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\title{Splicing factor SRSF1 acts as a novel molecular brake for CD4 T cell
activation and controls key molecular pathways in SLE}



\author[1]{Rhea Bhargava}%
\author[2]{Michelle Lee}%
\author[1]{Rohit Upadhyay}%
\author[2]{Vaishali Moulton}%
\affil[1]{Tulane University School of Medicine}%
\affil[2]{Beth Israel Deaconess Medical Center}%


\vspace{-1em}



  
  \date{May 07, 2026}


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\selectlanguage{english}
\begin{abstract}
T cells from systemic lupus erythematosus (SLE) patients exhibit a
hyperactive phenotype with defects in homeostasis, signaling and
cytokine production. We previously uncovered new roles for serine
arginine-rich splicing factor 1 (SRSF1) in the control of genes involved
in signaling and cytokine production in T cells. SRSF1 expression is
decreased in T cells from patients with SLE and low SRSF1 levels are
associated with severe disease activity. Mice with a T cell-conditional
deficiency of Srsf1 exhibit T cell hyperactivity, systemic autoimmunity,
and lupus-like nephritis. However, little is known about the molecular
targets controlled by SRSF1 and whether they are implicated in human
SLE. Our goal was to identify the molecular signatures controlled by
SRSF1 and evaluation by comparative bioinformatic analysis if these
genes and pathways are dysregulated in SLE. We curated publicly
available gene array datasets from SLE patients and compared them with
SRSF1-regulated genes in CD4 T cells from Srsf1-deficient mice. We
identified 169 overlapping genes controlled by SRSF1 that are aberrantly
expressed in T cells of SLE patients. Pathway analysis revealed genes
enriched in interferon signaling, cytokine production, cytokine receptor
interaction, cell migration and lysosomal clearance pathways. Our data
reveal that SRSF1 controls genes involved in T cell homeostasis,
activation, cytokine regulation/signaling and differentiation, which are
altered in patients with SLE. Therefore, SRSF1 is an important regulator
of T cell function and its deficiency may lead to a hyperactive T cell
phenotype in SLE patients. Targeting SRSF1 and the genes controlled by
this molecule to correct the aberrant T%
\end{abstract}%



\sloppy


\textbf{Splicing factor SRSF1 acts as a novel molecular brake for CD4 T
cell activation and controls key molecular pathways in SLE}

Rhea Bhargava\textsuperscript{1,2,3}*, Michelle
Lee\textsuperscript{1,3}, Rohit Upadhyay\textsuperscript{2}, Vaishali R.
Moulton\textsuperscript{1}

\textsuperscript{1}Department of Medicine, Division of Rheumatology and
Clinical Immunology,

Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA

\textsuperscript{2}John W. Deming Department of Medicine, Tulane
University School of Medicine, New Orleans, LA

\textsuperscript{3}Equal contribution co-first authors

*Corresponding author: Dr. Rhea Bhargava Email:
rbhargav@bidmc.harvard.edu

\textbf{Keywords:} SRSF1, SLE, CD4 T cells, transcriptomics, Comparative
bioinformatics

\textbf{Significance statement:} In this study, we identified 169
overlapping genes controlled by SRSF1, which were aberrantly expressed
in patients with SLE. Pathway analysis unveiled that these genes were
enriched in interferon signaling, cytokine production, cytokine receptor
interaction, cell migration, and lysosomal clearance pathways. The
findings also showed that SRSF1 plays a crucial role in regulating genes
involved in T cell homeostasis, activation, cytokine
regulation/signaling, and differentiation, all of which were found to be
altered in patients with SLE. Moreover, the study suggested that SRSF1
plays an important role in regulating T cell function and its deficiency
may lead to a hyperactive T cell phenotype in patients with SLE. These
findings shed light on the potential mechanisms underlying the
development of SLE and highlight potential of SRSF1 as a therapeutic
target for treating SLE.

