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\begin{document}

\title{Exhausted T cells in systemic lupus erythematosus patients in
long-standing remission.}



\author[1]{Guadalupe Lima}%
\author[2]{Francisco Treviño-Tello}%
\author[3]{Yemil Atisha-Fregoso}%
\author[1]{Luis Llorente}%
\author[1]{Hilda Fragoso-Loyo}%
\author[1]{Juan Jakez-Ocampo}%
\affil[1]{Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán}%
\affil[2]{Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubiran}%
\affil[3]{Elmezzi Graduate School of Molecular Medicine}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Introduction The mechanisms that drives SLE remission.The aim of the
present study was to measure CD4+ and CD8+ T cell exhaustion in SLE
patients in prolonged remission (PR-SLE) and compared them with patients
with active SLE (Act-SLE) and healthy subjects Methods We included 15
PR-SLE patients, 15 Act-SLE and 29 healthy subjects. T-cell exhaustion
was determined by flow cytometry according to the expression of PD-1,
Tim-3, 2B4, EOMES and T-bet in CD4+ and CD8+ T cells. Dimensionality
reduction using the t-Distributed Stochastic Neighbor Embedding
algorithm and Clustering Analysis was used for the identification of
relevant populations. Results Percentages of CD3+, CD4+ and CD8+ T cells
were similar among groups. We identified five subpopulations of CD8+ and
seven of CD4+ cells. The CD4+Tbet+CD45RO+ cells identified in the
unsupervised analysis were significantly increased in PR-SLE vs Act-SLE
(median: 10.20, IQR: 1.74-30.50 vs. 1.68, IQR: 0.4-2.83;
p\textless{}0.01). CD4+EOMES+ cells were also increased in PR-SLE vs
Act-SLE (5.24, IQR: 3.38-14.70 vs. 1.39, IQR: 0.48-2.87;
p\textless{}0.001). CD8+ EOMES+ cells were increased in PR-SLE vs
Act-SLE (37.6, IQR: 24.9-53.2 vs 8.13, IQR: 2.33-20.5;
p\textless{}0.001). Exhausted and activated T cells presented an
increased frequency of PD-1, CD57 and EOMES in SLE patients vs healthy
subjects. Conclusions Some subpopulations of T cells expressing markers
associated with exhaustion are increased in patients in remission,
supporting T-cell exhaustion as a tolerance mechanism in SLE. Exhaustion
of specific populations of T cells might represent a potential
therapeutic tool that will contribute to the goal of achieving sustained
remission in these patients.%
\end{abstract}%



\sloppy


\textbf{Exhausted T cells in systemic lupus erythematosus patients in
long-standing remission} .

Short title: Exhausted T cells in SLE

Guadalupe Lima*\textsuperscript{+}, Francisco
Trevi\selectlanguage{ngerman}ño-Tello*\selectlanguage{english}\textsuperscript{+}, Yemil
Atisha-Fregoso\selectlanguage{english}\textsuperscript{++SS}, Luis Llorente*, Hilda
Fragoso-Loyo*\selectlanguage{english}\textsuperscript{[?]}, Juan Jakez-Ocampo*\selectlanguage{english}\textsuperscript{[?]}

*Department of Immunology and Rheumatology, Instituto Nacional de
Ciencias Medicas y Nutricion Salvador Zubiran, Mexico City, Mexico.

\selectlanguage{english}\textsuperscript{++}Elmezzi Graduate School of Molecular Medicine at
Northwell Health, Manhasset, New York, USA.

\textsuperscript{SS}Institute of Molecular Medicine, the Feinstein
Institutes for Medical Research, Manhasset, New York, USA.

\selectlanguage{english}\textsuperscript{+}These authors have contributed equally to this work.

\selectlanguage{english}\textsuperscript{[?]}Correspondence:

Juan Jakez-Ocampo: jjakez\_ocampo@yahoo.com or Hilda Fragoso-Loyo:
hfragosol@gmail.com

Key words: Clinical remission in SLE; T cell exhaustion; Clustering
analysis.

Word count: 2625

\textbf{Abstract}

\textbf{Introduction}

The mechanisms that drives SLE patients to achieve remission are
unknown, one possible explanation might be T cell exhaustion. The aim of
the present study was to measure CD4+ and CD8+ T cell exhaustion in SLE
patients in prolonged remission (PR-SLE) and compared them with patients
with active SLE (Act-SLE) and healthy subjects

\textbf{Methods}

We included 15 PR-SLE patients, 15 Act-SLE and 29 healthy subjects.
T-cell exhaustion was determined by flow cytometry according to the
expression of PD-1, Tim-3, 2B4, EOMES and T-bet in CD4+ and CD8+ T
cells. Dimensionality reduction using the t-Distributed Stochastic
Neighbor Embedding algorithm and Clustering Analysis was used for the
identification of relevant populations.

\textbf{Results}

Percentages of CD3+, CD4+ and CD8+ T cells were similar among groups. We
identified five subpopulations of CD8+ and seven of CD4+ cells. The
CD4+Tbet+CD45RO+ cells identified in the unsupervised analysis were
significantly increased in PR-SLE vs Act-SLE (median: 10.20, IQR:
1.74-30.50 vs. 1.68, IQR: 0.4-2.83; p\textless{}0.01). CD4+EOMES+ cells
were also increased in PR-SLE vs Act-SLE (5.24, IQR: 3.38-14.70 vs.
1.39, IQR: 0.48-2.87; p\textless{}0.001). CD8+ EOMES+ cells were
increased in PR-SLE vs Act-SLE (37.6, IQR: 24.9-53.2 vs 8.13, IQR:
2.33-20.5; p\textless{}0.001). Exhausted and activated T cells presented
an increased frequency of PD-1, CD57 and EOMES in SLE patients vs
healthy subjects.

\textbf{Conclusions}

Some subpopulations of T cells expressing markers associated with
exhaustion are increased in patients in remission, supporting T-cell
exhaustion as a tolerance mechanism in SLE. Exhaustion of specific
populations of T cells might represent a potential therapeutic tool that
will contribute to the goal of achieving sustained remission in these
patients.



\textbf{Introduction}

Systemic lupus erythematosus (SLE) is an autoimmune, multifactorial
disease with broad tissue and organ involvement. Its manifestations are
usually heterogeneous, with a fluctuating and unpredictable course
{[}1{]}. Despite the advances in the treatment and understanding of its
pathogenesis, disease activity continues to be one of the major causes
of morbidity and mortality. Progressive accumulation of irreversible
damage is attributed to chronic inflammation, as well as to the
prolonged use of steroids and immunosuppressants {[}2{]}. Many studies
conducted in recent years have focused on the search for treatments and
interventions that reduce or prevent relapses in patients with SLE, with
the goal of reaching a sustained remission and limiting damage {[}3,
4{]}. Despite the information provided by these studies, very little is
known about the clinical and serological features that characterize the
group of patients with SLE who achieve sustained remission and the
cellular, genetic, or molecular factors involved in this task.

T-cell exhaustion is a complex process that has been increasingly
recognized in recent years in patients with chronic infections and
cancer. T-cell exhaustion is driven by persistent TCR stimulation in the
absence of enough co-stimulation and is characterized by limited
proliferation, cytokine production, and effector function {[}5{]}. There
is not a universally accepted set of markers that define the population
of exhausted T cells, however, is recognized that they express a variety
of inhibitory receptors that reduce their response after activation,
such as PD-1, Tim-3 or 2B4 and differentially express transcription
factors such as EOMES and T-bet.

