Introduction: Toll-like receptors play crucial roles in the sepsis-induced systemic inflammatory response. Septic shock mortality correlates with overexpression of neutrophilic TLR2 and TLR9, while the role of TLR4 overexpression remains a debate. In addition, TLRs are involved in the pathogenesis of viral infections such as COVID-19, where the single-stranded RNA of SARS-CoV-2 is recognized by TLR7 and TLR8, and the spike protein activates TLR4. Methods: In this study, we conducted a comprehensive analysis of TLRs 1–10 expressions in white blood cells from 71 patients with bacterial and viral infections. Patients were divided into 4 groups based on disease type and severity (sepsis, septic shock, moderate, and severe COVID-19) and compared to 7 healthy volunteers. Results: We observed a significant reduction in the expression of TLR4 and its co-receptor CD14 in septic shock neutrophils compared to the control group (p < 0.001). Severe COVID-19 patients exhibited a significant increase in TLR3 and TLR7 levels in neutrophils compared to controls (p < 0.05). Septic shock patients also showed a similar increase in TLR7 in neutrophils along with elevated intermediate monocytes (CD14+CD16+) compared to the control group (p < 0.005 and p < 0.001, respectively). However, TLR expression remained unchanged in lymphocytes. Conclusion: This study provides further insights into the mechanisms of TLR activation in various infectious conditions. Additional analysis is needed to assess their correlation with patient outcome and to evaluate the impact of TLR-pathway modulation during septic shock and severe COVID-19.

Toll-like receptors (TLRs) play a crucial role in severe infections. Overexpression of certain TLRs on neutrophils has been associated with increased mortality in patients with infection-induced shock, although the role of TLR4 overexpression is still debated. In addition, TLR7 and TLR8 recognize the SARS-CoV-2 virus, while the spike protein activates TLR4. In this study, we conducted a comprehensive analysis of TLR expression in white blood cells from patients with bacterial and viral infections. 71 patients were included, divided into 4 groups based on infection severity (sepsis, septic shock, moderate, and severe COVID-19), along with 7 control subjects. Notably, we observed a significant reduction in the expression of TLR4 and its co-receptor CD14 in neutrophils of patients with shock compared to controls. Furthermore, severe COVID-19 patients exhibited a significant increase in TLR3 and TLR7 levels in neutrophils compared to the control group. Patients with shock also showed a similar increase in TLR7 expression in neutrophils, along with elevated intermediate monocytes compared to controls. However, TLR expression remained unchanged in lymphocytes. This study provided further insights into the mechanisms of TLR activation in various severe forms of infection. Additional analysis is required to assess their correlation with patient outcomes and evaluate the potential impact of TLR-related therapy on neutrophil activation.

Toll-like receptors (TLR) are major sensors of the innate immune system, involved in the recognition of pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). These receptors are found in innate immune cells such as monocytes, lymphocytes, and neutrophils. Different TLRs are located within the cellular structure. TLR1, TLR2, TLR4, TLR5, TLR6, and TLR10 are localized on the cellular surface, whereas TLR3, TLR7, TLR8, and TLR9 are located intracellularly and are responsible for the detection of endosomal nucleic acids from viruses and bacteria.

When activated, TLR signaling pathways trigger an intracellular response, which leads to the production of pro-inflammatory cytokines, chemotactic factors, antimicrobial peptides, and interferons. During infections, these pathways play a crucial role in generating the cytokine storm that culminates in septic shock a life-threatening organ dysfunction characterized by organ dysfunction due to a dysregulated host response to infection [1, 2]. The pathophysiology of septic shock is characterized by an excessively amplified systemic inflammatory response from the host, mediated by immune cell activation in response to pathogen invasion [3]. Intense cellular activation triggers a systemic inflammatory response, as demonstrated by the release of leukocyte microparticles [4]. Among immune cells, neutrophils are particularly activated in septic shock and warrant further investigation [5]. TLR4, which recognizes lipopolysaccharides from bacterial membranes, is one of the most extensively studied TLRs. Notably, an increased expression of TLR2 and TLR4 has been reported in neutrophils and monocytes following an infection with Gram + bacteria [6], which was not observed by Martins et al. [7]. Elevated expressions of TLR2 and TLR9 proteins in neutrophils have been associated with lower survival rates in patients admitted to the Intensive Care Unit (ICU), whereas TLR4 has not shown a similar association [8]. Nonetheless, these studies often relied on measuring mRNA expression rather than protein expression. These inconsistent results highlight the challenges of accurately measuring TLR protein expression and may have implications for the understanding of TLR pathways and the use of TLR-modulating agents in humans. Many TLR-modulating agents have been tested in mice (mainly genetically modified with loss of function mutations) or in artificial sepsis models. Different TLR expression patterns in white blood cells (WBC) were observed compared to humans.

TLRs (TLR2, TLR3, TLR4, and TLR7 in particular) play a role in recognizing bacterial structures as well as detecting viral patterns [9]. Activation of the TLR signaling pathway is an early step for the recognition of viral genomic patterns of SARS-CoV-2 [9]. The viral single-stranded RNA (ssRNA) of SARS-CoV-2 is recognized by endosomal TLR7 and TLR8 [10, 11] while the spike protein of the SARS-CoV-2 activates TLR 4 [12]. However, there are conflicting reports about the specific role of TLRs during SARS-CoV-2 infection [13‒15]. The modulation of TLRs during sepsis could be of interest even if it is a matter of debate. As reported in the phase 2 ACCESS clinical trial [16], we ought to better understand why TLR4 blockade resulted in a higher mortality rate among mild sepsis patients compared to those with severe sepsis.

In order to improve our understanding of TLRs during infectious conditions, we conducted a prospective analysis of the expression of TLRs and CD180 (RP-105) – a toll-like related protein with high TLR4 homology involved in LPS sensing [17‒19] – in WBCs of patients with either bacterial (sepsis and septic shock) or viral infections (mild and severe COVID infections) and a control group. We also studied the expression of CD16 (FcγRIIIB) on neutrophils, which is associated with neutrophil maturation [20, 21] and has been described as under-expressed in patients with either sepsis [22, 23] or COVID-19 with elevated severity scores [24]. We also analyzed the subpopulations of monocytes (e.g., classical, intermediate, and non-classical subsets), which were reported to be modified in these conditions.

