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\begin{document}

\title{A role for IL-15 and sMAdCAM in Covid19 associated cellular immune
pathology}



\author[1]{Amit Kumar  Singh}%
\author[1]{Nandini Kasarpalkar}%
\author[1]{Shilpa Bhowmick}%
\author[1]{Gaurav Paradkar}%
\author[1]{Mayur Talreja}%
\author[1]{Karan Shah}%
\author[1]{Abhishekh Tiwari}%
\author[1]{Harsha Palav}%
\author[1]{Snehal Kaginkar}%
\author[1]{Rajiv Kulkarni}%
\author[1]{Ashwini Patil}%
\author[1]{Varsha Kalsurkar}%
\author[2]{Sachee Agrawal}%
\author[2]{Jayanthi Shastri}%
\author[3]{Rajesh Dere}%
\author[1]{Smita Mahale}%
\author[2]{Ramesh Bharmal}%
\author[1]{Vikrant Bhor}%
\author[1]{Vainav Patel}%
\affil[1]{NIRRH}%
\affil[2]{TN Medical College \& BYL Nair Hospital, Mumbai}%
\affil[3]{BKC COVID Jumbo Facility, Municipal Corporation of Greater Mumbai}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Immune cell dysregulation and lymphopenia characterize COVID-19
pathology in moderate to severe disease. While underlying inflammatory
factors have been extensively studied, homeostatic and mucosal migratory
signatures remain largely unexplored as causative factors. In this study
we evaluated the association of circulating IL-6, soluble mucosal
addressin cell adhesion molecule (sMAdCAM) and IL-15 with cellular
dysfunction characterizing mild and hypoxemic stages of COVID-19. A
cohort of SARS-CoV-2 infected individuals (n=125) at various stages of
disease progression together with healthy controls (n=16) were recruited
from COVID Care Centres (CCCs) across Mumbai, India. Multiparametric
flow cytometry was used to perform in-depth immune subset
characterization and to measure plasma IL-6 levels. sMAdCAM, IL-15
levels were quantified using ELISA. Distinct depletion profiles, with
relative sparing of CD8 effector memory and CD4+ regulatory T cells were
observed in hypoxemic disease within the lymphocyte compartment. An
apparent increase in the frequency of intermediate monocytes
characterized both mild as well as hypoxemic disease. IL-6 levels
inversely correlated with those of sMAdCAM and both markers showed
converse associations with observed lympho-depletion suggesting opposing
roles in pathogenesis. Interestingly, IL-15, a key cytokine involved in
lymphocyte activation and homeostasis, was detected in symptomatic
individuals but not in healthy controls or asymptomatic cases. Further,
negative association of plasma IL-15 with depleted T, B and NK subsets
suggested a compensatory production of this cytokine in response to the
profound lymphopenia. Finally, higher levels of plasma IL-15 and IL-6,
but not sMAdCAM, were associated with longer duration of
hospitalization.%
\end{abstract}%



\sloppy


\textbf{Introduction:}

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
pandemic continues to pose a global health crisis in spite of ongoing
interventions such as vaccination (1, 2). Pathology of COVID-19 displays
varied clinical manifestations ranging from no symptoms to critical
systemic disease (3--5). Hypoxemia is a key signature that discriminates
between mild and moderate to severe disease(6, 7). The role of
lymphopenia as a defining cellular immune correlate of moderate to
severe disease has been well established (8). However, underlying immune
homeostatic mechanisms that might contribute to this phenotype remain
largely unexplored (9, 10). Understanding and identifying such
relationships, in context of vaccination history, would help to guide
therapeutic efforts and to ensure optimal disease management of
COVID-19. In this study we evaluated the contribution of key
inflammatory, cellular homeostatic and mucosal migratory markers in
distinct stages of COVID-19 pathogenesis. Our results highlight
associations of IL-6, IL-15 and sMAdCAM with lymphopenia together with a
heretofore undescribed role for detectable plasma IL-15 as marker
associated with symptomatic progression.

