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\begin{document}

\title{Anti-inflammatory screening biomarkers on an LPS-induced RAW 264.7 cell
model: a Systematic Review and Meta-analysis.}



\author[1]{Bruno Facchin}%
\author[2]{Gustavo dos Reis}%
\author[2]{Guilherme Vieira}%
\author[2]{Eduarda Mohr}%
\author[2]{Júlia da Rosa}%
\author[2]{Iara Kretzer}%
\author[2]{Izabel Demarchi}%
\author[2]{Eduardo Dalmarco}%
\affil[1]{Federal University of Santa Catarina}%
\affil[2]{UFSC}%


\vspace{-1em}



  
  \date{June 05, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The inflammation is a response to some injury aimed at reestablishing
homeostasis of the affected tissue. The in vitro model using RAW 264.7
cells has been widely used, as experimental model to assess the
inflammatory response. However, there is still no consensus on which
inflammatory mediators should initially be measured to screen for
possible anti-inflammatory effects. To determine the rationality of
measuring different inflammatory mediators together with NO, we carried
out this systematic review and meta-analysis. A total of 17 studies were
included, and LPS-induced cells produced high NO levels compared to
non-LPS induced, and this production was not related to cell density.
TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}and IL-6, also showed high levels after cells had been
stimulated with LPS; however, these effects were related to cell
density. Finally, was found that measurement of NO levels alone may be
sufficient to screen for possible anti-inflammatory action in the
context of this model.%
\end{abstract}%



\sloppy


\textbf{Anti-inflammatory screening biomarkers on an LPS-induced RAW
264.7 cell model: a Systematic Review and Meta-analysis}

Bruno Matheus Facchin\textsuperscript{1}, Gustavo Oliveira dos
Reis\textsuperscript{1}, Guilherme Nic\selectlanguage{ngerman}ácio Vieira\textsuperscript{1},
Eduarda Talita Bramorski Mohr\textsuperscript{1}, Júlia Salvan da
Rosa\textsuperscript{1,2}, Iara Fabricia Kretzer\textsuperscript{2},
Izabel Galhardo Demarchi\textsuperscript{1,2,3}, Eduardo Monguilhott
Dalmarco\textsuperscript{1,2*}

\textsuperscript{1}Programa de Pós-Graduação em Farmácia (PPGFar),
Universidade Federal de Santa Catarina, Florianópolis, SC,
Brazil.\textsuperscript{2}Departamento de Análises Clínicas,
Universidade Federal de Santa Catarina, Florianópolis, SC,
Brazil.\textsuperscript{3}Programa de Pós-Graduação em Ciências da
Saúde, Universidade Estadual de Maringá, PR, Brazil.

\textbf{Conflicts of interest.}

None.

* Corresponding author Eduardo Monguilhott Dalmarco, phD Departamento de
Análises Clínicas - CCS, Universidade Federal de Santa Catarina, Campus
Universitário - Trindade, 88040-970, Florianópolis, SC, Brazil. email:
edalmarco@gmail.com and/or eduardo.dalmarco@ufsc.br

\textbf{Abstract}

The inflammation is a response to some injury aimed at reestablishing
homeostasis of the affected tissue. The in vitro model using RAW 264.7
cells has been widely used, as experimental model to assess the
inflammatory response. However, there is still no consensus on which
inflammatory mediators should initially be measured to screen for
possible anti-inflammatory effects. To determine the rationality of
measuring different inflammatory mediators together with NO, we carried
out this systematic review and meta-analysis. A total of 17 studies were
included, and LPS-induced cells produced high NO levels compared to
non-LPS induced, and this production was not related to cell density.
TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}and IL-6, also showed high levels after cells had been
stimulated with LPS; however, these effects were related to cell
density. Finally, was found that measurement of NO levels alone may be
sufficient to screen for possible anti-inflammatory action in the
context of this model.

\textbf{Keywords:} Lipopolysaccharide, LPS, \emph{in vitro} study,
macrophages, RAW 264.7, nitric oxide, tumor necrosis factor-alpha,
interleukin-1 beta, and interleukin- 6.

\textbf{Introduction}

Inflammation is a physiological process that occurs in response to an
infectious agent or tissue injury, in order to restore the homeostasis
of the affected tissue. This response involves a cascade of events
characterized by vascular and cellular involvement, with activation of
modulation pathways that depend on the type of harmful agent and the
stimulus generated (Fullerton \& Gilroy, 2016; Medzhitov, 2010; Nathan
\& Ding, 2010; Zarrin et al., 2021). If successful, the inflammatory
response tends to progress to the post-resolution phase, through a
coordinated series of molecular and cellular events that lead to the
restoration of tissue structure, organ function, and `adapted
homeostasis' (Sugimoto et al., 2016). However, uncontrolled inflammation
can lead to tissue damage that gives rise to chronic inflammatory
diseases, such as Alzheimer's disease, atherosclerosis, cardiovascular
diseases, and cancer (Cicchitti et al., 2015; Furman et al., 2019).

