Tanaka Arthur Choto1,2, Ian Makupe3, Andrew Zolani Cakana4, Elopy Nimele Sibanda3, Takafira Mduluza1,2. 1. Department of Biochemistry and Biotechnology, University of Zimbabwe, Harare, Zimbabwe. 2. Optics and Imaging, Doris Duke Medical Research Institute, College of Health Sciences, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa. 3. Gamma Allergy and Immunology Laboratory, Twin Palms Medical Centre, Harare, Zimbabwe. 4. The Haematology Centre, Harare, Zimbabwe.
Abstract
Coronavirus disease 2019 (COVID-19) is caused by a recently identified virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease is a pandemic. Although the hallmarks of severe COVID-19 have been established, the underlying mechanisms that promote severe pathology have not been thoroughly studied. A better understanding of the immune response in severe COVID-19 patients may help guide the development of therapeutic strategies and predict immuno-pathogenicity. This study was set to determine the lymphocyte and cytokine profiles associated with COVID-19 severity. A total of 43 hospitalised COVID-19 patients were recruited for the study and whole blood samples were drawn from each patient. Complete blood counts, lymphocyte subset profiles and C-reactive protein statuses of patients were determined. Cytometric bead array was performed to analyse the cytokine profiles of each patient. The demographic characteristics showed that the median age of the patients was 48.72 years, with an interquartile range from 40 to 60 years, and 69.77% of the patients were male. COVID-19 patients exhibited significantly low CD4+ lymphocyte expansion and leucocytosis augmented by elevated neutrophil and immature granulocytes. Stratification analysis revealed that reduced monocytes and elevated basophils and immature granulocytes are implicated in severe pathology. Additionally, cytokine results were noted to have significant incidences of interleukin 17A (IL-17A) expression associated with severe disease. Results from this study suggest that a systemic neutrophilic environment may preferentially skew CD4+ lymphocytes towards T-helper 17 and IL-17A promotion, thus, aggravating inflammation. Consequently, results from this study suggest broad activity immunomodulation and targeting neutrophils and blocking IL-17 production as therapeutic strategies against severe COVID-19.
Coronavirus disease 2019 (COVID-19) is caused by a recently identified virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease is a pandemic. Although the hallmarks of severe COVID-19 have been established, the underlying mechanisms that promote severe pathology have not been thoroughly studied. A better understanding of the immune response in severe COVID-19 patients may help guide the development of therapeutic strategies and predict immuno-pathogenicity. This study was set to determine the lymphocyte and cytokine profiles associated with COVID-19 severity. A total of 43 hospitalised COVID-19 patients were recruited for the study and whole blood samples were drawn from each patient. Complete blood counts, lymphocyte subset profiles and C-reactive protein statuses of patients were determined. Cytometric bead array was performed to analyse the cytokine profiles of each patient. The demographic characteristics showed that the median age of the patients was 48.72 years, with an interquartile range from 40 to 60 years, and 69.77% of the patients were male. COVID-19 patients exhibited significantly low CD4+ lymphocyte expansion and leucocytosis augmented by elevated neutrophil and immature granulocytes. Stratification analysis revealed that reduced monocytes and elevated basophils and immature granulocytes are implicated in severe pathology. Additionally, cytokine results were noted to have significant incidences of interleukin 17A (IL-17A) expression associated with severe disease. Results from this study suggest that a systemic neutrophilic environment may preferentially skew CD4+ lymphocytes towards T-helper 17 and IL-17A promotion, thus, aggravating inflammation. Consequently, results from this study suggest broad activity immunomodulation and targeting neutrophils and blocking IL-17 production as therapeutic strategies against severe COVID-19.
