-non-CMD). As GO terms for RNA sensing, double stranded (ds) RNA and single stranded (ss) RNA binding, were enriched in SLE-CMD patient samples, we further analyzed the expression of the leading edge genes in the top GO terms (Figure 3C). In contrast only downregulated genes in SLE-non-CMD patients were associated with any GO categories. For example, genes associated with blood coagulation, cell-cell junction, and cellular defense response were decreased in SLE-non-CMD blood samples (Figure 3D).
Figure 1 – DEG and pathway analysis of SLE vs HC whole blood transcriptomes.
(A) Principal component analysis (PCA) of whole blood transcriptomes from SLE group (n=11) and HC group (n=10); (B) Volcano plots of the gene expression comparison between SLE and HC. The horizontal axis represents the log2 (fold change) and the vertical axis represents the ‑log10 (P‑value). The red plots represent the selected DEGs with fold change >=2 and p<0.01; (C) GO terms enriched in SLE patients’ samples. Genes with p-values < 0.05 were selected as input and enriched terms with padj < 0.05 were selected.
Figure 2. Unique gene differentially expressed in SLE-CMD and SLE-non-CMD whole blood samples using HC as reference group.
(A, B) Venn diagram representing the unique or overlapping (A) upregulated or (B) downregulated genes in SLE-CMD and SLE-non-CMD when using HC as a reference group. SLE-CMD vs HC (a); SLE-non-CMD vs HC (b); DE genes were identified using DEseq2 v1.42.0 with padj < 0.1, log2FoldChange > 0.5 as up regulated gene cutoff, and log2FoldChang < 0.5 as down regulated gene cutoff. SLE-CMD (n=4), SLE-non-CMD (n=7), and HC (n=10); (C) Go pathway analysis for SLE-CMD and SLE-non-CMD common genes. 60 common genes were used for this test with default filters of pvalueCutoff = 0.05, qvalueCutoff = 0.2, minGSSize = 10, maxGSSize = 500.
Table 1 DEG between SLE-CMD and SLE-non-CMD
DE genes were identified using DEseq2 v1.42.0 with padj < 0.1. SLE-CMD (n=4) and SLE-non-CMD (n=7).
SYMBOL
|
ENTREZID
|
log2FoldChange
|
pvalue
|
Padj
|
DUS4L-BCAP29
|
1.15E+08
|
7.183865
|
2.33E-08
|
0.000334
|
ZNF727
|
442319
|
1.353635
|
3.65E-05
|
0.07473
|
PDPR
|
55066
|
0.924705
|
5.01E-07
|
0.003591
|
RGPD6
|
729540
|
0.86413
|
2.46E-05
|
0.058768
|
DZIP3
|
9666
|
0.671509
|
4.47E-05
|
0.080102
|
PYHIN1
|
149628
|
0.637926
|
2.08E-05
|
0.05418
|
GPR174
|
84636
|
0.557565
|
6.77E-05
|
0.097572
|
TIGIT
|
201633
|
0.533694
|
6.81E-05
|
0.097572
|
C1QTNF7
|
114905
|
-0.94902
|
6.53E-05
|
0.097572
|
PTCRA
|
171558
|
-0.98256
|
4.37E-05
|
0.080102
|
JUP
|
3728
|
-1.28328
|
7.27E-05
|
0.099248
|
PDE3A
|
5139
|
-1.4114
|
1.74E-05
|
0.04974
|
SEPTIN7P3
|
646913
|
-5.27252
|
1.39E-05
|
0.044204
|
TBC1D3
|
729873
|
-9.06604
|
1.03E-06
|
0.005896
|
Table 2 Common genes between SLE-CMD vs HC and SLE-non-CMD vs HC
DE genes were identified using DEseq2 v1.42.0 with padj < 0.1, log2FoldChange > 0.5 as up regulated gene cutoff, and log2FoldChang < 0.5 as down regulated gene cutoff. SLE-CMD (n=4), SLE-non-CMD (n=7), and HC (n=10).
Upregulated genes
|
Downregulated genes
|
SLC12A1
|
ZCCHC2
|
TBX21
|
SIGLEC1
|
MX1
|
C1orf21
|
OAS1
|
LY6E
|
RAB33A
|
OAS3
|
IFI27
|
COL6A2
|
OAS2
|
ANO5
|
CEP78
|
PRLR
|
FBXO39
|
GLB1L2
|
IFIH1
|
USP18
|
DLG5
|
IFIT3
|
IFIT1
|
PCDH1
|
IFI6
|
ISG15
|
CLDND2
|
XAF1
|
PLSCR1
|
SGCD
|
EPSTI1
|
SPATS2L
|
S1PR5
|
RSAD2
|
PGAP1
|
FCRL6
|
CMPK2
|
GRASLND
|
KLRC4
|
RTP4
|
CTXN2
|
TOGARAM2
|
DDX60
|
ZDHHC4P1
|
RN7SL653P
|
IFI44L
|
CCDC194
|
C19orf84
|
IFI44
|
HERC5
|
H4C12
|
HERC6
|
NKD1
|
|
Figure 3. Pathway analysis of DE genes unique to CMD or non-CMD SLE patients.
(A, B) Go pathway analysis for unique genes in (A) SLE-CMD and (B) SLE-non-CMD. Genes with p-values < 0.05 were selected as input and enriched terms with padj < 0.1 were selected. SLE-CMD (n=4), SLE-non-CMD (n=7), and HC (n=10); (C) Heatmap of SLE-CMD unique genes in the enriched GO terms. Green: up regulated; Red: down regulated; (D) Heatmap of unique genes in SLE-non-CMD in the enriched GO terms. Green: up regulated; Red: down regulated.
Analyzing a subset of genes relevant to RNA sensing, we observed that a panel of genes associated with IFN signaling and response to RNA and DNA sensing such as RIGI, DDX60, DHX58 and ZBP1 were significantly increased in SLE-CMD compared to HC and SLE-non-CMD (Figure 4A, B). In contrast, down regulated genes included the inhibitory receptor TIGIT, and markers of natural killer cells (NK) or invariant NK T cells (iNKT) KLRG1 and KLRC, suggesting that cells and pathways that might restrain cardiotoxic responses are decreased in CMD patient blood samples. Another interesting finding was the observation that IFNLR1, a component of the receptor for IFN lambda, was decreased in SLE-CMD compared to SLE-non-CMD and HC. Enzymes involved in ADP-ribosylation, a post-translational modification that regulates protein function, are also differentially upregulated in SLE-CMD patients compared to non-CMD. PARP9 and PARP14 were both increased and are part of a sub family of ADP-ribosylation enzymes that recognize mono-ADP-ribosylation (MAR) on proteins as opposed to poly-ADP-ribosylation (PAR). However, in patients with non-CMD, pathways relative to coagulation, platelet activity and cell adhesion are decreased specifically relative to SLE-CMD patients. These results suggest that RNA sensing pathways are associated with development of vascular changes associated with CMD in SLE, whereas reduced platelet activity and coagulation is associated with the reduced left ventricular function we observed in the non-CMD cohort in our previous cardiac MRI study [9]. This is the first study to identify gene signatures that may associate with SLE.
Figure 4 SLE-CMD unique gene signature related to RNA sensing and inflammation.
(A, B) Dot plot of enrichment of RNA sensing related genes in SLE-CMD(A) and Dot plot of enrichment of inflammation related genes in SLE-CMD (B) (Padj < 0.1, |log2FoldChange| > 0.5). SLE-CMD (n=4), SLE-non-CMD (n=7), and HC (n=10).