In this context, there is a continuous search for new anti-inflammatory
drugs, and many experimental models have been developed and extensively
used over the Years (Kabir \& Ansari, 2018; Patil et al., 2019). In
vitro models represent the beginning of this complex development, and
millions of potential compounds are proposed each year. Thus, fast and
reliable screening is required for the next level of pharmacological
research, optimizing the initial screening and reducing the use of
animals (Benam et al., 2015).~For this purpose, the use of in vitro
models using RAW 264.7 cells has become widespread in research
laboratories, due to their versatility. RAW 264.7 cells are similar to
monocytes/macrophages, originating from the cell line transformed by the
Abselon Leukemia Virus derived from BALB/c mice (Taciak et al., 2018).
In vitro models using these cells are the most commonly applied models
when screening for anti-inflammatory and immunomodulatory compounds.
This is because the RAW 264.7 cell line produces a robust and well-known
inflammatory response, especially when challenged by inflammatory
stimulants such as lipopolysaccharide (LPS), a Toll-like receptor 4
(TLR4) agonist. TLR4 stimulation activates intracellular signaling
cascades, which in turn, mobilizes nuclear transcription factors such as
NF-kB, This ultimately leads to the production of inflammatory
mediators, such as Nitric Oxide (NO), Tumor Necrosis Factor-alpha
(TNF-\selectlanguage{greek}α\selectlanguage{english}), Interleukin-1-beta (IL-1\selectlanguage{greek}β\selectlanguage{english}), and Interleukin-6 (IL-6), among
others (Elisia et al., 2018; Lawrence, 2009; Taciak et al., 2018). In
this context, the most common inflammatory biomarker used in the
screening for possible anti-inflammatory and immunomodulatory action of
any compound or drug is nitric oxide levels (NOx). Nitric oxide is
produced after the initial action of the phlogistic agent, culminating
in nitric oxide synthase (iNOS) induction and substantial production of
this inflammatory mediator (Lind et al., 2017; Saha \& Burns, 2020).

The RAW 264.7 cell line has been used in biological laboratories for
more than forty years, and the detection of NO levels is the most
commonly measured inflammatory mediator in these experiments. The
expected level of other proinflammatory mediators produced, after
stimulation of RAW cells with LPS, has not yet been explored and/or
standardized in any robust study (Taciak et al., 2018). Furthermore,
there is no consensus regarding the existence of a direct correlation
between NO production by activation of iNOS, and other important
inflammatory mediators such as TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}and IL-6, produced after
activation of nuclear transcription factors.~In order to standardize the
expected levels of NO, TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}and IL-6 production after LPS
stimulation of RAW 264.7 cells, and to determine the rationality of
measuring numerous inflammatory biomarkers in the initial stages of the
screening process of anti-inflammatory and immunomodulatory activities,
we decided to carry out this systematic review and meta-analysis.

\textbf{2. Methods}

We conducted this systematic review and meta-analysis in accordance with
the Preferred Reporting of Systematic Reviews and Meta-Analysis (PRISMA
statement, Page et al., 2021) and the Cochrane Handbook for Systematic
Reviews of Intervention (Higgins et al., 2019). This review was
registered in the Open Science Framework (DOI 10.17605/OSF.IO/8C3HT).
The review question was formulated according to the acronym PICOS
(population, intervention, control, outcomes, and study design): What is
the efficacy of Tumor Necrosis Factor-alpha, interleukin-1 beta, and
interleukin-6, as \emph{in vitro} inflammatory trial biomarkers,
compared to nitric oxide (gold standard) in LPS inflamed RAW 264.7
cells? (Supplementary Material S1 - Table 2).

\textbf{2.1 Literature Review}

The literature review was conducted in the PubMed, EMBASE, Scopus, and
Web of Science (Web of Knowledge), and Google Scholar databases from Jan
2015 through April 2020. The first phase established an investigation
according to the features and strategies of each electronic database
(Supplementary Material S2 - Table 3). All the researchers conducted
independent searches to define MeSH (Medical Subject Headings) terms and
search strategies, which were validated by three experts (IGD, IFK,
EMD). The researchers first screened the titles and then the abstracts
(BMF; GOR; ETBM; GNV). To retrieve the publications in PubMed, The MeSH
terms were organized into four blocks: Block 1 (markers of mediators of
inflammation): ''Interleukin-1'' OR ''Interleukin-6'' OR
''Inflammation'' OR ''Nitric Oxide'' OR ''Nitric Oxide Synthase Type
II'' OR ''Nitrites'' OR ''Tumor Necrosis Factor-alpha''; AND Block 2
(cells): ''Cells, Cultured'' OR ''Macrophages'' OR ''Macrophages,
Peritoneal'' OR ''Mice''; AND Block 3 (lipopolysaccharide):
''Endotoxins''; AND Block 4 (anti-inflammatory agents):
''Anti-Inflammatory Agents'' OR ''Anti-Inflammatory Agents,
Non-Steroidal'' OR ''Anti-Inflammatory Agents'' {[}Pharmacological
Action{]} OR ''Anti-Inflammatory Agents, Non-Steroidal''
{[}Pharmacological Action{]}. In the other databases, we searched for
the terms that would retrieve potential studies, and used the topic
strategy for the Web of Science (topic), Scopus
(title/keyword/abstract), and Embase (Emtree terms) databases, applying
filters for language (English), time period and article. In Google
Scholar, we used the following strategy: ''inflammation'' OR
''cytokines'' AND ``RAW 264.7'' AND ''lipopolysaccharide'' AND
''Anti-inflammatory agents'' (PDF file and period 2015-2020). The
references retrieved from the searches were organized in EndNote
Reference Manager and Rayyan QCRI web and mobile app.

\textbf{2.2. Study selection}

Duplicates were removed using EndNote Reference Manager, and manually in
the Rayyan web and mobile app for systematic review. In the first phase,
four blinded and independent authors (BMF, GOR, ETBM, and GNV) selected
an article by title and abstract, applying the eligibility criteria
according to the PICOS question (above) (Supplementary Material S1 -
Table 2). Any disagreement between the reviewers was solved by an expert
reviewer (IGD and JSR). Next, the full texts of the selected studies
were retrieved and independently assessed for eligibility by four
reviewers (BMF, GOR, ETBM, and GNV). Any disagreement was resolved
through discussion between the reviewers and an independent expert (IGD,
JSR, IFK, and EDM).