Study Design

This prospective single-center observational cohort study was conducted in the Strasbourg University Hospital from May 2021 to April 2022. We included patients with infections admitted to the emergency department or to the ICU, and healthy volunteers as the control group. Patients were considered for inclusion when suffering from infection and fulfilling the criteria for sepsis (sepsis group) and septic shock (septic shock group) according to the third international-consensus definition (SEPSIS-3) [1].

A positive reverse transcriptase-polymerase chain reaction (RT-PCR) confirmed COVID-19 infection, and patients with COVID-19 were categorized depending on disease severity. According to WHO guidelines [25], patients were considered in the “mild COVID” group if they had any of the various signs and symptoms of COVID-19, without shortness of breath, dyspnea, or abnormal chest imaging. Patients with one criterion beyond SpO2 lower than 94% while breathing room air, PaO2/FiO2 ratio below 300 mm Hg, respiratory rate upper than 30 cycles per minute, or lung infiltrates of more than 50% of lung parenchyma were classified into the severe COVID group.

The study protocol was approved by the Strasbourg University Hospital Ethic Committee (#NCT03559569). Written informed consent was obtained from all participants before their inclusion. If a patient was unable to consent, consent was obtained from their next of kin, and post hoc consent was obtained as soon as possible. Included patients were treated according to international guidelines. There was no exclusion criterion. For this study, we recorded all demographic characteristics, medical history, clinical signs, biological, and imaging data.

Blood Collection and Staining

Blood sampling was performed at admission on BD Vacutainer Plus Blood Collection Tubes (Becton Dickinson, Le Pont de Claix, France) that contain EDTA (ethylene-diamine-tetra-acetic acid) anticoagulant. Within 3 h, blood cells were stained using 14 monoclonal antibodies coupled to a fluorescent dye for the 10 TLRs, CD14, CD16, and CD19 antigens: CD19-APC (clone SJ25C1, #17-0198-42); TLR1-PE (clone GD2.F4, #12-9911-42); TLR3-PE (clone TLR3.7, #12-9039-82), TLR10-PE (clone 3C10C5, #12-2909-42) from Invitrogen; CD16-APC-H7 (clone 3G8, #560195); CD14-PE-Cy7 (clone Mφ9, #562698); TLR4-BV421 (clone TF901, #564401); CD180-BV421 (clone G28-8, #743624) from BD Biosciences; TLR2-PerCP (clone 383936, #FAB2616C); TLR5-A488(clone 624915, #FAB6704G); TLR7-PerCP (clone 533707, #IC5875C); TLR8-A488 (clone 935166, #IC8999G); TLR9-A405 (clone 26C593R, #IC36583V) from R&D Systems and TLR6- FITC (clone TLR6.127, #ab72362) from Abcam. TLR antibodies were chosen for their specificity and brightness and were previously validated in the peripheral blood cell populations of 20 healthy donors together with isotype controls (data not shown). In brief, blood samples were incubated with the antibodies for 20 min in the dark. Then a red blood cell lysing solution (#349202; BD Biosciences) was added, followed by 2 PBS washing. After the last centrifugation, the cell pellet was resuspended in PBS solution. For the endosomal TLRs (TLR3, TLR7, TLR8, TLR9), plasma cell permeabilization was performed using the Perm2 Solution® (#558052, BD Biosciences) prior to incubation of monoclonal antibody staining.

Sample Preparation for Flow Cytometry Analysis

Flow cytometry signals were acquired using a three-laser (blue, red, violet) 8-fluorochrome 10-parameter BD FACS Canto II flow cytometer (BD Biosciences, Franklin Lakes, USA). The initial PMT voltage configuration was run with unstained cells, while the compensation configuration was run with staining in a single tube of all fluorophores used. A total number of 1.106 neutrophils were acquired per sample. FCS files of all patients were analyzed simultaneously using Cytobank software (Beckman Coulter, Miami, USA). After doublet exclusion, neutrophils were identified as SSChigh CD14- CD16+, monocytes as SSClow CD14+, T lymphocytes as SSClow CD14- CD16- CD19-, B lymphocytes as SSClow CD14- CD16- CD19+ and NK cells as SSClow CD14- CD16+. Monocytes were subdivided as described by Selimoglu-Buet et al. [26] into classical (CD14+CD16-), intermediate (CD14+CD16+), and non-classical (CD14lowCD16+) monocytes, respectively.

Statistical Analysis

For clinical characteristics, qualitative variables were reported as numbers and percentages, and quantitative ones as median and interquartile range. Comparison between groups (mild vs. severe COVID-19 and sepsis vs. septic shock) were based on Fisher’s exact and Wilcoxon rank-sum tests for qualitative and quantitative data, respectively. For TLR expression assessed via flow cytometry analysis, data were expressed as median intensity of fluorescence (MFI) and interquartile range and were analyzed using the Kruskal-Wallis test for multiple group comparison. A p value <0.05 was considered to indicate statistical significance. Statistical analyses were performed with GraphPad Prism8® (GraphPad Software, Inc., CA, US) and RStudio (version 1.2.5001).

Clinical and Biological Characteristics of the Cohort

78 participants were included during the first COVID-19 infection wave between May 2021 and April 2022, of which 23 had severe COVID-19, 7 had mild COVID-19, 35 had septic shock, 6 had with sepsis, and 7 were healthy subjects. The baseline clinical and biological characteristics of the four groups are summarized in Table 1. No significant difference was observed between patients in terms of gender, comorbidities, or type of infection. Sepsis and septic shock groups were associated with Bacillus gram-negative microorganisms. Leukocyte, neutrophil, monocyte, and lymphocyte counts were not different between groups.

Table 1.