\textbf{Results:}

\textbf{Demographic and Clinical characteristics of study participants}

A total of 125 admitted hospital patients, laboratory-confirmed positive
for SARS-CoV-2 infection by quantitative RT-PCR of throat and nasal swab
samples were recruited for the study. According to the clinical
management protocol for COVID-19 (Version 3) issued by Ministry of
Health and Family Welfare, Government of India, patients were recruited
into different groups based on clinical severity(7). The patients
without evidence of breathlessness or hypoxia were classified into Mild
group. Mild patients were further segregated into Asymptomatic Mild (AM;
n=47) and Symptomatic Mild (SM; n=57) where SMs presented mild symptoms
like fever, cough, sore throat, headache etc. Patients with oxygen
saturation (SpO\textsubscript{2}) of \textless{} 93\% on room air who
required oxygen support to correct hypoxemia, were classified as having
Moderate (MD; n=10) COVID-19, whereas patients with oxygen saturation
(SpO\textsubscript{2}) of \textless{} 90\% on room air were considered
as having Severe (SV; n=11) COVID-19. In addition, a total of 16 healthy
participants with no apparent history of COVID-19 and seronegative (SN)
by Rapid antibody test, were also recruited as controls. The demographic
and clinical characteristics of all the study participants is summarised
in Table 1.

\textbf{Lymphopenia with relative sparing of Treg and CD8+ effector
memory subsets define hypoxemic COVID-19 progression.}

The absolute counts of total lymphocytes and T cells (both CD4+ and
CD8+) in MD and SV groups were significantly decreased compared to AM,
SM and SN (Supplementary figures S1 and S2A). Additionally, SV group
also showed significant depletion of total lymphocytes compared to MDs.
Similar depletion profiles (in MD and SV groups) were observed for both
T (CD4+ and CD8+) and non T (B and NK) cell compartments. However, no
significant difference was observed among mild patients (AM \& SM) and
compared to SN (Supplementary figure S2B-G). No apparent difference in
the frequency of these lymphocyte subsets was observed except for a
significant decrease for total T lymphocytes and a significant increase
for total non-T lymphocytes in both MD and SV compared to other groups
(Supplementary figure S2H-M). This suggested a pan lymphopenic phenotype
that was observed in hypoxemic (MD and SV) individuals. Additionally, to
evaluate the impact of lymphopenia on T cell compartment, distribution
of CD4+ and CD8+ T cell subsets was also evaluated(11). Based on the
surface expression of CD45RA and CCR7, CD4+ and CD8+ T cells were
differentiated into Na\selectlanguage{ngerman}ïve (T\textsubscript{N;} CD45RA+CCR7+), Central
memory (T\textsubscript{CM;} CD45RA-CCR7+), Effector memory
(T\textsubscript{EM;} CD45RA-CCR7-) and Terminally differentiated
(T\textsubscript{TD;} CD45RA+CCR7-) T cells (Supplementary figure S3).
While both MD and SV groups had significant depletion of absolute count
for all CD4+ T cell subsets (Figure 1A), our findings do not support
preferential depletion of any particular subsets in the CD4+ T cell
compartment (Figure 1B). In CD8+ T cell compartment, comparison of
absolute counts revealed profound depletion of all subsets in hypoxemic
(MD and SV) individuals (Figure 1C). In contrast to the CD4+ T cell
compartment however, we observed relatively lower frequency of naïve
(CD8+ T\textsubscript{N}) and concomitant elevation of effector memory
(CD8+ T\textsubscript{EM}) frequency (Figure 1D). This reflected a
distinct depletion profile for CD8+ T cells with relative sparing of
effector memory cells.

Interestingly, while memory subsets of CD4+ T cell compartment seemed to
be equally depleted in hypoxemic individuals, we report here depletion
in Treg count that was exacerbated in the SV group (Figure 1E). Also,
elevated frequency of CD4+ T regulatory cells in hypoxemic individuals
indicated selective preservation of this subset (Figure 1F). When the
non-lymphocyte monocyte compartment was evaluated, we observed an
elevated frequency of the intermediate (CD14+CD16++) subset that defined
both mild (AM, SM) and hypoxemic patients (Figure 1G).

