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Szymczak et al., Sci. Adv. 2021; 7 : eabd7600 6 January 2021
SCIENCE ADVANCES | RESEARCH ARTICLE
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DISEASES AND DISORDERS
Gene expression signatures of target tissues in type 1
diabetes, lupus erythematosus, multiple sclerosis,
and rheumatoid arthritis
F. Szymczak1,2*, M. L. Colli1*†, M. J. Mamula3, C. Evans-Molina4, D. L. Eizirik1,5†
Autoimmune diseases are typically studied with a focus on the immune system, and less attention is paid to
responses of target tissues exposed to the immune assault. We presently evaluated, based on available RNA
sequencing data, whether inflammation induces similar molecular signatures at the target tissues in type 1 diabetes,
systemic lupus erythematosus, multiple sclerosis, and rheumatoid arthritis. We identified confluent signatures,
many related to interferon signaling, indicating pathways that may be targeted for therapy, and observed a high
(>80%) expression of candidate genes for the different diseases at the target tissue level. These observations
suggest that future research on autoimmune diseases should focus on both the immune system and the target tissues,
and on their dialog. Discovering similar disease-specific signatures may allow the identification of key pathways
that could be targeted for therapy, including the repurposing of drugs already in clinical use for other diseases.
INTRODUCTION
The incidence of autoimmune diseases is increasing on a worldwide
basis, and the prevalence of some of the most severe autoimmune
diseases, i.e., type 1 diabetes (T1D), systemic lupus erythematosus
(SLE), multiple sclerosis (MS), and rheumatoid arthritis (RA), has
reached levels of prevalence ranging from 0.5 to 5% in different
regions of the world (1). There is no cure for these autoimmune
diseases, which are characterized by the activation of the immune
system against self-antigens. This is, in most cases, orchestrated by
autoreactive B and T cells that trigger and drive tissue destruction in
the context of local inflammation (2–5). While the immune targets
of T1D, SLE, MS, and RA are distinct, they share several similar
elements, including common variants that pattern disease risk, local
inflammation with contribution by innate immunity, and down-
stream mechanisms mediating target tissue damage. In addition,
disease courses are characterized by periods of aggressive auto-
immune assaults followed by periods of decreased inflammation
and partial recovery of the affected tissues (3,6–11). Endoplasmic
reticulum stress (12–15), reactive oxygen species (16–19), and
inflammatory cytokines, such as interleukin-1 (IL-1) and inter-
ferons (IFNs), are also shared mediators of tissue damage in these
pathologies (20–23).
Despite these common features, autoimmune disorders are tra-
ditionally studied independently and with a focus on the immune
system rather than on the target tissues. There is increasing evidence
that the target tissues of these diseases are not innocent bystanders
of the autoimmune attack but participate in a deleterious dialog
with the immune system that contributes to their own demise, as
shown by our group and others in the case of T1D [reviewed in
(3,24,25)]. Furthermore, in T1D, several of the risk genes for
the disease seem to act at the target tissue level—in this case, pan-
creatic cells—regulating the responses to “danger” signals, the dialog
with the immune system, and apoptosis (20,25,26). Against this
background, we hypothesize that key inflammation-induced mech-
anisms, potentially shared between T1D, SLE, MS, and RA, may
drive similar molecular signatures at the target tissue level. Dis-
covering these similar (or, in some cases, divergent) disease-specific
signatures may allow the identification of key pathways that could
be targeted for therapy, including the repurposing of drugs already
in clinical use for other diseases.
To test this hypothesis, we obtained RNA sequencing (RNA-seq)
datasets from pancreatic cells from controls or individuals affected
by T1D (27), from kidney cells from controls or individuals affects by
SLE (28), from optic chiasm from controls or individuals affected
by MS (29), and from joint tissue from controls or individuals
affected by RA (30). In some cases, we also compared these datasets
against our own datasets of cytokine-treated human cells (31).
These unique data were mined to identify similar and dissimilar
gene signatures and to search for drugs that may potentially revert
the observed signatures. Furthermore, we searched for the expres-
sion of candidate genes for the different autoimmune diseases at the
target tissue level and the signaling pathways downstream of these
candidate genes.
These studies indicate major common gene expression changes
at the target tissues of the four autoimmune disease evaluated, many
of them downstream of types I and II IFNs, and massive expression
of candidate genes (>80% in all cases). These findings support the
importance of studying the target tissue of autoimmune diseases, in
dialog with the immune system, to better understand the genetics
and natural history of these devastating diseases.
RESULTS
Metadata and global gene expression in the target tissues
of different autoimmune diseases
The metadata of the tissue donors evaluated in the present analysis
are shown in Table1. The number of samples is proportional to the
accessibility of the target tissues, with the highest number of sam-
ples available for joint tissue in RA. The age and sex of the patients
reflect the natural history of the different diseases, with younger
1ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles
(ULB), Brussels, Belgium. 2Interuniversity Institute of Bioinformatics in Brussels, Uni-
versité Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium. 3Section of
Rheumatology, Yale University School of Medicine, New Haven, CT, USA. 4Center
for Diabetes and Metabolic Diseases, Indiana University School of Medicine, India-
napolis, IN, USA. 5Indiana Biosciences Research Institute (IBRI), Indianapolis, IN, USA.
*These authors contributed equally to this work.
†Corresponding author. Email: mcolli@ulb.ac.be (M.L.C.); deizirik@ulb.ac.be (D.L.E.)
Copyright © 2021
The Authors, some
rights reserved;
exclusive licensee
American Association
for the Advancement
of Science. No claim to
original U.S. Government
Works. Distributed
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Commons Attribution
NonCommercial
License 4.0 (CC BY-NC).
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patients in the T1D group and a higher proportion of female patients
in the MS and SLE groups. Sex information was obtained from the
original metadata and, when not available, was inferred using chro-
mosomal marker information present in the transcriptome (see
Materials and Methods). Of note, while some of the samples used
for RNA-seq were obtained following fluorescence-activated cell
sorting (FACS) purification (e.g., pancreatic cells) (27), other samples
comprised a mixture of target cells and infiltrating immune cells.
Determination of the leukocyte marker CD45 expression in the dif-
ferent samples indicated a trend for higher presence of immune-
derived cells among samples obtained in T1D, MS, and RA, but not
in SLE (table S1). This contribution by immune cells was, however,
modest. For instance, while in the cell preparation the number of
transcripts per million (TPM) for CD45in the patient group was
16.4 (mean), the TPM values for the following cell markers were
as follows: INS (Insulin), 125.359; Sodium/potassium-transporting
ATPase gamma chain (FXYD2a), 65; GCK (Glucokinase), 20;
Homeobox protein Nkx-2.2 (NKX2-2), 28; Synaptotagmin 4 (SYT4),
36; Neurogenic Differenciation 1 (NEUROD1), 27; Homeobox pro-
tein Nkx-6.1 (NKX6-1), 27; and MAF BZIP Transcription Factor B
(MAFB), 23, indicating that the observed responses are driven, at
least in part, by the constitutive cells of the target tissues. Of note,
proinflammatory cytokines decrease the expression of several of
the cell markers (3,20,32) described above.
In the T1D and SLE datasets, but not in the MS and RA ones,
there was a trend for more up-regulated than down-regulated genes
in the target tissues, which was particularly marked in the T1D
dataset, with more than twofold higher number of up-regulated
genes as compared with the down-regulated ones (Fig.1A). Of note,
because of a statistically significant difference in the age of patients
with RA and their respective controls, we have included age as an
independent variable when determining the differentially expressed
genes in the joint tissue samples (see Materials and Methods).
