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\begin{document}

\title{Differentiation of circulating monocytes into macrophages with
metabolically activated phenotype regulates inflammation in dyslipidemia
patients.}



\author[1]{Elena Berenice Martínez-Shio, Ángel Martín Cárdenas-Hernández, Verónica Jiménez-Suárez, Laura Sherell Marín-Jáuregui, Claudia Castillo-Martin del Campo, Roberto González-Amaro, Carlos D Escobedo-Uribe}%
\author[1]{Adriana Elizabeth Monsiváis-Urenda}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 08, 2024}


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\textbf{Introduction}

Dyslipidemia is one of the main modifiable risk factors for the
development of cardiovascular diseases (CVD), being the ischemic heart
disease the leading cause of mortality in the world. Every year, more
people die from CVD than from any other reason, according to data from
the World Health Organization, it is estimated that 17.9 million people
died from this cause in 2019, which represents 32\% of all registered
deaths in the world . In addition to high blood pressure, diabetes,
obesity, and smoking, dyslipidemia is one of the main cardiovascular
risk factors. The latter it is defined as disorders in blood lipids
characterized by an increase in cholesterol and/or triglyceride levels
called hypercholesterolemia and triglyceridemia, respectively .

While most of the triglyceride and cholesterol content is obtained from
dietary sources, \emph{de novo} lipogenesis contributes significantly to
serum lipid content in people who have a high-carbohydrate diet. The
metabolic pathways by which the macromolecules obtained through the diet
are processed, such as glycolysis, Krebs cycle, oxidative
phosphorylation, beta oxidation, among others, have as their main
function the generation of energy, and an imbalance in these can promote
pathological processes, such as dyslipidemia . These metabolic pathways
not only provide energy for cellular homeostasis, but also control
immune cell functions. Immune cells at rest, use processes such as Krebs
cycle and oxidative phosphorylation for ATP generation, but cells with
pro-inflammatory phenotype such as M1 macrophages and activated T
lymphocytes tend to change to aerobic glycolysis, while M2 macrophages
and regulatory T lymphocytes induced in the periphery continue with
oxidative phosphorylation. Reprogramming of the metabolic state of
immune cells influences the generation of epigenetic changes which lead
to functional changes. This cellular metabolic state is affected by
systemic metabolism, either by nutrients availability or by signalling
pathways induced by each metabolite . These concepts are the basis of
innate immunological memory, this phenomenon, also called immune
training, is defined by metabolic changes originated after priming with
pathogens or sterile stimuli that lead to sustained functional changes
orchestrated mainly by epigenetic reprogramming, which are sustained
changes in gene expression and cellular physiology, which does not imply
permanent changes .

An immune cell type that attracts more attention in the immunometabolism
area is the macrophages population. Macrophages are phagocytic cells of
innate immunity with a broad functional spectrum, from pro-inflammatory
to anti-inflammatory phenotypes representing the extremes. Monocytes,
cells that develop from bone marrow precursors, travel in bloodstream
for a few days, then they migrate to tissue and become macrophages with
different phenotypes . Tissue-resident macrophages are long-lived cells
derived mostly from erythro-myeloid progenitors that emerge from the
yolk sac . The first to emerge are the primitive macrophages, which are
not derived from monocytes and seed every tissue. When erythro-myeloid
progenitors seed the fetal liver, they generate fetal monocytes that
differentiate into macrophages, and represent the most abundant
tissue-resident macrophage population . Furthermore, monocytes derived
from hematopoietic stem cells emerge from the fetal liver and
differentiate into long-lived macrophages, while adult hematopoiesis
begins in the bone marrow. Bone marrow -derived monocytes contribute to
the different populations of postnatal tissue resident macrophages .

Monocytes/ macrophages are recognized because their important roles in
regulating homeostasis and immune defense through their inflammatory or
tissue repair properties . The importance of metabolism in immune cells
for the programming of macrophages with their different functional
spectra suggests that metabolic pathways may play a role for long-term
functional changes in monocytes and macrophages during immune training .
The role of these cells is widely described in obesity, being the main
population present in the adipose tissue stromal~vascular fraction,
where there is an increase in the proliferation of macrophages coupled
with the recruitment of circulating monocytes to this tissue. Due to the
production of cytokines such as IL-1\selectlanguage{greek}β, \selectlanguage{english}IL-6 and TNF-\selectlanguage{greek}α, \selectlanguage{english}M1 macrophages
participate in the low-grade chronic inflammation that characterizes
obesity .