\textbf{Inclusion criteria}

We applied the PICOS strategy to establish the inclusion criteria.
Population (P): RAW 264.7 macrophages derived from murine/Mus musculus,
cell culture medium Dulbecco's Modified Eagle Medium (DMEM). The use of
this cell culture medium in the studies was chosen as an eligibility
criterion, according to the ATCC base medium recommendation for this
cell line (American Type Culture Collection - ATCC, 2018). Intervention
(I)/Exposure: Any form of intervention with LPS at a concentration of 1
\selectlanguage{greek}µ\selectlanguage{english}g/mL (inflammatory inductor of RAW 264.7 macrophages), and exposure
time of between 12 to 48 hours. Comparator(s)/control (C): RAW 264.7
macrophages not induced by LPS (basal control group). Outcome (O)/
measure(s): Levels or concentration of NO or metabolites (\selectlanguage{greek}µ\selectlanguage{english}M), and at
least one of the following cytokines:~ TNF\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}and IL-6~(pg
mL\textsuperscript{-1}). Only studies that showed NO production over 20
\selectlanguage{greek}µ\selectlanguage{english}M by LPS-induced RAW 264.7 macrophages were included. These conditions
were established based on previous studies published by our laboratory
using this experimental model (Biluca et al., 2020; Duarte et al., 2018;
Mohr et al., 2019). Other important references in the field have also
used the same experimental protocol (Chen et al., 2016; Jung et al.,
2013; Yoon et al., 2009). Study designs (S) included: Only original
articles from \emph{in vitro} studies (RAW 264.7 macrophages), with
separate control cells. Articles written in English, published in the
last five years (From January~1, 2015 to May 31, 2020).

\textbf{Exclusion criteria}

The exclusion criteria of the PICOS strategy were also applied,
following the recommended sequence of PICOS. These criteria were
selected based on the main guidelines for cell culture (American Type
Culture Collection - ATCC, 2018).~ In the first phase of eligibility,
the title and abstract were read and the following exclusion criteria
applied: 1) Not RAW 264.7 macrophages; 2) Not LPS induced RAW 264.7
macrophages; 3) Not reporting ``nitric oxide or nitrites'' AND
``TNF-alpha or IL-1 beta or IL-6''; 4) Study design: experimental
studies using exclusively animal models, clinical trials or other
studies involving humans, reviews, Guidelines, Letters, Editorials,
Comments, News, conference abstracts, thesis, and dissertations; Not
written in the English language; full texts unavailable. In the second
phase, the full text was read and some further exclusion criteria were
added: 5) LPS concentration different from 1 \selectlanguage{greek}µ\selectlanguage{english}g/mL; LPS-exposure time
different from 12, 24 or/and 48 hours; 6) ~``nitric oxide or nitrites''
AND TNF-alpha and/or IL-1 beta and/or IL-6 measurements presented as
gene or ratio of expression OR other measurements (\%, IC50); 7) Nitric
oxide or nitrite production equal or less than 20 \selectlanguage{greek}µ\selectlanguage{english}M in the LPS control
group; 8) Studies that did not use a basal control group (comparator:
RAW 264.7 macrophages not induced by LPS and another substance); 9) DMEM
not used as a cell culture medium; 10) Full texts unavailable; 11) NO
not measured by the Griess method; 12) atmospheric condition other than
5\% CO\textsubscript{2}\textsuperscript{5} mL; 15) NO and cytokines not
measured in the same experimental condition; 16) NO levels
\textgreater{}300 \selectlanguage{greek}µ\selectlanguage{english}M (the reactivity threshold); 17) Duplicate studies.

\textbf{2.3. Data Extraction~}

The articles were randomly distributed between four authors, who
extracted the data independently (BMF, GOR, ETBM, and GNV) (Urbaniak \&
Plous, 2013 - Research randomizer\textsuperscript{TM}). Data were
extracted from the text, figures, or tables and inserted into a
standardized table. If only a graph was available, the data were
extracted using a digital screen ruler. The extracted data were
validated by a pair of reviewers. Any discrepancies were resolved
through discussion or by consultation with a third investigator (IGD,
IFK, JSR, and EMD). Afterwards, the expert group validated the extracted
data in the standardized way. If the data were not reported, or unclear,
we contacted the authors by email (max. 3 attempts).

\emph{Data to be extracted:} 1st author, year of publication, country,
objectives, RAW 264.7 cultured cells (macrophage; Abelson murine
leukemia virus-transformed - \emph{Mus musculus} , mouse),
number/density of cells plated, medium supplementation, incubation
temperature, atmospheric carbon dioxide (\%), antibiotics, cell passage
number, the plate used for the test, cell confluence, number of
technical replicates and repetition of independent experiments.
Concentration of LPS (manufacture, 1 \selectlanguage{greek}µ\selectlanguage{english}g/mL), vehicle, dimethyl sulfoxide
concentration (DMSO), timing of incubation/exposure (12, 24 or 48h).
Mediators of inflammation production, continuous variables: Nitric
Oxide/nitrites (\selectlanguage{greek}µ\selectlanguage{english}M, mean, SD), and TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}or IL-6 (pg
mL\textsuperscript{-1}, mean, SD). If the type of error was unclear, it
was recorded as SEM. These data were converted to standard deviations
(Hozo et al., 2005). The authors' conclusions and risk/limitations
reported by the authors of original studies were also extracted. We
provided a narrative synthesis of the findings from the included
studies, structured around the type of intervention, target
characteristics, type of outcome, intervention content, and control
cells. We summarized the intervention effects for each study by
calculating the standardized mean differences in a meta-analysis.