There are few reports about the role of exhausted T cells in
autoimmunity, but they have linked their presence with an improved
prognosis in several autoimmune diseases {[}6{]}. There is also evidence
of an association of exhausted T cells and increased immune tolerance in
transplantation {[}7{]}. Because of this potentially favorable effect of
the exhaustion process in autoimmunity, we conducted this study with the
aim of measuring CD4+ and CD8+ T cell exhaustion by quantifying surface
inhibitory molecules and transcriptional factors by flow cytometry in
SLE patients in prolonged remission and to compare them with active SLE
patients and healthy subjects.

Material and Methods

\textbf{Patients and controls}

This was a cross-sectional study conducted at the Instituto Nacional de
Ciencias Medicas y Nutricion Salvador Zubiran, a tertiary care center in
Mexico City. We included 15 patients with SLE in long-term remission
without treatment (PR-SLE). Prolonged remission was defined as a
clinical SLEDAI of 0 (regardless of serological activity, i.e.,
anti-dsDNA antibodies and/or hypocomplementemia) and being free of
immunosuppressive or antimalarial treatment for at least 10 years
{[}8{]}. As controls, we included 15 patients with clinically active SLE
(Act-SLE) with a clinical SLEDAI 2K \selectlanguage{english}[?]3 (excluding hypocomplementemia and
high titers of anti-dsDNA) and 29 healthy subjects (HS). All the healthy
controls were confirmed negative for antinuclear antibodies and with no
personal or familial history of autoimmune diseases. All patients with
SLE met the SLICC 2012 classification criteria {[}9{]}.

\textbf{Flow Cytometry}

One sample of peripheral blood was obtained from each patient according
to standard procedures. EDTA-treated blood samples were analyzed by
8-color flow-cytometry (Becton Dickinson Canto II Cytometer) using
several combinations of fluorescence-labeled antibodies distributed in
the following panels: Panel 1: CD3 FITC, CD4 APC/Cy7,~~CD25 BV421, CD127
APC and FOXP3 PE; Panel 2: CD3~~PerCP/Cy5, CD4 APC/Cy7, CD8 Alexa Fluor
488, HLA-DR PE, CD244 (2B4) APC,~~CD279 (PD-1) BV421, CD38 BV510 and
PE/Cy7 CD366 (Tim-3); Panel 3: CD3 PE/Cy7, CD4 PE/Cy5, CD8 BV510, CD27
APC/Cy7, CD57 APC, CD45RO FITC, T-bet BV421 and EOMES PE; Panel 4: CD3
APC, CD4 BV510, CD8 PE, CD45RA FITC, CD197 APC/Cy7. All antibodies were
purchased from Biolegend Inc. (San Diego, CA) except EOMES PE
(eBioscience). Briefly, 250 \selectlanguage{greek}µ\selectlanguage{english}L of blood was incubated with
fluorochrome-conjugated antibodies for 30 min at room temperature prior
to lysis with RBC Lysis Buffer (Biolegend Inc.) and fixation with 3\%
formaldehyde in PBS.

OneFlow  Setup Beads (BD Biosciences) were used to adjust instrument
settings, set fluorescence compensation, and check instrument
sensitivity. `Fluorescence minus one' controls were used to determine
positive and negative staining boundaries for each fluorochrome. Two
hundred and fifty thousand events were recorded for each sample and
analyzed with the FlowJo\selectlanguage{ngerman}® software v.10.7.1 (FlowJo, LLC, Oregon, USA).
FlowAI plugin was used to perform an automatic or interactive quality
control on the .fcs data {[}10{]}. Lymphocyte population was identified
in an SSC-A versus FSC-A plot, followed by doublets exclusion in a
FSC-Height (FSC-H) by FSC-Area (FSC-A) scatter plot. Phenotypes used to
define mayor and minor T cell populations are depicted in Table 3.

\textbf{Dimensionality Reduction and Clustering Analysis}

After excluding death cells and doublets, the resulting flow cytometry
data files were gated on the CD3+ population using FlowJo 10.7.1. Prior
to further analysis, all groups were down-sampled to equal cell numbers
and concatenated in a single file. We then used a tSNE algorithm to
reduce dimensionality of the data followed by Self Organized Maps
(FlowSOM) {[}11{]} for unsupervised clustering of the data into
subpopulations. The resulting subpopulations were further analyzed for
specific marker expression and frequency in each group. We also compared
populations that have been reported in the literature and that were
manually gated independently of the findings of the unsupervised
clustering. These populations were: CD4/CD8 PD-1+, CD4/CD8 T-bet+,
CD4/CD8 2B4, CD4/CD8 Tim-3, CD4/CD8 EOMES, CD4/CD8 CD57,~CD4/CD8
CD38+HLA-DR+ (activated T cells) and CD4/CD8 CD38+HLA-DR+PD-1+
(exhausted T cells).

\textbf{Statistical analysis}

Descriptive statistics: Continuous data is presented as median with
interquartile ranges (IQR; 25 - 75). For comparisons of quantitative
variables between 2 groups, the Mann-Whitney U test was used, while the
Kruskal-Wallis test followed by Dunn's test was used to compare \selectlanguage{english}[?]3
groups. Categorical values are presented as number and percentage.
Chi-squared test was used to compare categorical variables between
groups. The statistical analysis was performed using the SPSS software
v.21.0 (IBM). P value \selectlanguage{english}[?] 0.05 was considered significant.

The study was approved by the Institutional Review Board of the
Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran
(Ref. 2232). All subjects gave their informed consent to participate
according to the Declaration of Helsinki.

\textbf{Results}

\textbf{Clinical and demographic characteristics}

Of the fifteen patients with PR-SLE, fourteen (93.3\%) were women, with
a median age of 49 years (48-60). All Act-SLE patients were women with a
median age of 33 years (30-46) and 28 (97\%) of the healthy subjects
were women, median age 43 years (32-52). Patients with Act-SLE presented
lymphopenia, with lower lymphocyte counts when compared with PR-SLE and
healthy controls (1.12 x103 / \selectlanguage{greek}µ\selectlanguage{english}L {[}0.74 - 1.71{]} vs 1.67 x103 {[}1.23
- 2.76{]} vs 2.17 x103 {[}1.61 - 2.41{]} p = 0.030) The demographic and
clinical characteristics at the time of recruitment are detailed in
Table 1.

No differences were found between in the SLICC 2012 classification
criteria at diagnosis between both groups of patients. At inclusion,
disease duration in PR-SLE patients was 25.6 years (19.1 - 35.4) vs 9.25
(5.75 - 12.5) in Act-SLE patients (p \textless{}0.001). The median time
in remission was 16.9 years (12.1 - 20.6). The PR-SLE group had a
SLEDAI-2k score of 0 (0-2) versus 8 (6-16) in the Act-SLE group (p =
0.001). The clinical manifestations, activity indices and accumulated
damage, and treatment of active patients are described in Table 2.