In past years, it has been shown that when treating monocyte derived
macrophages with high concentrations of insulin, glucose and palmitate,
characteristic of metabolic syndrome, a different pro-inflammatory
phenotype is induced. These cells present surface markers and
transcription factors different from classical macrophages and were
called metabolically activated macrophages (MMe) . MMe present surface
molecules such as CD36, which binds to long chain fatty acids and
facilitates their transport in the cell, participating in the use of
lipids in muscle, storage of adipose energy and absorption of intestinal
fat ; ABCA1 is a cholesterol efflux pump in the elimination pathway of
cellular lipids that are then collected by apoA-I, forming high-density
lipoproteins (HDL) ; and PLIN2, which is a protein expressed on the
lipid droplet membrane . These MMe have been described in metabolic
syndrome and have been found in adipose tissue during obesity,
performing beneficial and detrimental functions during diet-induced
obesity in mice , and in mammary adipose tissue promoting tumorigenesis
during obesity . MMe produce pro-inflammatory cytokines, although in a
lesser extent than classic M1 macrophages. The expression of their
characteristic surface markers, as well as the attenuated inflammatory
response, is mainly mediated by the transcription factor PPAR-\selectlanguage{greek}γ , \selectlanguage{english}that
could be contributing to the chronic low-grade inflammatory state
present in metabolic syndrome and obesity.

Due to dietary overload, the metabolites produced by the different
metabolic pathways can be used for alternative pathways in organs and
tissues, such as adipose tissue, modifying and defining systemic
metabolic responses It is not completely clear whether the change from a
healthy systemic metabolic state to a pathological one, such as the
dyslipidemic state, lead to changes causing immune training influencing
polarization to different cell types.

The aim of this study was to evaluate if high cholesterol and
triglycerides levels, main feature of dyslipidemia, are promoting immune
training in peripheral blood cells, functioning as a first stimulus,
conditioning monocytes to present a metabolic phenotype and leading them
to polarization into metabolically activated macrophages. We found that
monocytes with metabolic phenotype expressing CD36, ABCA1 and PLIN2, are
present in systemic circulation. \emph{In vitro} stimulation showed that
MMe from patients with dyslipidemia, play a dynamic role with production
of pro- and anti-inflammatory cytokines.



\textbf{Materials and methods}

\textbf{Patients}

Thirty-two patients with diagnosis of dyslipidemia were included.
Dyslipidemia was defined by lipid serum levels: Cholesterol
\textgreater{}200 mg/dl and/or triglycerides \textgreater{}150 mg/dl.
All patients were without pharmacological treatment at the time of the
study. Twenty-two healthy individuals with normal lipid levels were also
included (cholesterol \textless{}200 mg/dl and triglycerides
\textless{}150 mg/dl). All subjects were recruited from the University
Health Center of the Universidad Aut\selectlanguage{ngerman}ónoma de San Luis Potosí. None of
our included subjects showed evident infectious or autoimmune disease,
previous acute myocardial infarction, hypertension, diabetes or cancer.
Clinical data were collected for all patients, including sex, age, and
tobacco use.

Clinical and demographic data were collected and are summarized in table
1. The Research Ethics Committee of the Faculty of Stomatology of the
UASLP approved this study (CEI-FE-014-019). This work was carried out
following The Code of Ethics of the World Medical Association
(Declaration of Helsinki) for experiments involving humans.

\textbf{Peripheral blood mononuclear cells isolation}

Human peripheral blood (20 ml) samples were collected from patients and
healthy donors in vacutainer tubes (BD Vacutainer tubes with EDTA K2; BD
Vacutainer RST tube) through venepuncture; complete blood and serum were
obtained. Peripheral blood mononuclear cells (PBMC) were isolated from
peripheral venous blood by density gradient centrifugation with
Ficoll-Hypaque (Sigma Aldrich). Cellular viability was evaluated by
staining with trypan blue. PBMC were used for staining and monocyte
isolation.

\textbf{Monocyte isolation}

Monocytes were isolated from PBMC by incubation with anti-CD14 antibody
coupled to magnetic microspheres (EasySep Human CD14 Positive Selection
Kit II), the tube with the mixture was placed on an EasySep magnet for
immunomagnetic positive selection of CD14\textsuperscript{+} cells
(StemCell). Monocytes were used for cell culture and an aliquot stored
in RNAlater (Sigma Aldrich) for RNA isolation.

\textbf{Cell culture}

For macrophage differentiation, monocytes were cultured at 37°C with 5\%
CO\textsubscript{2} in 24-well plates (Costar) for 6 days in
supplemented RPMI medium (10\% fetal bovine serum, 10nM glutamine, 100
\selectlanguage{greek}μ\selectlanguage{english}g/ml streptomycin, 100 U/ml penicillin) and 50 ng/ml GM-CSF for M1
macrophages, then incubated for 24 hours with IFN-\selectlanguage{greek}γ \selectlanguage{english}(100 ng/ml) and LPS
(100 ng/ml). For MMe and M2 macrophages, monocytes were incubated for 6
days with M-CSF (50 ng/ml), and then incubated for 24 hours with 15mM
glucose (Sigma Aldrich), 10nM insulin (Sigma Aldrich) and 0.4mM
palmitate (Sigma Aldrich) to polarize to MMe, or with IL-4 (20 ng/ml) to
obtain M2 macrophages. Cells were harvested using EDTA and further
evaluation by flow cytometry was performed. Culture media were collected
for cytokine measures.