Coronavirus disease 2019 (COVID-19), is an infectious disease caused by a recently discovered coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the disease is a pandemic. The emergence and outbreak of the SARS-CoV-2 infections is considered to have occurred in December 2019, when pneumonia cases of unknown aetiology were identified in Wuhan, China [1]. Following the outbreak of COVID-19 cases in China, it did not take more than 4 months for COVID-19 cases to be vastly spread throughout the world. As a consequence, the spread of COVID-19 was declared a pandemic [2]. The COVID-19 pandemic presented an unprecedented burden to healthcare settings globally, with the progression of the pandemic being driven by successive waves of infection [3, 4].COVID-19 is a notably heterogeneous disease according to clinical reports [5, 6]. Clinical presentations in COVID-19 patients can range from being an asymptomatic infection to critical illness that requires hospitalisation. At least 14% of infected patients show severe symptoms, often linked with imbalanced immune responses [7]. Critical COVID-19 cases are characterised by a cytokine storm syndrome and acute respiratory distress syndrome (ARDS), which may eventually lead to death [5, 6]. Dysregulated immune responses have been speculated to be the leading cause of morbidity and mortality [8]. A difficult task in the context of COVID-19 is providing comprehensive evidence of the underlying mechanisms that drive disease heterogeneity. Ideally, once enough evidence has been provided, the results may help guide the development of therapeutic strategies and predict immuno-pathogenicity [9, 10]. Akin to previous studies on SARS-CoV and MERS-CoV, patterns in the immune response and COVID-19 progression have a proximal association and may play a key role in disease severity [11, 12]. Hallmarks of severe COVID-19 cases have been widely described to be lymphopenia, aberrant granulocytes and monocytes, a cytokine storm and an increased neutrophil-to-lymphocyte ratio (NLR) [10, 13–15]. However, innate immune cells, particularly neutrophils, have been suggested to be the main mediators of immunopathology [16].This current study was set to determine the lymphocyte subset and cytokine profiles associated with COVID-19 severity. COVID-19 severity in this case was determined by a NLR stratification and neutrophilia status stratification of hospitalised COVID-19 patients. Previous studies, systematic reviews and meta-analyses have demonstrated that the NLR can be utilised to diagnose and predict COVID-19 severity and outcome with remarkable accuracy [17-19]. More importantly, this approach significantly helps to ascertain the degree by which neutrophils modulate the immunopathology in COVID-19 patients.
Materials and methods
Study participants and clinical data
This study was granted ethical approval by the Medical Research Council of Zimbabwe (MRCZ/A/2602). The study was carried out in accordance to the principles and ethical guidelines of the International Declaration of Helsinki, the guidelines for Good Clinical Practice in Zimbabwe and the Medical Research Council Ethical Guidelines for Research. A total of 43 COVID-19 patients and 28 healthy individuals as controls were recruited for the study from Parirenyatwa General Hospital, Harare, Zimbabwe. Patient were recruited without a pre-determined inclusion criteria (no inclusion with regards to age and sex), between 1 July 2020 and 30 November 2020. The patients’ age and sex were recorded making use of the information collected as part of the hospital’s procedures. All recruited participants were confirmed their COVID-19 positive status as part of the hospital’s procedures, by detecting the presence of SARS-CoV-2 genetic material using reverse transcriptase polymerase chain reaction (RT-PCR). Peripheral blood was drawn from each recruited patient on hospitalisation and the blood was collected into ethylenediaminetetraacetic acid (EDTA) vacutainers for subsequent assays.
Determination of complete blood counts
A haematology analyser, Sysmex XN-3000™ (Sysmex Corporation, Kobe, Japan), was used to determine the complete blood counts of recruited patients within 2 hours of receiving whole blood specimens. The white blood cell differential (WDF) channel was used to determine differential white blood cell counts (lymphocytes, neutrophils, eosinophils and immature granulocytes). The global cell counts were determined by the white cell nucleated (WNR) channel. The complete blood counts were measured by aspirating approximately 88 μl of whole blood within 2 hours of reception. The haematology analyser then automatically determined the haematological parameters, making use of the radio frequencies (RF) and direct current (DC) method, hydrodynamic focusing, fluorescent flow cytometry and cyanide free sulfolyser method. The complete blood count results were recorded and captured for further analysis.