Characteristics of patients on admission

CharacteristicsSevere COVID (n = 23)Mild COVID (n = 7)Mild versus Severe COVIDSeptic Shock (n = 35)Sepsis (n = 6)Sepsis versus Septic Shock
p valuep value
Age, year, Med [IQR] 59 [54–64] 81 [68–86] 0.008 68 [62–74] 61 [48–77] 0.555 
Male sex, n (%) 17 (73.9) 5 (71.4) 25 (74) 3 (11) 0.361 
SAPS II, Med [IQR] 32 [29–44] NA 69 [50–85] 56 [31–82] 0.086 
SOFA inclusion, Med [IQR] 4 [4–6] 1 [0–6] 0.054 13 [10–15] 4 [3–10] 0.010 
Preexisting conditions, n (%) 
 Chronic hypertension 11 (47.8) 5 (71.4) 0.399 28 (80) 3 (10) 0.143 
 Diabetes 6 (26) 4 (57.1) 0.181 5 (14) 2 (33) 0.268 
 Chronic heart failure 1 (4.3) 6 (17.1) 0.567 
 Ischemic heart disease 1 (14.3) 0.233 3 (8.6) 2 (33) 0.148 
 Chronic kidney disease 6 (17.1) 0.567 
 Liver cirrhosis 1 (2.9) 
 Chronic obstructive pulmonary disease 10 (28.6) 0.307 
Cancer 1 (4.3) 1 (14.3) 0.418 11 (31.4) 1 (16.7) 0.651 
Infection, n (%) 
Source of infection 
 Lung 23 (100) 7 (100) 18 (51.4) 3 (50) 
 Abdominal  3 (8.6) 
 Urinary tract  8 (22.8) 3 (50) 0.316 
 Bloodstream infection  15 (42.9) 2 (11.7) 
 Nosocomial infection 5 (21.7) 0.304 6 (17.1) 0.567 
Immunosuppression 1 (2.9) 
Involved bacteria 
 Cocci gram positive    5 (14.3) 1 (16.7) 
Bacillus gram negative    20 (57.1) 4 (66) 
Organ-support at inclusion 
 MAP min, mm Hg 70 [62–81] NA  59 [53–64] 68 [62–78] 0.028 
 PaO2/FiO2 ratio, mm Hg 115 [85–135] NA  156 [112–232] 186 [72–390] 0.875 
Invasive mechanical ventilation, n (%) 11 (52.2) 0.029 26 (74.3) 2 (33.3) <0.04 
Biological findings, Med [IQR] 
 Serum lactate, mmol/L 1.1 [0.8–1.9] 0.8 [0.7–1] 0.024 3 [1.9–4.7] 2.2 [1–3.6] 0.297 
 Leucocytes count (G/L) 7.9 [5.7–10.9] 6.6 [5.3–13.4] 0.848 9.8 [5.3–20.9] 16.9 [15.5–23.7] 0.237 
 Neutrophil count (G/L) 6.1 [4.5–8.5] 5.8 [3.8–10.9] 0.795 9.3 [5.9–19.5] 14.1 [12–21.1] 0.404 
 Monocyte count (G/L) 0.5 [0.3–0.6] 0.7 [0.3–1] 0.212 0.6 [0.2–1.] 1 [0.4–1.4] 0.272 
 Lymphocyte count (G/L) 0.9 [0.6–1] 0.7 [0.5–1.3] 0.614 0.5 [0.4–0.9] 1 [0.6–1.4] 0.12 
 NEUT-SFL (AU) 51 [47–53] 49 [49–52] 0.873 65 [55–75] 48 [48–57] 0.04 
 ISTH ≥5, n (%) 15 (43) 0.07 
 JAAM ≥4, n (%) 13 (37) 0.152 
SIC ≥4, n (%) 32 (94) 4 (66) <0.03 
Outcome, n (%) 
 Day 7 mortality 5 (14) 3 (50) 0.076 
 In-ICU mortality 2 (9) 11 (31) 2 (33) 0.919 
 In-hospital mortality 2 (9) 14 (80) 3 (50) 0.679 
 Day-28 mortality 7 (20) 3 (50) 0.143 
CharacteristicsSevere COVID (n = 23)Mild COVID (n = 7)Mild versus Severe COVIDSeptic Shock (n = 35)Sepsis (n = 6)Sepsis versus Septic Shock
p valuep value
Age, year, Med [IQR] 59 [54–64] 81 [68–86] 0.008 68 [62–74] 61 [48–77] 0.555 
Male sex, n (%) 17 (73.9) 5 (71.4) 25 (74) 3 (11) 0.361 
SAPS II, Med [IQR] 32 [29–44] NA 69 [50–85] 56 [31–82] 0.086 
SOFA inclusion, Med [IQR] 4 [4–6] 1 [0–6] 0.054 13 [10–15] 4 [3–10] 0.010 
Preexisting conditions, n (%) 
 Chronic hypertension 11 (47.8) 5 (71.4) 0.399 28 (80) 3 (10) 0.143 
 Diabetes 6 (26) 4 (57.1) 0.181 5 (14) 2 (33) 0.268 
 Chronic heart failure 1 (4.3) 6 (17.1) 0.567 
 Ischemic heart disease 1 (14.3) 0.233 3 (8.6) 2 (33) 0.148 
 Chronic kidney disease 6 (17.1) 0.567 
 Liver cirrhosis 1 (2.9) 
 Chronic obstructive pulmonary disease 10 (28.6) 0.307 
Cancer 1 (4.3) 1 (14.3) 0.418 11 (31.4) 1 (16.7) 0.651 
Infection, n (%) 
Source of infection 
 Lung 23 (100) 7 (100) 18 (51.4) 3 (50) 
 Abdominal  3 (8.6) 
 Urinary tract  8 (22.8) 3 (50) 0.316 
 Bloodstream infection  15 (42.9) 2 (11.7) 
 Nosocomial infection 5 (21.7) 0.304 6 (17.1) 0.567 
Immunosuppression 1 (2.9) 
Involved bacteria 
 Cocci gram positive    5 (14.3) 1 (16.7) 
Bacillus gram negative    20 (57.1) 4 (66) 
Organ-support at inclusion 
 MAP min, mm Hg 70 [62–81] NA  59 [53–64] 68 [62–78] 0.028 
 PaO2/FiO2 ratio, mm Hg 115 [85–135] NA  156 [112–232] 186 [72–390] 0.875 
Invasive mechanical ventilation, n (%) 11 (52.2) 0.029 26 (74.3) 2 (33.3) <0.04 
Biological findings, Med [IQR] 
 Serum lactate, mmol/L 1.1 [0.8–1.9] 0.8 [0.7–1] 0.024 3 [1.9–4.7] 2.2 [1–3.6] 0.297 
 Leucocytes count (G/L) 7.9 [5.7–10.9] 6.6 [5.3–13.4] 0.848 9.8 [5.3–20.9] 16.9 [15.5–23.7] 0.237 
 Neutrophil count (G/L) 6.1 [4.5–8.5] 5.8 [3.8–10.9] 0.795 9.3 [5.9–19.5] 14.1 [12–21.1] 0.404 
 Monocyte count (G/L) 0.5 [0.3–0.6] 0.7 [0.3–1] 0.212 0.6 [0.2–1.] 1 [0.4–1.4] 0.272 
 Lymphocyte count (G/L) 0.9 [0.6–1] 0.7 [0.5–1.3] 0.614 0.5 [0.4–0.9] 1 [0.6–1.4] 0.12 
 NEUT-SFL (AU) 51 [47–53] 49 [49–52] 0.873 65 [55–75] 48 [48–57] 0.04 
 ISTH ≥5, n (%) 15 (43) 0.07 
 JAAM ≥4, n (%) 13 (37) 0.152 
SIC ≥4, n (%) 32 (94) 4 (66) <0.03 
Outcome, n (%) 
 Day 7 mortality 5 (14) 3 (50) 0.076 
 In-ICU mortality 2 (9) 11 (31) 2 (33) 0.919 
 In-hospital mortality 2 (9) 14 (80) 3 (50) 0.679 
 Day-28 mortality 7 (20) 3 (50) 0.143 