\textbf{Flow cytometry}

500,000 to 1x10\textsuperscript{6} PBMC or macrophages were labelled
after Fc receptor blockade with 10\% human AB serum by 15 minutes. Then,
cell surface staining was performed with the following monoclonal
anti-human antibodies labelled with different fluorochromes: CD14-FITC
(BioLegend), CD80-PE (BioLegend), CD163-PE (BD Pharmigen), CD206-APC
(Invitrogen), CD36-PerCp (Novus) and ABCA1-PE (Novus). After incubation
and washing, cells were fixed and permeabilized with Foxp3 Fix/Perm kit
(eBioscience) and stained with antibodies against CD68-APC (Invitrogen),
PLIN2-APC (Novus) or IL-10-PE (BioLegend). Cells were analysed with the
FACSCanto II flow cytometer (BD Biosciences, Becton Dickinson) and the
FlowJo software (Tree Star, Ashland, OR).

\textbf{Soluble cytokine quantification}

To measure multiple soluble cytokine levels in serum and culture
supernatants from dyslipidemia patients and control subjects, we
performed a cytometric bead assay (BD CBA, inflammatory cytokine array).
Briefly, serum and supernatants samples were incubated with capture
beads and detector antibodies; then, sandwich complexes were formed with
these reagents and the analyte. These complexes were measured by flow
cytometry, according to size and fluorescence characteristics of both
the beads and the detector antibody, in an Accuri C6 cytometer using
FCAP array software for analyses.

\textbf{Real-Time Quantitative PCR}

To extract total RNA from monocytes, a RNeasy Mini Kit (Qiagen) was
used. Once obtained RNA, cDNA was synthesized starting off from 0.5 \selectlanguage{greek}μ\selectlanguage{english}g
of total RNA using high-capacity cDNA reverse transcription kit (Applied
Biosystems). For real-time qPCR (real-time quantitative polymerase chain
reaction), a SYBR Green master kit (Applied Biosystems) was used to
evaluate PPAR-\selectlanguage{greek}γ \selectlanguage{english}and \selectlanguage{greek}β-\selectlanguage{english}actin mRNA relative levels in monocytes. Relative
PPAR-\selectlanguage{greek}γ \selectlanguage{english}mRNA expression was normalized against endogenous gen \selectlanguage{greek}β-\selectlanguage{english}actin
mRNA. The amounts of PPAR-\selectlanguage{greek}γ \selectlanguage{english}mRNA relative to control were calculated
with \selectlanguage{greek}Δ\selectlanguage{english}C\textsubscript{T} method.

\textbf{Statistical analysis}

Data were analysed with GraphPad Prism, 5.01 software. Data were
analysed for normality with the Pearson test, Kolmogorov-Smirnov test,
D'Agostino, and Shapiro-Wilk test. Flow cytometry data were evaluated by
paired or unpaired Student t-test in case of a normal distribution;
otherwise, we used the Mann-Whitney U test or Wilcoxon signed-rank
sum-test. In some cases, data were analysed by one-way ANOVA, Friedman
test, or two-way ANOVA test. The analysis of correlations between
variables was based on Spearman's rank test. P \textless{}0.05 was
considered statistically significant. The data are shown as mean and SD,
or median with interquartile range (IQR).

\textbf{Results}

\textbf{Identification of circulating monocytes with a metabolic
phenotype.}

Peripheral blood was obtained from healthy individuals to analyse
metabolic markers in circulating monocytes. Cell staining was performed
as described in materials and methods. Cells were analysed by
multi-parametric flow cytometry. We used ABCA1, CD36 and PLIN2 to define
the metabolic population (Fig 1A). Interestingly, with this strategy, we
proved by the first time the presence in bloodstream of monocytes with
metabolic phenotype (MoMe). We could identify two populations according
to the ABCA1 and CD36 expression (3.4\%, 1.93\%-9\%, median and IQR) or
ABCA1, CD36 and PLIN2 expression (2.05\%, 0.95\%-4.17\%, median and IQR
respectively) (Fig 1B). Since a common marker for the study of monocytes
is CD14, we decided to evaluate this molecule expression in metabolic
monocytes (Fig 1C), we found that in each subject, there are metabolic
monocytes with different CD14 expression levels. We found a range from a
small population that does not express this marker (6.83\%,
3.33\%-14.08\%median and IQR), to CD14 positive metabolic monocytes with
dim (31.05\%, 20.48\%-40.78\%, median and IQR) and high expression, but
the most predominant fraction was the one with high expression of CD14
(p\textless{}0.0001, 58.75\%, 43.13\%-75.33\%, median and IQR).