Flow cytometric determination of lymphocyte subsets
The Becton Dickinson (BD) Multitest™ IMK kit, (Becton, Dickinson and Company BD Biosciences, San Jose, California, United States of America) was used to determine the lymphocytes subset populations. The 1X lysing solution was prepared by diluting the 10X concentrate of the BD Multitest™ IMK kit lysing solution with deionised water. Two 12 × 75 mm BD Trucount™ tubes were labelled with the sample identification number and the letters A and B to differentiate each tube. After verifying that the BD Trucount™ bead pellet was intact at the bottom of the BD Trucount™ tube, 20 μl of BD Multitest™ CD3/CD8/CD45/CD4 reagent was placed in tube A. Similarly, 20 μl of BD Multitest™ CD3/CD16+CD56/CD45/CD19 reagent was placed in tube B. In both cases, the reagent was pipetted to the bottom of the tube making sure to avoid the pellet. A volume of 50 μl of whole blood sample was placed at the bottom of both tubes. Both samples were stained by reverse pipetting, and were vortexed to ensure thorough mixing of the blood and the solution. After mixing, the tubes were incubated for 15 minutes in the dark at room temperature. A volume of 450 μl of 1X BD Multitest™ IMK kit lysing solution was added into both tubes and both tubes were vortexed to ensure thorough mixing. Subsequently, both tubes were incubated for another 15 minutes in a dark environment at room temperature. The samples in both tubes were sequentially analysed using a BD FACSCalibur™ flow cytometer (Becton, Dickinson and Company BD Biosciences, San Jose, California, United States of America), after incubation. BD Multiset™ software (Becton, Dickinson and Company BD Biosciences, San Jose, California, United States of America) was used to acquire and automatically measure the lymphocyte subsets of the samples. The procedure was repeated after the reception of each whole blood sample. The lymphocyte subset profile results were recorded and captured for further analysis.
Qualitative analysis of serum C-reactive protein
Qualitative analysis of serum C-reactive protein was carried out using a C-reactive protein latex agglutination test kit (Fortress Diagnostics Limited, Antrim, Northern Ireland, United Kingdom). A volume of 50 μl of the serum sample and a drop of the positive control were placed on a card, in separate black circles. The latex reagent was re-suspended and a drop of the latex reagent was added to each black circle with the sample and positive control. The latex reagent was spread entirely over the area of the circle to ensure thorough mixing of the mixture. The cards were then rotated at 100 revolutions per minute for 2 minutes. Presence of agglutination clumps similar to the positive control indicated a positive result (C-reactive protein of at least 6 mg/l). The process was done for all serum samples and the results were captured for analysis.