Data are expressed as median (Med) with interquartile range (IQR) and as numbers and percentages for qualitative variables [n (%)]. Fisher exact test was used for qualitative data, and Wilcoxon test for median comparison.

FiO2, Fraction inspired Oxygen; ICU, Intensive Care Unit; ISTH, International Society of Thrombosis and Hemostasis; JAAM, Japanese Association for Acute Medicine; MAP, Mean Arterial Pressure; NA, Not Available; NEUT-SFL, NEUTrophil-Side Fluorescence Light; PaO2, Partial Pressure of Oxygen; SAPS II, Simplified Acute Physiology Score II; SIC, Sepsis-Induced Coagulopathy; SOFA, Sepsis Organ Failure Assessment; yr, year.

TLR Antigenic Expression in Circulating WBC Populations

The expression of the ten human TLRs was analyzed in neutrophils, monocytes, and lymphocytes (T, B, CD16+ NK cells) using multi-parametric flow cytometry. No significant expression of TLRs was observed in T or CD16+ NK cells. However, the TLR-related protein CD180 was the only detected protein in B cells. In neutrophils, antibody staining for TLRs 1–10 and CD180 revealed the expression of TLR8 and TLR9. TLR4 showed a large variation in its expression between groups, while TLR2, TLR3, and TLR7 were weakly expressed. In monocytes, only TLR2, TLR4, and CD180 were detected.

TLR4 and CD14 Antigenic Expression in Neutrophils and Monocytes Populations

TLR4 was studied in conjunction with its co-receptor CD14, which is expressed in neutrophils and monocytes. CD14 enhances the activation of TLR4 by LPS and regulates the subsequent internalization of the LPS-activated TLR4. In neutrophils, TLR4 was under-expressed in patients with septic shock (MFI = 19,273 vs. 111,691, p < 0.001), as well as in those with sepsis (MFI 23,745, p < 0.001) (Fig. 1a right panel). CD14 expression was also significantly lower in the septic shock patient group than in healthy volunteers (MFI 2,028 vs. 3,025, p < 0.02) (Fig. 1a, left panel). In the septic shock patient group, a lower expression of TLR4 was associated with reduced CD14 expression on the surface of neutrophils, suggesting altered cellular trafficking of the TLR4-CD14 complex under these conditions. No significant changes in TLR4 or CD14 expression were observed in the groups of COVID-19 patients. In monocytes, TLR4 expression was not different from that of healthy subjects in all groups (Fig. 1b, right panel). CD14 was significantly less expressed in monocytes of patients with sepsis, septic shock (MFI 54,854 and 64,294, p < 0.01 for both), and severe COVID-19 (MFI 62,5887, p < 0.001) compared to healthy donors (MFI 99,380) (Fig. 1b, left panel). This prompted us to investigate TLR4 expression in different monocyte subsets: classical (CD14+CD16-), intermediate (CD14+CD16+), and non-classical (CD14-CD16+) monocytes. In septic shock patients, the proportion of classical monocytes subset was lower than in healthy donors (74.9% vs. 87.4% p = 0.01), whereas the proportion of intermediate monocytes was higher (14.4% vs. 3.7%, p < 0.001). TLR4 expression remained unchanged within these different monocyte subsets, and no significant variation in the proportion of different monocyte subsets was observed in the other groups.

Fig. 1.

Box plot representation of cell surface expression of TLR4 and CD14 in neutrophils (a) and monocytes (b) in septic shock and COVID-19 patients compared to healthy donors. The results are presented as mean fluorescence intensity (MFI). The main body of the boxplots show the 1st and 3rd quartiles. Horizontal lines in each box represent the median. *p < 0.01, **p < 0.005, ***p < 0.0005.

Fig. 1.

Box plot representation of cell surface expression of TLR4 and CD14 in neutrophils (a) and monocytes (b) in septic shock and COVID-19 patients compared to healthy donors. The results are presented as mean fluorescence intensity (MFI). The main body of the boxplots show the 1st and 3rd quartiles. Horizontal lines in each box represent the median. *p < 0.01, **p < 0.005, ***p < 0.0005.

Close modal

Other TLRs and CD180 (RP-105) Expression

TLR7, which recognizes single-stranded RNA in endosomes, was more significantly expressed in neutrophils of patients in the severe COVID-19 group (MFI 1,053 vs. 513, p = 0.0193, Fig. 2) and in the septic shock group compared to healthy subjects (MFI = 1,005 vs. 413, p < 0.05, respectively, Fig. 2).