\textbf{Contribution of sMAdCAM and IL-6 to COVID-19 associated
lymphopenia}

To assess the contribution of known soluble inflammatory cytokines and
mucosal migration markers to the observed cellular immune signatures we
undertook the evaluation of circulating IL-6 and sMAdCAM levels in our
cohort. As expected, we observed elevated IL-6 levels, with the highest
being observed in hypoxemic individuals, in plasma of all groups
compared to seronegative controls (Figure 2A). Additionally, extending
our previously reported results (12) obtained with sMAdCAM in mild
infection, we observed a progressive decline in these levels across mild
as well as hypoxemic patients that seemed converse to the pattern
observed for IL-6 (Figure 2B). Indeed, the levels of these 2 markers
were significantly negatively correlated (Figure 2C). Further, it was
interesting to note that neither in the case of IL-6 or sMAdCAM was it
possible to discriminate between AM and SM individuals. Next,
correlation analysis was undertaken to delineate putative relationships
between the aforementioned soluble markers and cellular subsets
described above (Supplementary Figure S4). Intriguingly and reflective
of their apparently divergent relationship, both IL-6 and sMAdCAM showed
significant opposing correlations with absolute counts of lymphocytes, T
cells (CD4+ and CD8+), B cells and NK cells supporting their clear,
albeit, opposing roles in COVID-19 associated lymphopenia (Figure 2
D-I). We also noted a unique negative correlation of IL-6 with Treg
counts together with a heretofore unreported positive correlation of
sMAdCAM levels with CD8+ effector memory T cell counts (Supplementary
Figure S5A-B). Elevated LPS levels, associated with microbial
translocation and severe COVID-19 disease were observed in hypoxemic
individuals (Supplementary Figure S5C). Furthermore, and possibly
related to altered monocyte frequencies observed ex vivo (Figure 1G), a
negative correlation of sMAdCAM levels occurred with frequencies of
intermediate monocytes (Supplementary Figure S5D). Gut pathology
associated markers LPS and sMAdCAM were poorly correlated with only the
latter exhibiting major correlations to lymphopenia.

\textbf{Plasma IL-15 levels discriminate symptomatic and asymptomatic
mild COVID-19}

Having observed distinct depletion profiles of CD4+ and CD8+ T cells,
the relative sparing of CD8+ effector memory T cells and non T cell
lymphopenia (B and NK cells) we surmised that IL-15, a major homeostatic
and activation cytokine for these subsets needed to be evaluated in
COVID-19 pathogenesis. We observed elevated levels of circulating IL-15
in lymphopenic, hypoxemic individuals and remarkably, as shown in Figure
3A, in symptomatic mild but not asymptomatic patients. All hypoxemic
individuals had detectable levels of IL-15 followed by 70\% of
individuals with symptomatic infection and only 18\% (2 out of 11) with
asymptomatic infection (Figure 3B). Indeed, of the 2 asymptomatic
individuals that had detectable levels of IL-15 at sampling, one later
progressed to hypoxemia. No individuals within the SN control group had
detectable levels of plasma IL-15. Underlying a strong role for this
cytokine, possibly related to homeostatic repopulation in lymphopenia we
observed significant negative correlations between depleted subsets and
circulating IL-15 levels across all groups of COVID-19 affected
individuals (Figure 3C-H and Supplementary Figure S4). Also, as observed
for IL-6, sMAdCAM levels were negatively correlated with those of IL-15
(Figure 3I).

Finally, in a rudimentary analysis, we evaluated the possible prognostic
value of our single determinations of IL-15 together with IL-6 and
sMAdCAM in predicting duration of hospitalization independently. For
this analysis, days of hospitalization post sampling, including those
from individuals with undetectable values for these markers (considered
as 0) were plotted as shown in Figure 3J-L. Participants were grouped
into Low and High categories based on median values of these markers.
Proportional hazards analysis revealed significantly longer
hospitalization for individuals in the High IL-15 and IL-6 groups. This
value was above \textgreater{}4.20 pg/ml of IL-15 and
\textgreater{}14.39 pg/ml for IL-6.