Analysis of serum cytokine concentrations
The BD™ Human Th1/Th2/Th17 cytometric bead array (CBA) (Becton, Dickinson and Company BD Biosciences, San Jose, California, United States of America) kit was used to analyse cytokines in serum samples of COVID-19 patients. The kit allowed simultaneous detection of a set of cytokines (IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ and IL-17A). The instrument used to detect the cytokines, the BD FACSCalibur™ flow cytometer (Becton, Dickinson and Company BD Biosciences, San Jose, California, United States of America), was setup according to the manual. The setup was done to configure the gating parameters of the instrument using the BD CellQuest Pro™ software (Becton, Dickinson and Company BD Biosciences, San Jose, California, United States of America). Lyophilised cytokine standards were reconstituted in 2 ml of the assay diluent for 15 minutes, and the tube was labelled as the top standard. After equilibration at room temperature, serial dilutions were made by transferring 300 μl of the top standard into the respective tubes containing 300 μl of assay diluent to create dilution ratios of 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 1:128, and 1:256. Capture beads were then vortexed vigorously and the cocktail of capture beads the mixture was centrifuged at 200 g using a Hermle ZK364 centrifuge (Maschinenfabrik Berthold Hermle AG, Gosheim, Germany). After centrifuging, the capture beads were aspirated and re-suspended in serum enhancement buffer by adding the volume lost during aspiration. A volume of 50 μl of mixed capture beads was added to all tubes (50 μl aliquoted sample [COVID-19 hospitalised patients and healthy controls] and cytokine standards). The samples were incubated overnight in a dark environment to allow binding. The samples were then washed with 1 ml of wash buffer at 200 g for 5 minutes, after incubation. The pellet was then re-suspended in 300 μl wash buffer after centrifuging. The BD FACSCalibur™ flow cytometer (Becton, Dickinson and Company BD Biosciences, San Jose, California, United States of America) was used to acquire all samples and an acquisition template was used to record the results. The results were subsequently loaded for analysis on the FCAP Array™ application (version 3.0 for Windows® OS Becton, Dickinson and Company BD Biosciences, San Jose, California, United States of America). The software determined the mean fluorescence intensities (MFIs), which were fitted to a logistic curve-fitting equation to determine the concentrations of the cytokines. The determined concentrations of cytokines were captured for further analysis.
Ethics statement
This study was conducted after the protocol was reviewed and approved by the Medical Research Council of Zimbabwe, approval MRCZ/A/2602. Permission to conduct the study was also obtained from the Joint Research Ethics Committee of Parirenyatwa Group of Hospitals and the University of Zimbabwe College of Health Sciences. After having thorough discussion on the procedures and purpose of the study and before commencement of data collection, written consent was obtained from the participants.
Data analysis
After all the results were captured, statistical analyses and tests were done. Patients were stratified by their neutrophil to lymphocyte ratio (NLR) and neutrophilia status to determine the profiles that correlated with severe COVID-19. Calculation of the NLR was done by dividing the neutrophil absolute counts by the lymphocyte absolute count. Therefore, values above 7.5 were categorised in the high NLR group, which indicated severe immunopathology and values below 7.5 were categorised in the low NLR group, which indicated less severe immunopathology. The rationale behind using 7.5 as the cut-off value was to target neutrophilia patients (neutrophil count > 7.5 cells x 109/L) and/or lymphopenia patients (lymphocyte count > 7.5 cells x 109/L). These targeted patients are more likely to be severe COVID-19 cases. Therefore, a ratio of 7.5 and above was deemed as a suitable cut-off value. Patients were also stratified according by neutrophil counts, neutrophilia (> 7.5 cells x 109/L) and non-neutrophilia patients (< 7.5 cells x 109/L), in order to investigate how neutrophils exert their effects on lymphocyte subset expansion. The Mann-Whitney U test and Kruskal-Wallis test were used to compare the distributions. Spearman’s correlation and Fischer’s exact tests were used to investigate association between variables. All data was analysed using statistical software, STATA (version 16.0, StataCorp Limited Liability Company, Texas, USA) and Graphpad Prism 5® (Version 5.0, Graph pad Software Inc, San Diego, United States of America). Results with p-values less than 0.05 (< 0.05), were statistically significant.
Results
Demographic characteristics of recruited COVID-19 patients
The aim of this study was to determine the lymphocyte and cytokine profiles that are associated with COVID-19 severity. Therefore, a total of 43 hospitalised COVID-19 patients were recruited for the study from Parirenyatwa General Hospital in Harare. Demographic analysis showed that 13 (30.23%) of the patients were female and 30 (69.77%) of the patients male. Additionally the median age of the collective group of patients was 48.72 years and the interquartile range was 40 years to 60 years (Table 1).
Table 1
Demographic characteristics of the patients recruited for the study.