Fig. 2.

Box plot representation of intracellular expression of TLR3 and TLR7 in neutrophils in septic shock and COVID-19 patients compared to healthy donors. The results are presented as mean fluorescence intensity (MFI). The main body of the boxplots show the 1st and 3rd quartiles. Horizontal lines in each box represent the median. *p < 0.05, **p < 0.005.

Fig. 2.

Box plot representation of intracellular expression of TLR3 and TLR7 in neutrophils in septic shock and COVID-19 patients compared to healthy donors. The results are presented as mean fluorescence intensity (MFI). The main body of the boxplots show the 1st and 3rd quartiles. Horizontal lines in each box represent the median. *p < 0.05, **p < 0.005.

Close modal

TLR3 expression in neutrophils was higher in the severe COVID-19 patient group (MFI = 2,812, p = 0.003) than its weak expression in the control group (MFI = 1,024). However, TLR3 expression in neutrophils did not show a significant difference between the other groups and the control group. TLR2, also weakly expressed in neutrophils, was slightly increased in the septic shock patient group compared to the control group (p = 0.02). The expression of CD180 (RP-105), which is expressed in monocytes and B cells and is thought to prevent TLR4 activation at least in mice, was not changed in any of the conditions tested [27].

Neutrophil Immaturity in Severe Infections and Pro-Inflammatory Monocytes during Septic Shock

During neutrophil maturation, CD16 is expressed in the late metamyelocyte and band stages. In humans, immature and mature neutrophils are identified as CD16low/CD10-and CD16high/CD10+ respectively. However, CD16 expression can be diminished during apoptosis. CD16 is also acquired by human NK cells during their maturation process. We observed that CD16 expression in neutrophils was significantly lower in the septic shock patients group compared to the control group (p < 0.0001) and, to a lesser extent, in the sepsis patients group as well (p < 0.01) (Fig. 3). This pattern was not observed in both groups of COVID-19 patients, regardless of the severity. No significant difference was observed between groups in CD16 expression in NK cells.

Fig. 3.

Box plot representations of cell surface CD16 expression in septic shock and COVID-19 patients compared to healthy donors. The results are presented as mean fluorescence intensity (MFI). The main body of the boxplots show the 1st and 3rd quartiles. Horizontal lines in each box represent the median. **p < 0.0001, #p < 0.001. The box ranges from the first (Q1) to the third quartile (Q3). The line across the box indicates the median. The whiskers are lines extending from Q1 and Q3.

Fig. 3.

Box plot representations of cell surface CD16 expression in septic shock and COVID-19 patients compared to healthy donors. The results are presented as mean fluorescence intensity (MFI). The main body of the boxplots show the 1st and 3rd quartiles. Horizontal lines in each box represent the median. **p < 0.0001, #p < 0.001. The box ranges from the first (Q1) to the third quartile (Q3). The line across the box indicates the median. The whiskers are lines extending from Q1 and Q3.

Close modal

In this study, we assessed the expression of the ten human TLRs proteins in WBCs, early after hospital admission (emergency department or intensive care unit), in 4 groups of patients: mild and severe COVID, sepsis, and septic shock. A significant decrease in TLR4 expression was observed in neutrophils of patients with sepsis, septic shock, and severe COVID-19 infection and, to a lesser extent, a decrease of CD14 expression in septic shock patients.

Our findings differ from other studies that have shown an increase in TLR2 and TLR4 during sepsis and septic shock [6, 28]. Conversely, Silva et al. [29] found no changes in the protein expression of TLR2, TLR4, or TLR9 and an upregulation of TLR5 in human sepsis. Another study did not report any modification in TLR4 expression in sepsis [8].

The decrease of TLR4 and CD14 in septic shock patients may be supported by previous pathophysiologic studies. In a cell line model, it was demonstrated that TLR4 is internalized after exposure to LPS, and this internalization depends on CD14. Internalization of TLR4 and CD14 terminates the initial phase of MYD88 signaling and initiates a second phase of TRIF-dependent signaling, leading to an IFN-type 1 response [30]. It is known that when endosomal TLR4 interacts with TRIF, it sustains NFκB stimulation [31] and initiates ubiquitination and degradation of TLR4. This mechanism, following endosome maturation and lysosomal degradation of TLR4, determines the duration and magnitude of the TRIF-dependent signaling [32‒34], which was demonstrated in a cell line model.

The current observational study on the expression of Toll-like receptors (TLRs) during septic conditions sets the stage for future investigations specifically addressing TLR4 and CD14 cell trafficking in neutrophils and monocytes. Assessing the subcellular localization of TLR4 and CD14 becomes crucial, as various cutting-edge therapeutic strategies for sepsis and septic shock may target TLR4/CD14 cell trafficking. Indeed several approaches were attempted to promote the internalization and elimination of the TLR4/CD14 complex, such as using a small synthetic TLR4 ligand (1Z105, which was studied in mice as an influenza vaccine adjuvant [35]) or an agonistic antibody directed against TLR4/MD2 (UT12) [36, 37]. Elsewhere, recent studies suggested that the endosomal signaling of TLR4 is negatively regulated by prostaglandin E2 (PGE2) [38]. Furthermore, it has been shown that the internalization of TLR4 may depend, in part, on clathrin, where the inhibition of clathrin-dependent LPS internalization may decrease TRIF-dependent responses [39]. Endocytosis of TLR4 is also negatively regulated by CD13 metallopeptidase [40]. Finally, yet importantly, TLR4 activation may be modulated via proteins that can link to LPS. Soluble CD14 molecules capture LPS and enhance its elimination in the liver through HDL binding. Thrombin-derived C peptides may inhibit the binding of LPS to CD14, as recently described [41].

We also observed in this study an increase in intermediate monocytes and a decrease in classical monocytes in septic shock patients. The decrease of CD16 was more pronounced in septic shock as compared to sepsis patients. In this context, an elevated proportion of CD16dim neutrophils (immature subset) has been associated with an increased risk of death during septic shock [23, 42]. Furthermore, it has been recently shown that experimental administration of LPS in humans leads to an increase of CD16dim immature neutrophils [20].