\textbf{ABSTRACT}

T cells from systemic lupus erythematosus (SLE) patients exhibit a
hyperactive phenotype with defects in homeostasis, signaling and
cytokine production. We previously uncovered new roles for serine
arginine-rich splicing factor 1 (SRSF1) in the control of genes involved
in signaling and cytokine production in T cells. SRSF1 expression is
decreased in T cells from patients with SLE and low SRSF1 levels are
associated with severe disease activity. Mice with a T cell-conditional
deficiency of \emph{Srsf1} exhibit T cell hyperactivity, systemic
autoimmunity, and lupus-like nephritis. However, little is known about
the molecular targets controlled by SRSF1 and whether they are
implicated in human SLE. Our goal was to identify the molecular
signatures controlled by SRSF1 and evaluation by comparative
bioinformatic analysis if these genes and pathways are dysregulated in
SLE. We curated publicly available gene array datasets from SLE patients
and compared them with SRSF1-regulated genes in CD4 T cells from
Srsf1-deficient mice. We identified 169 overlapping genes controlled by
SRSF1 that are aberrantly expressed in T cells of SLE patients. Pathway
analysis revealed genes enriched in interferon signaling, cytokine
production, cytokine receptor interaction, cell migration and lysosomal
clearance pathways. Our data reveal that SRSF1 controls genes involved
in T cell homeostasis, activation, cytokine regulation/signaling and
differentiation, which are altered in patients with SLE. Therefore,
SRSF1 is an important regulator of T cell function and its deficiency
may lead to a hyperactive T cell phenotype in SLE patients. Targeting
SRSF1 and the genes controlled by this molecule to correct the aberrant
T cell phenotype may lead to potential novel therapeutics.

\textbf{INTRODUCTION}

Systemic lupus erythematosus (SLE) is a debilitating chronic systemic
autoimmune disease, which afflicts women in the childbearing years and
is among the leading causes of mortality in young women (Dall'Era,
2013). Aberrant gene regulation, signaling and function of T lymphocytes
are key features of immune dysregulation in lupus patients and mice
(Katsuyama et al., 2018; Moulton and Tsokos, 2015), and are therefore
potential therapeutic targets and biomarkers for disease management.
Delineating the molecular signatures and pathways underlying defective
immune cells and the molecules which control them is critical to
identifying new molecular targets. RNA sequencing/transcriptomic
profiling approaches have enabled the identification of molecular
signatures underlying the defects in immune cells in various autoimmune
diseases including SLE.

T cells from patients with SLE exhibit several signaling and gene
regulation defects, which contribute to their hyperactivity and
dysfunction (Katsuyama et al., 2018; Moulton and Tsokos, 2015). For
example, individuals with SLE have reduced transcript of CD3 zeta chain
resulting in defective proximal T cell receptor (TCR) signaling and
their hyperactivity (Kammer et al., 2002). Recruitment of alternative
downstream signaling molecules such as Syk kinase lead to increased
strength of signaling with increased calcium flux (Clements and
Koretzky, 1999; Kammer et al., 2002). Cytokine defects include low IL-2
production and increased inflammatory cytokines such as IL-17 have also
been noted in T cells and have established roles in SLE pathogenesis
(Alcocer-Varela and Alarcon-Segovia, 1982; Bengtsson et al., 2000;
Laurence et al., 2007; Linker-Israeli et al., 1983; Lourenco and La
Cava, 2009; Ytterberg and Schnitzer, 1982). Additionally, an elevated
type I interferon signature has been shown in SLE patients
(Alcocer-Varela and Alarcon-Segovia, 1982; Bengtsson et al., 2000;
Laurence et al., 2007; Linker-Israeli et al., 1983; Lourenco and La
Cava, 2009; Ytterberg and Schnitzer, 1982) which impacts multiple cells
of both the innate and adaptive immune systems. Yet the molecules that
control these global programs of TCR activation, differentiation,
cytokine production and cytokine signaling are not fully known.

Serine arginine rich splicing factor 1 (SRSF1) is the prototype member
of the SR family of splicing factors and is a key controller of
constitutive and alternative splicing events (Das et al., 2013). While
SRSF1 has been shown to control genes involved in cell survival (Maslon
et al., 2014), its roles in T cells or the immune system and in
autoimmune disease are virtually unknown. By discovery approaches of
oligo-pulldown and mass spectrometric proteomics screening, we
identified SRSF1 binding the mRNA of the TCR-associated CD3 zeta chain
and showed that SRSF1 positively regulates its expression in human T
cells (Moulton et al., 2015; Moulton et al., 2014; Moulton et al., 2008;
Moulton and Tsokos, 2010). We demonstrated that SRSF1 controls genes
involved in T cell signaling and cytokine production in human T cells
(Moulton et al., 2015; Moulton et al., 2013; Moulton and Tsokos, 2010).
Furthermore, we found that T cells from SLE patients exhibit low levels
of SRSF1, which is associated with severe disease (Moulton et al.,
2013); and importantly, overexpression of SRSF1 rescues IL-2 production
in SLE T cells (Moulton et al., 2013). Furthermore, we have recently
shown in proof-of-concept in vivo studies that selective deletion of
SRSF1 in T cells in mice leads to T cell hyperactivity and lupus-like
systemic autoimmune disease (Katsuyama et al., 2019; Katsuyama et al.,
2021; Katsuyama and Moulton, 2021). We have also shown that SRSF1 is
necessary for T cell homeostasis and its deficiency correlates with
comorbidities including lymphopenia in SLE patients (Katsuyama et al.,
2020). While we have identified by transcriptomics profiling a large
number of putative genes and pathways controlled by SRSF1 in T cells
from mice (Katsuyama et al., 2019), the relevance of these molecular
signatures in the context of SLE pathogenesis remains unknown.