\textbf{Discussion:}

In this study we describe cellular immune signatures characterizing mild
(asymptomatic as well as symptomatic) and hypoxemic (moderate and
severe) COVID-19 disease. We further elucidate the role of sMAdCAM, an
important mucosal and lymphocyte migration marker and IL-15, a cytokine
regulating leukocyte activation and homeostasis, in the observed
cellular immune signatures.

Our results clearly illustrate, as has been well described (13--16),
leukopenic profiles only in hypoxemic COVID-19 affected individuals.
Further, in-depth analysis of T cell subsets revealed distinct depletion
profiles in operation within CD4+ and CD8+ T cell compartments. While
the CD4+ T cell compartment did not show any preferential depletion
across naïve and memory subsets, we report here an asymmetric depletion
profile in hypoxemic COVID-19 infection characterized by drastic
depletion of naïve CD8+ T cells with relative sparing of the CD8+
effector memory T cell subset. These results extend the limited data
available for T cell subsets in a stratified manner to include distinct
stages of COVID-19 pathogenesis (10, 17). The CD4+ T regulatory cell
subset represents a critical part of the response to viral infections
(18, 19) that may play both protective as well as exacerbatory roles in
COVID-19 pathogenesis. Our analysis revealed an incremental decrease in
absolute count of this subset from moderate to severe pathogenesis
compared to healthy controls. However, both stages of hypoxemic
infection showed a relative preservation of this subset evidenced
through elevated frequencies compared to healthy controls. As has been
reported very recently this elevated frequency may represent suppressive
(protective) as well as inflammatory roles played by these cells,
markedly higher, in severe disease (20). Our study also evaluated the
non-lymphocyte compartment in terms of monocytes and their profiles in
mild and hypoxemic COVID-19 disease. This compartment has been known to
modulate inflammatory responses to a variety of viral infections (21,
22). Interestingly, as opposed to the T cell compartment and when
compared with healthy seronegative individuals, a pan-disease signature
was delineated within monocytes comprising of elevated frequencies of
intermediate monocytes. Also, concomitant decrease in frequency of
non-classical monocytes was observed in mild infection irrespective of
symptoms.

In an effort to understand factors influencing the observed cellular
depletion and dysfunction in our cohort we evaluated plasma markers that
could influence inflammation, mucosal leukocyte migration and
homeostasis. Extending and strengthening our previous results on the
converse relationship between sMAdCAM and IL-6 observed in non-hypoxemic
COVID-19 progression (12), we report a similar and inversely correlated
relationship between sMAdCAM and IL-6 levels across mild and hypoxemic
stages of COVID-19 disease. Decreased sMAdCAM levels observed following
SARS-CoV-2 infection in our study complement recently reported findings
of reduced circulating lymphocytes expressing integrin
\selectlanguage{greek}α\selectlanguage{english}\textsubscript{4}\selectlanguage{greek}β\selectlanguage{english}\textsubscript{7} in peripheral blood of COVID-19
patients. Furthermore, the restoration of both sMAdCAM levels (in our
study) and \selectlanguage{greek}α\selectlanguage{english}\textsubscript{4}\selectlanguage{greek}β\selectlanguage{english}\textsubscript{7} expressing lymphocytes
(23) was observed in convalescent individuals. Taken together, these
results highlight a clear role for altered mucosal homing in the
pathogenesis of this disease Furthermore, for the first time, we show
that this converse relationship extends to opposing correlations with
observed cellular immune signatures defining COVID-19 pathogenesis.
Notably, elevated circulating LPS (a marker for gut inflammation), in
the context of SARS-CoV-2 infection, has only been reported for severe
and ICU patients (24). In our study we also observed increased plasma
LPS levels in hypoxemic individuals compared to seronegative controls as
opposed to our previous work with a cohort of mild COVID-19 (12).
Importantly, there was no correlation between circulating LPS levels and
either sMAdCAM or IL-6 levels. These results suggest that the altered
trajectory of sMAdCAM levels is less related to gut persistence mediated
inflammation and more to altered mucosal leukocyte migration. Taken
together, our results show that IL-6 and sMAdCAM may contribute towards
disparate aspects of disease progression where circulating levels of the
former are more closely related to inflammatory sequalae and development
of anti-viral B cell responses (25, 26) and those of the latter with
restoration of mucosal homeostasis of leukocytes. Indeed, we have
recently highlighted a role for the modulation of sMAdCAM and expression
of its receptor integrin alpha-4 beta-7 present on monocytes,
lymphocytes in the context of HIV pathogenesis (27).