In the setting of COVID-19, the interaction between virus particles and TLRs induces TLR activation leading to the production of pro-inflammatory cytokines [43], as observed by an upregulation of TLR3 and TLR7 in COVID-19 patients. TLR3, TLR7, TLR8, and TLR9 are known to respond to double-stranded RNA, single-stranded RNA, and unmethylated CpG DNA in endosomal compartments [44]. It has been suggested that TLR2, TLR3, TLR4, TLR7/8, and TLR9 contribute to antiviral responses against SARS-CoV-2 [11]. TLR3 senses dsRNA in endosomes, and its stimulation leads to the TRIF-dependent production of pro-inflammatory cytokines and type I IFNs. TLR7 and TLR8 recognize ssRNA in SARS-CoV-2-infected cellular spheroids and activate the MyD88-dependent signaling [45]. In addition to TLRs, other receptors may be involved in COVID-19 response, such as RIG-I-like receptors (RLRs), Nod-like receptors (NLRs), AIM2-like receptors (ALRs), C-type lectin receptors (CLRs), and intracellular DNA sensors, like cGAS [46]. These receptors were not investigated in our study.

We found no modification of monocyte subpopulations in COVID patients, contrary to the mild increase of classical monocyte subset and the slight decrease in non-classical monocytes described by Parackova et al. [42]. Carissimo et al. [47] proposed that an abnormal immature neutrophil ratio could serve as an early marker for severe COVID-19. In our series, CD16 expression appeared to decrease in neutrophils of severe COVID patients, without reaching statistical significance.

Thus, our study offers a novel holistic approach to TLR protein expression during viral and bacterial infections of varying severity. Despite the limitations of this study, in which the groups were small sample-sized (particularly mild COVID and sepsis) and the nature of the study being monocentric, our results warrant further confirmation with larger cohort studies to determine whether our findings could be generalized.

On the other hand, this study only offers an accurate description of TLR expression. Our results reinforce the need for a better in-depth understanding of TLR pathways and open the door toward functional tests to better understand the impact of TLR internalization and its modulation in circumstances of an exaggerated inflammatory response, directly or indirectly.

Our data highlights the specific modulation of TLR protein expression in neutrophils during severe infectious conditions. The modulation of cell trafficking may represent an interesting way of modulation of TLR-mediated signals. It remains crucial to better characterize cell trafficking of TLRs, notably TLR4, and to better comprehend the clinical correlation of TLR modulation to patient outcome in a larger sample-sized group of patients to assess their potential beneficial effects before the initiation of targeted-agent testing in preclinical models.

We wish to thank Dr. Naser Al Barzan for his careful and critical reading of the manuscript.

The study protocol was approved by the Strasbourg University Hospital Ethic Committee (#NCT03559569). Written informed consent was obtained from all participants before their inclusion. If a patient was unable to consent, consent was obtained from their next of kin, and post hoc consent was obtained as soon as possible.

All authors declare having no conflict of interest related to this research.

The “Association D’Aide aux Insuffisants Respiratoires d’Alsace-Lorraine (ADIRAL, Strasbourg, France)” funded the doctoral research of LC. The association played no role in the data preparation and the writing of the manuscript.

Louise Chomel, Ferhat Meziani, and Laurent Mauvieux participated in designing the research. Julien Demiselle, Pierrick Le Borgne, Hamid Merdji, Julie Helms, and Ferhat Meziani were involved in patients recruitment. Louise Chomel, Mathieu Vogt, Laurent Miguet, Julien Demiselle, Marine Tschirhart, and Valentin Morandeau made the experiments. Louise Chomel and Laurent Mauvieux performed the data analysis. Louise Chomel, Julien Demiselle, Ferhat Meziani, and Laurent Mauvieux participated in the writing and editing of the manuscript.

The data that support the findings of this study are not publicly available due to privacy concerns but are available to bona fide researchers upon a reasonable request to the corresponding author.