The etiology of SLE is thought to be from an interplay between genetics,
environmental and hormonal factors. Besides rare cases of monogenic
lupus as seen with specific complement deficiencies, multiple genes may
influence an individual's risk of developing SLE along with
environmental triggers. High-throughput sequencing and transcriptomic
profiling can be used to quantify the expression levels of large numbers
of genes simultaneously. When combined with bioinformatic analysis and
supplementation with data from specific genetically engineered animal
models, one can delineate key pathways controlled by these specific
pathogenic genes in complex diseases.

Our recent studies have shown that T cell conditional SRSF1-ko mice
develop lupus like disease due to aberrant T cell function. We have
uncovered differentially expressed genes in these mice. In this study we
sought to examine the various genes that are differentially expressed in
CD4 T cells in individuals with SLE and identify pathways that are
possibly regulated by SRSF1. To this end, we performed comparative
bioinformatic analyses between transcriptomic profiles of CD4 effector T
cells from Srsf1-knockout (KO) mice with transcriptomes of CD4 T cells
from patients with SLE. We further performed pathway analysis along with
RNA -- protein interaction predictions to determine genes controlled by
SRSF1 in patients with active SLE.

\textbf{METHODS}

\emph{Transcriptomics profiling dataset from Srsf1-ko mice}

We used previously generated transcriptomics profiling data from
effector CD4 T (Teff) cells from Srsf1-ko mice (Moulton and Tsokos,
2015). Teff cells were generated by stimulating na\selectlanguage{ngerman}ïve CD4 T cells from
spleens of \emph{Srsf1} -T cell-ko or control WT (n=3) mice for 72h with
CD3/CD28 antibodies. RNA from CD4 T effector cells was subjected to
RNA-sequencing (R-seq). DAVID Bioinformatics Resource 6.8 Gene ID
Conversion was used to convert mouse to human gene names.

\emph{Transcriptomics datasets from SLE Patients}

To identify publicly available gene array data in CD4+ T lymphocytes
from patients with SLE, we used the NCBI gene expression omnibus (GEO)
2R datasets, PubMed, and Google Scholar databases. Studies that did not
use microarray or RNA sequencing were excluded from our search. Studies
that used naïve CD4+ T cells or only whole blood and peripheral blood
mononuclear cells (PBMC) without isolation of CD4+ T cells were also
excluded. We used the search word ``Lupus'' in (GEO)2R and identified
two datasets (GSE51997 and GSE4588) that matched our criteria. We
included 6 active SLE (SLEDAI range 6-22, ANA positive) and 4 healthy
controls from GSE51997 and 8 active SLE and 10 healthy controls from
GSE4588. Next, we used the search words ``Lupus RNA seq'' in PubMed and
identified one dataset (Crow, 2014) that matched our criteria for active
SLE (n=15) and 10 healthy controls (p\textless{}0.05). Finally, we used
Google Scholar and searched the keywords ``RNA seq'', ``T cells'', and
``Lupus'' using timeline limits between 2008 till 2019. Two studies fit
our requirements and we included them in our study (Liu et al., 2020).
From one study (Reyes et al., 2019), we used 5 active SLE and 5 healthy
controls (p\textless{}0.05). The Liu study had analyzed individuals with
SLE according to organ involvement. We included from this study, 4 SLE
(skin), 4 SLE (renal and skin symptoms), 4 SLE (renal and joint
symptoms), and 4 healthy controls (p\selectlanguage{english}[?]0.001). In total, 46 active SLE and
33 healthy controls from the five datasets from our search were included
in our analysis.