While most immune markers of pathogenesis in COVID-19 have been reported
to be associated with mild, moderate and severe disease, a marker that
clearly discriminates between symptomatic and asymptomatic pathogenesis
remains elusive. Our study is the first to demonstrate the presence of
plasma IL-15 as such a marker where detection of this cytokine is
clearly segregated based on the symptomatic status of patients with
incrementally higher levels detected in mild symptomatic to hypoxemic
individuals. In fact, of the 2 (out of 11) mild asymptomatic individuals
that had detectable levels of this cytokine, one was found
retrospectively to have progressed to a hypoxemic stage. Also, negative
correlation of plasma IL-15 levels with lymphopenic profiles (T, B, NK)
are indicative of its homeostatic restorative production (28--30) in the
face of systemic depletion of these subsets. Circulating IL-15 levels
have been implicated as a contributory factor to hospitalization time,
disease severity and mortality in some settings (31, 32). Indeed, a
preliminary analysis in our cohort, with respect to days of
hospitalization supported this finding. Our study further demonstrates
the utility of using it as an early biomarker predicting progression to
symptomatic and severe disease.

\textbf{Conclusion:}

In summation, our results delineate a role for inflammatory, homeostatic
and migratory immune mediators in COVID-19 associated lymphopenia, of
which, plasma IL-15 detection may be an early prognostic marker.

\textbf{Methods}

\textbf{Study population, setting, and data collection:} Sixteen
seronegative controls and a total of 125 in-patients individuals were
recruited, following informed consent, for the study from the COVID Care
Centres associated with BYL Nair hospital and T N medical college,
Municipal Corporation of Greater Mumbai (MCGM), Mumbai following
approval of institutional ethics committees. We obtained demographic
data, clinical history at presentation, and laboratory results during
admission.

Blood samples for the study were handled in accordance with ICMR
guidelines for biosafety. 1-3ml of whole blood was collected in EDTA
vacutainers. Aliquots of whole blood were processed for absolute cell
count and immunophenotyping as described below. Plasma was separated by
centrifugation at 400 g for 10 minutes. IgG and IgM antibodies against
SARS-CoV-2 were detected in fresh plasma samples using Rapid test from
Voxpress (Voxtur Bio LTD, India) and Chemiluminescence immunoassay
(CLIA) directed against SARS -CoV-2 anti-NC IgG. Remaining plasma
samples were aliquoted and stored at -80\textsuperscript{0} C until
batch analysis of cytokines, LPS (Lipopolysaccharide) and Soluble MAdCAM
(sMAdCAM).

\textbf{Assay for absolute count of T cells, B cells and NK cells}

BD multitest 6 colour TBNK reagent and BD
Trucount\textsuperscript{TM}tubes were used to enumerate absolute count
of T cells, CD4 T cells, CD8 T cells, B cells, and Natural killer (NK)
cells using 50 \selectlanguage{greek}μ\selectlanguage{english}l of fresh EDTA stabilised blood following
stain/lyse/no-wash protocol. Data acquisition was performed on BD FACS
Aria Fusion (SORP) Flow cytometer (BD Biosciences) where 5000 events
gated on beads count were acquired. Data analysis was carried out using
FlowJo software (BD biosciences)