\textbf{2.4. Meta-analysis}

A minimum of 3 studies were used to perform a meta-analysis. If the
studies presented the same type of intervention (LPS) and control, with
the same outcome measure, we pooled the results using a meta-analysis of
random effects, with standardized mean differences for continuous
outcomes, calculating the 95\% confidence intervals and two-sided P
values for each outcome. In studies where the effects of clustering have
not been taken into account, was to adjust the standard deviations for
the design effect. Statistical significance was defined as a
p-value\emph{\textless{}} 0.05 (Z test). Cochran's Q was used to assess
heterogeneity, Q was used to calculate the excess variance. A p-value
can be calculated for Q, indicating whether all studies will share a
common effect size (p \textless{} 0.05) or not (p \textgreater{} 0.05),
We used univariate meta-regression, where sufficient data existed
(\textgreater{} 10 comparisons per variable). We also used the
I\textsuperscript{2} test to investigate heterogeneity (p \textgreater{}
0.1, heterogeneity, I\textsuperscript{2} \textgreater{} 50\%). A
Stratified meta-analysis was used to explore heterogeneity in effect
estimates according to subgroups, and evidence of publication bias was
assessed using funnel plot analysis. For the stratified analyses,
subgroup analyses were carried out. The following characteristics of the
study were examined as a potential source of heterogeneity: time of
LPS-exposure (24 hours); interval of nitric oxide/nitrites concentration
on a positive control group (20-50 \selectlanguage{greek}µ\selectlanguage{english}M/ \textgreater{} 50 \selectlanguage{greek}µ\selectlanguage{english}M) (studies
showing mM/mL or another scale were converted to \selectlanguage{greek}µ\selectlanguage{english}M); and the method
used to determine cell density (cells per well or cells per mL). The
results of studies reported as zero were used 0.1 to perform a
meta-analysis. The sensitivity analysis was performed after removing
outliers, according to the assay range of the cytokines ELISA kit: TNF:
10 - 6.000 pg mL\textsuperscript{-1}, IL-1: 3 - 3.000 pg
mL\textsuperscript{-1}, and IL-6: 5 - 2.000 pg mL\textsuperscript{-1}.

\textbf{2.5. Quality assessment}

The risk of bias was assessed based on the OECD ``Guidance Document for
Describing Non-Guideline In Vitro Test Method'' and the SciRAP (Beronius
et al., 2018) tool for evaluating in vitro toxicity studies. Two
reviewers independently assessed the quality of each study (BMF and
ETBM). Any discrepancies were resolved by the expert and topic reviewers
(IGD and EMD). We assessed the reporting quality, methodological
quality, and relevance of the in vitro studies. For the reporting and
methodological quality, the response options for signaling each question
were ``Not determined, Fulfilled Partially, Fulfilled, or Not
fulfilled''. For the relevance criteria, the response options for the
report items were ``Not determined, Directly relevant, Indirectly
relevant, or Not relevant''. These color profiles are especially useful
for determining where the strengths and weaknesses of a study lay. A
numerical score for reporting quality and methodological quality was
calculated. The calculation of the scores can be simply explained as the
SciRAP score: \{{[}F + (PF*0,5){]}/T{]}*100\} (where F is the number of
fulfilled criteria multiplied by their individual weights, PF is the
number of partially fulfilled criteria multiplied by their individual
weights and T is the total number of criteria multiplied by their
individual weights). In other words, the score is the percentage of
fulfilled and partially fulfilled criteria included in the evaluation,
taking the weight of individual criteria into account, where partially
fulfilled criteria (subjective analysis) contribute half the value of
fulfilled criteria. The SciRAP score can have a value ranging from 0
(all criteria are judged as ``not fulfilled'') to 100 (all criteria are
judged as ``fulfilled''). The output from the SciRAP evaluation was
based on the ranking of studies according to their relative reliability,
and dividing the studies into different categories of reliability, e.g.
the Klimisch categories ``reliable without restrictions'', ``reliable
with restrictions'', and ``not reliable''. The ranking and
categorization are done on a case-by-case basis. The SciRAP does not
dictate cut-off values or categorization based on SciRAP scores or color
profiles.

\textbf{3. Results}

After an exhaustive search in five databases we retrieved a total of
13,073 articles. This number was reduced after automatic application of
a filter to remove duplicates (Endnote) and manual removal of duplicates
in Rayyan QCRI web and mobile app (Ouzzani et al., 2016), leaving a
total of 7,868 articles to be submitted to the first phase round of
application of eligibility criteria (Fig.1).

After applying the first-round of exclusion criteria (title and
abstract), a total of 6,542 articles were excluded, leaving 1,326
articles to be read in full. In this second selection phase, more
specific criteria were applied to further refine the search, leading to
the exclusion of more than 1,309 articles (Supplementary Material S3 -
https://osf.io/jexud/). The remaining 17 articles were sent for data
extraction (Fig.1).

The objective of most of the studies (15) was to assess the biological
activity of plants, extracts, or chemical compounds isolated from them.
One study worked with a fraction of egg yolk levitins (Meram \& Wu,
2017) and another with Celecoxib (Pang et al., 2019), a non-steroidal
anti-inflammatory in the class of specific cyclooxygenase-2 enzyme
inhibitors. Thirteen of the studies were carried out in Asia; five in
China (Guo et al., 2016; He et al., 2019; Pang et al., 2019; Sun et al.,
2017; Zhang et al., 2015) six in South Korea (M.-J. Kim et al., 2019; Y.
S. Kim et al., 2016; H. A. Lee et al., 2017; S.-B. Lee et al., 2017;
Lim, Kim, et al., 2015; Lim, Lee, et al., 2015), one in India (Ghate et
al., 2018), and one in Indonesia (Laksmitawati et al., 2016). Three were
conducted in the USA, two in Brazil (Da Silva et al., 2019; Mohr et al.,
2019) and one in Canada (Meram \& Wu, 2017). Only one of the studies
included was from Europe (Turkey) (Karatoprak et al., 2019). Altogether,
ten authors performed the NO dosage plus three of the cytokines included
in our research (TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}and IL-6) (Ghate et al., 2018; Guo et
al., 2016; M.-J. Kim et al., 2019; Y. S. Kim et al., 2016; Laksmitawati
et al., 2016; Lim, Kim, et al., 2015; Lim, Lee, et al., 2015; Meram \&
Wu, 2017; Mohr et al., 2019; Sun et al., 2017), four authors measured NO
and two of the cytokines (Da Silva et al., 2019; He et al., 2019; S. B.
Lee et al., 2017; Zhang et al., 2015) and only three studies included NO
and only one cytokine (Karatoprak et al., 2019; H. A. Lee et al., 2017;
Pang et al., 2019) (Table 1). As stated above, all the studies included
in this review aimed to evaluate the possible anti-inflammatory activity
of isolated compounds, plant extracts, or drugs. In some studies, the
authors reported have linked this activity not only to the reduction in
the production of pro-inflammatory mediators, but also to the inhibition
of nuclear transcription factors such as NF\selectlanguage{greek}κ\selectlanguage{english}B and protein kinases such
(JNK, ERK, and P38 MAPKs).