\emph{Comparative bioinformatics data analysis of transcriptomics data}

To compare the DEGs derived from RNA-sequencing data from the
T-cell-\emph{Srsf1} -cKO mice and from gene array datasets from SLE
patients, we used the MIT comparison tool BaRC
(\emph{http://barc.wi.mit.edu/tools/compare/} ). Next, comparative
analysis of mouse and human gene array data was performed using
Metascape (\emph{http://metascape.org/gp/index.html\#/main/step1} ) to
identify overlapping gene signatures in SLE patients controlled by
SRSF1. Data was analyzed for differentially expressed (DE) genes, gene
set enrichment, Kyoto encyclopedia of genes and genomes (KEGG) and gene
ontology (GO) pathways. In addition, omicX FunCoup tool
(\emph{https://omictools.com/funcoup-tool} ) was used for analysis of
protein-protein interactions (Figure 3c). STRING tool
(\emph{https://string-db.org/} ) was used to identify co-expressed genes
for Srsf-1 and our mouse and human gene lists (Figure 3d). Both STRING
and FunCoup were used to visualize interactomes between mouse and human
gene array data (Figure 3c). (GEO)2R Profile Graph was used for further
analysis of Srsf1 gene expression levels in GEO dataset GSE51997.
RPINbase (\emph{http://rpinbase.com/Explore} ) was used for RNA -protein
interactions.

\textbf{RESULTS}

\textbf{Workflow Set-up and datasets curated for comparative
bioinformatics analysis}

We have recently described new roles for the splicing factor SRSF1 in
the control of genes involved in signaling and cytokine production in T
cells and SLE (Moulton et al., 2015; Moulton et al., 2014; Moulton et
al., 2013; Moulton and Tsokos, 2010). We have also generated novel T
cell conditional Srsf1-KO mice which demonstrates SRSF1 as a novel
regulator of immune-response-related genes and pathways in CD4 effector
T cells from T cell conditional Srsf1-KO mice (Moulton and Tsokos,
2015). These mice develop T cell hyperactivity, systemic autoimmunity
and lupus like disease (Moulton and Tsokos, 2015). In this study we
evaluated by comparative bioinformatics analysis of transcriptomic data,
pathogenic pathways that are likely controlled by SRSF1 in SLE. Figure 1
shows a schematic of the workflow used for data analysis. Table 1
displays the five SLE patient datasets selected for analysis.
RNA-sequencing was done in effector CD4 T cells from mice to evaluate
genes controlled by SRSF1 (Katsuyama et al., 2019). The mouse effector
CD4 T cells RNA-sequencing data analysis yielded 612 DE genes (Figure
2A) compared to control mice at the 2-fold cutoff with a p
value\textless{}0.05. Of these, 312 genes were significantly upregulated
and 300 genes were downregulated.

\textbf{Identification of differentially expressed genes (DEG) in CD4 T
cells in SLE patients}

We analyzed publicly available gene array data from CD4 T cells in a
total of 46 individuals with SLE and 33 healthy controls. The NCBI gene
expression omnibus (GEO)2R database and published research in the
literature were used to curate publicly available gene array datasets in
SLE patients. Of these, only studies utilizing CD4 T cells were included
for analysis. Ultimately, a total of five datasets, three using
microarray technology and two utilizing RNA-sequencing were included for
analysis. Following data normalization, preprocessing, and filtering
with the criteria of adjusted p\textless{}0.05 and
\textbar{}log\textsubscript{2}FC\textbar{}\textgreater{}0.8, a total of
4179 genes were identified as differentially expressed in CD4 T cells of
patients with SLE compared with healthy controls. Among these, 2306 were
upregulated and 2002 were downregulated (Figure 2b). The overlapping
differentially regulated genes in SLE patients and SRSF1 KO mice were
evaluated to identify those that may be regulated by SRSF1 in humans
(Figure 2c) and the relationship between adjusted p value
(\textless{}0.05) and log2 fold expression of overlapping genes was
plotted (Figure 2d).