\textbf{Healthy individuals and dyslipidemic patients show similar
levels of metabolic monocytes.}

One common characteristic of obesity and metabolic syndrome is the
chronic inflammatory state . It has been documented the macrophages in
these pathologies undergo a phenotypic switch from anti-inflammatory to
inflammatory phenotype. To determine whether diseases such as
dyslipidemia are conditioning circulating monocytes to express metabolic
markers, we evaluated the percentages of these cells from subjects with
elevated lipid levels (triglycerides \textgreater{}150 mg/dl, total
cholesterol \textgreater{}200 mg/dl) and compared them with the
percentages from healthy subjects. We evaluated the cell populations
with 2 (ABCA1, CD36) and 3 (ABCA1, CD36, PLIN2) phenotypic markers and
found that metabolic monocytes percentages do not differ between healthy
individuals and those with dyslipidemia (not statistically significant
(NS); Fig 2A), and CD14 expression seems not to have influence in this
context (NS; Fig 2B). Additionally, relative expression of PPAR-\selectlanguage{greek}γ \selectlanguage{english}mRNA
in monocytes from healthy individuals and patients with dyslipidemia was
similar (NS; Fig 2A). Furthermore, we did not find a relationship
between the percentages of metabolic monocytes and lipid levels (NS; Fig
2C) or body mass index (NS; Fig 2D) in subjects with dyslipidemia.

\textbf{Dyslipidemia patients display higher levels of pro-inflammatory
monocytes than healthy subjects.}

We also determined the levels of M1- and M2-like monocytes in healthy
subjects and those with dyslipidemia based on the CD14 expression (Fig
3A). There is a higher percentage of M1-like monocytes expressing CD14
high (\textsuperscript{++}) (p= 0.0003, 45.99\% \selectlanguage{ngerman}± 23.23\% vs 57.52\% ±
26.47\%, arithmetic mean and SD; Fig 3B), and a higher percentage of
M2-like monocytes expressing CD14 dim (\textsuperscript{+}) in the
subjects with dyslipidemia compared to the control group (p
\textless{}0.0001, 3.8\% ± 6.67\% vs 3.66\% ± 2.22\%, arithmetic mean
and SD; Fig 3C). When evaluating the
CD14\textsuperscript{++}CD68\textsuperscript{+}CD80\textsuperscript{+}and
CD14\textsuperscript{+}CD163\textsuperscript{+}CD206\textsuperscript{+}populations,
we found higher percentages of pro-inflammatory monocytes in the
dyslipidemia patients (p= 0.0355, 51.65\%, 60.80\%-26.53\% vs 28\%,
39.13\%-13.63\%, median and IQR; Fig 3B). However, no differences
between M2-like monocytes from healthy and dyslipidemia groups, were
found (p= 0.5383; Fig 3C).

\textbf{Metabolic stimuli promote differentiation to metabolic,
classical, and alternative macrophages.}

We aimed to investigate the effects of high concentrations of molecules
such as, glucose, insulin, and palmitate, in the polarization of
macrophages into different phenotypes. Once the monocytes were purified
from healthy and dyslipidemia subjects, cells were incubated with the
metabolic stimuli mentioned above, dyed, and analysed by flow cytometry
(Fig 4A). We obtained different macrophages subsets such as M1
(CD14\textsuperscript{+}CD68\textsuperscript{+}CD80\textsuperscript{+}),
M2
(CD14\textsuperscript{+}CD163\textsuperscript{+}CD206\textsuperscript{+})
and MMe
(CD14\textsuperscript{+}ABCA1\textsuperscript{+}CD36\textsuperscript{+}PLIN2\textsuperscript{+}),
being M1 the most abundant subset among the polarized cells (p= 0.0004;
Fig 4B).

\textbf{Higher differentiation of circulating monocytes into
metabolically activated macrophages (MMe) in dyslipidemia.}

Since the population of MMe has been recently described, there is of
interest to consider different subpopulation levels based on the
expression of one, two or three metabolic markers. When we compared MMe
obtained from culture from dyslipidemia and from healthy subjects, we
found that the percentage of differentiated MMe from the group with
dyslipidemia is higher than the percentage from the control group (Fig
5A) when macrophages were evaluated with 2 (p= 0.0249, 2.71\%,
2.05\%-3.31\% vs 3.41\%, 2.3\%-5.08\%, median and IQR) or 3 metabolic
markers (p= 0.0340, 2.08\%±1.14\% vs 2.88\%±1.35\%, arithmetic mean and
SD). Moreover, in dyslipidemia subjects, we observed a positive
correlation between the BMI and the percentage of MMe (p= 0.0401, r=
0.4217; Fig 5B). Nevertheless, there was no relationship between the
monocyte derived-MMe percentage and blood lipid levels either in
dyslipidemia patients (NS; Fig 5C) or control subjects. Despite an
expected relationship between ABCA1 median fluorescence intensity (MFI)
and HDLc levels (Fig 5D), no significant correlation was observed
between these parameters in dyslipidemia subjects (p= 0.9133).
Interestingly, there was a positive correlation between ABCA1 MFI and
LDLc concentrations (p= 0.0019, r= 0.5897; Fig 5D). Regarding
macrophages M1 and M2 monocytes differentiated in presence of GM-CSF,
LPS and IFN-\selectlanguage{greek}γ \selectlanguage{english}or M-CSF and IL-4, respectively, we found no difference in
the percentages of neither M1 nor M2 macrophages, between controls and
patients with dyslipidemia (NS; Fig 5E).