1.
Singer
M
,
Deutschman
CS
,
Seymour
CW
,
Shankar-Hari
M
,
Annane
D
,
Bauer
M
, et al
.
The third international consensus definitions for sepsis and septic shock (Sepsis-3)
.
JAMA
.
2016
;
315
(
8
):
801
10
.
2.
Delabranche
X
,
Quenot
JP
,
Lavigne
T
,
Mercier
E
,
François
B
,
Severac
F
, et al
.
Early detection of disseminated intravascular coagulation during septic shock: a multicenter prospective study
.
Crit Care Med
.
2016
;
44
(
10
):
e930
9
.
3.
Hotchkiss
RS
,
Moldawer
LL
,
Opal
SM
,
Reinhart
K
,
Turnbull
IR
,
Vincent
JL
.
Sepsis and septic shock
.
Nat Rev Dis Primers
.
2016
;
2
:
16045
.
4.
Delabranche
X
,
Boisramé-Helms
J
,
Asfar
P
,
Berger
A
,
Mootien
Y
,
Lavigne
T
, et al
.
Microparticles are new biomarkers of septic shock-induced disseminated intravascular coagulopathy
.
Intensive Care Med
.
2013
;
39
(
10
):
1695
703
.
5.
Stiel
L
,
Meziani
F
,
Helms
J
.
Neutrophil activation during septic shock
.
Shock
.
2018
;
49
(
4
):
371
84
.
6.
Härter
L
,
Mica
L
,
Stocker
R
,
Trentz
O
,
Keel
M
.
Increased expression of toll-like receptor-2 and -4 on leukocytes from patients with sepsis
.
Shock
.
2004
;
22
(
5
):
403
9
.
7.
Martins
PS
,
Brunialti
MKC
,
Martos
LSW
,
Machado
FR
,
Assunçao
MS
,
Blecher
S
, et al
.
Expression of cell surface receptors and oxidative metabolism modulation in the clinical continuum of sepsis
.
Crit Care
.
2008
;
12
(
1
):
R25
.
8.
Lenz
M
,
Draxler
DF
,
Zhang
C
,
Kassem
M
,
Kastl
SP
,
Niessner
A
, et al
.
Toll like receptor 2 and 9 expression on circulating neutrophils is associated with increased mortality in critically ill patients
.
Shock
.
2020
;
54
(
1
):
35
43
.
9.
Birra
D
,
Benucci
M
,
Landolfi
L
,
Merchionda
A
,
Loi
G
,
Amato
P
, et al
.
Covid 19: a clue from innate immunity
.
Immunol Res
.
2020
;
68
(
3
):
161
8
.
10.
Khanmohammadi
S
,
Rezaei
N
.
Role of Toll-like receptors in the pathogenesis of COVID-19
.
J Med Virol
.
2021
;
93
(
5
):
2735
9
.
11.
Jung
HE
,
Lee
HK
.
Current understanding of the innate control of toll-like receptors in response to SARS-CoV-2 infection
.
Viruses
.
2021
;
13
(
11
):
2132
.
12.
Sohn
KM
,
Lee
SG
,
Kim
HJ
,
Cheon
S
,
Jeong
H
,
Lee
J
, et al
.
COVID-19 patients upregulate toll-like receptor 4-mediated inflammatory signaling that mimics bacterial sepsis
.
J Korean Med Sci
.
2020
;
35
(
38
):
e343
.
13.
Jaiswal
SR
,
Mehta
A
,
Bhagwati
G
,
Lakhchaura
R
,
Aiyer
H
,
Khamar
B
, et al
.
Innate immune response modulation and resistance to SARS-CoV-2 infection: a prospective comparative cohort study in high risk healthcare workers [Internet]
. Infectious Diseases (except HIV/AIDS); 2020 Oct [cited 2023 Feb 16]. Available from: http://medrxiv.org/lookup/doi/10.1101/2020.10.20.20214965.
14.
Zhang
Q
,
Bastard
P
,
Liu
Z
,
Le Pen
J
,
Moncada-Velez
M
,
Chen
J
, et al
.
Inborn errors of type I IFN immunity in patients with life-threatening COVID-19
.
Science
.
2020
;
370
(
6515
):
eabd4570
.
15.
Shaath
H
,
Vishnubalaji
R
,
Elkord
E
,
Alajez
NM
.
Single-cell transcriptome analysis highlights a role for neutrophils and inflammatory macrophages in the pathogenesis of severe COVID-19
.
Cells
.
2020
;
9
(
11
):
2374
.
16.
Tidswell
M
,
Tillis
W
,
Larosa
SP
,
Lynn
M
,
Wittek
AE
,
Kao
R
, et al
.
Phase 2 trial of eritoran tetrasodium (E5564), a toll-like receptor 4 antagonist, in patients with severe sepsis
.
Crit Care Med
.
2010
;
38
(
1
):
72
83
.
17.
Bastiaansen
AJNM
,
Karper
JC
,
Wezel
A
,
de Boer
HC
,
Welten
SMJ
,
de Jong
RCM
, et al
.
TLR4 accessory molecule RP105 (CD180) regulates monocyte-driven arteriogenesis in a murine hind limb ischemia model
.
PLoS One
.
2014
;
9
(
6
):
e99882
.
18.
Candel
S
,
Sepulcre
MP
,
Espín-Palazón
R
,
Tyrkalska
SD
,
de Oliveira
S
,
Meseguer
J
, et al
.
Md1 and Rp105 regulate innate immunity and viral resistance in zebrafish
.
Dev Comp Immunol
.
2015
;
50
(
2
):
155
65
.
19.
Kimoto
M
,
Nagasawa
K
,
Miyake
K
.
Role of TLR4/MD-2 and RP105/MD-1 in innate recognition of lipopolysaccharide
.
Scand J Infect Dis
.
2003
;
35
(
9
):
568
72
.
20.
Bongers
SH
,
Chen
N
,
van Grinsven
E
,
van Staveren
S
,
Hassani
M
,
Spijkerman
R
, et al
.
Kinetics of neutrophil subsets in Acute, subacute, and chronic inflammation
.
Front Immunol
.
2021
;
12
:
674079
.
21.
Guérin
E
,
Orabona
M
,
Raquil
MA
,
Giraudeau
B
,
Bellier
R
,
Gibot
S
, et al
.
Circulating immature granulocytes with T-cell killing functions predict sepsis deterioration
.
Crit Care Med
.
2014
;
42
(
9
):
2007
18
.
22.
Hanna
MOF
,
Abdelhameed
AM
,
Abou-Elalla
AA
,
Hassan
RM
,
Kostandi
I
.
Neutrophil and monocyte receptor expression in patients with sepsis: implications for diagnosis and prognosis of sepsis
.
Pathog Dis
.
2019
;
77
(
6
):
ftz055
.
23.
Demaret
J
,
Venet
F
,
Friggeri
A
,
Cazalis
MA
,
Plassais
J
,
Jallades
L
, et al
.
Marked alterations of neutrophil functions during sepsis-induced immunosuppression
.
J Leukoc Biol
.
2015
;
98
(
6
):
1081
90
.
24.
Hoffmann
J
,
Etati
R
,
Brendel
C
,
Neubauer
A
,
Mack
E
.
The low expression of fc-gamma receptor III (CD16) and high expression of fc-gamma receptor I (CD64) on neutrophil granulocytes mark severe COVID-19 pneumonia