\textbf{Identification of common genes and pathways in CD4 T cells
controlled by SRSF1 and implicated in SLE patients.}

We first converted DEGs identified in Srsf1-KO mice to their human
homologues (Supplementary table 1). 41 genes from the upregulated group
and 30 genes from the downregulated group were excluded since these
genes were exclusive to mice and not found in humans (Supplementary
table 2). Next, we filtered the DEGs from human SLE patients and the
human homologues from SRSF1 KO mice for adjusted p value of
\textless{}0.05 and separated the genes that were shared in these two
data sets. From this analysis, we were able to identify 73 genes that
were upregulated in CD4 T cells of Srsf1-KO mice and in individuals with
SLE while 42 genes were downregulated in both datasets. These genes are
listed in Supplementary table 3. To identify the pathways represented by
these DEGs in mice and humans, we performed gene ontology (GO) analysis
(Figure 3). The most significant biological processes (BP) represented
by these DEGs involved the immune system particularly those pertaining
to immune responses and metabolism. Specifically, differentially
expressed upregulated genes involved pathways of leucocyte activation,
cytokine production and signaling (Figure 3A). In addition, we found the
dataset enriched in BPs of the defense response against viruses and
bacteria. The downregulated DEGs were significantly enriched in
regulation of protein complex assembly, peptidyl serine phosphorylation
and cell morphogenesis involved in differentiation (Figure 3B).

We next performed pathway analysis for these overlapping DEGs (Figure
4). Upregulated DEGs in CD4 T cells of both SRSF1 KO mice and
individuals with SLE were involved in biologic processes regulating
IL-10 signaling, positive regulation of cytokine signaling, leukocyte
differentiation and cellular response to biotic stimulus (Figure 4a).
Downregulated common DEGs from human homologues of SRSF1 KO mice and
individuals with SLE identified BPs regulating the HIF1 PID pathway and
regulation of protein establishment (Figure 4a). The top 5 pathways are
displayed in Table 2. Amongst the differentially upregulated genes,
cytokine production particularly IL-10 signaling was highlighted, while
the PID-HIF1 pathway was significantly represented by the differentially
downregulated genes.

In one dataset extracted from the study by Liu et al (Liu et al., 2020),
individuals with SLE were grouped according to organ involvement
including skin and/or renal and/or joint disease (Liu et al., 2020).
This gave us an opportunity to perform pathway analysis in common DEGs
between SRSF1-controlled genes and with genes segregated with organ
involvement in 4 SLE (skin), 4 SLE (skin and renal disease), and 4 SLE
(skin, renal and joint disease) patients (Figure 5). While most biologic
processes were similar, BPs involving B cell proliferation were
exclusively involved in individuals with skin and kidney disease.
Regulation of innate immune pathways appeared to be involved in
individuals with joint disease (Figure 5).

\textbf{Identification of major protein interaction networks in SLE that
may be controlled by SRSF1}

Next, we performed interactome analysis of common DEGs between human
homologues of upregulated genes controlled by SRSF1 and individuals with
SLE to identify protein-protein interaction (PPI) networks regulated by
SRSF1 (Figure 6). 151 functional interactions were identified in the
upregulated DEG group with a PPI enrichment p-value of \textless{}
1.0e-16. Based on the information in the STRING database, the PPI
network was constructed (Figure 6). We further identified top 10 genes
as HUB genes with the most interactions- OAS2, IL10, IFIT3, CXCL10,
CCR5, TMEM176, CSF, CCR2, CCNB2 and CCNA2 (Supplementary table 4).
Additionally, using the MCODE plugin we identified clusters (highly
interconnected regions) in a network. We selected the top 3 significant
modules and analyzed the cellular pathways of the genes involved in
these modules (Supplementary Table 5).

We performed similar analysis for the downregulated DEG cluster and
found only 5 interactions with a PPI enrichment p-value of 0.338. This
indicates that this set of proteins are essentially a random collection
of proteins that are not very well connected through physical
protein-protein interactions. Using the MCODE plugin the only module
identified in this cluster was for hemostasis (Supplementary Table 5).

\textbf{Identification of RNA binding targets for SRSF1 in individuals
with SLE}

SRSF1 is the prototype member of the serine arginine (SR) family of
splicing factors (Das et al., 2013). The SR proteins have a modular
domain structure with an N-terminal RNA-binding domain (RBD) and a
C-terminal RS (arginine/serine-rich) domain which is involved in
protein-protein interactions (van Der Houven Van Oordt et al., 2000).
Within its RBD, SRSF1 has two RNA recognition motifs (RRM), which
recognize specific RNA sequences within target genes (Aubol et al.,
2018). To identify targets that may be regulated by RNA-binding of
SRSF1, we evaluated the protein sequence of SRSF1 for RNA binding
partners using RPINbase. The entire list of predicted hits is displayed
in Supplementary table 6. We then filtered this list based on
overlapping DEG in the Srsf1-KO mice and individuals with SLE. We
analyzed the main pathways that were identified after enrichment
analysis from upregulated DEG from SRSF KO mice and individuals with SLE
that are regulated at the RNA level by SRSF1 (Figure 7A). The
interactions between these are displayed in the interactome map in
Figure 7A (left) and are displayed as a GO tree (right). For the
downregulated overlapping DEGs, we did not find specific pathway
enrichment however the genes regulated by SRSF1 at the RNA level were
found to regulate mainly cell metabolic processes and signaling (Figure
7B). Overall, these data suggest that the genes identified through this
analysis are likely RNA-binding targets of and regulated by SRSF1.