Analysis of the gene patterns in target tissues
of autoimmune diseases indicates up-regulation of
IFN-related pathways
Enrichment analysis of these disease-modified genes (Fig.1,BtoE)
indicated similarities and differences between the different autoimmune
diseases. Thus, both T1D and SLE have several up-regulated IFN-
related pathways among the top up-regulated ones (Fig.1,BandC);
IFN pathways were also detected as enriched for MS and RA, but
not among the 20 top ones [e.g., MS: IFN- signaling normalized
enrichment score (NES) = 2.26 (P adj. < 0.007); RA, IFN- signaling
NES = 2.64 (P adj. < 0.004)]. This similar enrichment in IFN-related
genes can also explain the appearance of SLE as the top up-regulated
pathway in T1D (Fig.1B). Up-regulated pathways related to antigen
presentation or antigen-related activation of immune cells were present
for the four diseases (Fig.1,BtoE), in line with their autoimmune
nature, while complement cascades were preeminent in MS (Fig. 1D)
and RA (Fig.1E), but less so in T1D and SLE. To evaluate whether
these observed IFN-induced signatures originate, at least in part,
from nonimmune cells in the target tissues, we reanalyzed available
single-cell(sc)/nucleus(sn)–RNA-seq data focusing on nonimmune
cells in affected tissues in T1D [pancreatic cells (33)], SLE [kidney
epithelial cells (34)], MS [brain neurons (35)], and RA [synovial fi-
broblasts (36)] (fig. S1A), confirming that there is a significant IFN
signature in the target of the four autoimmune diseases as measured
by an IFN response score, defined as the average expression of known
IFN-stimulated genes (ISGs; see Materials and Methods) (34,37).
The down-regulated pathways tended to be more disease specific
and related to the dysfunction of the target organ. Thus, for T1D,
there was down-regulation of pathways involved in “integration of
energy metabolism,” a key step for insulin release, and in “regulation
of gene expression in cells,” which reflects the down-regulation of
several transcription factors (TFs) critical for the maintenance of
cell phenotype and function (e.g., PDX1 and MAFA) (38) (Fig.1B),
while in RA, there was a decrease in collagen chain trimerization, an
important step for proper collagen folding (Fig.1E) (39). Moreover,
down-regulation of pathways involved in lipid metabolism was en-
riched in MS samples (Fig.1D). Supporting that, disruption of lipid
metabolism in oligodendrocytes compromises the lipid-rich myelin
production/regeneration, a hallmark of MS, both in invitro studies
(40) and in samples obtained from individuals with MS (41).
Gene set enrichment analysis (GSEA) of the sc/sn–RNA-seq
data of nonimmune cells from the four autoimmune diseases (fig.
S1, B to E) confirmed several up-regulated pathways in common,
including IFN signaling (present for all diseases, although not always
among the top 20 shown), T1D (which appears in three of the four
diseases), allograft rejection, etc. As observed in the bulk RNA-seq
analysis, there were less similarities between diseases regarding the
down-regulated pathways.
We also analyzed the intersection between significantly up- and
down-regulated genes of the bulk RNA-seq of the four diseases using
another criterion, namely, considering genes as significantly modi-
fied if they presented a false discovery rate <0.10 without a fold change
Table 1. Summary of the metadata for the RNA-seq samples of the four autoimmune diseases. RNA-seq data from four studies of target tissues in
autoimmune diseases were retrieved from the Gene Expression Omnibus (GEO) portal (https://ncbi.nlm.nih.gov/geo/), reanalyzed, and quantified with Salmon
using GENCODE 31 as the reference. N/A, data nonavailable. For the sex column: M, male; F, female.
Disease Target tissue Samples (n) Age (mean ± SD) Sex (n)Source
Patients Controls Patients Controls Patients Controls
T1D Pancreatic
cells 4 12 20.3 ± 5.6 16.1 ± 5.8 3 M/1 F 8 M/4 F GSE121863 (27)
SLE Kidney cells 20 7 ~40 N/A 2 M/18 F* 7 F* GSE98422 (28)
MS Optic chiasm 5 5 56.2 57.6 5 F* 5 F* GSE100297 (29)
RA Joint tissue 57 28 55.9 ± 16.7 35.2 ± 16.2 33 F/24 M 14 F/14 M GSE89408 (30)
*Predicted using genes expressed in Y chromosome and XIST gene.
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threshold (fig. S2, A and B). This showed a higher similarity among
up- than down-regulated genes, but there were few genes in com-
mon between the four diseases. On the basis of a hypergeometric
test to search for gene set enrichment for the cases where there were
>50 genes in common between two and three diseases, we identified
IFN signaling, antigen processing, and presentation and cytokine
signaling, among others. It was, however, difficult to find common
pathways among the down-regulated genes. A limitation of this ap-
proach is that we can only analyze genes that pass a fixed statistical
cutoff, which makes the results very susceptible to the number of
samples studied, as presently observed for the higher intersection
between RA (a disease with a much higher number of samples) and
the other autoimmune diseases. This type of analysis must thus
be redone as more samples become available for the different
diseases.
To obtain more detailed information on the (dis)similarities
between the different autoimmune diseases, avoiding the pitfalls
mentioned above for threshold-based analysis, we performed the
rank-rank hypergeometric overlap (RRHO) analysis (Fig.2) (42),
a genome-wide approach that compares two equally ranked data-
sets using a threshold-free algorithm (see Materials and Methods).
The main similarities between the diseases were observed among
up- regulated genes, while there was no major intersection of com-
monly down-regulated genes between datasets (Fig.2). This finding
is in line with the above-described observation that down-regulated
genes tended to be target-tissue related (Fig.1, B toE). cells in
Direction
Up-regulated
Down-regulated
REGULATION OF GENE EXPRESSION IN BETA CELLS (14/21)
HSF1 ACTIVATION (20/26)
CILIUM ASSEMBLY (77/196)
RESPONSE TO METAL IONS (7/11)
CYTOSOLIC TRNA AMINOACYLATION (15/24)
ATTENUATION PHASE (15/23)
DEFECTS IN BIOTIN BTN METABOLISM (7/8)
INTRAFLAGELLAR TRANSPORT (23/52)
TRNA AMINOACYLATION (22/42)
BIOTIN TRANSPORT AND METABOLISM (7/11)
METALLOTHIONEINS BIND METALS (6/8)
INTEGRATION OF ENERGY METABOLISM (44/100)
VALINE LEUCINE AND ISOLEUCINE DEGRADATION (18/44)
HSP90 CHAPERONE CYCLE (22/51)
RIBOSOME (55/86)
HEME BIOSYNTHESIS (8/10)