Variable
Descriptive Statistic of Recruited COVID-19 Patients
n
43
Age in years, median (Q1, Q3)
48.72 (40, 60)
Sex:
Female, n (%)
13 (30.23)
Male, n (%)
30 (69.77)
Summary of haematological features and lymphocyte profiles
After complete blood counts and lymphocyte profiles were measured, the results summarised in Tables 2 and 3 respectively. The results were and recorded together with their reference ranges. The reference ranges were used as guidelines to infer anomalies caused by COVID-19. Therefore, COVID-19 patients were characterised by CD4+ T-cell lymphopenia and their white blood cell differentials were skewed towards higher neutrophil and immature granulocyte percentages and lower lymphocyte percentages.
Table 2
Summary of haematological characteristics of hospitalised COVID-19 patients.
Variable
Hospitalised COVID-19 Patients
Reference Range
Median (Q1, Q3)
White Blood Cell Count (x 109/L)
10.045 (7.665, 12.585)
4.5–11
Red Blood Cell Count (x 1012/L)
4.43 (3.385, 4.93)
4.65–6.5
Haemoglobin Count (g/dl)
12.46 (10.25, 14.15)
13–18
Haematocrit (%)
43 (33.75, 47.7)
43–55
Mean Corpuscular Volume (fl)
93.95 (88.6, 103.7)
77–95
Mean Corpuscular Haemoglobin (pg)
28.55 (27.15, 30.2)
27–32
Mean Corpuscular Haemoglobin Concentration (g/dl)
29.5 (28.25, 31.3)
32–36
Red Cell Distribution Width Coefficient (%)
16.25 (14.45, 17.7)
11.5–14.5
Red Cell Distribution Width Standard Deviation (fl)
56.63545 (49, 60.6)
40–55
Platelets (x 109/L)
206.5 (149.5, 336)
140–440
Neutrophil Count (x 109/L)
7.145 (3.93, 10.75)
2–7.5
Neutrophil Percentage (%)
78.55 (60.55, 85.55)
51–76
Lymphocyte Count (x 109/L)
1.105 (0.69, 2.085)
1–4
Lymphocyte Percentage (%)
13.1 (6.5, 23.75)
20–40
Eosinophil Count (x 109/L)
0.01 (0, 0.03)
0–0.45
Eosinophil Percentage (%)
0.1 (0, 0.4)
0–5
Monocyte Count (x 109/L)
0.58 (0.17, 0.85)
0.18–0.8
Monocyte Percentage (%)
5.3 (2.35, 8.4)
5–8
Basophil Count (x 109/L)
0.025 (0.01, 0.04)
0–0.2
Basophil Percentage (%)
0.3 (0.13, 0.6)
0–0.2
Immature Granulocyte Count (x 109/L)
0.31 (0.1, 0.62)
0–0.03
Immature Granulocyte Percentage (%)
2.7 (1.2, 6.4)
0–0.5
Neutrophil to Lymphocyte Ratio
6.02(2.5, 12.2)
-
Table 3
Summary of lymphocyte profiles of hospitalised COVID-19 patients.
Spearman’s correlation analysis of leucocyte subsets
A two-tailed Spearman’s correlation test was carried out at 95% confidence interval, to investigate association between leucocyte subsets. The results of the analysis were displayed in a correlation matrix (Fig 1) and a p-value table (Table 4). Lymphocyte counts showed the strongest positive correlation with monocyte counts (r = 0.63; p < 0.0001). Basophil demonstrated significant correlations with all leucocyte subsets. Basophils were positively correlated with eosinophil counts (r = 0.40; p = 0.014), neutrophil counts (r = 0.40; p = 0.013) and immature granulocyte counts (r = 0.55; p = 0.001). Conversely, basophils were negatively correlated with monocyte counts (r = - 0.34, 0.034) and lymphocyte counts (r = - 0.41; p = 0.011). Immature granulocytes also displayed significant correlations with neutrophils, they exhibited a positive correlation with neutrophil counts (r = 0.38; p = 0.033).
Fig 1
Spearman’s correlation matrix of cell populations in COVID-19 patients.