\textbf{Evaluation of DEGs in a cohort of SLE patients with low SRSF1
levels}

Since we have previously demonstrated that SRSF1 is decreased in a
cohort of individuals with SLE (Moulton et al., 2013), we determined the
prevalence of decreased SRSF1 levels amongst the available data. We
found one cohort (Kyogoku et al., 2013) in which individuals with SLE
had decreased SRSF1 levels compared to healthy controls (Figure 8A and
B). We then evaluated the DEGs that were overlapping between the
Srsf1-KO mice and this cohort of SLE patients with low SRSF1. These
genes are listed in Supplementary Table 7. In this dataset, 290 genes
were significantly (adjusted p\textless{}0.05) altered between patients
with active SLE compared to healthy controls (Figure 8C). The top
pathways represented in the DE genes in the Srsf1-ko mice were cell
cycle, Th1 and Th2 differentiation, Th17 differentiation and
cytokine-cytokine receptor interaction (Moulton and Tsokos, 2015).
Overall, the CD4 Teffs showed an elevated T cell activation gene
signature. Pathway analysis of the 290 DE genes in SLE patients
identified interferon signaling, cytokine production, cytokine receptor
interaction, cell migration and lysosomal clearance pathways.
Overlapping genes between human and mouse transcriptomics data were
analyzed. Specifically, we found 11 genes (CCR1, RHOG, ELL2, IFI16,
IFIT3, OAS2, ZER1, PRKD2, RGS3, SAT1, SOCS1) to be significantly altered
in active SLE patients, which were regulated by SRSF1 (Figure 8C, Table
3). Of these genes, IFI16, IFIT3 and OAS2 are associated with Type I
interferon pathway (Chang et al., 2013; Sadler and Williams, 2008;
Thompson et al., 2014), which is clearly known to be elevated and
pathogenic in development of lupus (Crow, 2014). Hence, these
comparative analyses suggest that SRSF1 controls genes, involved in T
cell homeostasis, activation, cytokine regulation/signaling and
differentiation, which are altered in patients with active SLE.

\textbf{DISCUSSION}

Low levels of SRSF1 has been associated with the severity of SLE in
human patients as well as in mice model ((Katsuyama et al., 2020;
Katsuyama and Moulton, 2021)). Specifically, T cell--restricted
Srsf1-deficient mice develop systemic autoimmunity, lupus-nephritis, and
an elevated T cell activation gene signature. In this study we attempted
to evaluate if the pathways controlled by SRSF1 in lupus prone mice can
be translated to the human SLE population. To this end, we compared the
transcriptomic profiles controlled by SRSF1 in CD4 T cells from mice
lacking SRSF1 which develop lupus like disease with the transcriptomes
of CD4 T cells from patients with SLE. We found common genes and
pathways between these gene sets and pathway signatures indicating that
the target genes of SRSF1 are dysregulated in SLE and may be implicated
in the pathogenesis of human autoimminity.

Our data indicate that a significant portion of upregulated DEGs are
enriched in cytokine regulation and leucocyte activation and
differentiation in SLE patients and SRSF1 KO mice (Table 2).
Downregulated DEGs are primarily involved in the immune response,
protein complex assembly and cell homeostasis (Table 2) (Kozyrev et al.,
2012; Sharabi et al., 2018; Wu et al., 2018). These features are in
accordance with the features of immune abnormalities typical of
autoimmune diseases and are consistent with prior studies validating our
current methodology (Chang et al., 2013; Crow, 2014; Sadler and
Williams, 2008; Sharabi et al., 2018; Thompson et al., 2014; Wu et al.,
2018). Based on the PPI network, the present study identified the top 10
hub genes (Supplementary Table 4), of which IFIT3 and OAS2 are
interferon-inducible genes whose encoding proteins are involved in the
innate immune response to viral infection (Liu et al., 2020; Schmeisser
et al., 2010), and are associated with a poor prognosis in SLE (Kyogoku
et al., 2013; Liao et al., 2016; Wu et al., 2018). Chemokine and
cytokine signaling has been described to be altered in SLE (Kaul et al.,
2016) and our findings are consistent with this result.