\textbf{Major Lymphocyte Populations}

There were no differences in the relative percentage of CD3+, CD4+ or
CD8+ T cells between groups. Among the subpopulations of CD4+ T cells,
only the central memory compartment was significantly different between
groups, being expanded in the PR group, compared with the other two.
More differences were observed in the subpopulations of CD8+ T cells.
Similar to what we observed within CD4+ T cells, patients in PR showed
an increased central memory compartment, and a concomitant reduction in
the percentage of na\selectlanguage{ngerman}ïve CD8+ cells, when compared with the other groups.
Meanwhile, patients with Act-SLE presented a decreased percentage of
CD8+ effector memory cells. The percentages of the subpopulations
analyzed and the p values for the comparisons are described in Table 3.

\textbf{T cell exhaustion markers and populations}

As there is no clear consensus, and just little information about the
specific marker combination that describes specific subpopulations of
exhausted T cells in patients with SLE, we initially used an
unsupervised approach to explore the behavior and clustering of surface
markers and transcription factors that have been reported as relevant in
exhausted T cells (see methods for the complete list of markers
included). As a first step, we generated tSNE maps using concatenated
files with an equal number of CD3+ cells from all the patients included
in each group. Figures 1A and 1B show the tSNE plots for each group, and
the distribution of the included markers across the plots. In order to
identify the clustering of these markers in subpopulations, we used a
self-organizing maps algorithm {[}11{]}. We could identify five
subpopulations of CD8+, seven of CD4+ cells and three CD3+CD4-CD8-. The
heatmaps and tables showing the relative expression of markers in each
cluster across all the identified subpopulations are shown in figure 1C.
Overlapping of these populations on the tSNE plots are shown in 1D.
Histograms represent the relative expression of a CD3+ CD8+ cell
population that seems to be exclusive to the PR-SLE group, in both in
panel II and III (2.86\% and 3.75\% respectively) and which expresses
significantly transcription factors and surface markers associated with
senescence and depletion.

\textbf{Populations differentially represented in the PR when compared
with the Act-SLE group} .

Within the CD4+ compartment, a population of Tbet+CD45RO+ cells
identified in the unsupervised analysis, which represents 10.20\%
(1.74--30.50) of the CD4+ cells in the PR group, was significantly
increased compared with the Act-SLE (1.68, IQR: 0.42--2.83) and HS
(4.08, IQR: 1.64--5.48). The CD4+EOMES+ cells were also increased in the
PR group compared with Act-SLE and HS. The other population that was
significantly different between PR and Act-SLE was the CD4+Tbet+ cells,
which were increased in the PR group, and similar to the HS.

Within the CD8+ T cells, the proportion of PD1+ cells was apparently
increased only in the PR group, even though the difference was only
significant when compared with the HS. The CD8+2B4+ cells, identified
through the unsupervised clustering, were increased in the PR vs Act-SLE
group. The most striking difference was observed in the EOMES+ cells,
with a median of 37.6 (24.9--53.2) in the PR group, vs 12.9 (6.78--24.8)
in the HS and 8.13 (2.33--20.5) in the Act-SLE groups.

\textbf{Differences between SLE patients and healthy subjects}

We observed a significant increase in the frequency of expression of
cell activation and exhaustion markers in both the CD4+ and the CD8+
compartment between the SLE patients and HS group. The T cell
subpopulations in SLE patients presented an increase in the frequency of
PD-1, CD57, EOMES, and exhausted and activated populations when compared
with the HS group. Table 4 and Figure 2 shows all the populations that
were identified as differentially expressed between groups.

\textbf{Discussion}

Remission in SLE implies absence of major signs and symptoms, which
translate into a lower probability of adverse outcomes when this state
is sustained. In 2016, the conclusions from an international consensus
of experts on the definitions of remission in SLE (DORIS) were published
{[}12{]}. It was established that to catalog a patient in remission it
is important to rely on activity indices (as SLEDAI, BILAG or ECLAM).
The consensus also highlights the importance of differentiating patients
in remission who are under treatment (low doses of glucocorticoids,
maintenance immunosuppressants and/or biological maintenance) from those
without treatment (or with antimalarials). All the patients classified
and included as in remission in this study, had a sustained clinical
SLEDAI of 0, and were free of medication, including antimalarials for at
least 10 years.

Patients with SLE, even after achieving remission for a long time, are
not considered cured, and there is evidence confirming a subjacent
autoimmune response: presence of autoantibodies or low complement
{[}8{]} or relapses after long periods in remission (over 10 years)
{[}13{]}. How the altered immune response is kept under control in
patients with lupus that achieve remission is not understood and
understudied. Our results support T cell exhaustion as a contributor in
this process.

Using an unsupervised flow cytometric analysis, we found that multiple
populations that express markers that have been associated with T cell
exhaustion like PD-1, CD57 and EOMES are altered in patients with SLE
when compared with HS. Remarkably, not all the populations with markers
of exhaustion were different between SLE patients and HS, finding
consistent with previous reports, that suggest that opposed to what
occurs in models of chronic viral infection, patients with autoimmune
disease have a selective upregulation of exhaustion associated receptors
{[}6{]}. This phenomenon suggests that the signals that induce
exhaustion in each condition are somehow context specific.

Exhausted T cells have a differential transcriptome and epigenome when
compared with effector T cells {[}14, 15{]}, and exhaustion is
considered a stable differentiation fate of T cells, even though it can
be reversed by therapeutic interventions. The presence of exhausted T
cells in patients with an active autoimmune disease, suggests that cells
that are activated by an ongoing inflammatory response or by the
persistent presence of an autoantigen, cannot get costimulatory signals
above certain threshold, and are acquiring an exhausted phenotype
{[}16{]}. The function of these cells is not completely known, but in
patients with cancer, exhausted cells in the tumor microenvironment
contribute with the tumor immune evasion, or induced tumor tolerance.
This deleterious effect in cancer might have a positive implication in
other diseases, when tolerance has a positive connotation, as
transplantation and autoimmunity {[}17{]}.

In patients with autoimmune diseases, T cell exhaustion has been
associated more with the progression than with the onset of the disease
{[}6, 18{]}. In this regard, this is the first study that identifies
specific subpopulations of exhausted T cells in patients with lupus in
long-term remission.

The mechanisms underlying the hyporesponsive state of exhausted cells
are also subject of study. We found an increased frequency of CD4 and
CD8 EOMES+ cells in patients in remission. This transcription factor,
member of the T-bet family, has also been described as altered in
chronic viral infection {[}19{]} and cancer {[}20{]}. Remarkably, in
patients with cancer, EOMES is necessary for the induction of cytotoxic
anti-tumor T cells, however, the deletion of one allele of this
transcript factor reduced the number of exhausted T cells and improved
tumor control {[}20{]}. This dual effect emphasizes the fine tuning
involved in the generation of an exhausted versus effector response in T
cells. A deeper understanding of the mechanisms that induce exhaustion
of specific populations of T cells that are pathogenic in patients with
SLE, might represent a potential therapeutic tool that will contribute
to the goal of achieving sustained remission.

There are some limitations of this study. The study is transversal,
however, to identify and study longitudinally patients in long-term
remission free of medication before they achieve this status is probably
not feasible. We believe this group of patients provides an ideal group
for this analysis, as there is no confounding effect caused by
medications and there is no recent evidence of disease activity. We
studied a relatively reduced number of markers associated with
exhaustion, however, these were carefully selected and allowed us to
identify populations that were differentially present in the groups of
interest.