All the studies included in our research (17) were considered for a
meta-analysis of NO. In all the manuscripts, NO was produced
significantly in favor of the LPS induced cells compared to the
non-induced cells (mean difference: 32.22 \selectlanguage{greek}µ\selectlanguage{english}M, CI95\%: 25.81-38.63;
p-value \textless{}0.001; i\selectlanguage{ngerman}²=100\%) (Fig. 2A), In order to assess
whether the difference between cell density of the studies could
interfere in the NO levels, we conducted a subgroup analysis. It was
found that cell density did not contribute to the increase or decrease
in NO levels produced by LPS-induced RAW 264.7 cells (1 - 2.5 x
10\textsuperscript{5} cells mL\textsuperscript{-1}: 30.61 \selectlanguage{greek}µ\selectlanguage{english}M; CI95\%:
13.54 -47.68; 3 - 5 x 10\textsuperscript{5} cells
mL\textsuperscript{-1}: 33.10 \selectlanguage{greek}µ\selectlanguage{english}M; CI95\%: 28.77 - 37.43; p-value
\textgreater{}0.05, i\selectlanguage{ngerman}²=0\%) (Fig. 3A). Since the placement range is
10\textsuperscript{5} cells mL-1, independently of the plate used (6-96
wells), the NO levels increased significantly when induced by 1 \selectlanguage{greek}µ\selectlanguage{english}g
mL\textsuperscript{-1} LPS compared to non-induced cells.

Similar to the NO results, TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}and IL-6 levels production were
significantly increased by induced-LPS cells, compared to non-induced
cells (Fig. 2B, 2C and 2D), respectively.

TNF-\selectlanguage{greek}α \selectlanguage{english}was the cytokine with the largest number of studies~included for
meta-analysis (15) (Ghate et al., 2018; Guo et al., 2016; C He et al.,
2019; Karatoprak et al., 2019; M.-J. Kim et al., 2019; Y. S. Kim et al.,
2016; Laksmitawati et al., 2016; S.-B. Lee et al., 2017; Lim, Kim, et
al., 2015; Lim, Lee, et al., 2015; Meram \& Wu, 2017; Mohr et al., 2019;
Pang et al., 2019; Sun et al., 2017; Zhang et al., 2015). The TNF-\selectlanguage{greek}α
\selectlanguage{english}levels were significantly higher in the induced-LPS cells compared to
the non-induced LPS cells (mean difference: 47233 pg
mL\textsuperscript{-1}, CI95\% 46729-47737; p-value:\textless{}0.001,
i\selectlanguage{ngerman}²=100\%) (Fig. 2B). Removing the outliers (S. B. Lee et al., 2017; Lim,
Kim, et al., 2015; Lim, Lee, et al., 2015; Meram \& Wu, 2017; Pang et
al., 2019; Sun et al., 2017) results (that exceed the reaction limits)
the TNF production was also higher in the LPS-induced cells (mean
difference: 1373 pg mL\textsuperscript{-1}, CI95\% 884-1862;
p-value:\textless{}0.001, i²=100\%) (Supplementary Material S4 - Fig
5A).

In the subgroup meta-analysis of the effect of cell density, TNF-\selectlanguage{greek}α
\selectlanguage{english}production was favored by the increase in cell density (1 - 2.5 x
10\textsuperscript{5} cells mL-1: 8546 pg mL\textsuperscript{-1};
CI95\%: 8007-9086; 3 - 5 x 10\textsuperscript{5} cells mL-1: 105412~pg
mL\textsuperscript{-1}; CI95\%: 102006 - 108818; p-value
\textless{}0.001, i\selectlanguage{ngerman}²=100\%) (Fig. 3B). This was also observed when
removing the outliers (1 - 2.5 x 10\textsuperscript{5} cells mL-1: 754
pg mL\textsuperscript{-1}; CI95\%: 178 - 1329; 3 - 5 x
10\textsuperscript{5} cells mL-1: 1373 pg mL\textsuperscript{-1};
CI95\%: 884- 1862; p-value \textless{}0.001, i²=93.2\%) (Supplementary
Material S5 - Fig. 6A).

Regarding NO and cytokine levels, the group with NO production of
between 20-50 \selectlanguage{greek}µ\selectlanguage{english}M (67704 pg mL\textsuperscript{-1}; CI95\%: 67207 --
68201; p-value \textless{}0.001; I\selectlanguage{ngerman}²=100\%) showed a higher production of
TNF-\selectlanguage{greek}α \selectlanguage{english}levels compared to those \textgreater{} 50 \selectlanguage{greek}µ\selectlanguage{english}M (6204~ pg
mL\textsuperscript{-1}; CI95\%: 5045- 7363; p-value \textless{}0.001,
I\selectlanguage{ngerman}²=100\%) (subgroup p-value \textless{}0.001, I²=100\%) (Fig 4A).
Despite this, when removing the outliers, TNF-\selectlanguage{greek}α \selectlanguage{english}production was higher in
the NO cells, producing above 50 \selectlanguage{greek}µ\selectlanguage{english}M (20-50 \selectlanguage{greek}µ\selectlanguage{english}M: 636 pg
mL\textsuperscript{-1}; CI95\%: 445- 826; \textgreater{}50 \selectlanguage{greek}µ\selectlanguage{english}M: 1373 pg
mL\textsuperscript{-1}; CI95\%: 884- 1862; p-value \textless{}0.01,
I\selectlanguage{ngerman}²=86.3\%) (Supplementary Material S6 - Fig 7A).