CARGO TRAFFICKING TO THE PE RICILIARY MEMBRANE (22/50)
CREB1 PHOSPHORYLATION (5/11)
TP53 REGULATES METABOLIC GENES (38/83)
DEFECTS IN VITAMIN AND COFACTOR METABOLISM (11/21)
INTEGRIN CELL SURFACE INTERACTIONS (45/80)
ECM RECEPTOR INTERACTION (46/81)
TYPE I DIABETES MELLITUS (19/32)
CELL ADHESION MOLECULES CAMS (47/118)
TRANSCRIPTIONAL REGULATION OF GRANULOPOIESIS (36/73)
CELL SURFACE INTERACTIONS AT THE VASCULAR WALL (50/121)
INTERLEUKIN 10 SIGNALING (23/35)
INTERFERON SIGNALING (65/182)
ANTIGEN PROCESSING AND PRESENTATION (26/57)
ALLOGRAFT REJECTION (20/25)
AUTOIMMUNE THYROID DISEASE (20/29)
INTESTINAL IMMUNE NETWORK FOR IGA PRODUCTION (26/37)
GRAFT VERSUS HOST DISEASE (20/26)
HEMATOPOIETIC CELL LINEAGE (46/71)
INTERFERON ALPHA BETA SIGNALING (33/56)
CYTOKINE CYTOKINE RECEPTOR INTERACTION (101/183)
IMMUNOREGULATORY INTERACTIONS BETWEEN LYMPHOID AND NON-LYMPHOID CELL (61/101)
INTERFERON GAMMA SIGNALING (46/87)
LEISHMANIA INFECTION (40/62)
SYSTEMIC LUPUS ERYTHEMATOSUS (63/105)
−2 −1 0123
Normalized enrichment score
RIBOSOME (76/86)
NONSENSE MEDIATED DECAY NMD INDEPENDENT OF THE EXON JUNCTION (77/94)
EUKARYOTIC TRANSLATION INITIATION (83/118)
SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE (76/111)
SELENOAMINO ACID METABOLISM (79/116)
NONSENSE MEDIATED DECAY NMD (78/114)
INFLUENZA INFECTION (81/153)
REGULATION OF EXPRESSION OF SLITS AND ROBOS (79/164)
ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX (37/59)
SENESCENCE ASSOCIATED SECRETORY PHENOTYPE SASP (24/75)
SIGNALING BY ROBO RECEPTORS (89/209)
RRNA PROCESSING IN THE NUCLEUS AND CYTOSOL (84/191)
PRC2 METHYLATES HISTONES AND DNA (13/41)
DEPURINATION (13/34)
NUCLEAR RECEPTOR TRANSCRIPTION PATHWAY (9/50)
RRNA PROCESSING (85/203)
DNA METHYLATION (12/33)
MEIOTIC SYNAPSIS (17/53)
ESTROGEN DEPENDENT GENE EXPRESSION (28/115)
ERCC6 CSB AND EHMT2 G9A POSITIVELY REGULATE RRNA EXPRESSION (14/44)
FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TR IC (15/23)
TRNA AMINOACYLATION (25/42)
GLUTATHIONE SYNTHESIS AND RECYCLING (9/12)
CHONDROITIN SULFATE BIOSYNTHESIS (7/18)
KERATAN SULFATE DEGRADATION (8/12)
DISEASES ASSOCIATED WITH N GLYCOSYLATION OF PROTEINS (12/17)
COPII MEDIATED VESICLE TRANSPORT (33/66)
CARGO CONCENTRATION IN THE ER (21/31)
GOLGI TO ER RETROGRADE TRANSPORT (63/125)
ALPHA LINOLENIC OMEGA3 AND LINOLEIC OMEGA6 ACID METABOLISM (5/12)
ANTIVIRAL MECHANISM BY IFN STIMULATED GENES (38/81)
DISEASES ASSOCIATED WITH GLYCOSAMINOGLYCAN METABOLISM (20/38)
ANTIGEN PRESENTATION (16/25)
COPI DEPENDENT GOLGI TO ER RETROGRADE TRAFFIC (51/92)
COPI MEDIATED ANTEROGRADE TRANSPORT (54/94)
NF KB ACTIVATION (6/12)
TRANSPORT TO THE GOLGI AND SUBSEQUENT MODIFICATION (89/172)
INTERFERON SIGNALING (69/181)
ER TO GOLGI ANTEROGRADE TRANSPORT (78/145)
INTERFERON ALPHA BETA SIGNALING (35/56)
−3 −2 −1 0
12
Normalized enrichment score
CHOLESTEROL BIOSYNTHESIS (18/24)
ACTIVATION OF GENE EXPRESSION BY SREBF SREBP (19/42)
STEROID BIOSYNTHESIS (10/16)
MISCELLANEOUS TRANSPORT AND BINDING EVENTS (9/24)
UNBLOCKING OF NMDA RECEPTORS GLUTAMATE (9/21)
RAS ACTIVATION (6/20)
HEME BIOSYNTHESIS (6/11)
REGULATION OF CHOLESTEROL BIOSYNTHESIS BY SREBP SREBF (20/55)
PROTEIN PROTEIN INTERACTIONS AT SYNAPSES (19/87)
COMPLEMENT AND COAGULATION CASCADES (34/58)
FC EPSILON RECEPTOR FCERI SIGNALING (55/162)
SIGNALING BY THE B CELL RECEPTOR BCR (56/142)
FCGAMMA RECEPTOR FCGR DEPENDENT PHAGOCYTOSIS (42/121)
CELL SURFACE INTERACTIONS AT THE VASCULAR WALL (65/159)
IMMUNOREGULATORY INTERACTIONS BETWEEN LYMPHOID AND NON-LYMPHOID CELL (72/147)
FCERI MEDIATED CAPLUS2 MOBILIZATION (32/64)
REGULATION OF ACTIN DYNAMICS FOR PHAGOCYTIC CUP FORMATION (41/96)
FCERI MEDIATED NF KB ACTIVATION (46/112)
ANTIGEN ACTIVATES B CELL RECEPTOR BCR (38/64)
FCERI MEDIATED MAPK ACTIVATION (32/65)
ROLE OF LAT2 NTAL LAB ON CALCIUM MOBILIZATION (32/49)
BINDING AND UPTAKE OF LIGANDS BY SCAVENGER RECEPTORS (46/75)
CD22 MEDIATED BCR REGULATION (34/39)
FCGR ACTIVATION (35/48)
SCAVENGING OF HEME FROM PLASMA (35/48)
ROLE OF PHOSPHOLIPIDS IN PHAGOCYTOSIS (36/61)
INITIAL TRIGGERING OF COMPLEMENT (47/56)
CREATION OF C4 AND C2 ACTIVATORS (41/48)
COMPLEMENT CASCADE (54/83)
−2 02
Normalized enrichment score
BC
DE
3400 3716
Rheumatoid arthritis
Up−regulated Down−regulated
0
1000
2000
3000
4000
328
125
Type 1 diabetes
Up−regulated Down−regulated
0
100
200
300
Multiple sclerosis
151
121
Systemic lupus erythematosus
Sy
stemic lupus er
y
thematosus
yy
Up−regulated Down−regulated
0
50
100
150
Number of differentially expressed genes in target tissues
4
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A
OLFACTORY TRANSDUCTION (40/81)
COLLAGEN CHAIN TRIMERIZATION (25/41)
NCAM SIGNALING FOR NEURITE OUT GROWTH (29/53)
OLFACTORY SIGNALING PATHWAY (37/79)
NCAM1 INTERACTIONS (18/32)
NUCLEAR RECEPTOR TRANSCRIPTION PATHWAY (23/44)
PROTEIN PROTEIN INTERACTIONS AT SYNAPSES (34/72)
CYP2E1 REACTIONS (3/5)
G ALPHA S SIGNALLING EVENTS (68/164)
NEUREXINS AND NEUROLIGINS (23/48)
SYNTHESIS OF 16 20 HYDROXYEICOSATETRAENOIC ACIDS HETE (3/6)
COLLAGEN BIOSYNTHESIS AND MODIFYING ENZYMES (32/64)
SYNTHESIS OF EPOXY EET AND DIHYDROXYEICOSATRIENOIC ACIDS DHET (5/28)
MUSCLE CONTRACTION (57/155)
G ALPHA 12 13 SIGNALLING EVENTS (38/74)
STRIATED MUSCLE CONTRACTION (10/22)
PHASE 0 RAPID DEPOLARISATION (21/33)
INTERACTION BETWEEN L1 AND ANKYRINS (20/28)
NON INTEGRIN MEMBRANE ECM INTERACTIONS (29/56)
ECM PROTEOGLYCANS (33/64)
CELL SURFACE INTERACTIONS AT THE VASCULAR WALL (69/160)
FC EPSILON RECEPTOR FCERI SIGNALING (102/172)
FCGAMMA RECEPTOR FCGR DEPENDENT PHAGOCYTOSIS (66/130)
SYSTEMIC LUPUS ERYTHEMATOSUS (87/116)
FCERI MEDIATED MAPK ACTIVATION (55/75)
REGULATION OF ACTIN DYNAMICS FOR PHAGOCYTIC CUP FORMATION (65/106)
IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL (107/149)
FCERI MEDIATED NF KB ACTIVATION (85/122)
FCERI MEDIATED CAPLUS2 MOBILIZATION (50/74)
SIGNALING BY THE B CELL RECEPTOR BCR (99/153)
COMPLEMENT CASCADE (49/85)
BINDING AND UPTAKE OF LIGANDS BY SCAVENGER RECEPTORS (49/81)
ANTIGEN ACTIVATES B CELL RECEPTOR (53/75)
INITIAL TRIGGERING OF COMPLEMENT (46/62)
ROLE OF LAT2 NTAL LAB ON CALCIUM MOBILIZATION (44/59)
CD22 MEDIATED BCR REGULATION (45/50)
ROLE OF PHOSPHOLIPIDS IN PHAGOCYTOSIS (49/70)
CREATION OF C4 AND C2 ACTIVATORS (46/54)
FCGR ACTIVATION (49/58)
SCAVENGING OF HEME FROM PLASMA (49/54)
−2 024
Normalized enrichment score
218
551
0
200
400
Up−regulated Down−regulated
Fig. 1. Overview of the number of differentially expressed genes and the signaling pathways activated in the target tissues of four autoimmune diseases.