Our results confirm the presence of certain subpopulations of exhausted
T-cells in patients in lupus that are unequivocally in remission. This
suggests a potential role of T-cell exhaustion as a mechanism of
tolerance in patients with SLE. The populations identified in the
present study warrant further investigation as prognostic markers in
longitudinal studies of these patients, and in independent cohorts. The
identification of the specific mechanisms behind the origin of these
populations might provide new therapeutic options in SLE.

\textbf{Aknowledgements}

This research was supported by a grant from the Consejo Nacional de
Ciencia y Tecnología-Fondo Sectorial de Investigación en Salud
(CONACYT-FOSIS), Grant No.289732, México. This work was the Master's in
Science degree thesis of Francisco Treviño-Tello, Universidad Anáhuac
del Norte, México.

\textbf{Abbreviations}

SLE Systemic lupus erythematosus

PR-SLE SLE in long-term remission

Act-SLE Clinically active SLE

HS Healthy subjects

PD-1 Programmed cell death protein 1

Tim-3 T-cell immunoglobulin and mucin domain-3

2B4 Natural killer cell receptor

EOMES Eomesodermin

T-bet T-box transcription factor TBX21

CD Cluster of differentiation

\textbf{Author contributions}

GL and FT-T performed immunological experiments, analyzed results. YA-F
and LL conceived and supervised experiments, analyzed results, and wrote
the manuscript. HF-L supervised and analyzed clinical data and results.
JJ-O analyzed clinical data, analyzed results, and wrote the manuscript.

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Systemic Lupus Erythematosus. \emph{Arthritis Care Res (Hoboken)}
2019;\textbf{71} (6):822-28.

14.\selectlanguage{english} Pauken KE, Sammons MA, Odorizzi PM, Manne S, Godec J, Khan O, Drake
AM, Chen Z, et al. Epigenetic stability of exhausted T cells limits
durability of reinvigoration by PD-1 blockade. \emph{Science}
2016;\textbf{354} :1160-65.

15.\selectlanguage{english} Sen DR, Kaminski J, Barnitz RA, Kurachi M, Gerdemann U, Yates KB,
Tsao H-W, Godec J, et al., The epigenetic landscape of T cell
exhaustion. \emph{Science} 2016; \textbf{354} :1165-69.

16.\selectlanguage{english} McKinney EF, Smith KGC. Metabolic exhaustion in infection, cancer
and autoimmunity. \emph{Nat Immunol} 2018;\textbf{19} : 213-21.

17.\selectlanguage{english} Pawelec G. Is There a Positive Side to T Cell Exhaustion?\emph{Front
Immunol} 2019; \textbf{10} :111.

18.\selectlanguage{english} Long SA, Thorpe J, DeBerg HA, Gersuk V, Eddy J, Harris KM, Ehlers M,
Herold KC, et al. Partial exhaustion of CD8 T cells and clinical
response to teplizumab in new-onset type 1 diabetes. \emph{Sci
Immunol}2016; \textbf{1} : eaai7793.

19.\selectlanguage{english} Paley MA, Kroy DC, Odorizzi PM, Johnnidis JB, Dolfi DV, Barnett BE,
Bikoff EK, Robertson EJ, et al. Progenitor and terminal subsets of CD8+
T cells cooperate to contain chronic viral infection.
\emph{Science}2012; \textbf{338} :1220-5.

20.\selectlanguage{english} Li J, He Y, Hao J, Ni L, Dong C. High Levels of Eomes Promote
Exhaustion of Anti-tumor CD8 (+) T Cells. \emph{Front Immunol}
2018;\textbf{9} : 2981.