The correlation matrix was obtained by analysing the relationship between the white blood cell differential counts of COVID-19 patients. The correlations were determined by r values in the matrix. Each cell was colour coded according to a heat-map that depicts the measure of correlation, blue for a positive correlation and red for a negative correlation.
Table 4
Probability values of Spearman’s correlation between leucocytes.
Neutrophil Count
Lymphocyte Count
Eosinophil Count
Monocyte Count
Basophil Count
Immature Granulocyte Count
Neutrophil Count
0.248
0.886
0.377
0.013
0.033
Lymphocyte Count
0.248
0.151
< 0.0001
0.011
0.059
Eosinophil Count
0.886
0.151
0.061
0.014
0.455
Monocyte Count
0.377
< 0.0001
0.061
0.034
0.825
Basophil Count
0.013
0.011
0.014
0.034
0.001
Immature Granulocyte Count
0.033
0.059
0.455
0.825
0.001
Spearman’s correlation matrix of cell populations in COVID-19 patients.
The correlation matrix was obtained by analysing the relationship between the white blood cell differential counts of COVID-19 patients. The correlations were determined by r values in the matrix. Each cell was colour coded according to a heat-map that depicts the measure of correlation, blue for a positive correlation and red for a negative correlation.
Qualitative analysis of C-reactive protein
Qualitative analysis of CRP expression was carried out using serum samples. The results showed that 27 patients (85.17%) were CRP positive, whilst 5 patients (14.29%) were CRP negative (Table 5). A Fischer’s exact test was carried out to determine an association between CRP expression and the patients’ NLR scores. CRP expression was observed to be significantly associated with NLR scores of the recruited patients (p = 0.046) (Table 5). While 70.59% of the patients in the low NLR category were CRP positive, 100% of the patients within the high NLR category were CRP positive.
Table 5
Distribution of C-reactive positive patients with respect to neutrophil lymphocyte ratio.
Laboratory Test
COVID-19 patients with low NLR
COVID-19 patients with high NLR
p
C-reactive Protein:
Positive, n (%)
12 (70.59)
15 (100)
0.046
Negative, n (%)
5 (29.41)
0 (0)
Haematological features of COVID-19 patients stratified by NLR
After NLR stratification, it was observed that there was no significant statistical difference in the distribution of the haematological features and NLR was not associated with the demographic features of the patients (S8 Table). Notably, the high NLR group had decreased mean corpuscular volume values and mean corpuscular haemoglobin values, whilst the platelets counts increased for the same group.
Distribution of granulocytes and monocytes in COVID-19 patients stratified by NLR
The population counts of immature granulocytes, eosinophils, monocytes and basophils were presented in column scatter plots (Fig 2). When the Mann-Whitney U test was used to compare the distributions, basophil counts and immature granulocyte counts were noted to be significantly higher for patients within the high NLR group (p = 0.0391; p = 0.0165) (Fig 2A and 2C). However, the basophil counts were also noted to be within the upper and lower limit (UL and LL) of the normal ranges. Monocyte counts were observed to be significantly higher for the low NLR groups (p = 0.0438) (Fig 2D) and the distribution was spread above the UL of the normal range. Eosinophils were observed to have no significant statistical difference in distribution of the 2 groups.
Fig 2
Column scatter plots of leucocyte subset counts of COVID-19 patients.
The patients were stratified by their NLR scores. Each graph shows the distribution of a subset of leucocytes, which are basophils (A), eosinophils (B), immature granulocytes (C) and monocyte (D). Each column represents an NLR group, showing the counts observed in each patient, the median and interquartile ranges. An analysis of the distribution was made using the Mann-Whitney U test at 95% significance interval. The upper limit (UL) and lower limit (LL) demarcate the normal ranges.
Column scatter plots of leucocyte subset counts of COVID-19 patients.