\textbf{Metabolically activated macrophages from patients and controls
produce equal amounts of cytokines.}

To evaluate the MMe function, we recollected supernatants from the
macrophage cultures treated with metabolic stimuli in order to determine
concentration of different cytokines. First, we compared the cytokines
levels produced by pro-inflammatory macrophages M1 and MMe. In the
control group, we found higher levels of IL-12 (p= 0.0156, 0.97\%,
0.33\%-1.67\% vs 2.07\%, 1.71\%-2.15\%, median and IQR; Fig 6A) in the
MMe supernatants, while IL-1\selectlanguage{greek}β, \selectlanguage{english}IL-6, IL-8, IL-10 and TNF\selectlanguage{greek}α \selectlanguage{english}were equally
produced by MMe and M1 macrophages (data not shown). Interestingly, in
dyslipidemia patients (Fig 6B), we found a higher concentration of IL-10
in the MMe supernatants (p= 0.0391, 4.21\%, 2.4\%-4.61\% vs 56.33\%,
2.98\%-104\%, median and IQR) and higher IL-6 levels in M1 supernatants
(p= 0.0156, 390.8\%, 1726\%-55.84\% vs 273.5\%, 570.8\%-31.3\%, median
and IQR). Since it is known that when culturing monocytes with metabolic
stimuli there is also differentiation not only towards MMe, but also
towards M1 and M2 macrophages, we evaluated the intracellular expression
of IL-10 by flow cytometry in MMe to corroborate that these cells, in
fact, produce IL-10 (85.8\%, 82.5\%-86.3\%, median and IQR; Fig 6B).
When we evaluated cytokine concentrations, produced by MMe from healthy
and dyslipidemia subjects, we did not observe differences between groups
(NS, Fig 6C). Additionally, we did not find differences among the
cytokine concentrations in serum samples from healthy and dyslipidemia
subjects, with the exception of IL-12 (p= 0.0003, 0.43\%, 0.52\%-0.12\%
vs 3.12\%, 4.05\%-2.63\%, median and IQR, Fig 6D), that was higher in
the group with dyslipidemia.

\textbf{Discussion}

Dyslipidemia is an important public health problem in industrialized
countries, great importance due to its contribution to the development
of cardiovascular diseases . Dyslipidemia can have different
presentations, whether there is a high level of triglycerides, a high
level of total cholesterol, HDL cholesterol, LDL cholesterol, or any
combination of high serum lipid levels . Systemic metabolism, in
addition to storing and providing energy to the body, influences
cellular metabolism. Immune cells, after stimulation by some antigens,
suffer metabolic changes that directly influence their functional
spectrum. In innate immune cells, the metabolic changes after activation
lead to epigenetic changes that condition cells to respond differently
to a second stimuli, either enhancing or attenuating their functional
phenotype, a physiological condition known as immune training .

In metabolic diseases such as dyslipidemia, monocyte phenotype may be
shaped by high blood lipid levels. We hypothesized that elevated serum
levels of cholesterol and triglycerides observed in patients with
dyslipidemia, could induce the expression of metabolic markers in
circulating monocytes, providing them with a phenotype similar to the
recently described metabolic activated macrophages onocytes are cells
with a short half-life in circulation of around 1 day; however, studies
suggest reprogramming can take place at the level of progenitor cells in
the bone marrow trained monocytes have been observed 3 months after
priming .

It has been reported that the phenotype of metabolic monocytes was
CD36\textsuperscript{+} ABCA1\textsuperscript{+} ; however, in that
study the phenotype was missing PLIN2 and CD14 markers, and the
metabolic markers were evaluated individually, with possible overlap
with other subpopulations such as M2. This is the first study reporting
circulating monocytes with the complete metabolic phenotype (MoMe) in
healthy subjects. This population correspond to approximately 3.4\% of
total monocytes, with the majority of these cells showing high CD14
expression. This could be related to its pro-inflammatory functional
spectrum because its similarities, at least in terms of CD14 expression,
to the intermediate monocyte phenotype (CD14high CD16low), to which
proinflammatory properties are attributed .

When evaluating the percentages of the different populations of
circulating monocytes, we found that healthy subjects showed similar
levels of MoMe and M2-like monocytes, which are predominantly
CD14\textsuperscript{+} (dim). Regarding the M1-like monocytes,
predominantly CD14\textsuperscript{++} (high), higher frequencies were
found in dyslipidemia subjects. This is in accordance with the
literature, as hypercholesterolemia induces loss of hematopoietic stem
and progenitor cells quiescence and enhances the propensity to generate
pro-inflammatory monocytes . We consider that the levels of MoMe may not
be influenced by high levels of lipids since the effect of this
metabolic alteration has an impact on epigenetic mark and not
necessarily in the expression of metabolic receptors. In innate immune
cells that are subjected to a stimulus, whether infectious or sterile,
undergo long-term metabolic, transcriptional, and epigenetic
reprogramming . It is when these cells are subjected to a secondary
stimulus, equal to or different from the first stimulus, when they
present a greater response capacity, for example, increased
proliferation, receptors expression and cytokines production . We need
further studies to evaluate the possible epigenetic modifications,
including levels of histone acetylation and methylation, in order to
clarify this hypothesis.