(A) Number of protein-coding genes differentially expressed in four autoimmune diseases. Each RNA-seq data set was quantified with Salmon using GENCODE 31 as the
reference. Differential expression was assessed with DESeq2. The numbers within the bars represent the protein-coding genes with |fold change| >1.5 and an adjusted
P value <0.05. RNA-seq sample numbers (n) are as follows: T1D (n = 4 for patients, n = 10 for controls), SLE (n = 20 for patients, n = 7 for controls), MS (n = 5 for patients, n =
5 for controls), and RA (n = 56 for patients, n = 28 for controls). Results for the RA samples were adjusted by age as an independent variable. (B to E) Gene set enrichment
analysis (GSEA) of T1D (B), SLE (C), MS (D), and RA (E) target tissues. After quantification using Salmon and differential expression with DESeq2, genes were ranked accord-
ing to their fold change. Then, the fGSEA algorithm (76) was used along with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases to deter-
mine significantly modified pathways. Bars in red and blue represent, respectively, a positive and negative enrichment in the associated pathway. The x axis shows the
normalized enrichment score (NES) of the fGSEA analysis, and the y axis the enriched pathways. The numbers at the end of the signaling pathway names represent,
respectively, (i) the number of genes present in the leading edge of the GSEA analysis and (ii) the total number of genes present in the gene set.
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T1D, in particular, showed a strong correlation with regard to up-
regulated genes with SLE, RA, and MS (Fig.2). The functional en-
richment analysis of these up-regulated overlapping pathways
showed concordance for both types I and II IFN signaling for nearly
all disease pairs (Fig.3). Pathways related to neutrophil degranula-
tion were highly up-regulated when comparing MS against T1D
(Fig.3B), SLE (Fig.3D), or RA (Fig.3F); this pathway also appeared
highly in common between T1D and RA (Fig.3C).
We next investigated the potential TFs controlling the observed
interdisease similarities. For this purpose, we evaluated the enrichment
of TF binding site motifs in the promoter region of up-regulated
genes from the pairwise analysis of autoimmune diseases (fig. S3).
In line with the predominance of IFN-related pathways observed in
Fig.3, there was a high prevalence of common binding site motifs
for IFN-induced TFs, including IFN-stimulated response element
(ISRE), IFN regulatory factor 1 (IRF1), and IRF2, particularly when
comparing T1D versus SLE (fig. S3A) and T1D versus RA (fig.
S3C). To examine whether these TFs are expressed by constitutive cells
of the target tissues, we have reevaluated the TF expression in non-
immune cells present in sc/sn–RNA-seq of the target tissues from the four
autoimmune diseases. Since the presently available methods for sc/
sn–RNA-seq only detect on average 1000 to 5000 genes per cell (43),
which is 75 to 80% lower than the total number of genes identified
by bulk cell RNA-seq (>20,000 genes), we selected for this analysis the
top 10 TFs presenting the highest expression in the affected target tissues.
By this approach, we observed that the majority of these TFs are ex-
pressed by nonimmune cells from the target tissues (fig. S3G). In agree-
ment with this observation, we have previously shown that exposure of
the human cell line EndoC-H1 to INF leads to the activation of s ever al
of the same TFs identified, including signal transducer and activator
of transcription 1 (STAT1), STAT2, STAT3, IRF1, and IRF9 (31,48).
To assess whether a putative invivo type I IFN signaling in the
context of different autoimmune diseases activates similar pathways
in the target tissues, we compared gene expression of primary human
islets (31) and skin keratinocytes (44) exposed invitro to IFN- for
8 and 6 hours, respectively (fig. S4). There were approximately 40%
differentially expressed genes in common between these two tissues
(fig. S4A), leading to the induction of pathways such as IFN signal-
ing and antigen presentation/processing (fig. S4B) that were similar
to the pathways observed in target tissues from patients affected by
T1D (Fig.1B and fig. S5) or SLE (Fig.1C and fig. S5).
It is noteworthy that when comparing SLE versus T1D and SLE
versus RA (Fig.2, A andB), there were a large number of genes
up-regulated in one disease but down-regulated in the other. A more
detailed analysis of these oppositely regulated genes (fig. S6) indi-
cated that neutrophil degranulation and signaling by RHO GTPases
(guanosine triphosphatases) were among the most enriched gene sets.
A similar observation was made regarding SLE versus RA, where
neutrophil degranulation was also the most represented gene set.
This apparent disagreement between genes regulating neutrophil
degranulation in SLE and other autoimmune diseases may reflect
the presence of two distinct populations of neutrophils in patients
with SLE that have functional differences in pathways controlling
chemotaxis, phagocytosis, and degranulation (45). Other dissimilarities
include the anti-inflammatory IL-10 signaling and groups related to
the regulation of the dialog between immune and resident cells, such
as “immunoregulatory interaction between a lymphoid and non-
lymphoid cell” and “PD-1 (programmed cell death protein 1) signal ing.”
The availability of the above-described datasets allowed us to
mine the overlapping genes in the target tissues of the different au-
toimmune diseases to search for common therapeutic targets, with
the potential to find drugs to be repurposed (Fig.4). As a proof of
concept, we identified dihydrofolate reductase inhibitors as a po-
tential therapeutic target for several pairs of autoimmune diseases
(Fig.4,BtoD and F), and methotrexate, a member of this class, is
already routinely used for the treatment of different autoimmune
diseases, including RA (46) and SLE (47). Bromodomain inhibitors
were also observed as common perturbagens between T1D and SLE
(Fig.4A) and SLE versus RA (Fig.4E). This is in line with our recent
observations that two of these bromodomain inhibitors, JQ1 and
T1D
SLE
RA
MS
MS
RA
SLE
T1D
MS
T1D
SLE
RA
MS
T1D
624
734
NS
NS
494
NS NS
NS 194
NS NS
187
1080
NS
NS
NS
675
NS NS
NS 416
NS NS
NS
RA
SLE
AB
Fig. 2. RRHO analysis comparing the gene expression signatures of target tissues among four autoimmune diseases demonstrates a high degree of similarity
between up-regulated genes. (A) Genes were ranked by their fold change from the most down- to the most up-regulated ones and then submitted to the RRHO algo-
rithm. The level map colors display the adjusted log P values of the overlap (the P values were adjusted using the Benjamini and Yekutieli method) between genes
up-regulated in both datasets (bottom left quadrant), down-regulated in both (top right quadrant), up-regulated in the left-hand pathology and down in the bottom part
(top left quadrant), and down in the left-hand pathology and up-regulated in the bottom part pathology (bottom right quadrant). (B) The panel displays the number of
genes significantly overlapping in each pairwise analysis (A). NS, not significant quadrant.
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I-BET-151, protect human cells against the deleterious effects of
IFN- (31). There were additional interesting candidates, some
with a profile covering multiple diseases, such as phosphoinositide
3-kinase (PI3K) (T1D versus SLE, SLE versus RA, and MS versus RA)
and janus kinase (JAK) inhibitors (SLE versus RA and MS versus RA),
while others acting on specific pairs of diseases, namely, bile acids
(T1D versus MS) and fibroblast growth factor receptor (FGFR) in-
hibitors (SLE versus MS) (Fig.4). Of note, clinical trials are currently
evaluating the effects of the bile acid tauroursodeoxycholic acid
(TUDCA) in patients with recent-onset T1D (ClinicalTrials.gov,
NCT02218619) and MS (ClinicalTrials.gov, NCT03423121).