For the analysis of correlation between NO and IL-6 levels, a total of
13 studies were included in the meta-analysis (Da Silva et al., 2019;
Ghate et al., 2018; Guo et al., 2016; M.-J. Kim et al., 2019; Y. S. Kim
et al., 2016; Laksmitawati et al., 2016; H A Lee et al., 2017; S. B. Lee
et al., 2017; Lim, Kim, et al., 2015; Lim, Lee, et al., 2015; Meram \&
Wu, 2017; Mohr et al., 2019; Sun et al., 2017). Initially, we observed
that the production of IL-6 was significantly higher in the cells
induced by LPS (mean difference: 2905 pg mL\textsuperscript{-1}; CI95\%:
2788- 3021; p\textless{}0.001; i²=100\%) (Fig. 2C). Removing outliers
(Da Silva et al., 2019; S.-B. Lee et al., 2017; Lim, Kim, et al., 2015;
Lim, Lee, et al., 2015; Meram \& Wu, 2017), this was also observed (mean
difference: 391 pg mL\textsuperscript{-1}; CI95\%: 296- 485;
p\textless{}0.001; i²=100\%) (Supplementary Material S4 - Fig. 5B).
Regarding cell density, there was no difference between the subgroups (1
- 2.5 x 10\textsuperscript{5} cells mL\textsuperscript{-1}: 2843 pg
mL\textsuperscript{-1}; CI95\%: 2665 -3021; 3 - 5 x
10\textsuperscript{5} cells mL\textsuperscript{-1}: 2957 pg
mL\textsuperscript{-1}; CI95\%: 2777- 3138; p-value \textgreater{}0.05,
i²=0\%) (Fig. 3C). Removing outliers, the highest cell density showed a
higher production of IL-6 levels (1 - 2.5 x 10\textsuperscript{5} cells
mL-1: 276 pg mL\textsuperscript{-1}; CI95\%: 137- 415; 3 - 5 x
10\textsuperscript{5} cells mL-1: 505 pg mL\textsuperscript{-1}; CI95\%:
370- 641; p-value \textless{}0.05, i²=81.4\%)~(Supplementary Material S5
- Fig. 6B). Regarding the NO subgroups, the production of IL-6 was
higher in the 20-50uM group (Fig. 4B), but with the removal of outliers,
there was no significant difference between the groups (Supplementary
Material S6 - Fig. 7B).

For IL-1\selectlanguage{greek}β \selectlanguage{english}meta-analysis, 12 studies were initially included (Da Silva et
al., 2019; Ghate et al., 2018; Guo et al., 2016; C He et al., 2019;
M.-J. Kim et al., 2019; Y. S. Kim et al., 2016; Laksmitawati et al.,
2016; Lim, Kim, et al., 2015; Lim, Lee, et al., 2015; Meram \& Wu, 2017;
Mohr et al., 2019; Sun et al., 2017; Zhang et al., 2015). As previously
mentioned, IL-1\selectlanguage{greek}β \selectlanguage{english}production was significantly higher in the LPS-induced
cells compared to non-induced cells (mean difference: 1860 pg
mL\textsuperscript{-1}; CI95\%: 1813- 1907; p\textless{}0.001; i\selectlanguage{ngerman}²=100\%)
(Fig 2D). After removing outliers (Sun et al., 2017), IL-1\selectlanguage{greek}β \selectlanguage{english}levels were
also increased (mean difference: 548 pg mL\textsuperscript{-1}; CI95\%:
517- 580; p-value \textless{}0.001; i\selectlanguage{ngerman}²=100\%) (Supplementary Material S4
- Fig. 5C).

In the analysis by subgroups according to cell density, IL-1\selectlanguage{greek}β \selectlanguage{english}production
was higher with the increase in cell density (1 - 2.5 x
10\textsuperscript{5} cells mL-1: 125 pg mL\textsuperscript{-1}; CI95\%:
79- 172; 3 - 5 x 10\textsuperscript{5} cells mL\textsuperscript{-1}:
2727 pg mL\textsuperscript{-1}; CI95\%: 2636 - 2819; p-value
\textless{}0.001, i\selectlanguage{ngerman}²=100\%)~(Fig. 3D). After removal of outliers, IL-1\selectlanguage{greek}β
\selectlanguage{english}levels increased significantly with cell density (1 - 2.5 x
10\textsuperscript{5} cells mL\textsuperscript{-1}: 125 pg
mL\textsuperscript{-1}; CI95\%: 79 - 172; 3 - 5 x
10\textsuperscript{5}cells mL\textsuperscript{-1}: 790 pg
mL\textsuperscript{-1}; CI95\%: 732 - 848; p-value \textless{}0.001,
i\selectlanguage{ngerman}²=99.7\%)~(Supplementary Material S5 - Fig. 6C).

For the subgroup analysis of NO levels, IL-1\selectlanguage{greek}β \selectlanguage{english}production was higher in
the cells that produced less NO (20-50 \selectlanguage{greek}µ\selectlanguage{english}M) (mean difference: 2678 pg
mL\textsuperscript{-1}; CI95\%: 2590- 2767; p-value \textless{}0.001;
i\selectlanguage{ngerman}²=100\%) compared to those \textgreater{} 50 \selectlanguage{greek}µ\selectlanguage{english}M (223 pg
mL\textsuperscript{-1}; CI95\%: 168 - 279; p-value \textless{}0.001;
i\selectlanguage{ngerman}²=100\%)~(Fig. 4C). We observed that one study with IL-1\selectlanguage{greek}β \selectlanguage{english}production
above 3000 pg mL\textsuperscript{-1} (Sun et al., 2017) was responsible
for this difference. This study was therefore treated as an outlier and
removed from the analysis. After this, a significant difference was
observed in the group with NO greater than 50 \selectlanguage{greek}µ\selectlanguage{english}M (20-50 \selectlanguage{greek}µ\selectlanguage{english}M: 734 pg
mL\textsuperscript{-1}; CI95\%: 679- 789; \textgreater{}50 \selectlanguage{greek}µ\selectlanguage{english}M: 223 pg
mL\textsuperscript{-1}; CI95\%: 168- 279; p-value \textless{}0.01,
i\selectlanguage{ngerman}²=100\%) (Supplementary Material S6 - Fig. 7C).