It is well known that patients with complicated metabolic diseases such
as diabetes mellitus 2 or metabolic syndrome, are characterized by a
pro-inflammatory state where molecules as glucose, insulin, and
palmitate, are elevated, regulating inflammation and with M1 macrophages
playing a key role preserving the inflammatory environment . However, it
has been proposed that these metabolites also promote the presence of
metabolically activated macrophages (MMe), which display a different
phenotype from the classically activated (M1) .

Since monocytes from dyslipidemia subjects are exposed to high
triglycerides and cholesterol levels, we hypothesize that such condition
serves as a priming stimuli driving macrophages stimulated in vitro with
high concentrations of glucose, insulin, and palmitate to a stronger
polarization into the MMe phenotype.

In dyslipidemia patients and healthy subjects, metabolic stimuli promote
polarization into pro-inflammatory MMe, pro-inflammatory M1 and
anti-inflammatory M2 macrophages, being M1 the most abundant polarized
subset. This was expected free fatty acids (FFA) such as palmitat (the
most prevalent dietary FFA, induce metabolic inflammation via TLR4
through NF-kB, MAPK and PI3K, and induce proinflammatory cytokines
production such as IL-6, TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-8, and IL-1\selectlanguage{greek}β .

\selectlanguage{english}While no difference was observed between the percentages of MoMe
comparing control and dyslipidemic subjects, \emph{in
vitro}differentiated monocyte-derived MMe percentages are higher in
dyslipidemia than in the control group. This finding suggests,
accordingly with our hypothesis, that elevated triglycerides and
cholesterol levels serve as the first stimulus inducing immune training
in monocytes, which after \emph{in vitro} stimulation with glucose,
insulin and palmitate in high concentrations, respond differently to
those not exposed to elevated lipids in circulation.

Furthermore, in dyslipidemic conditions, the higher the body mass index
(BMI) the higher the MMe percentage. It has been previously reported
that macrophages from adipose tissue, show a positive correlation
between CD36 expression and BMI . Nevertheless, this is the first work
reporting the correlation between monocyte-derived MMe (full phenotype)
frequencies and BMI.

ABCA1, which is present in the plasma membranes of cells such as
monocytes/ macrophages, participates in cholesterol efflux donating it
to ApoA1 to form HDL, inducing anti-inflammatory signaling . In , we did
not find a relationship between serum HDL concentrations and ABCA1
expression, but we found a direct association between LDL concentrations
and ABCA1 expression. This finding suggests that the differentiation
into MMe may be a result of the metabolic impairment and are not
associated in a direct manner to the metabolism of lipids. However, we
observed that high levels of LDLc are related to a greater expression of
ABCA1 in MMe, suggesting a high cholesterol efflux and HDL formation.
The above, could indicate a beneficial role of this cell population in
late stages of dyslipidemia. It would be interesting to evaluate
molecules such as SIRT6, involved in the expression of ABCA1 .

Regarding the MMe function, we confirm that although they produce
pro-inflammatory cytokines such as IL-6 , they do so to a lesser extent
than classic M1 macrophages in subjects with dyslipidemia.
Interestingly, we also found that these MMe produce IL-10, a cytokine
with potent anti-inflammatory and regulatory activities , and that their
production is higher in MMe compared to M1. Regarding the polarization
to classic M1 and alternative M2 macrophages, we found no differences
between healthy people and those with dyslipidemia; in this sense,
Baardman and colleagues have shown that hypercholesterolemia alters
macrophage metabolism and phenotype, attenuating the inflammatory
phenotype in M1 macrophages.

Our results support the idea that pre-existing metabolic alterations
such as elevated serum lipid levels, condition monocytes to
differentiate into a specific phenotype after a second stimulus. This is
supported by our results from our \emph{in vitro} model where the second
stimulus corresponds to the combination of high concentrations of
glucose, insulin and palmitate. We observed a preferent polarization of
monocytes into MMe in dyslipidemia group compared to healthy subjects.
Interestingly, we observed that MMe from dyslipidemia patients produce
higher levels of the regulatory cytokine IL-10. It has been previously
described that MMe have a dynamic role in obesity (18). MMe have an
important role in controlling metabolic alterations of high-fat diet fed
mice at early stages, however, even when maintaining their metabolic
markers expression, their function becomes detrimental at advanced
stages of obesity. It is clear that our experimental approach focuses in
monocyte derived macrophages and not in adipose tissue associated
macrophages. However, the production of IL-10 as a regulatory mechanism
by monocyte derived MMe results of great relevance. In this regard, it
is important to point out that our study was carried out in young people
with dyslipidemia, who did not present other comorbidities. For this
reason, we cannot predict whether metabolically activated monocytes/
macrophages would have a different behavior pattern in subjects with a
longer time of evolution of the disease, different metabolic alterations
or in the presence of other comorbidities. To elucidate if the chronic
exposure of circulating metabolic monocytes to high concentrations of
metabolites (such as glucose or free fatty acids) leads to a different
functional profile, further follow-up studies should be carried out.