.
Diagnostics
.
2022
;
12
(
8
):
2010
.
25.
Information on COVID-19 Treatment, Prevention and
.
COVID-19 treatment guidelines
. [cited 2023 Nov 9]. Available from: https://www.covid19treatmentguidelines.nih.gov/.
26.
Selimoglu-Buet
D
,
Wagner-Ballon
O
,
Saada
V
,
Bardet
V
,
Itzykson
R
,
Bencheikh
L
, et al
.
Characteristic repartition of monocyte subsets as a diagnostic signature of chronic myelomonocytic leukemia
.
Blood
.
2015
;
125
(
23
):
3618
26
.
27.
Schultz
TE
,
Blumenthal
A
.
The RP105/MD-1 complex: molecular signaling mechanisms and pathophysiological implications
.
J Leukoc Biol
.
2017
;
101
(
1
):
183
92
.
28.
Tansho-Nagakawa
S
,
Ubagai
T
,
Kikuchi-Ueda
T
,
Koshio
O
,
Koshibu
Y
,
Kikuchi
H
, et al
.
Analysis of membrane antigens on neutrophils from patients with sepsis
.
J Infect Chemother
.
2012
;
18
(
5
):
646
51
.
29.
Silva
SC
,
Baggio-Zappia
GL
,
Brunialti
MKC
,
Assunçao
MSC
,
Azevedo
LCP
,
Machado
FR
, et al
.
Evaluation of Toll-like, chemokine, and integrin receptors on monocytes and neutrophils from peripheral blood of septic patients and their correlation with clinical outcomes
.
Braz J Med Biol Res
.
2014
;
47
(
5
):
384
93
.
30.
Roy
S
,
Karmakar
M
,
Pearlman
E
.
CD14 mediates Toll-like receptor 4 (TLR4) endocytosis and spleen tyrosine kinase (Syk) and interferon regulatory transcription factor 3 (IRF3) activation in epithelial cells and impairs neutrophil infiltration and Pseudomonas aeruginosa killing in vivo
.
J Biol Chem
.
2014
;
289
(
2
):
1174
82
.
31.
Brubaker
SW
,
Bonham
KS
,
Zanoni
I
,
Kagan
JC
.
Innate immune pattern recognition: a cell biological perspective
.
Annu Rev Immunol
.
2015
;
33
:
257
90
.
32.
Ciesielska
A
,
Matyjek
M
,
Kwiatkowska
K
.
TLR4 and CD14 trafficking and its influence on LPS-induced pro-inflammatory signaling
.
Cell Mol Life Sci
.
2021
;
78
(
4
):
1233
61
.
33.
Watts
C
.
Location, location, location: identifying the neighborhoods of LPS signaling
.
Nat Immunol
.
2008
;
9
(
4
):
343
5
.
34.
Husebye
H
,
Halaas
Ø
,
Stenmark
H
,
Tunheim
G
,
Sandanger
Ø
,
Bogen
B
, et al
.
Endocytic pathways regulate Toll-like receptor 4 signaling and link innate and adaptive immunity
.
EMBO J
.
2006
;
25
(
4
):
683
92
.
35.
Sato-Kaneko
F
,
Yao
S
,
Lao
FS
,
Shpigelman
J
,
Messer
K
,
Pu
M
, et al
.
A novel synthetic dual agonistic liposomal TLR4/7 adjuvant promotes broad immune responses in an influenza vaccine with minimal reactogenicity
.
Front Immunol
.
2020
;
11
:
1207
.
36.
Tanaka
A
,
Nakamura
S
,
Seki
M
,
Fukudome
K
,
Iwanaga
N
,
Imamura
Y
, et al
.
Toll-like receptor 4 agonistic antibody promotes innate immunity against severe pneumonia induced by coinfection with influenza virus and Streptococcus pneumoniae
.
Clin Vaccin Immunol
.
2013
;
20
(
7
):
977
85
.
37.
Rajaiah
R
,
Perkins
DJ
,
Ireland
DDC
,
Vogel
SN
.
CD14 dependence of TLR4 endocytosis and TRIF signaling displays ligand specificity and is dissociable in endotoxin tolerance
.
Proc Natl Acad Sci U S A
.
2015
;
112
(
27
):
8391
6
.
38.
Perkins
DJ
,
Richard
K
,
Hansen
AM
,
Lai
W
,
Nallar
S
,
Koller
B
, et al
.
Autocrine-paracrine prostaglandin E2 signaling restricts TLR4 internalization and TRIF signaling
.
Nat Immunol
.
2018
;
19
(
12
):
1309
18
.
39.
Wang
Y
,
Yang
Y
,
Liu
X
,
Wang
N
,
Cao
H
,
Lu
Y
, et al
.
Inhibition of clathrin/dynamin-dependent internalization interferes with LPS-mediated TRAM–TRIF-dependent signaling pathway
.
Cell Immunol
.
2012
;
274
(
1–2
):
121
9
.
40.
Ghosh
M
,
Subramani
J
,
Rahman
MM
,
Shapiro
LH
.
CD13 restricts TLR4 endocytic signal transduction in inflammation
.
J Immunol
.
2015
;
194
(
9
):
4466
76
.
41.
Petruk
G
,
Puthia
M
,
Samsudin
F
,
Petrlova
J
,
Olm
F
,
Mittendorfer
M
, et al
.
Targeting Toll-like receptor-driven systemic inflammation by engineering an innate structural fold into drugs
.
Nat Commun
.
2023
;
14
(
1
):
6097
.
42.
Parackova
Z
,
Zentsova
I
,
Bloomfield
M
,
Vrabcova
P
,
Smetanova
J
,
Klocperk
A
, et al
.
Disharmonic inflammatory signatures in COVID-19: augmented neutrophils’ but impaired monocytes’ and dendritic cells’ responsiveness
.
Cells
.
2020
;
9
(
10
):
2206
.
43.
Patra
R
,
Chandra Das
N
,
Mukherjee
S
.
Targeting human TLRs to combat COVID-19: a solution
.
J Med Virol
.
2021
;
93
(
2
):
615
7
.
44.
McNab
F
,
Mayer-Barber
K
,
Sher
A
,
Wack
A
,
O’Garra
A
.
Type I interferons in infectious disease
.
Nat Rev Immunol
.
2015
;
15
(
2
):
87
103
.
45.
Bortolotti
D
,
Gentili
V
,
Rizzo
S
,
Schiuma
G
,
Beltrami
S
,
Strazzabosco
G
, et al
.
TLR3 and TLR7 RNA sensor activation during SARS-CoV-2 infection
.
Microorganisms
.
2021
;
9
(
9
):
1820
.
46.
Sun
H
,
Chan
JFW
,
Yuan
S
.
Cellular sensors and viral countermeasures: a molecular arms race between host and SARS-CoV-2
.
Viruses
.
2023
;
15
(
2
):
352
.
47.
Carissimo
G
,
Xu
W
,
Kwok
I
,
Abdad
MY
,
Chan
YH
,
Fong
SW
, et al
.
Whole blood immunophenotyping uncovers immature neutrophil-to-VD2 T-cell ratio as an early marker for severe COVID-19
.
Nat Commun
.
2020
;
11
(
1
):
5243
.