The patients were stratified by their NLR scores. Each graph shows the distribution of a subset of leucocytes, which are basophils (A), eosinophils (B), immature granulocytes (C) and monocyte (D). Each column represents an NLR group, showing the counts observed in each patient, the median and interquartile ranges. An analysis of the distribution was made using the Mann-Whitney U test at 95% significance interval. The upper limit (UL) and lower limit (LL) demarcate the normal ranges.
Analysis of cytokines expressed by COVID-19 patients
After the patients were stratified by their NLR scores, a Kruskal-Wallis test was used to determine significant differences in cytokine expression. Serum IL-2 and IL-4 were not detected in the samples of the patients (Fig 3B and 3C). Contrastingly, there was a prevalent expression of IL-10 and IL-6 amongst the collective group of recruited patients. Patients within the high NLR group were noted to have significantly higher expression of IL-10 (p < 0.001) than the control group and low NLR patients also exhibited significantly higher expression of IL-10 (p < 0.01) than the control group. However, there was no significant difference in IL-10 expression between the high and low NLR groups (Fig 3E). Similarly, both the high and low NLR group exhibited a significantly higher expression of IL-6 (p < 0.001) compared to the control. No significant difference in IL-6 expression was observed between the low and high NLR groups (Fig 3D). Although high NLR patients were noted to express significantly higher levels of IFN-γ in serum compared to the healthy controls (p < 0.01), there was no statistical difference in expression between the high and low NLR groups (Fig 3A). TNF-α was mainly expressed by patients within the low NLR group and a strong statistical difference in TNF-α expression compared to the control group (p < 0.01) was observed. There was no significant statistical difference in TNF-α in expression between the low NLR and high NLR groups (Fig 3G). The most significant finding of this study was observed when significantly higher incidences in IL-17A expression were noted within the high NLR group. Patients with the high NLR group were noted to express significantly higher levels of IL-17A compared to low NLR patients (p < 0.01) and the control group (p < 0.001) (Fig 3F).
Fig 3
Column scatter plots of cytokines expressed by COVID-19 patients.
The patients were stratified by their NLR scores. Each graph shows the concentration of the cytokines, IFN-γ (A), IL-2 (B), IL-4 (C), IL-6 (D), IL-10 (E), IL-17A (F) and TNF-α (G). The scatter plot of each NLR group and the healthy controls shows the concentration of the cytokines for each patient, the median and interquartile ranges. An analysis of the distributions was performed using the Kruskal-Wallis test at 95% significance interval, with Dunn’s multiple comparison post-test.
Column scatter plots of cytokines expressed by COVID-19 patients.
The patients were stratified by their NLR scores. Each graph shows the concentration of the cytokines, IFN-γ (A), IL-2 (B), IL-4 (C), IL-6 (D), IL-10 (E), IL-17A (F) and TNF-α (G). The scatter plot of each NLR group and the healthy controls shows the concentration of the cytokines for each patient, the median and interquartile ranges. An analysis of the distributions was performed using the Kruskal-Wallis test at 95% significance interval, with Dunn’s multiple comparison post-test.
Distribution of lymphocyte subsets in COVID-19 patients stratified by neutrophilia status
Neutrophils exert versatile functions in the immune system, thus, a neutrophilia stratification was carried out to investigate how neutrophils may contribute to lymphocyte subset expansion. However, there no significant statistical differences in distribution that were noted (S1 Fig). Notably, neutrophilia patients had higher CD4+ lymphocyte percentages, CD16+, CD56+ and CD16+CD56+ lymphocyte percentages and CD4+/CD8+ lymphocyte ratios. Non-neutrophilia patients exhibited higher CD3+ lymphocyte counts and CD19+ lymphocyte counts (S1 Fig).