Expression of candidate genes for the different autoimmune
diseases at the target tissue level
We have previously shown that isolated human pancreatic islets ex-
press a large number of risk genes for T1D (20,24,26,48), and we
presently examined whether this is also the case for the target tis-
sues in other autoimmune diseases (table S2). Confirming our pre-
vious findings, 81% of risk genes for T1D were expressed in human
cells; similar findings were observed for the target tissues for SLE
(92%), MS (83%), and RA (88%). The autoimmune assault changed
the expression of >65% of these candidate genes for joint tissue RA
(table S2), but the number of disease-induced and significantly
modified genes was much smaller for the other autoimmune diseases,
probably because of limited statistical power associated to the num-
ber of samples analyzed (>80 samples studied in the case of RA and
between 10 and 27 for the other diseases). The list of risk genes ex-
pressed in the target tissues is available in data file S1. An overview
of these candidate genes and their coexpression in different auto-
immune diseases is provided in Fig.5. Genes related to antigen pre-
sentation [human lymphocyte antigen (HLA)–DQB1 and HLA-DRB1]
and to type I IFN signaling (TYK2) are present in all target tissues
INTERLEUKIN 9 SIGNALING
OAS ANTIVIRAL RESPONSE
NICOTINATE METABOLISM
NICOTINAMIDE SALVAGING
NEGATIVE REGULATORS OF DDX58 IFIH1 SIGNALING
INTERLEUKIN 2 FAMILY SIGNALING
INTERLEUKIN 4 AND INTERLEUKIN 13 SIGNALING
ANTIVIRAL MECHANISM BY IFN STIMULATED GENES
COMPLEMENT CASCADE
IMMUNOREGULATORY INTERACT LYMPH NON LYMPH
INTERFERON GAMMA SIGNALING
INTERFERON ALPHA BETA SIGNALING
EXTRACELLULAR MATRIX ORGANIZATION
INTERFERON SIGNALING
0.0250.050 0.075 0.100
Gene ratio
0.04
0.03
0.02
0.01
P.adjust
Count
10
20
30
IRAK4 DEFICIENCY TLR2 4
ENDOSOMAL VACUOLAR PATHWAY
DISEASES OF IMMUNE SYSTEM
SYNDECAN INTERACTIONS
PD 1 SIGNALING
TRANSLOCATION OF ZAP 70 TO IMMUNOLOGICAL SYNAPS E
PHOSPHORYLATION OF CD3 AND TCR ZETA CHAINS
DAP12 INTERACTIONS
GENERATION OF SECOND MESSENGER MOLECULES
INTERLEUKIN 2 FAMILY SIGNALING
NON INTEGRIN MEMBRANE ECM INTERACTIONS
INTERLEUKIN 10 SIGNALING
ANTIGEN PROCESSING CROSS PRESENTATION
INTERFERON ALPHA BETA SIGNALING
INTERLEUKIN 4 AND INTERLEUKIN 13 SIGNALING
INTERFERON GAMMA SIGNALING
EXTRACELLULAR MATRIX ORGANIZATION
INTERFERON SIGNALING
IMMUNOREGULATORY INTERACT LYMPH NON LYMPH
NEUTROPHIL DEGRANULATION
0.03 0.06 0.09
Gene ratio
0.006
0.004
0.002
P.adjust
Count
20
40
60
DEPURINATION
INTERLEUKIN 10 SIGNALING
ERCC6 CSB AND EHMT2 G9A POSITIVELY REGULATE RRNA EXPRESSION
NUCLEOSOME ASSEMBLY
SIRT1 NEGATIVELY REGULATES RRNA EXPRESSION
ACTIVATED PKN1
DNA METHYLATION
CONDENSATION OF PROPHASE CHROMOSOMES
PRC2 METHYLATES HISTONES AND DNA
TRANSCRIPTIONAL REGULATION OF GRANULOPOIESIS
DNA DAMAGE TELOMERE STRESS INDUCED SENESCENCE
SENESCENCE ASSOCIATED SECRETORY PHENOTYPE SASP
HATS ACETYLATE HISTONES
MHC CLASS II ANTIGEN PRESENTATION
HDACS DEACETYLATE HISTONES
CELL CYCLE CHECKPOINTS
RHO GTPASE EFFECTORS
NEUTROPHIL DEGRANULATION
M PHASE
SIGNALING BY RHO GTPASES
0.050 0.075 0.100 0.125 0.150
Gene ratio
1.5 × 10−10
1.0 × 10−10
5.0 × 10−11
P.adjust
Count
20
30
40
50
GLUTATHIONE SYNTHESIS AND RECYCLING
TYPE I HEMIDESMOSOME ASSEMBLY
INTERLEUKIN 21 SIGNALING
ENDOSOMAL VACUOLAR PATHWAY
NICOTINATE METABOLISM
NICOTINAMIDE SALVAGING
ANTIGEN PRESENTATION
COLLAGEN BIOSYNTHESIS AND MODIFYING ENZYMES
INITIAL TRIGGERING OF COMPLEMENT
ANTIGEN PROCESSING CROSS PRESENTATION
COLLAGEN FORMATION
REGULATION OF INSULIN LIKE GROWTH FACTOR
COMPLEMENT CASCADE
INTERFERON GAMMA SIGNALING
IMMUNOREGULATORY INTERACT LYMPH NON LYMPH
INTERFERON ALPHA BETA SIGNALING
BIOLOGICAL OXIDATIONS
EXTRACELLULAR MATRIX ORGANIZATION
INTERFERON SIGNALING
NEUTROPHIL DEGRANULATION
0.0250.050 0.075
Gene ratio
0.04
0.03
0.02
0.01
P.adjust
Count
10
20
30
REGULATION OF TRANSCRIPTION BY TP53
INTERLEUKIN 21 SIGNALING
TNFS BIND THEIR PHYSIOLOGICAL RECEPTORS
INTERFERON ALPHA BETA SIGNALING
INTERLEUKIN 2 FAMILY SIGNALING
G0 AND EARLY G1
ANTIGEN PROCESSING CROSS PRESENTATION
RHO GTPASES ACTIVATE FORMINS
RESOLUTION OF SISTER CHROMATID COHESION
MITOTIC SPINDLE CHECKPOINT
TNFR2 NON CANONICAL NF KB PATHWAY
INTERLEUKIN 4 AND INTERLEUKIN 13 SIGNALING
INTERFERON GAMMA SIGNALING
CHEMOKINE RECEPTORS BIND CHEMOKINES
MITOTIC METAPHASE AND ANAPHASE
PEPTIDE LIGAND BINDING RECEPTORS
CELL CYCLE CHECKPOINTS
INTERFERON SIGNALING
IMMUNOREGULATORY INTERACT LYMPH NON LYMPH
G ALPHA I SIGNALLING EVENTS
0.025 0.050 0.075 0.100
Gene ratio
0.03
0.02
0.01
P.adjust
Count
3
6
9
12
15
TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR
DISEASES OF IMMUNE SYSTEM
IRAK4 DEFICIENCY TLR2 4
TRANSLOCATION OF ZAP 70 TO IMMUNOLOGICAL SYNAPS E
GENERATION OF SECOND MESSENGER MOLECULES
PHOSPHORYLATION OF CD3 AND TCR ZETA CHAINS
ACTIVATED PKN1
DNA METHYLATION
PD 1 SIGNALING
CONDENSATION OF PROPHASE CHROMOSOMES
SENESCENCE ASSOCIATED SECRETORY PHENOTYPE SASP
CHEMOKINE RECEPTORS BIND CHEMOKINES
ANTIGEN PROCESSING CROSS PRESENTATION
COSTIMULATION BY THE CD28 FAMILY
MHC CLASS II ANTIGEN PRESENTATION
INTERLEUKIN 10 SIGNALING
INTERFERON GAMMA SIGNALING
INTERFERON SIGNALING
IMMUNOREGULATORY INTERACT LYMPH NON LYMPH
NEUTROPHIL DEGRANULATION
0.05 0.10
Gene ratio
1 × 10
−3
5 × 10
−4
P.adjust
Count
10
20
30
AB
C
D
EF
Fig. 3. Functional enrichment analysis of overlapping genes among four autoimmune diseases demonstrates signaling pathways concordance. (A to F) Genes
significantly overlapping between different pairs of autoimmune diseases in the RRHO analysis (Fig. 2B) were selected for enrichment analysis using the clusterProfiler
tool with the Reactome database. The top 20 gene sets are represented according to their adjusted P values (Benjamini and Hochberg correction) and their gene ratio
(no. of modified genes/total gene set size). Diseases were analyzed in pairs. Enrichment analysis of genes significantly up-regulated in the target tissues of both (A) T1D
and SLE, (B) T1D and MS, (C) T1D and RA, (D) SLE and MS, (E) SLE and RA, and (F) MS and RA.
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for the four autoimmune diseases. Reactome (49) analysis of risk
genes in T1D (data file S2) identified ILs and IFN signaling as
important pathways. IFN signaling also appears pro-eminently for
kidney tissue in SLE, optic chiasm in MS, and joint tissue in RA (data fi le
S2), but there are also clusters related to defense against the autoimmune
assault, including PD-1 (for all diseases) and IL-10 signaling (for SLE
and MS only); PD-1–PDL1 (programmed death ligand 1) is probably
also an important defense mechanism of human cells in T1D (50).
To evaluate whether the observed candidate genes are expressed
in nonimmune cells from the target tissues studied, we have used a
similar approach as done for the TF analysis (fig. S3G) and revised
sc/sn–RNA-seq data from nonimmune cells in affected tissues in T1D
(33), SLE [kidney epithelial cells (34)], MS [brain neurons (35)], and
RA [synovial fibroblasts (36)]. This confirmed that >80% of the top
50 risk genes are expressed by the target cells (fig. S1, F to I). Of
note, the present limitations of the sc–RNA-seq method regarding
the number of genes detected (commented upon above) may explain
why less candidate genes are observed in single cells (fig. S1, F to I)
than in whole tissue or FACS-sorted bulk cells (data file S1).