\textbf{Flow Cytometry}

For phenotypic characterization, immunostaining of 200 \selectlanguage{greek}μ\selectlanguage{english}l of fresh
peripheral whole blood with following fluorescently labelled monoclonal
antibodies, anti-CD3 (Clone:SK7), anti-CD4 (Clone: RPA-T4), anti-CD8
(Clone: SK1), anti-CD25 (Clone: M-A251), anti-CD127 (Clone: HIL-7R-M21),
anti-CCR7 (Clone: 150503), anti-CD45RA (Clone: HI100), anti-CD14 (Clone:
M5E2 ) and anti-16 (Clone: 3G8) was performed using stain/lyse/wash
protocol as described earlier (11). Data acquisition was performed on BD
FACS Aria Fusion (SORP) flow cytometer (BD Biosciences) where at least
100,000 events in lymphocyte scatter gate were acquired. Data analysis
was carried out using FlowJo software. IL-6 determination was carried
out using a cytometric bead array (CBA, BD Biosciences) to quantify
cytokine levels in plasma of study participants as per manufacturers'
protocol.

\textbf{ELISA}

Plasma IL-15, sMAdCAM-1 and LPS levels were quantified using Human IL-15
DuoSet ELISA kit (R\&D Systems), Human MAdCAM-1 DuoSet ELISA kit (R\&D
Systems) and Human Lipopolysaccharides ELISA kit (MyBioSource)
respectively, following manufacturers' recommendations. The values below
the level of detection were assigned as zero.

\textbf{Statistics}

Statistical analysis was performed in GraphPad Prism 8 using non
parametric tests. Statistical significance of differences between groups
were assessed using Mann-Whitney U-test. Spearman's rank-order
correlation was used to analyse the association between participant
attributes that had detectable values in our assays. Kaplan--Meier
curves were compared using Log-rank (Mantel-Cox) test. Statistical
significance was accepted at p\textless{}0.05.

\textbf{Competing Interests:} The authors declare that they have no
competing interests including those of any financial nature.

\textbf{Author Contribution:} Sample processing, performing experiments,
data analysis and figure generation (Amit Kumar Singh, Nandini
Kasarpalkar, Shilpa Bhowmick). Participant recruitment, sample
processing and data analysis (Gaurav Paradkar, Mayur Talreja). Sample
processing (Abhishek Tiwari, Harsha Palav, Snehal Kaginkar). Participant
recruitment (Rajiv Kulkarni, Karan Shah). Sample collection (Ashwini
Patil, Varsha Kalsurkar). Performed experiments and Data analysis
(Sachee Agrawal, Jayanthi Shastri). Scholarly advice (Rajesh Dere,
Ramesh Bharmal, Smita Mahale). Conceived study, analysed data, compiled
results and wrote manuscript (Vikrant Bhor and Vainav Patel). All
authors revised the manuscript and gave final approval for publication.

\textbf{Acknowledgement:} We are extremely grateful to Director General,
Indian Council of Medical Research (ICMR), Ministry for Health \& Family
Welfare, Government of India for encouraging and supporting with
resources, the pursuit of important research questions relevant to
COVID-19 pathogenesis. We would also like to thank Dr. Noora Pathan, Dr.
Mahesh Chaurasia, Dr. Rasika Satpute and Dr. Dharmesh Balsarkar for
facilitating recruitment of study participants and Mr. Omkar Arekar for
the assistance provided in data entry. Also, none of this work would
have been possible without the active participation and support of
participants to whom we are grateful.

\textbf{Funding:} Funding was through Institutional intramural core
support from ICMR-NIRRH.

\textbf{Ethical Approval:} Institutional Ethical Committee approval from
ICMR-NIRRH and T N medical college, Municipal Corporation of Greater
Mumbai (MCGM) was received for the study and informed consent of all
participating subjects was obtained.

\textbf{Data Sharing:} Raw/Source data are available on request but are
not shared here due to privacy/ethical concerns. Sharing of this data
will also be subject to local ethics approval if applicable.