In conclusion, this is the first study in which monocytes with metabolic
phenotype (expressing CD36, ABCA1 and PLIN2) in systemic circulation are
described by multiparametric analysis. Our study highlights the dual
role of metabolically activated macrophages, which regulate the
pro-inflammatory milieu characteristic of dyslipidemia through the
production of IL-6 and IL-10.

\textbf{Acknowledgements}

We thank the University Health Center from the Universidad Aut\selectlanguage{ngerman}ónoma de
San Luis Potosí. This work was supported by CONACyT.

\textbf{Disclosure}

The authors declare that they have no disclosures related to this
manuscript.

\textbf{Author contributions}

All authors were involved in drafting or revising the article, and all
authors approved the final version to be published.

\textbf{Data sharing and data accessibility}

All data pertinent to this article are included herein.



\textbf{References}



\textbf{FIGURE LEGENDS}

\textbf{Fig 1.} \textbf{Circulating monocytes express metabolic
markers.} Peripheral blood was obtained from healthy and dyslipidemic
individuals to analyse metabolic markers in circulating monocytes. (A)
Cells were analysed by multi-parametric flow cytometry. (B) Bloodstream
monocytes with metabolic phenotype (MoMe) levels according to ABCA1,
CD36 (n= 19) and PLIN2 (n= 20) expression in healthy subjects. (C) CD14
expression in metabolic monocytes. Data shown in panels B and C had a
non-Gaussian distribution and correspond to the median and Q1 and Q3.
Groups in panel C were compared using one-way ANOVA (Friedman test). *p
\textless{} 0.05, **p \textless{} 0.01, ***p \textless{} 0.001.

\textbf{Fig 2. Metabolic monocytes in healthy individuals and in
patients with dyslipidemia.} Bloodstream MoMe percentages from subjects
with elevated lipid levels (triglycerides \textgreater{}150 mg/dl, total
cholesterol \textgreater{}200 mg/dl) and healthy subjects. (A) MoMe
percentages evaluated with 2 (n= control 19, dyslipidemia 31) and 3 (n=
control 20, dyslipidemia 32) metabolic markers. Relative expression of
PPAR-\selectlanguage{greek}γ \selectlanguage{english}mRNA in healthy individuals (n= 4) and in patients with
dyslipidemia (n= 8). (B) Different MoMe subsets according to their CD14
expression, divided into CD14 negative (n= control 20, dyslipidemia 31),
dim (n= control 19, dyslipidemia 31) and high (n= control 19,
dyslipidemia 31) expression. (C) Correlations between metabolic
monocytes percentages and total cholesterol (n= 29) or triglycerides (n=
29) or body mass index (D, n= 27) in subjects with dyslipidemia. All
data shown had a non-Gaussian distribution and correspond to the median
and Q1 and Q3. Groups were compared using Mann-Whitney test in panel A
and B. Spearman r analysis was performed in panel C. Linear regression
was performed. P value represents the likelihood of a nonzero slope.

\textbf{Fig 3. Evaluation of M1- and M2-like monocytes levels in healthy
subjects and in patients with dyslipidemia.} (A) Using flow cytometry,
starting from monocytes gate defined by FSC and SSC parameters, doble
cells were eliminated and then, CD14 expression was evaluated. (B) CD14
expression in M1-like monocytes (n=26) from dyslipidemia subjects and
CD14\textsuperscript{++}CD68\textsuperscript{+}CD80\textsuperscript{+}comparation
between patients (n= 26) and controls (n= 8). (C) CD14 expression in
M2-like monocytes from subjects with dyslipidemia (n=26) and
CD14\textsuperscript{+}CD163\textsuperscript{+}CD206\textsuperscript{+}comparation
between patients (n= 26) and controls (n= 8). Data shown in bars graph B
had a non-Gaussian distribution and correspond to the median and Q1 and
Q3. Data shown in individual values graphs and bars graph C had a normal
distribution and correspond to the arithmetic mean and SD. Groups were
compared using paired \emph{t} test, Mann-Whitney test, or unpaired
\emph{t} test. *p \textless{} 0.05, ***p \textless{} 0.001, ****p
\textless{} 0.0001