\textbf{Table 1. Clinical Characteristics of Prolonged Remission SLE
Patients, Active SLE and Healthy Subjects.}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& Healthy Subjects & Active SLE & PR-SLE & p\tabularnewline
\midrule
\endhead
Demographics & \textbf{N = 29} & \textbf{N = 15} & \textbf{N = 15}
&\tabularnewline
Sex & & & & 0.61\tabularnewline
Female (\%) & 28 (96.6) & 15 (100) & 14 (93.3) &\tabularnewline
Age (years) & 43 (32 -- 52) & 33 (30 -- 46) & 49 (48 -- 60) &
\textbf{\textless{} 0.01}\tabularnewline
Body Mass Index & 24.74 (23.66 -- 26.37) & 23.12 (20.57 -- 25.43) &
24.45 (21.3 -- 32.88) & 0.47\tabularnewline
Smoker (\%) & 9 (31) & 6 (40) & 5 (33.3) & 0.70\tabularnewline
Laboratory characteristics & & & &\tabularnewline
Positive ANA (\%) & 0 (0) & 14 (93.3) & 15 (100) & \textbf{\textless{}
0.001}\tabularnewline
ESR & 7.5 (4 -- 10) & 5 (2 -- 12) & 3 (2 -- 5) & 0.15\tabularnewline
Positive ESR (\%) & 0 (0) & 1 (6.7) & 1 (6.7) & 0.35\tabularnewline
Hemoglobin (g/dl) & 14.4 (14.1 -- 15.2) & 13.5 (12.6 -- 14.7) & 14.5
(12.6 -- 15.7) & 0.10\tabularnewline
Hematocrit (\%) & 44.9 (43.1 -- 45.6) & 40.6 (37.4 -- 44.2) & 42.1 (37.6
-- 45.6) & \textbf{0.01}\tabularnewline
Leukocytes (x10\textsuperscript{3}) & 6.3 (5.6 -- 6.9) & 5.7 (4.1 --
9.2) & 5.3 (4.0 -- 6.9) & 0.30\tabularnewline
Neutrophils (x10\textsuperscript{3}) & 3.84 (3.29 -- 4.27) & 4.04 (2.94
-- 6.10) & 3.36 (1.78 -- 3.82) & \textbf{0.04}\tabularnewline
Lymphocytes (x10\textsuperscript{3}) & 2,17 (1.61 -- 2.41) & 1.12 (0.74
-- 1.71) & 1.67 (1.23 -- 2.76) & \textbf{0.03}\tabularnewline
Platelets (K/\selectlanguage{greek}µ\selectlanguage{english}L) & 260.0 (235.0 -- 289.0) & 263.0 (237.0 -- 283.0) &
217.0 (150.0 -- 256.0) & \textbf{\textless{} 0.01}\tabularnewline
n/a: not applicable. ANA: antinuclear antibodies. ESR: Erythrocyte
sedimentation rate. Results for quantitative variables are shown in
Median with Interquartile Range, and Kruskal Wallis test was used to
perform comparisons between groups. For quantitative variables measured
only in two groups, Mann Whitney U test was used to perform comparisons
between both groups. The qualitative variables are presented in
frequencies, using Chi squared test to compare between groups. Mann
Whitney U test was performed to compare between two groups as a post Hoc
test, in which PR-SLE patients are older than Active SLE patients (p =
0.001), and older than the healthy subjects (p = 0.017). & n/a: not
applicable. ANA: antinuclear antibodies. ESR: Erythrocyte sedimentation
rate. Results for quantitative variables are shown in Median with
Interquartile Range, and Kruskal Wallis test was used to perform
comparisons between groups. For quantitative variables measured only in
two groups, Mann Whitney U test was used to perform comparisons between
both groups. The qualitative variables are presented in frequencies,
using Chi squared test to compare between groups. Mann Whitney U test
was performed to compare between two groups as a post Hoc test, in which
PR-SLE patients are older than Active SLE patients (p = 0.001), and
older than the healthy subjects (p = 0.017). & n/a: not applicable. ANA:
antinuclear antibodies. ESR: Erythrocyte sedimentation rate. Results for
quantitative variables are shown in Median with Interquartile Range, and
Kruskal Wallis test was used to perform comparisons between groups. For
quantitative variables measured only in two groups, Mann Whitney U test
was used to perform comparisons between both groups. The qualitative
variables are presented in frequencies, using Chi squared test to
compare between groups. Mann Whitney U test was performed to compare
between two groups as a post Hoc test, in which PR-SLE patients are
older than Active SLE patients (p = 0.001), and older than the healthy
subjects (p = 0.017). & n/a: not applicable. ANA: antinuclear
antibodies. ESR: Erythrocyte sedimentation rate. Results for
quantitative variables are shown in Median with Interquartile Range, and
Kruskal Wallis test was used to perform comparisons between groups. For
quantitative variables measured only in two groups, Mann Whitney U test
was used to perform comparisons between both groups. The qualitative
variables are presented in frequencies, using Chi squared test to
compare between groups. Mann Whitney U test was performed to compare
between two groups as a post Hoc test, in which PR-SLE patients are
older than Active SLE patients (p = 0.001), and older than the healthy
subjects (p = 0.017). & n/a: not applicable. ANA: antinuclear
antibodies. ESR: Erythrocyte sedimentation rate. Results for
quantitative variables are shown in Median with Interquartile Range, and
Kruskal Wallis test was used to perform comparisons between groups. For
quantitative variables measured only in two groups, Mann Whitney U test
was used to perform comparisons between both groups. The qualitative
variables are presented in frequencies, using Chi squared test to
compare between groups. Mann Whitney U test was performed to compare
between two groups as a post Hoc test, in which PR-SLE patients are
older than Active SLE patients (p = 0.001), and older than the healthy
subjects (p = 0.017).\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2. SLICC 2012 criteria, disease activity, accrual damage,
and treatment in the SLE patients.}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& Active SLE & PR-SLE & P\tabularnewline
\midrule
\endhead
& \textbf{N = 15} & \textbf{N = 15} &\tabularnewline
Duration of SLE (years) & 9.25 (5.75 -- 12.5) & 25.6 (19.1 -- 35.4) &
\textbf{\textless{} 0.001}\tabularnewline
Remission time (years) & n/a & 16.9 (12.1 -- 20.6) & n/a\tabularnewline
SLICC 2012 SLE classification criteria items & \textbf{7 (6 -- 9)} &
\textbf{7 (6 -- 8)} & 0.50\tabularnewline
Acute cutaneous lupus & 12 (80) & 10 (66.7) & 0.63\tabularnewline
Chronic cutaneous lupus & 1 (6.7) & 0 (0) & 0.55\tabularnewline
Nasal or oral ulcers & 6 (40) & 9 (60) & 0.39\tabularnewline
Alopecia & 2 (13.3) & 4 (26.7) & 0.62\tabularnewline
Synovitis & 13 (86.7) & 13 (86.7) & 0.92\tabularnewline
Serositis & 2 (13.3) & 3 (20) & 0.82\tabularnewline
Renal & 6 (40) & 4 (26.7) & 0.50\tabularnewline
Neurologic & 0 (0) & 3 (20) & 0.18\tabularnewline
Autoimmune Hemolytic Anemia & 1 (6.7) & 1 (6.7) & 0.96\tabularnewline
Leuko-lymphopenia & 11 (73.3) & 12 (80) & 0.18\tabularnewline
Thrombocytopenia & 3 (20) & 3 (20) & 0.87\tabularnewline
Positive ANA & 14 (93.3) & 15 (100) & 0.55\tabularnewline
Positive anti-dsDNA & 15 (100) & 11 (73.3) &
\textbf{0.04}\tabularnewline
Positive anti-Smith & 4 (26.7) & 0 (0) & \textbf{0.01}\tabularnewline
Antiphospholipid antibodies & 8 (53.3) & 6 (40) & 0.69\tabularnewline
Hypocomplementemia & 12 (80) & 9 (60) & 0.38\tabularnewline
Positive Coombs test & 4 (26.7) & 0 (0) & 0.06\tabularnewline
SLEDAI 2K & \textbf{8 (6 -- 16)} & \textbf{0 (0 -- 2)} &
\textbf{\textless{} 0.001}\tabularnewline
Arthritis & 7 (46.6) & 0 (0) & \textbf{0.001}\tabularnewline
Hematuria & 4 (26.6) & 0 (0) & \textbf{0.01}\tabularnewline
Proteinuria & 8 (53.3) & 0 (0) & \textbf{\textless{}
0.001}\tabularnewline
Pyuria & 5 (33.3) & 0 (0) & \textbf{\textless{} 0.01}\tabularnewline
Low complement & 11 (73.3) & 7 (46.6) & 0.23\tabularnewline
Increased anti-dsDNA titers & 12 (80.0) & 5 (33.3) &
\textbf{0.02}\tabularnewline
Fever & 1 (6.6) & 0 (0) & 0.45\tabularnewline
Serological SLEDAI 2K items & & &\tabularnewline
C3 (mg/dL) & 81 (67 -- 128) & 113 (79 -- 128) & 0.24\tabularnewline
Low C3 (\%) & 10 (66.7) & 4 (26.7) & 0.07\tabularnewline
C4 (mg/dL) & 11 (8 -- 20) & 19 (12 -- 30) & 0.10\tabularnewline
Low C4 (\%) & 10 (66.7) & 7 (46.7) & 0.42\tabularnewline
anti-dsDNA (UI/mL) & 225.1 (20.2 -- 586.9) & 7.2 (4.5 -- 12.8) &
\textbf{\textless{} 0.01}\tabularnewline
SLICC/ACR DI & 0 (0 -- 1) & 1 (0 -- 1) & 0.10\tabularnewline
Secondary APS & 2 (13.3) & 6 (40) & 0.36\tabularnewline
Treatment & \textbf{15 (100)} & \textbf{0 (0)} &
\textbf{n/a}\tabularnewline
Prednisone & 7 (46.7) & n/a & n/a\tabularnewline
Dose (mg) & 15 (6.25 -- 27.5) & n/a & n/a\tabularnewline
Immunosupressor & 13 (86.6) & n/a & n/a\tabularnewline
Azathioprine & 5 (33.3) & n/a & n/a\tabularnewline
Methotrexate & 4 (26.7) & n/a & n/a\tabularnewline
Mycophenolate mofetil & 4 (26.7) & n/a & n/a\tabularnewline
Antimalarial & 10 (73.3) & n/a & n/a\tabularnewline
n/a: not applicable. ANA: antinuclear antibodies. SLEDAI: SLE Disease
Activity Index. SLICC: Systemic Lupus International Collaborating
Clinics. SLICC / ACR DI: Systemic Lupus International Collaborating
Clinics / American College of Rheumatology Damage Index. APS:
Antiphospholipid Syndrome. Results for quantitative variables are shown
in Median with Interquartile Range, and Kruskal Wallis test was used to
perform comparisons between groups. For quantitative variables measured
only in two groups, Mann Whitney U test was used to perform comparisons
between both groups. The qualitative variables are presented in
frequencies, using Chi squared test to compare between groups. Only the
SLEDAI 2K items that were present in at least one study subject from
either SLE group are shown. Prednisone dose is shown from the 7 Active
SLE patients who had it prescribed. & n/a: not applicable. ANA:
antinuclear antibodies. SLEDAI: SLE Disease Activity Index. SLICC:
Systemic Lupus International Collaborating Clinics. SLICC / ACR DI:
Systemic Lupus International Collaborating Clinics / American College of
Rheumatology Damage Index. APS: Antiphospholipid Syndrome. Results for
quantitative variables are shown in Median with Interquartile Range, and
Kruskal Wallis test was used to perform comparisons between groups. For
quantitative variables measured only in two groups, Mann Whitney U test
was used to perform comparisons between both groups. The qualitative
variables are presented in frequencies, using Chi squared test to
compare between groups. Only the SLEDAI 2K items that were present in at
least one study subject from either SLE group are shown. Prednisone dose
is shown from the 7 Active SLE patients who had it prescribed. & n/a:
not applicable. ANA: antinuclear antibodies. SLEDAI: SLE Disease
Activity Index. SLICC: Systemic Lupus International Collaborating
Clinics. SLICC / ACR DI: Systemic Lupus International Collaborating
Clinics / American College of Rheumatology Damage Index. APS:
Antiphospholipid Syndrome. Results for quantitative variables are shown
in Median with Interquartile Range, and Kruskal Wallis test was used to
perform comparisons between groups. For quantitative variables measured
only in two groups, Mann Whitney U test was used to perform comparisons
between both groups. The qualitative variables are presented in
frequencies, using Chi squared test to compare between groups. Only the
SLEDAI 2K items that were present in at least one study subject from
either SLE group are shown. Prednisone dose is shown from the 7 Active
SLE patients who had it prescribed. & n/a: not applicable. ANA:
antinuclear antibodies. SLEDAI: SLE Disease Activity Index. SLICC:
Systemic Lupus International Collaborating Clinics. SLICC / ACR DI:
Systemic Lupus International Collaborating Clinics / American College of
Rheumatology Damage Index. APS: Antiphospholipid Syndrome. Results for
quantitative variables are shown in Median with Interquartile Range, and
Kruskal Wallis test was used to perform comparisons between groups. For
quantitative variables measured only in two groups, Mann Whitney U test
was used to perform comparisons between both groups. The qualitative
variables are presented in frequencies, using Chi squared test to
compare between groups. Only the SLEDAI 2K items that were present in at
least one study subject from either SLE group are shown. Prednisone dose
is shown from the 7 Active SLE patients who had it
prescribed.\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3. CD4+ and CD8+ memory subpopulations and T regs.}