Since SRSF1 is an RNA binding protein, we evaluated its binding partners
amongst the differentially expressed overlapping genes in our mouse
model and individuals with SLE. Pathway analysis for upregulated DE
genes identified that interferon signaling including processes regulated
through OAS2, cytokine regulation including IL-10 pathway regulation and
chemokine signaling maybe regulated by this property of SRSF1 (Figure
7). This evaluation also identifies key genes in CD4 T cells that are
regulated by SRSF1 and are aberrant in individuals with SLE (Figure 7,
Supplementary Table 5). Through module analysis of the PPI network, the
present study determined that the development of SLE through SRSF1 was
closely associated with the chemokine binding to chemokine receptor and
regulation of cytosolic calcium concentration (Supplementary 4). The
overlapping downregulated genes indicated that the HIF PID pathway may
be controlled by SRSF1 in CD4 T cells. Calcium flux represents a key
component of signaling events that follow stimulation of lymphocytes
(Gronski et al., 2009), because it directs events that determine the
fate of the involved cells. Calcium signaling in systemic lupus
erythematosus (SLE) lymphocytes is increased following engagement of
immune receptors. In addition, HIF shifts the balance between follicular
regulatory and helper T cells but also regulate metabolism, numbers of
follicular helpers, and molecules they express to promote antibody
production. HIF and hypoxia influence CD4+ T cell provision of effector
cytokines in guidance to class switching, in part through mediation of
cytokine-specific metabolic programs in the T cell help to humoral
immunity (39). Interestingly NT5E encodes CD73 which is mostly expressed
in B cells however in SLE, CD73 has been shown to be a marker of
regulatory T cells, and its abnormal expression in Treg cells may
participate in the pathogenesis of SLE (40). Among the upregulated
genes, CCR1 and CCR5 were prominently featured (Table 2). Inhibition of
several chemokine receptors including CCR1 have been shown to prevent
lupus nephritis in lupus prone mice along with amelioration of
autoimmunity in SLE mouse models (41-42). CCR5 is involved in the
recruitment of inflammatory cells into tissues, and mechanisms
modulating CCR5 expression and function interfere in SLE development,
influencing the clinical course of the disease (43)

To further narrow down processes that are controlled by SRSF1, we
evaluated common genes and pathways in SRSF1 KO mice and SLE patients
with low expression of SRSF1 (Figure 8, Table 3). Overlapping genes
between human and mouse transcriptomics data specifically identified 11
genes (CCR1, RHOG, ELL2, IFI16, IFIT3, OAS2, ZER1, PRKD2, RGS3, SAT1,
SOCS1) to be significantly altered in active SLE patients and were
regulated by SRSF1 as confirmed by our mouse RNA-seq analysis.

These findings are hypothesis generating and enhance the current
findings which have been limited to mouse models. Some of these genes
(PTEN, mTOR pathway S6) and proteins (inflammatory cytokines) have been
validated by RT-PCR, western blot \& flow cytometry in mice (Katsuyama
et al., 2019); however, further confirmation and functional analysis for
the molecules identified will be performed in future studies.

In conclusion, these hub genes may have various roles in the occurrence
and development of the SLE, leading to damage of multiple systems in
SLE. Combined with bioinformatics analysis, the current study identifies
key genes and cellular pathways regulated by SRSF1, involved in aberrant
T cell function in human SLE.

\textbf{Acknowledgement:} This study was supported by the NIH K99 grant
(7K99AI162843-02) to RB.

\textbf{Declaration of Competing Interest:} The authors declare that
they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in
this paper.

\textbf{FIGURE LEGENDS}

\textbf{Figure 1. Workflow of bioinformatic comparative analysis of
transcriptomics datasets.}

Schematic shows the workflow of comparative analysis of transcriptomics
data of differentially expressed genes (DEG) from CD4 T cells from
Srsf1-ko mice and SLE patients.