Our selection of studies involved strict inclusion criteria, as specific
results of the studied markers were necessary for the inclusion or
exclusion of the studies in/from this review. For this reason, and as
expected, both for NO and for all the cytokines studied showed a
publication bias in a non-heterogeneous (asymmetric way), as
demonstrated by the analysis of funnel graphs (Supplementary Material S7
- Fig. 8A, B, C and D).

\emph{Quality assessment of included studies}

The agreement between the reviewers regarding the report and
methodological quality was 82.35\% and 94.12\%, respectively. All
discrepancies were removed by two experts. Regarding the methodological
quality, most of the studies (15) had a low risk of bias (Da Silva et
al., 2019; Guo et al., 2016; He et al., 2019; M.-J. Kim et al., 2019; Y.
S. Kim et al., 2016; Laksmitawati et al., 2016; H. A. Lee et al., 2017;
S. B. Lee et al., 2017; Lim, Kim, et al., 2015; Lim, Lee, et al., 2015;
Meram \& Wu, 2017; Mohr et al., 2019; Pang et al., 2019; Sun et al.,
2017; Zhang et al., 2015) and two had a moderate risk of bias (Ghate et
al., 2018; Karatoprak et al., 2019) (Supplementary Material S8 - Table
4B, and S9). No study had a high risk of bias. Regarding the quality of
the reports, eight of the 17 studies had a moderate risk, while 9 had a
low risk of bias.

In all the SCIRAP evaluation criteria, there was an item that was not
covered by almost any of the studies, but one that we consider crucial
for the quality of the articles. For example, in the test compound and
controls session, only one author (H. A. Lee et al., 2017) described the
vehicle used to dilute the compounds. Similarly, in the test system
session, only two authors reported the number of cell passages used to
perform the experiments~(M.-J. Kim et al., 2019; Mohr et al., 2019), and
no author provided information on the measures taken to track or avoid
contaminations by mycoplasma, bacteria, fungi or virus. Furthermore,
only two authors (Guo et al., 2016; H. A. Lee et al., 2017) provided
complete information regarding the toxicity of the test compound as well
as the phlogistic agent (LPS) (Supplementary Material S8 - Table 4A, and
S9).

In the methodological quality, the lack of information about the vehicle
used, as well as the lack of tests to verify the interference of
possible cytotoxicity of the compounds in the results, were the negative
aspects that were present in practically all the studies. In addition, 9
studies did not provide information on replicates or repetitions of the
experiments to verify the reproducibility/reliability of the results (Da
Silva et al., 2019; Ghate et al., 2018; Karatoprak et al., 2019; M.-J.
Kim et al., 2019; Y. S. Kim et al., 2016; S. B. Lee et al., 2017; Lim,
Kim, et al., 2015; Lim, Lee, et al., 2015; Sun et al., 2017). It is
important to note that all the studies provided complete information and
were extremely reliable in regard to the administration of the test
compound. This was to be expected, given the strictness of the
pre-established eligibility criteria (Supplementary Material S8 - Table
4B, and S9).

In the relation of the phlogistic agent (LPS), we considered the LPS
effects with indirect relevance for all the studies that did not provide
information on the manufacturers or sources of LPS strains used (Da
Silva et al., 2019; Ghate et al., 2018; Karatoprak et al., 2019; M.-J.
Kim et al., 2019; Laksmitawati et al., 2016) (Supplementary Material S8
- Table 4C, and S9). The other items (test systems, endpoint and
concentrations) were considered of direct relevance.~ For the tested
system, as expected, all the studies were of direct relevance, because
the experimental model in RAW cells induced by LPS was the most used to
mimic the inflammatory process \emph{in vitro} , and was one of the
eligibility criteria used for the inclusion of studies. Likewise, in
terms of the endpoint and concentration, all the articles were also of
direct relevance, as the studies had to meet specific eligibility
criteria at the time of collection/treatment, and a particular
concentration of LPS. All the studies were considered reliable, with
restrictions (Supplementary Material S8 - Fig 9), as none completely met
with the guidelines in regard to the reporting and methodological
assessment. In summary, the studies presented some restrictions as to
the origin of the cell, a number of cell passages (15), and
microbiological control (All studies). Also, some studies did not report
toxicity (15), while others did not report experimental and biological
replicates (9).

\textbf{4. Discussion}

In this systematic review and meta-analysis, we demonstrated the
efficacy of TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β, \selectlanguage{english}and IL-6 as \emph{in vitro} inflammatory
biomarkers in the LPS-induced RAW 264.7 cell model, and showed that NO
levels still are the gold standard test to trial anti-inflammatory
agents.

As expected, NO levels increased significantly when RAW 264.7 cells were
induced by LPS compared to non-induced cells, independently of cell
density, but cytokine levels changed according to cell density. To
ensure better screening for novel drugs, the trial could be initiated by
screening for drugs showing NO levels higher than 20 \selectlanguage{greek}µ\selectlanguage{english}M by LPS-induced
RAW 264.7 cells with statistical significance compared to non-induced
cells. After this initial screening, it was found that all the cytokines
studied were excellent inflammatory biomarkers, since in all the studies
where NO produced by cells was \textgreater{} 20 \selectlanguage{greek}µ\selectlanguage{english}M, the cytokines were
significantly increased. Moreover, all cytokine levels presented a
positive correlation with the cell density used in the experiments.
However, significant heterogeneity was found among the studies, mainly
due to the different methodologies presented by the authors. Therefore,
it is essential to standardize the inflammation model in RAW 264.7
cells, following some specific guidelines for in vitro experimentation.