T cells populations

\textbf{Healthy Subjects}

\textbf{Active SLE}

\textbf{PR-SLE}

\textbf{p}

\textbf{N = 29}

\textbf{N = 15}

\textbf{N = 15}

\textbf{CD3+}

\%

60.8 (56.2 -- 69.2)

68.0 (53.7 -- 78.2)

67.7 (61.4 -- 74.4)

0.23

\textbf{CD3+CD4+}

\%

35.4 (33.5 -- 43.1)

39.4 (25.8 -- 46.7)

39.0 (31.4 -- 46.6)

0.92

Na\selectlanguage{ngerman}ïve (CD45RA+CD197+)

\%

27.9 (19.4 -- 33.1)

23.2 (7.98 -- 40.2)

19.3 (11.3 -- 32.5)

0.28

CM (CD45RA-CD197+)

\%

25.3 (16.1 -- 34.6)

26.5 (16.2 -- 44.5)

40.7 (22.1 -- 45.3)

\selectlanguage{english}\textbf{0.04\textsuperscript{++}}

EM (CD45RA-CD197-)

\%

40.9 (31.2 -- 53.0)

39.6 (36.2 -- 62.7)

39.9 (26.9 -- 47.5)

0.66

TEMRA (CD45RA+CD197-)

\%

2.26 (1.40 -- 4.26)

2.63 (0.78 -- 4.09)

0.92 (0.66 -- 2.14)

0.11\selectlanguage{english}\textsuperscript{+}

CD25+

\%

9.81 (5.68 -- 14.8)

11.2 (8.27 -- 22.4)

15.3 (9.13 -- 32.0)

0.09\selectlanguage{english}\textsuperscript{+}

CD127\textsuperscript{low}

\%

5.19 (4.20 -- 6.83)

5.42 (4.96 -- 6.85)

5.74 (4.29 -- 9.15)

0.88

CD25+CD127\textsuperscript{low}FOXP3+

\%

1.16 (0.38 -- 2.13)

0.54 (0.23 -- 1.52)

1.13 (0.25 -- 1.79)

0.37

\textbf{CD3+CD8+}

\%

18.4 (14.8 -- 21.9)

18.9 (13.4 -- 22.3)

19.0 (12.5 -- 25.2)

0.95

Naive (CD45RA+CD197+)

\%

21.5 (14.2 -- 25.5)

24.9 (15.3 -- 51.7)

14.7 (8.59 -- 24.4)

\selectlanguage{english}\textbf{0.04\textsuperscript{++}}

CM (CD45RA-CD197+)

\%

3.13 (1.71 -- 5.67)

2.88 (1.42 -- 6.45)

8.73 (3.74 -- 10.3)

\selectlanguage{english}\textbf{0.01\textsuperscript{++, ++}}

EM(CD45RA-CD197-)

\%

41.4 (36.1 -- 45.2)

27.8 (18.0 -- 38.8)

38.0 (28.8 -- 50.0)

\textbf{0.04\textsuperscript{*}}

TEMRA (CD45RA+CD197-)

\%

33.8 (24.0 -- 38.8)

34.1 (19.0 -- 45.0)

35.2 (24.4 -- 46.6)

0.95

Results are shown in Median with Interquartile Range, and Kruskal Wallis
test was used to perform comparisons between groups. Mann Whitney U test
was performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. \selectlanguage{english}+
Healthy Subjects vs PR-SLE. \selectlanguage{english}+ p \textless{} 0.05. \selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.01.
\selectlanguage{english}+\selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.001. \selectlanguage{english}++ Act-SLE vs PR-SLE. \selectlanguage{english}++ p \textless{} 0.05. \selectlanguage{english}++\selectlanguage{english}++ p
\textless{} 0.01. \selectlanguage{english}++\selectlanguage{english}++\selectlanguage{english}++ p \textless{} 0.001.

Results are shown in Median with Interquartile Range, and Kruskal Wallis
test was used to perform comparisons between groups. Mann Whitney U test
was performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. \selectlanguage{english}+
Healthy Subjects vs PR-SLE. \selectlanguage{english}+ p \textless{} 0.05. \selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.01.
\selectlanguage{english}+\selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.001. \selectlanguage{english}++ Act-SLE vs PR-SLE. \selectlanguage{english}++ p \textless{} 0.05. \selectlanguage{english}++\selectlanguage{english}++ p
\textless{} 0.01. \selectlanguage{english}++\selectlanguage{english}++\selectlanguage{english}++ p \textless{} 0.001.

Results are shown in Median with Interquartile Range, and Kruskal Wallis
test was used to perform comparisons between groups. Mann Whitney U test
was performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. \selectlanguage{english}+
Healthy Subjects vs PR-SLE. \selectlanguage{english}+ p \textless{} 0.05. \selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.01.
\selectlanguage{english}+\selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.001. \selectlanguage{english}++ Act-SLE vs PR-SLE. \selectlanguage{english}++ p \textless{} 0.05. \selectlanguage{english}++\selectlanguage{english}++ p
\textless{} 0.01. \selectlanguage{english}++\selectlanguage{english}++\selectlanguage{english}++ p \textless{} 0.001.