\textbf{Fig 4. Metabolic stimuli promote differentiation to
metabolically activated (MMe), classical (M1), and alternative
macrophages (M2).} Monocytes were purified from healthy and dyslipidemia
subjects, cells were incubated with metabolic stimuli, dyed, and
analysed by flow cytometry for the expression of characteristic
polarization markers, M1
(CD14\textsuperscript{+}CD68\textsuperscript{+}CD80\textsuperscript{+}),
M2
(CD14\textsuperscript{+}CD163\textsuperscript{+}CD206\textsuperscript{+})
and MMe
(CD14\textsuperscript{+}ABCA1\textsuperscript{+}CD36\textsuperscript{+}PLIN2\textsuperscript{+}).
(A) Cell culture conditions and MMe gating strategy. (B) Comparison of
MMe, M1 and M2 percentages, obtained from the incubation with metabolic
stimuli of monocytes from dyslipidemia patients (n= 9) and control
subjects (n= 6). Groups were compared using two-way repeated measures
ANOVA. *p \textless{} 0.05, ***p \textless{} 0.001

\textbf{Fig 5.} \textbf{Metabolically activated (MMe), classical (M1),
and alternative (M2) macrophage differentiation in dyslipidemia and
healthy subjects.} MMe (glucose, insulin, palmitate), M1 (IFN-\selectlanguage{greek}γ, \selectlanguage{english}LPS)
and M2 (IL-4) macrophages obtained from culture from dyslipidemia and
healthy subjects. (A) MMe comparison between control and dyslipidemia
subjects evaluated with 2 (n= control 21, dyslipidemia 28; median and
IQR) and 3 (n= control 21, dyslipidemia 28; arithmetic mean and SD)
metabolic markers. (B) Correlation between MMe percentages and body mass
index (n= 24). (C) Correlation between MMe percentages and triglycerides
(n= 27), total cholesterol (n= 27), HDLc (n= 27) and LDLc (n= 26). (D)
ABCA1 median fluorescence intensity compared with HDLc (n= 27) and LDLc
(n= 25). (E) Monocyte-derived M1 (n= control 9, dyslipidemia 23) and M2
(n= control 9, dyslipidemia 22) macrophages in control and dyslipidemia
subjects. Groups were compared using Mann-Whitney test or
unpaired\emph{t} test. Spearman r analysis was performed. Linear
regression was performed. \emph{P} value represents the likelihood of a
nonzero slope. *p \textless{} 0.05

\textbf{Fig 6.} \textbf{Cytokine production by macrophages from
dislypidemic patients and healthy controls.} Supernatants from
polarization cultures with metabolic stimuli or LPS were collected and
analysed by flow cytometry using a Cytometric Bead Array kit. (A) IL-12
quantification in M1 and MMe cell culture supernatants from healthy
subjects (n= 7). (B) IL-10 (n= 8) and IL-6 (n= 7) quantification in M1
and MMe cell culture supernatants from patients. Representative plot of
flow cytometry (n= 3) for
CD14\textsuperscript{+}CD36\textsuperscript{+}PLIN2\textsuperscript{+}macrophages
expressing IL-10 (red) compared with undyed control (blue). (C) Cytokine
concentrations produced by MMe from healthy and dyslipidemia subjects.
(D) IL-12 quantification in serum from control (n= 7) and dyslipidemia
subjects (n= 13). Data shown in A, B and D had a non-Gaussian
distribution and correspond to the median and Q1 and Q3. Groups were
compared using Wilcoxon matched-pairs signed rank test or Mann-Whitney
test. Groups were compared using two-way ANOVA. *p \textless{} 0.05,
***p \textless{} 0.001

\textbf{TABLES}

\textbf{Table 1. Demographic and clinical data of dyslipidemia patients
and control subjects}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& \textbf{Dyslipidemia} & \textbf{Control} & \textbf{p}\tabularnewline
\midrule
\endhead
\textbf{n} & 32 & 22 & -\tabularnewline
\selectlanguage{ngerman}\textbf{Age in years (mean±SD)} & 19.5, 25.5-18 & 24, 28-18 &
NS\tabularnewline
\textbf{Sex (F/M)} & M: 23, F: 9 & M: 15, F: 7 & -\tabularnewline
\textbf{Total cholesterol (mg/dl)} & 208.5, 223.3-201 & 158.5, 186.5-141
& \textless{}0.0001\tabularnewline
\textbf{Triglycerides (mg/dl)} & 192.5, 240.8-119.8 & 102, 129.3-70.75 &
\textless{}0.0001\tabularnewline
\textbf{HDLc (mg/dl)} & 44.3, 62.2-35.43 & 50.4, 68.98-41.7 &
0.1628\tabularnewline
\textbf{LDLc (mg/dl)} & 125.5, 135.7-106.9 & 80.35, 113.8-64.3 &
0.0005\tabularnewline
\bottomrule
\end{longtable}

Data correspond to median and Q3-Q1. Abbreviations: F, female; HDLc,
high-density lipoprotein cholesterol; LDLc, low-density lipoprotein
cholesterol; M, male; mg/dl, milligram/deciliter; NS, not statistically
significant.

\textbf{FIGURES}

Figure 1\selectlanguage{english}
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Figure 3\selectlanguage{english}
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Figure 4\selectlanguage{english}
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Figure 5\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

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\end{figure}

Figure 6\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
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