DISCUSSION
In the present study, we tested the hypothesis that target tissues
from four different autoimmune diseases, namely, T1D, SLE, MS,
and RA, engage in a “dialog” with the invading immune cells that
leaves “molecular footprints.” These footprints may share simi-
larities, as local inflammation is a common outcome of these dis-
eases, and point to common mechanisms that can be targeted by
therapy.
The analysis of the gene expression patterns of the target tissues
in the different diseases showed up-regulation of type I and II IFN–
related pathways, which is in line with observations made in the
peripheral blood of individuals with T1D (51), SLE (52,53), MS (54),
and RA (55). These descriptive similarities were confirmed by com-
paring the ranking of the up-regulated genes via RRHO, a method
that allows the comparison between differentially expressed genes
in control and diseased tissue from two different diseases, outlining
the similarities and/or dissimilarities between the modified genes in
both diseases. Here, we observed clear but different degrees of over-
lap between the diseases mostly regarding the up-regulated expression
PC3
VCAP
A375
A549
HA1E
HCC515
HT29
MCF7
HEPG2
Summary
DNA synthesis inhibitor
Bromodomain inhibitor
MTOR inhibitor
DNA-dependent protein kinase inhibitor
MEK inhibitor
PI3K inhibitor
Apolipoproteins LOF
Cell cycle inhibition GOF
NADH ubiquinone oxidoreductase core subunits GOF
Ribonucleotide reductase inhibitor
Perturbagen classes
−94.40
−92.66
−91.68
−89.20
−89.18
−87.91
−84.74
−82.99
−81.84
−81.72
Median tau score
Cell type
PC3
VCAP
A375
A549
HA1E
HCC515
HT29
MCF7
HEPG2
Summary
Dihydrofolate reductase inhibitor
Trace amine receptors LOF
Bile acid
Ribonucleotide reductase inhibitor
Cyclooxygenase inhibitor
Calmodulin antagonist
−95.92
−85.01
−84.82
−84.05
−83.56
−80.52
Median tau score
Cell type
PC3
VCAP
A375
A549
HA1E
HCC515
HT29
MCF7
HEPG2
Summary
DNA synthesis inhibitor
Apolipoproteins LOF
Ribonucleotide reductase inhibitor
Dihydrofolate reductase inhibitor
Thymidylate synthase inhibitor
Calmodulin antagonist
Baculoviral IAP repeat domain containing LOF
−98.17
−94.84
−91.46
−90.13
−85.40
−84.04
−82.91
Median tau score
Cell type
PC3
VCAP
A375
A549
HA1E
HCC515
HT29
MCF7
HEPG2
Summary
Benzodiazepine receptor agonist
FGFR inhibitor
MDM inhibitor
Dihydrofolate reductase inhibitor
Apolipoproteins LOF
Bacterial 30S ribosomal subunit inhibitor
Glycogen synthase kinase inhibitor
−97.69
−96.69
−95.33
−87.71
−84.94
−82.63
−80.97
Median tau score
Cell type
PC3
VCAP
A375
A549
HA1E
HCC515
HT29
MCF7
HEPG2
Summary
Cell cycle inhibition GOF
DNA synthesis inhibitor
Minor histocompatibility antigens LOF
Apolipoproteins LOF
Ribonucleotide reductase inhibitor
PI3K inhibitor
JAK inhibitor
C2 domain containing LOF
CDK inhibitor
Protein synthesis inhibitor
DNA-dependent protein kinase inhibitor
HIF activator
Bromodomain Inhibitor
BCL inhibitor
EGFR inhibitor
Glycogen synthase kinase inhibitor
MEK inhibitor
Leucine-rich repeat kinase inhibitor
Proteasome inhibitor
Vesicular transport LOF
−
99.74
−
99.20
−
98.80
−
97.97
−
97.28
−
97.10
−
96.61
−
93.02
−
91.69
−
90.52
−
89.51
−
89.41
−
87.79
−
84.58
−
84.06
−
83.15
−
82.24
−
81.68
−
81.47
−
81.18
median tau score
Cell type
PC3
VCAP
A375
A549
HA1E
HCC515
HT29
MCF7
HEPG2
Summary
Ribonucleotide reductase inhibitor
JAK inhibitor
Dihydrofolate reductase inhibitor
Minor histocompatibility antigens LOF
PI3K inhibitor
Protein synthesis inhibitor
Ribosomal 40S subunit LOF
Serotonin receptor agonist
−98.03
−88.38
−86.81
−85.56
−84.90
−84.66
−84.12
−81.42
Median tau score
Cell type
−100.0
01
00.00
Tau score scale
A
B
C
D
E
F
Perturbagen classes
Perturbagen classes
Perturbagen classes
Perturbagen classes
Perturbagen classes
Fig. 4. Mining overlapping genes among target tissues in four autoimmune diseases allows the identification of potential common therapeutic targets. (A to F) After
determining statistically which genes were overlapped in pairs of autoimmune diseases from the RRHO analysis (Fig. 2), the top 150 overlapping genes were submitted
to the Connectivity Map database to identify perturbagen classes driving an opposite signature (negative tau score) to the one present in the target tissues of the four
autoimmune diseases. Only classes with a median tau score <−80 were considered. (A to F) Perturbagen classes driving down the genomic signatures of up-regulated
genes. The same methodology and conditions have been applied for subsequent analysis: (A) T1D and SLE, (B) T1D and MS, (C) T1D and RA, (D) SLE and MS, (E) SLE and
RA, and (F) MS and RA. EGFR, epidermal growth factor receptor. LOF, Loss of Function; GOS, Gain of Function; IAP, inhibitor of Apoptosis; FGFR, Fibroblast Growth Factor
Receptor; MDM, Murine Double Minute; HIF, Hypoxia Inducible Factor; BCL, B-Cell Lymphoma.
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patterns. In support of the robustness of the present findings, these
similarities were present despite the fact that the original RNA-
seq data were obtained by different research teams, using different
extraction and sequencing processes, and that there were major
differences between the studies regarding age and sex of the pa-
tients and respective controls (many of these differences were inher-
ent to the diseases studied, e.g., SLE is more common in females).
The observed similarities in pathway activation between target
tissues were translated into the identification of several classes of
drugs that could potentially be used to treat more than one autoimmune
disease (Fig.4). Among them, JAK inhibitors, which act down-
stream of the types I and II IFN receptors by blocking activation of
the kinases JAK1 and JAK2, are of particular interest. These inhibi-
tors were recently approved for the treatment of RA (56) and had
promising results in a phase 2 clinical trial of patients with SLE (57).
In line with this, JAK inhibitors prevent the proinflammatory and
proapoptotic effects of IFN- on human pancreatic cells (31) and
revert established insulitis in diabetes-prone NOD (nonobese dia-
betic) mice (58). Another class of drugs presently identified for po-
tential use in several autoimmune diseases are the PI3K inhibitors.
These drugs target a family of lipid kinases that phosphorylate
phosphoinositides from cell membranes, modulating cellular pro-
cesses such as cell growth, metabolism, and immune responses. In
agreement with our analysis, inhibitors of the PI3K isoforms and
have beneficial effects in animal models of MS (59), SLE (60), and
RA (61). PI3K inhibitors, however, may have opposite effects on
different tissues. Thus, PI3K inhibitors exacerbate inflammatory
responses in the airways and gut, tissues often exposed to pathogens,
leading to severe cases of pneumonitis and colitis (62). This indicates
that selection of potential new therapeutic agents needs to consider
also the specific characteristics of the target tissue(s). This is in
agreement with our present observations of tissue-specific down-
regulated pathways in different diseases, such as pathways related to
maintenance of the cell phenotype in T1D, or down-regulation of
pathways involved in collagen folding in joint tissues from RA.
There have been previous attempts to perform individual drug
repurposing on these pathologies [e.g., (63,64)]. Our present study
attempts to expand this approach, potentially leading to drug re-
purposing for multiple autoimmune diseases, for instance, in the
case of JAK inhibitors. Repurposing already-studied drugs provides
the benefits of having their pharmacodynamic and pharmacokinetic
profiles already well studied, which considerably reduces the bench-
to-bedside time frame (65), and helping the treating physicians to
survey for previously detected side effects.