\textbf{Figure 2. Differentially expressed genes (DEG) in CD4 T cells
from \emph{Srsf1}-ko mice overlap with genes aberrantly expressed in CD4
T cells from patients with SLE.}

\textbf{A} . Graph shows numbers of DEGs in effector CD4 T cells from
Srsf1-ko mice compared to control mice. \textbf{B.} Publicly available
gene expression datasets from CD4 T cells from patients with SLE were
curated from NCBI GEO and literature search. Graph shows cumulative
numbers of DEG in SLE patients compared to healthy individuals from five
datasets. \textbf{C.} Venn diagram shows numbers of overlapping genes
between \emph{Srsf1} -ko mice and SLE patients. \textbf{D.} Graph shows
relationship between adjusted p value and log2fold expression of
overlapping genes with p\textless{}0.05.

\textbf{Figure 3. Top pathways and interactome represented by DEGs in
Srsf1-ko mice and SLE patients reveal pathways in lymphocyte activation,
cytokine signaling and interferon response.}

\textbf{A} . and \textbf{B} . Metascape heatmap graphs show the top
pathways associated with the upregulated and downregulated DEG
in\emph{Srsf1} -ko mice (\textbf{A} ) and SLE patients (\textbf{B} ).
Network analysis on the right shows interactome represented by the DEGs.

\textbf{Figure 4. SRSF1 controls genes involved in T cell cytokine
production/signaling and differentiation which are aberrantly regulated
in SLE.}

Pathway enrichment analysis of overlapping DEGs from CD4 T cells
from\emph{Srsf1} -ko mice and SLE patients was performed in Metascape.
Graphs show the top pathways represented by the DEGs that overlap
between. Network analysis on the right shows interactions between DEGs
and thickness of connecting lines indicates strength of interactions.

\textbf{Figure 5. Molecular profiles stratified by target organ
involvement in SLE patients}

Pathway analysis of overlapping DEGs from one dataset (Liu et al., 2020)
of SLE patients with distinct organ involvement. Image shows relevant
pathways associated with DEGs genes overlapping between \emph{Srsf1} -ko
mice and SLE patients with respective SLE organ characteristics. (S =
SLE with no organ involvement, SK = SLE with skin and kidney
involvement, SKJ = SLE with skin, kidney and joint involvement.

\textbf{Figure 6. Interactions of DEGs co-expressed with SRSF1.}

Schematic shows co-expression analysis of DEGs with SRSF1 in \emph{Homo
sapiens} .

\textbf{Figure 7. Interaction analysis of pathways enriched by RNA
binding targets of SRSF1.}

\textbf{A.} Pathway interactions of upregulated genes. On the right is
shown the GO terms tree of upregulated genes. \textbf{B.} Table shows
pathways enriched by downregulated gene targets of SRSF1.

\textbf{Figure 8. Evaluation of overlapping DEGs in active SLE patients
with decreased \emph{Srsf1} levels.}

\textbf{A.} RNA-sequencing data from one dataset (Kyogoku et. al.,
GSE51997) (Kyogoku et al., 2013) of active SLE patients versus healthy
controls was analyzed using NCBI GEO2R for \emph{Srsf1} mRNA
expression.\textbf{B} . Graph shows significant decrease of \emph{Srsf1}
expression levels in CD4+ T cells in SLE patients (n=6) versus healthy
controls (n=4) (p=0.004). \textbf{C.} Venn diagram shows numbers of
overlapping genes.

\textbf{Table 1.} Table shows the sources of SLE datasets curated from
NCBI GEO2R and literature searches.

\textbf{Table 2.} Table shows top five pathways represented by the
overlapping DEGs between SLE patients and Srsf1-ko mice.

\textbf{Table 3.} Table shows 11 genes from one dataset (Kyogoku et al.,
2013) and associated biological processes and protein function of each
gene.

\textbf{Supplementary Table 1.} List of human homologues of DEGs
identified in Srsf1-KO mice

\textbf{Supplementary Table 2.} List of excluded mouse specific DEGs
(not found in humans equivalent data)

\textbf{Supplementary table 3} . Overlapping mouse and human DEGs

\textbf{Supplementary Table 4.} Selection and analysis of cellular
pathways of the genes involved in the top 3 significant modules using
MCODE

\textbf{Supplementary Table 5.} List of predicted targets regulated by
RNA-binding of SRSF1 protein

\textbf{Supplementary Table 6.} List of predicted targets regulated by
RNA-binding of SRSF1 protein.

\textbf{Supplementary Table 7.} List of the overlapping DEGs between
Srsf1-KO mice and cohort of SLE patients with low SRSF1.



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