Considering our results, it is important to highlight that due to the
model chosen for the review and the characteristics of the included
studies, a meta-analysis was performed. The model applied to use LPS as
a phlogistic agent is one of the main components of gram-negative
bacteria (Romerio \& Peri, 2020). This agent has the characteristic of
activating Toll-like receptors present in cells of the immune system,
including the macrophages. In Gram-negative bacterial infections, for
example, in the early stages of the sepsis process, this interaction
between LPS and cellular receptors leads to the activation of
intracellular signaling cascades activating many nuclear transcriptional
factors. This results in massive secretion of several pro-inflammatory
mediators, such as NOx, TNF-\selectlanguage{greek}α, \selectlanguage{english}IL-1\selectlanguage{greek}β \selectlanguage{english}and IL-6, the focus of our research
(Ciesielska et al., 2021; Poltorak et al., 1998). For this reason, the
parallel increase of NO and cytokines when culture cells are stimulated
with LPS was already expected (Dai et al., 2018). However, an important
technical detail in the cell culture must be taken into account, which
is the incubation time of the cells after LPS induction. Although the
action of TLR4 by LPS, ends with the production of many inflammatory
mediators, their release by the cells, presents a different time-course
of secretion (Hobbs et al., 2018). This fact can produce different
results in the levels of these inflammatory mediators, making comparison
between them unfeasible (Liu et al., 2017; Rahman \& McFadden, 2011).

As previously commented in the introduction section, this model is
widespread among research laboratories worldwide, as it provides rich
information on the inflammatory response and helps initiate the search
for new anti-inflammatory and immunomodulatory drugs (Elisia et al.,
2018; Taciak et al., 2018). However, there is still a lack of
standardization regarding LPS doses, cell induction time, and cell
density used, among others. To resolve these shortcomings in the
standardization of this inflammatory model, we were very judicious in
the application of eligibility criteria and in the data extraction, in
order to ensure the reliability of the results obtained.

Our results show, as expected, that there is an increase in NO
production when RAW 264.7 cells are induced by LPS (Moore et al., 2019;
Ranaweera et al., 2020). Thus, considering the characteristics of the
studies included in the meta-analysis, it is plausible to affirm that
the NO measurement in the model in question is sufficient for the
purposes of initial inflammatory screening, considering that the
production of the pro-inflammatory cytokines in question accompanies the
same behavior as that observed with the determination of NO levels, at
least, when the time of cell incubation after LPS stimulation is between
12h and 24h. This data is important because, to our knowledge, there is
no standardization among researchers in regard to the choice of initial
inflammatory mediators to measure in this model, in screening for the
possible anti-inflammatory or immunomodulatory activity of some compound
or extract from a natural product. Moreover, we also found that there is
no consensus among researchers regarding the biomarkers used for
screening new anti-inflammatory drugs, cell density, LPS concentration
used, time of induction, and use of culture medium supplementation.

Thus, taking into account the information collected, we strongly advise
that the use of NO measurements be standardized when used to screen for
possible compounds with anti-inflammatory activity. Obviously, taking
into account the particularities of each laboratory is extremely
important, however, the use of LPS at a dose of 1ug
mL\textsuperscript{-1}, cell density between 1 and
9.9x10\textsuperscript{5}, and incubation time of between 12 and 24
hours at 37\selectlanguage{ngerman}°C, appear to be the minimum requirements for this
standardization (Supplementary Material S10).

Finally, regarding the risk of bias and heterogeneity of included
studies in this meta-analysis, we believe that due to this lack of
standardization among published papers, researchers have selected a
range of different mediators for their experiments. In our study, it was
demonstrated that only NO levels are sufficient to draw the initial
conclusions when seeking to determine whether some compound should be
submitted to the next levels of anti-inflammatory experimentation, when
the culture of Raw 264.7 cells induced by LPS is used as an initial
experimental platform. The use of a single marker is also economically
viable, as the technique is easy to perform, with good reproducibility
and low cost, besides being quicker than using several markers.

Regarding the strengths, in this study, all steps were performed in
pairs and validated by an expert. Also, a meta-analysis and risk of bias
were performed. No time was limited to literature search. Some
limitations were considered regarding heterogeneity between studies and
the rigorous inclusion criteria.

\textbf{5. Conclusion}

The measurement of NO levels is a sufficient and rational option to
begin the trial of anti-inflammatory and immunomodulatory action of any
compound or product using the RAW 264.7 model as an experimental
platform. However, future experiments need to follow some rules of
standardization. In this context, our study suggests a basic checklist
to help researchers in future experiments with NO and cytokines using
RAW 264.7 macrophages, which can help prevent the unnecessary use of
animals and waste of resources.

\textbf{Contributors}

BMF, GOR, ETBM and GNV performed the literature research, designed the
data extraction form, and performed the data extraction and data
analysis. IGD, IFK, EMD critically reviewed the analyzed the data. BMF
wrote the paper. IGD, IFK, EMD critically reviewed subsequent drafts.
All authors approved the final version of the manuscript for submission.
All authors had full access to all of the data in the study and took
responsibility for the integrity of the data and the accuracy of the
data analysis.

\textbf{Funding}

This study was financed in part by the Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior {[}Coordination for the Improvement of Higher
Education Personnel{]} -- Brazil (CAPES) -- Finance Code 001.

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\verb`Table 1. Summary of the studies.docx`  available at \url{https://authorea.com/users/735636/articles/711850-anti-inflammatory-screening-biomarkers-on-an-lps-induced-raw-264-7-cell-model-a-systematic-review-and-meta-analysis}

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