More than 80% of candidate genes for which a single-nucleotide
polymorphism (SNP)–trait link has been deemed significant are ex-
pressed in the target tissues of the different autoimmune diseases
studied. This is in line with our previous observations in T1D
(20,26, 48), where these candidate genes probably regulate cell
responses to “danger signals,” such as viral infections, and the signal
transduction of type I IFNs (23). The fact that similar observations
are now made in the target tissues of SLE, MS, and RA (present
data) suggests that future studies in these diseases should also con-
sider the impact of candidate genes acting at the target tissue level.
Of note, and to detect eQTL (Expression quantitative trait loci) in
target tissues, it may be necessary to expose them to relevant stimuli,
such as proinflammatory cytokines in the case of T1D (26).
The present observations, showing the expression of candidate
genes in the target tissues of autoimmune diseases, may contribute
to explain why certain people have different innate immune responses
at the tissue level to seemingly similar triggers (such as viral infec-
tions or other danger signals), leading to different outcomes, e.g.,
progressive tissue damage or resolution of inflammation and return
to homeostasis. For instance, diverse polymorphisms in candidate
genes for T1D may contribute to disease at the cell level by regu-
lating antiviral responses, innate immunity, activation of apoptosis,
and, at least for a few of them, cell phenotype (24,25,66).
The candidate genes presently observed as overlapping between
target tissues of two or more diseases are mostly related to inflam-
matory mediators, particularly the signal transduction of IFNs, sug-
gesting that similarities between these diseases are dependent, at least
in part, on the genetically mediated regulation of local immune re-
sponses. These findings may have therapeutic implications. For
instance, one of the candidate genes in common between all the
four autoimmune diseases is TYK2, a key component of the JAK-
STAT signaling pathway. TYK2 inhibitors are already in phase 3
clinical trial for another autoimmune disease, psoriasis (67), and
two different TYK inhibitors protect human cells against the del-
eterious effects of IFN- (68). Targeting IFN pathways at an early
step of its signal transduction may not be, however, a sufficiently
specific approach, and the role of IFNs may vary according to the
stage of disease and the genetic background of the affected individuals.
The success of IFN-blocking therapies in human SLE and other
rheumatic diseases remains to be proven (69).
The data generated in the present study contribute to a better
understanding of the communication between the immune system
and the target tissues in T1D, SLE, MS, and RA, and strengthen the
putative implication of the target tissues in these autoimmune dis-
eases. These findings also indicate a role for similar candidate genes
expressed in target tissues of two or more diseases and indicate po-
tential new therapeutic agents to target key similar pathways. As a
whole, these observations suggest that future research on the genetics
and pathogenesis of autoimmune diseases should focus on both the
immune system and their target tissues and on their dialog.
ATG5 TNFSF4 IRF5
WDFY4 ETS1 BLK
ARID5B FLI1 YDJC
HCFC1 TMEM187
RAD51B IRAK1 UBE2L3
HLA-DQA1 SPRED2
LBH HLA-DQA2
33
T1D
MS RA
SLE
107
77
103
Fig. 5. Venn diagrams of risk genes expressed in the target tissues of the four
autoimmune diseases shows shared candidate genes among them. Venn dia-
gram representing risk genes identified in GWAS studies in target tissues for T1D,
SLE, MS, and RA. For each disease, the risk genes were extracted from the GWAS
Catalog (www.ebi.ac.uk/gwas/) and selected as described in Materials and Meth-
ods. In brief, each list was curated according to their relationship to the disease,
and only genes with a P value <0.5 × 10−8 for their SNP-trait relationship were kept.
Last, an intersection between the four lists was performed and represented as a
Venn diagram. Numbers in the diagram represent the numbers of genes present in
each subgroup, and genes overlapping between diseases are displayed by their
HGNC symbols. A gene was considered as expressed if it presents a mean TPM >
0.5 in either the patient or control group. N/A, not applicable (no gene in common).
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Limitations
The study’s first limitation relates to the scarcity of RNA-seq data
for target tissues in autoimmune diseases, particularly in the cases
where these tissues are difficult to access, such as in T1D or MS. This
decreases the power of the analysis and may bias the data in favor of
diseases where a larger number of samples were available (e.g., RA).
Another issue is the stage of the disease, as the impact of the im-
mune system on the target tissues may differ in the early and late
phases of the disease [for instance, in the case of T1D, innate rather
than adaptive immunity may have a major role at earlier stages
(3,25, 70)]. Unfortunately, and because of the scarcity of samples
in, for instance, T1D or MS, this stage issue is difficult to address. It
is noteworthy that despite these limitations, it was still possible to
obtain clear conclusions from the available data.
Another potential limitation is that immune cells are present in
the target tissue preparations analyzed (although there was a statis-
tically significant increase in the expression of the immune marker
CD45 only in T1D and RA), which may affect the gene expression
pathways described above. The facts that (i) an IFN signature is
present in nonimmune cells of the diseased tissues analyzed and
these nonimmune cells express several candidate genes for the dis-
eases studied (fig. S1); (ii) at least in the case of a pure human cell
line, EndoC-H1 cells, exposure to IFN- induces a gene signature
that is similar to that observed in cells obtained from patients af-
fected by T1D (31); and (iii) histological analysis of pancreatic islets
from patients with T1D show expression of HLA class I (ABC) (71),
HLA-E (31), PDL1 (50), CXCL10 (72), and STAT1 (71) in pancreat-
ic cells, taken as a whole, suggest that at least part of the observed
gene signatures originate from the target tissues and cannot be ex-
plained by the immune infiltration alone. Future follow-up studies
based on direct histological staining of the specific cells involved are
required to define the exact contribution of immune and nonim-
mune cells in the affected target tissues.
MATERIALS AND METHODS
Target tissue bulk RNA-seq processing and analysis
For each dataset, control and patient target tissue gene expressions
were quantified using Salmon version 0.13.2 (73) with parameters
“--seqBias –gcBias --validateMappings.” GENCODE version 31
(GRCh38) (74) was chosen as the reference genome and has been
indexed with the default k-mer values. Differential expression was
performed with DESeq2 version 1.24.0 (75). For each gene included
in DESeq2’s model, a log2 fold change was computed and a Wald
test statistic was assessed with a P value and an adjusted P value. In
this study, we consider a gene as differentially expressed when |fold
change| >1.50 and adjusted P value <0.05. Since there was a statisti-
cal difference in the age between patients with RA and controls, for
this particular dataset, we have taken age as an independent variable
in the general linear model performed by DESeq2. To introduce age
as a confounding factor in the analysis, we performed a binning on
the ages and assigned each donor a group, respectively: 10 to 29, 30
to 49, 50 to 69, and >70 years old. All the other parameters of the
DESeq2 analysis were the same as for the others target tissues.
sc/sn–RNA-seq processing and analysis
We have obtained the expression matrices containing the processed
reads from transcriptome studies of the following target tissues: (i)
sc–RNA-seq from cryo-banked islets obtained from three donors
with T1D and three controls matched for body mass index, age, sex,
and storage time, performed using the SmartSeq-2 protocol as de-
scribed in (33) and accessible under the Gene Expression Omnibus
(GEO) number GSE124742; (ii) sc–RNA-seq from kidney biopsies
from 24 patients with LN and 10 control samples acquired from
living donor kidney biopsies using a modified CEL-Seq2 protocol as
described in (34) and accessible in the ImmPort repository (acces-
sion code SDY997); (iii) sc–RNA-seq from snap-frozen brain tissue
blocks obtained at autopsies from 10 patients with MS (1 primary
progressive MS, 9 secondary progressive MS) and 9 nonaffected in-
dividuals processed using the 10x Genomics Single-Cell 3′ system
as described in (35) and accessible on Sequence Read Archive (SRA;
accession number PRJNA544731); and (iv) sc–RNA-seq of synovial
tissues from ultrasound-guided biopsies or joint replacements of
36 patients with RA and 15 patients with osteoarthritis, as reference
controls, using the CEL-Seq2 protocol as described in (34) and
available at ImmPort (accession code SDY998). After that, we nor-
malized the gene expression levels by transforming the counts to
log2(CPM + 1) (counts per million).
For the purpose of reproducibility, we have kept the same cell
identity classification defined in the original sc/sn–RNA-seq study
(33–36). To represent nonimmune cells on the target tissues, we have
selected (i) in T1D, the cells isolated from pancreatic islets; (ii) in
SLE, all the kidney epithelial cells from the kidney biopsy; (iii) in MS,
all the cells from different clusters of brain neurons; and (iv) in RA,
all the cells from the fibroblast clusters of joint synovial tissues.