Results are shown in Median with Interquartile Range, and Kruskal Wallis
test was used to perform comparisons between groups. Mann Whitney U test
was performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. \selectlanguage{english}+
Healthy Subjects vs PR-SLE. \selectlanguage{english}+ p \textless{} 0.05. \selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.01.
\selectlanguage{english}+\selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.001. \selectlanguage{english}++ Act-SLE vs PR-SLE. \selectlanguage{english}++ p \textless{} 0.05. \selectlanguage{english}++\selectlanguage{english}++ p
\textless{} 0.01. \selectlanguage{english}++\selectlanguage{english}++\selectlanguage{english}++ p \textless{} 0.001.

Results are shown in Median with Interquartile Range, and Kruskal Wallis
test was used to perform comparisons between groups. Mann Whitney U test
was performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. \selectlanguage{english}+
Healthy Subjects vs PR-SLE. \selectlanguage{english}+ p \textless{} 0.05. \selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.01.
\selectlanguage{english}+\selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.001. \selectlanguage{english}++ Act-SLE vs PR-SLE. \selectlanguage{english}++ p \textless{} 0.05. \selectlanguage{english}++\selectlanguage{english}++ p
\textless{} 0.01. \selectlanguage{english}++\selectlanguage{english}++\selectlanguage{english}++ p \textless{} 0.001.

Results are shown in Median with Interquartile Range, and Kruskal Wallis
test was used to perform comparisons between groups. Mann Whitney U test
was performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. \selectlanguage{english}+
Healthy Subjects vs PR-SLE. \selectlanguage{english}+ p \textless{} 0.05. \selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.01.
\selectlanguage{english}+\selectlanguage{english}+\selectlanguage{english}+ p \textless{} 0.001. \selectlanguage{english}++ Act-SLE vs PR-SLE. \selectlanguage{english}++ p \textless{} 0.05. \selectlanguage{english}++\selectlanguage{english}++ p
\textless{} 0.01. \selectlanguage{english}++\selectlanguage{english}++\selectlanguage{english}++ p \textless{} 0.001.