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Table 1. Abridged clinical data of study participants\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& \textbf{Seronegative Healthy} & \textbf{Asymptomatic mild} &
\textbf{Symptomatic mild} & \textbf{Moderate} &
\textbf{Severe}\tabularnewline
\midrule
\endhead
Number of Participants & 16 & 47 & 57 & 10 & 11\tabularnewline
Age\textsuperscript{a} (years) & 26 (24-46) & 39 (18-75) & 37 (18-71) &
64 (47-75) 66 (31-81) & 64 (47-75) 66 (31-81)\tabularnewline
Gender (M/F) SpO\textsubscript{2} levels & 8/8 - & 33/14 41/16
\textbf{-} & 33/14 41/16 \textbf{-} & 6/4 9/2 \textless{}93\%
\textless{}90\% & 6/4 9/2 \textless{}93\% \textless{}90\%\tabularnewline
Duration of Hospitalisation\textsuperscript{a} (days) & - & 8 (1-41) 7
(1-41) & 8 (1-41) 7 (1-41) & 12 (2-27) 18 (8-38) & 12 (2-27) 18
(8-38)\tabularnewline
\bottomrule
\end{longtable}

\textbf{Footnote:} \textsuperscript{\textbf{a}} Data are expressed as
the median (range). SpO\textsubscript{2} levels at the time of
recruitment are reflected in the table

\textbf{Legends:}

\textbf{Figure 1:} \textbf{Distribution of different immune subsets
among study participants (A-B)} Variation across seronegative (SN),
asymptomatic mild (AM), symptomatic mild (SM), moderate (MD) and severe
(SV) groups in absolute count and frequency of CD4+ T cells subsets (N=
Na\selectlanguage{ngerman}ïve, CM= central memory, EM= Effector Memory and TD= Terminally
differentiated), respectively. (\textbf{C-D)} Variation across different
study groups in absolute count and frequency of CD8+ T cells subsets,
respectively. (\textbf{E-F)} Variation across different groups in Tregs
count (E) and frequency(F). (\textbf{G)} Variation in frequency of
monocyte subsets across different study groups. Statistical significance
was calculated by Mann-Whitney U-test; *, p \textless{} 0.05; **, p
\textless{} 0.01, ***, p\textless{}0.001 ; and ****, p\textless{}0.001.

\textbf{Figure 2: Evaluation of IL-6 and sMAdCAM along with correlation
of cellular immune subsets in study participants} Variation in levels of
(A) IL-6 and (B) sMAdCAM in seronegative(SN), asymptomatic mild (AM),
symptomatic mild (SM), moderate (MD) and severe (SV) groups.
Relationship between (C) IL-6 and sMAdCAM and their association with
different cellular subsets (D) Lymphocyte count (E) Total T cell count
(F) CD4+ T cell count (G) CD8+ T cell count (H) B cell count (I) NK cell
count. Statistical significance was calculated by Mann-Whitney U-test;
*, p \textless{} 0.05; **, p \textless{} 0.01, ***, p\textless{}0.001 ;
and ****, p\textless{}0.001. Correlation analysis was performed using
non parametric Spearman Rank Correlation test.

\textbf{Figure 3: Distribution of IL-15 and its correlation with immune
cells among study participants.} Variation in levels of IL-15 (A) and
detection rate (B) in seronegative (SN), asymptomatic mild (AM),
symptomatic mild (SM), moderate (MD) and severe (SV) groups. Association
of IL-15 and with different cellular subsets (C) Lymphocyte count (D)
Total T cell count (E) CD4+ T cell count (F) CD8+ T cell count (G) B
cell count (H) NK cell count (I) Soluble MAdCAM. (J-L) Kaplan Meier
plots depicting hospitalization duration post recruitment associated
with plasma levels of (J) IL-15 (K) sMAdCAM and (L) IL-6. Individuals
were categorized into a low and a high group on the basis of median
levels. Statistical significance was calculated by Mann-Whitney U-test;
*, p \textless{} 0.05; **, p \textless{} 0.01, ***, p\textless{}0.001;
and ****, p\textless{}0.001. Correlation analysis was performed using
non parametric Spearman Rank Correlation test. For Kaplan Meier
comparisons, Log-rank (Mantel-Cox) test was used.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1(2)/Figure-1(2)}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2(2)/Figure-2(2)}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3(2)/Figure-3(2)}

\end{center}
\end{figure}

\selectlanguage{english}
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