Sex determination
For most, but not all, target tissues, sex information was available in
the metadata on the GEO website. To compensate for this lack of
information, we inferred the sex based on the expression of 40 genes
exclusively coded on the Y chromosome and the female-expressed
XIST (X-inactive specific transcript) (data file S1). We created a ma-
chine learning model on the basis of a linear discriminant analysis
algorithm that we trained on the expression of both controls and
patient expression matrices in RA. The training was supervised
with the sex described in the metadata as the desired outcome. We
then tried our model to predict the sex of patients on different target
tissues (i.e., T1D and MS) where the outcome was known, accord-
ing to their metadata, which provided only one prediction different
from the expected outcome (96% accuracy). This allowed us to esti-
mate the sex ratio in the studies missing this information in the
available metadata.
Risk genes
Risk genes associated with each disease were identified using genome-
wide association study (GWAS) catalog (www.ebi.ac.uk/gwas/; con-
sulted January 2020). The candidate genes were selected on the basis
of the following criteria: (i) T1D, SLE, MA, and RA as the disease/
trait evaluated by the study; (ii) a P value of <0.5 × 10−8 for the lead
SNP; (iii) selecting the reported genes linked to the lead SNP
described by the original study; and (iv) expression of the reported
genes in the target tissue (TPM > 0.5). An overlap between the
four lists of genes was then performed and represented as a Venn
diagram.
ISG score
To evaluate for the presence of types I and II IFN signatures on the
target tissues of the four autoimmune diseases, we have calculated
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for each cell from the sc/sn–RNA-seq an ISG score. This ISG score
was calculated as the average expression of known ISGs listed on
data file S1. The statistical difference between groups was deter-
mined using a two-tailed Mann-Whitney U test.
RRHO analysis
To compare the genomic signatures of the target tissues of the four
autoimmune diseases, we used an RRHO (42) mapping, an unbiased
method to uncover the concordances and discordances between
two similarly ranked lists. Briefly, for a pair of diseases, the full list
of genes is ranked according to their fold change from the most
down-regulated to the most up-regulated gene. Then, an inter-
section of shared genes is performed, and the analysis of the rank-
ing order of genes is performed with a hypergeometric test.
The visual output of this analysis is an RRHO level map (Fig.2A),
where the hypergeometric P value for enrichment of k overlapping
genes is calculated for all possible threshold pairs for each experi-
ment, generating a matrix where the indices are the current rank in
each experiment. P values for each test are then log transformed and
reported on a heatmap to display the degrees of similarities accord-
ing to four quadrants representing the concordance or the
discordance in gene ranking in the two differential expression
analysis (e.g., up-regulated in one disease and down-regulated in
the other).
Functional enrichment analysis
The functional enrichment analysis was based on results from the
differential expression analysis. Genes from bulk RNA-seq data wer e pr e-
ranked according to the Wald test statistic of the differential expres-
sion results from DESeq2. For sc/sn–RNA-seq data, we filtered out
genes that were expressed in less than 10% of all cells to minimize
the dropout impact on the overall gene expression. The remaining
genes were then preranked according to the log2 fold change of the
differential expression results from DESeq2. We used fGSEA (76)
along with the Kyoto Encyclopedia of Genes and Genomes (KEGG)
(77) and Reactome (49) databases as the references to determine which
pathways were positively or negatively enriched in the target tissue
of each disease. Default parameters were used, except for the num-
ber of permutations (10,000) for the most accurate P values. For bulk
RNA-seq data, results with an adjusted P value <0.05 (Benjamini-
Hochberg correction) were then sorted according to their NES. For
sc/sn–RNA-seq data, results with an adjusted P value <0.15
(Benj amini-Hochberg correction) were then sorted according to
their NES.
To determine the functional enrichment in genes up-regulated
in pairs of diseases, we used a hypergeometric test included in the
clusterProfiler package (78) on the genes overlapping significantly
in the RRHO mapping. The Reactome (49) database was used as the
reference for the gene sets. Default parameters were used, and
P values were adjusted with the Benjamini-Hochberg correction.
Venn diagrams
Genes differentially expressed with an adjusted P value <0.10
(Benjamini-Hochberg correction) were selected. The gene lists of all
diseases were then overlapped and represented as a Venn diagram
of up- or down-regulated genes. In case of an overlap of >50 genes, the
gene list was processed using a hypergeometric test with the Reactome
database as the reference. Defaults parameters were used, and P values
were adjusted with the Benjamini-Hochberg correction.
TF binding site analysis
Motif discovery for TF binding site in the promoter regions of
up-regulated genes was done using the script findMotifs.pl from the
HOMER (79) tools suite with parameters “-start -2000 -end 2000.”
The promoter regions were considered as ±2000 base pairs from the
gene transcription start site. Known TF binding site motifs uncov-
ered and included in the study have a P value <0.05.
Therapeutic target identification
For each RRHO analysis result, we picked the top 150 up-regulated
genes shared between two diseases and processed this list with the
Connectivity Map dataset (80) using the cloud-based CLUE software
platform (https://clue.io). This allowed us to query the database for
compounds that are driving down the input genomic signatures,
revealing potential drugs that could be repurposed to treat one or
more diseases. We focused then on perturbagen classes that dis-
played a negative median tau score and retained as potential drug
candidates only classes with a median tau score <−80.
Statistical analysis
TPM values are given according to their means ± SD. Results con-
sidered as significant in this study have a P value (or an adjusted
P value when applicable) <0.05. For gene expression, we considered
that a gene is differentially expressed if |fold change| >1.5 and ad-
justed P value <0.05, unless explicitly stated.
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/
content/full/7/2/eabd7600/DC1
View/request a protocol for this paper from Bio-protocol.
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Acknowledgments
Funding: D.L.E. acknowledges the support of a grant from the Welbio–FNRS (Fonds
National de la Recherche Scientifique), Belgium, the Dutch Diabetes Fonds (DDFR), Holland,
startup funds from the Indiana Biosciences Research Institute (IBRI), Indianapolis, Indiana,
USA, and the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement
nos. 115797 (INNODIA) and 945268 (INNODIA HARVEST), supported by the European
Union’s Horizon 2020 Research and Innovation Programme. These Joint Undertakings
receive support from the Union’s Horizon 2020 research and innovation programme and
“EFPIA,” “JDRF,” and “The Leona M. and Harry B. Helmsley Charitable Trust.” C.E.-M.
acknowledges the support of NIH grants R01 DK093954, VA Merit Award I01BX001733, JDRF
2-SRA-2018-493-A-B, and JDRF 2-SRA-2019-834-S-B (to C.E.-M. and D.L.E.); M.M. is supported
by the JDRF. Author contributions: M.L.C., F.S., M.J.M., C.E-M., and D.L.E. conceived the
analysis. M.L.C. and D.L.E. supervised the analysis. F.S. and M.L.C. performed the
bioinformatics analyses. F.S., M.L.C., and D.L.E. wrote the manuscript, and all authors revised
it. D.L.E. provided the main funding. Competing interests: The authors declare that they
have no competing interests. Data and materials availability: All data needed to evaluate
the conclusions in the paper are present in the paper and/or the Supplementary Materials.
Raw RNA-seq data are accessible from the GEO repository (https://ncbi.nlm.nih.gov/geo/)
via their GSE codes as stated in Table 1. sc/sn–RNA-seq data are accessible from the
repositories indicated in Materials and Methods (see above). Additional data related to this
paper may be requested from the authors.
Submitted 10 July 2020
Accepted 16 November 2020
Published 6 January 2021
10.1126/sciadv.abd7600
Citation: F. Szymczak, M. L. Colli, M. J. Mamula, C. Evans-Molina, D. L. Eizirik, Gene expression
signatures of target tissues in type 1 diabetes, lupus erythematosus, multiple sclerosis, and
rheumatoid arthritis. Sci. Adv. 7, eabd7600 (2021).
on January 6, 2021http://advances.sciencemag.org/Downloaded from
multiple sclerosis, and rheumatoid arthritis
Gene expression signatures of target tissues in type 1 diabetes, lupus erythematosus,
F. Szymczak, M. L. Colli, M. J. Mamula, C. Evans-Molina and D. L. Eizirik
DOI: 10.1126/sciadv.abd7600
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