\textbf{Table 4. T cell exhaustion in CD4+ and CD8+ lymphocytes.}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& & \textbf{Healthy Subjects} & \textbf{Active SLE} & \textbf{PR-SLE} &
\textbf{p}\tabularnewline
\midrule
\endhead
& & \textbf{N = 29} & \textbf{N = 15} & \textbf{N = 15} &\tabularnewline
\textbf{CD4+} & & & & &\tabularnewline
\textbf{PD-1+} & \% & 31.40 (24.80 -- 40.60) & 43.40 (32.10 -- 52.30) &
42.90 (31.80 -- 63.60) & \textbf{\textless{} 0.01*, +}\tabularnewline
\textbf{Tim-3+} & \% & 17.90 (11.60-32.75) & 11.90 (7.99-53.40) & 7.85
(3.18-32.30) & 0.13\tabularnewline
\textbf{CD38+HLA-DR+ (Activated)} & \% & 4.01 (2.56 -- 5.72) & 6.86
(5.09 -- 12.1) & 8.18 (4.66 -- 10.5) & \textbf{\textless{} 0.01***,
+++}\tabularnewline
\emph{POP 4 2B4+} & \% & 2.79 (0.85 -- 8.69) & 5.27 (2.47 -- 11.10) &
5.05 (3.02 -- 7.97) & 0.10\tabularnewline
\emph{POP 5 CD38+PD-1+} & \% & 9.09 (5.63 -- 13.25) & 15.20 (6.78 --
24.60) & 14.30 (8.57 -- 24.10) & \textbf{0.01+}\tabularnewline
\emph{POP 6 CD38+HLA-DR+PD-1+} & \% & 1.02 (0.62 -- 1.92) & 3.48 (1.59
-- 4.20) & 2.28 (1.47 -- 3.96) & \textbf{\textless{} 0.001***,
+++}\tabularnewline
\textbf{CD57+} & \% & 3.13 (1.79 -- 6.25) & 4.19 (3.66 -- 8.57) & 5.22
(2.12 -- 10.4) & 0.24+\tabularnewline
\textbf{EOMES+} & \% & 1.59 (0.87 -- 2.49) & 1.39 (0.48 -- 2.87) & 5.24
(3.38 -- 14.70) & \textbf{\textless{} 0.001}++++\textbf{,
+++}\tabularnewline
\textbf{T-bet+} & \% & 4.93 (2.28 -- 8.32) & 1.69 (1.30 -- 4.02) & 5.05
(3.23 -- 9.16) & \textbf{0.01++}\tabularnewline
\emph{POP 0 CM CD57+} & \% & 3.90 (1.42 -- 6.58) & 4.90 (1.45 -- 39.60)
& 12.00 (5.41 -- 35.60) & \textbf{0.01+}\tabularnewline
\emph{POP 1 CD45RO+T-bet+} & \% & 4.08 (1.64 -- 5.48) & 1.68 (0.42 --
2.83) & 10.20 (1.74 -- 30.50) & \textbf{\textless{} 0.01+,
++++}\tabularnewline
\emph{POP 4 EM CD27-CD45RO+} & \% & 2.36 (1.32 -- 4.38) & 2.15 (0.45 --
12.50) & 3.16 (1.29 -- 7.42) & 0.57\tabularnewline
\textbf{CD8+} & & & & &\tabularnewline
\textbf{PD-1+} & \% & 27.9 (23.9 -- 35.4) & 27.4 (13.6 -- 42.9) & 38.6
(30.3 -- 49.1) & \textbf{0.02+}\tabularnewline
\textbf{Tim-3+} & \% & 3.16 (1.77-7.34) & 2.58 (2.23-3.95) & 3.09
(0.64-6.22) & 0.84\tabularnewline
\textbf{CD38+HLA-DR+ (Activated)} & \% & 6.28 (2.72 -- 8.07) & 9.38
(6.30 -- 17.3) & 12.5 (8.47 -- 18.7) & \textbf{\textless{} 0.01
++}\tabularnewline
\textbf{CD38+HLA-DR+PD-1+ (Exhausted)} & \% & 1.24 (0.88 -- 1.94) & 4.69
(1.70 -- 10.2) & 4.38 (2.09 -- 9.19) & \textbf{\textless{} 0.001***,
+++}\tabularnewline
\emph{POP 0 2B4+} & \% & 58.9 (49.3 -- 68.3) & 46.7 (27.1 -- 60.8) &
68.4 (49.7 -- 79.0) & \textbf{0.02++}\tabularnewline
POP 2 CD38+PD-1+ & \% & 6.24 (4.38 -- 9.23) & 12.0 (8.26 -- 26.9) & 13.6
(10.7 -- 27.7) & \textbf{\textless{} 0.0001**, +++}\tabularnewline
POP 7 CD38+HLA-DR+2B4+ & \% & 2.23 (1.17 -- 3.06) & 7.11 (3.66 -- 11.80)
& 8.97 (4.00 -- 14.80) & \textbf{\textless{} 0.0001**,
+++}\tabularnewline
\textbf{CD57+} & \% & 28.0 (21.5 -- 36.6) & 38.0 (31.4 -- 45.4) & 37.6
(26.2 -- 52.4) & \textbf{0.02++}\tabularnewline
\textbf{EOMES+} & \% & 12.9 (6.78 -- 24.8) & 8.13 (2.33 -- 20.5) & 37.6
(24.9 -- 53.2) & \textbf{\textless{} 0.001++, ++++++}\tabularnewline
\textbf{T-bet+} & \% & 32.8 (18.9 -- 44.2) & 26.4 (10.7 -- 44.2) & 47.8
(23.0 -- 70.1) & 0.09++\tabularnewline
\emph{POP 3 CD27+CD45RO+T-bet+} & \% & 2.06 (1.00 -- 6.02) & 2.70 (0.76
-- 5.91) & 5.75 (3.51 -- 10.10) & \textbf{0.02+}\tabularnewline
\emph{POP 5 DP CD4+CD8+CD27+CD45RO+} & \% & 0.15 (0.10 -- 0.30) & 0.20
(0.13 -- 0.36) & 0.28 (0.23 -- 0.47) & 0.15\tabularnewline
\emph{POP 6 CD27-CD45RO-CD57+T-bet+EOMES+} & \% & 0.81 (0.37 -- 3.06) &
1.42 (0.33 -- 6.27) & 4.54 (0.88 -- 9.78) &
\textbf{0.02+}\tabularnewline
\emph{POP 7 DN CD4-CD8-CD57+T-bet+} & \% & 0.23 (0.11 -- 0.49) & 0.32
(0.07 -- 0.53) & 0.37 (0.21 -- 0.68) & 0.35\tabularnewline
Bold type represents populations identified by traditional manual
gating. Pop populations that were inferred from FlowSOM Heat Map are in
cursive. Results are shown in Median with Interquartile Range, and
Kruskal Wallis test was used to perform comparisons between groups. Mann
Whitney U test was performed to compare between two groups as a post Hoc
test and are represented by the following symbols. * Healthy Subjects vs
Act SLE. * p \textless{} 0.05. ** p \textless{} 0.01. *** p \textless{}
0.001. + Healthy Subjects vs PR-SLE. + p \textless{} 0.05. ++ p
\textless{} 0.01. +++ p \textless{} 0.001. ++ Act-SLE vs PR-SLE. ++ p
\textless{} 0.05. ++++ p \textless{} 0.01. ++++++ p \textless{} 0.001. & Bold
type represents populations identified by traditional manual gating. Pop
populations that were inferred from FlowSOM Heat Map are in cursive.
Results are shown in Median with Interquartile Range, and Kruskal Wallis
test was used to perform comparisons between groups. Mann Whitney U test
was performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. +
Healthy Subjects vs PR-SLE. + p \textless{} 0.05. ++ p \textless{} 0.01.
+++ p \textless{} 0.001. ++ Act-SLE vs PR-SLE. ++ p \textless{} 0.05. ++++ p
\textless{} 0.01. ++++++ p \textless{} 0.001. & Bold type represents
populations identified by traditional manual gating. Pop populations
that were inferred from FlowSOM Heat Map are in cursive. Results are
shown in Median with Interquartile Range, and Kruskal Wallis test was
used to perform comparisons between groups. Mann Whitney U test was
performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. +
Healthy Subjects vs PR-SLE. + p \textless{} 0.05. ++ p \textless{} 0.01.
+++ p \textless{} 0.001. ++ Act-SLE vs PR-SLE. ++ p \textless{} 0.05. ++++ p
\textless{} 0.01. ++++++ p \textless{} 0.001. & Bold type represents
populations identified by traditional manual gating. Pop populations
that were inferred from FlowSOM Heat Map are in cursive. Results are
shown in Median with Interquartile Range, and Kruskal Wallis test was
used to perform comparisons between groups. Mann Whitney U test was
performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. +
Healthy Subjects vs PR-SLE. + p \textless{} 0.05. ++ p \textless{} 0.01.
+++ p \textless{} 0.001. ++ Act-SLE vs PR-SLE. ++ p \textless{} 0.05. ++++ p
\textless{} 0.01. ++++++ p \textless{} 0.001. & Bold type represents
populations identified by traditional manual gating. Pop populations
that were inferred from FlowSOM Heat Map are in cursive. Results are
shown in Median with Interquartile Range, and Kruskal Wallis test was
used to perform comparisons between groups. Mann Whitney U test was
performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. +
Healthy Subjects vs PR-SLE. + p \textless{} 0.05. ++ p \textless{} 0.01.
+++ p \textless{} 0.001. ++ Act-SLE vs PR-SLE. ++ p \textless{} 0.05. ++++ p
\textless{} 0.01. ++++++ p \textless{} 0.001. & Bold type represents
populations identified by traditional manual gating. Pop populations
that were inferred from FlowSOM Heat Map are in cursive. Results are
shown in Median with Interquartile Range, and Kruskal Wallis test was
used to perform comparisons between groups. Mann Whitney U test was
performed to compare between two groups as a post Hoc test and are
represented by the following symbols. * Healthy Subjects vs Act SLE. * p
\textless{} 0.05. ** p \textless{} 0.01. *** p \textless{} 0.001. +
Healthy Subjects vs PR-SLE. + p \textless{} 0.05. ++ p \textless{} 0.01.
+++ p \textless{} 0.001. ++ Act-SLE vs PR-SLE. ++ p \textless{} 0.05. ++++ p
\textless{} 0.01. ++++++ p \textless{} 0.001.\tabularnewline
\bottomrule
\end{longtable}

Figure 1. Dimensional Reduction Analysis and Unsupervised clustering of
subpopulations of CD3+ T cells using markers associated with cell
exhaustion in Panels 2 and 3 (described previously in methods).

A) t-Distributed Stochastic Neighbor Embedding (tSNE) plots for each
study group (HS, Act-SLE and PR-SLE). Individual cell subsets of CD3+
cells from each subject in each group were randomly down-sampled to an
equal number~of~events and concatenated in a single file. B) Relative
expression of selected markers shown in tSNE plots. C) Heat maps of
selected markers using the Self Organizing Maps (FlowSOM) algorithm
according to the fluorescence intensity of the markers by flow
cytometry. Eight clusters were identified and named Pop 0 to Pop 7). The
heatmap shows the relative fluorescence intensity and hierarchical
clustering of each of the markers included in the subpopulations
generated. Percentage relative to CD3+ cells is shown in tables below.
D) Distribution of clusters identified in C overlaid on the t-SNE plots
in each study group. In both panels, a CD8+ subpopulation that expresses
exhaustion and senescence markers was identified exclusively in the
PR-SLE group. Overlaid histograms (HS: green; Act-SLE: red; PR-SLE:
purple) show relative frequencies of most abundant markers.

Figure 2. Bar graphs showing the frequencies (mean \selectlanguage{ngerman}±SEM) of lymphocytes
populations derived from traditional manual gating and FlowSOM clusters
identified in the heatmap in panel a. Mann Whitney U test was performed
to compare between two groups and differences are represented by the
following symbols: * p \textless{} 0.05. ** p \textless{} 0.01. *** p
\textless{} 0.001. **** p \textless{} 0.0001. HS (healthy subjects);
Act-SLE (active SLE); PR-SLE (prolonged remission SLE).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figura1/Figura1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figura2/Figura2}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