Correlation between lymphocyte subset and monocytes
A Spearman’s correlation test between monocyte percentages and the lymphocyte subset counts was done to investigate the correlation between monocytes and lymphocytes. Graphs show a strong and statistically significant correlation between monocyte percentages and all lymphocyte subset counts, except CD16+CD56+ lymphocytes (Fig 4). CD19+ lymphocytes exhibited the strongest correlation (r = 0.747; p < 0.0001), followed by CD3+ lymphocytes (r = 0.636; p < 0.0001), then CD8+ lymphocytes (r = 0.560; p = 0.00011) and finally, CD4+ lymphocytes (r = 0.480; p = 0.001).
Fig 4
Scatter plot showing the correlation between different lymphocyte subsets and monocyte percentage.
Each graph shows dot plots of all monocyte percentages and lymphocyte subset counts. The graphs show CD3+ lymphocytes (A), CD8+ lymphocytes (B), CD4+ lymphocytes (C), CD16+CD56+ lymphocytes (D) and CD19+ lymphocytes (E). The correlations were analysed by a two tailed Spearman’s correlation test at 95% significance interval, where p < 0.05 was considered significant and p < 0.01; p < 0.001 were used to determine the magnitude of significance.
Scatter plot showing the correlation between different lymphocyte subsets and monocyte percentage.
Each graph shows dot plots of all monocyte percentages and lymphocyte subset counts. The graphs show CD3+ lymphocytes (A), CD8+ lymphocytes (B), CD4+ lymphocytes (C), CD16+CD56+ lymphocytes (D) and CD19+ lymphocytes (E). The correlations were analysed by a two tailed Spearman’s correlation test at 95% significance interval, where p < 0.05 was considered significant and p < 0.01; p < 0.001 were used to determine the magnitude of significance.
Discussion
The spread of COVID-19 is difficult to control and the disease continues to claim lives. The progression of the disease has been driven by successive waves of infections regionally [4]. Consequently, the SARS-CoV-2 virus brought an unprecedented threat globally. According to preceding reports, COVID-19 is markedly heterogeneous and at least 14% of all infected individuals may exhibit severe symptoms [7, 16, 20]. More importantly, severe COVID-19 cases are distinctively characterised by neutrophilia and lymphopenia, which may lead to impaired viral clearance and poor outcomes [10, 15, 17]. Guided by initial studies, this study was set to investigate how neutrophilia and lymphopenia are linked with the cellular responses and cytokine responses in COVID-19 patients. Results from this study provide a unique perspective on the immunopathology of COVID-19, with a particular focus on neutrophilia and lymphopenia.A total of 43 COVID-19 patients were recruited from Parirenyatwa General Hospital, 69.77% of which were male and 30.23% were female. Although there are limited reports that describe the demographic characteristics of hospitalised COVID-19 patients in sub-Saharan Africa, the demographic characteristics from this study were consistent with an earlier retrospective study [21]. The study was conducted in in the Democratic Republic of the Congo (DRC) and the median age from the study was noted to be 46 years and of the 766 COVID-19 patients, 65.6% were male [21]. Comparably, the median age and the interquartile range were noted to be 48.72 (40, 60) years and 69.77% of the patients were male. These demographic features are indicative of the population that is at risk of hospitalisation. However, a wider retrospective study may help ascertain these insights.Analysis of complete blood counts of COVID-19 patients revealed that the percentages of leucocyte subsets were skewed towards higher neutrophil and immature granulocyte percentages. Whilst neutrophils and immature granulocytes had higher percentages, CD4+ T-lymphocytes populations and percentages of most patients were observed to be low. Neutrophil, immature granulocyte and CD4+ T-lymphocyte populations may be heavily implicated in the immunopathology of COVID-19. A meta-analysis study observed that the levels of neutrophils increase while the levels lymphocyte decrease [22]. Such an imbalance entails prolonged innate immune responses, which may promote release of cytotoxic granules at sites of infection, NETosis and enhanced coagulation [23]. Prolonged innate immune responses may overpower lymphocytes that dampen inflammatory responses [23]. Furthermore, since CD4+ T-lymphocytes were observed to be low in most patients, it is important to investigate human immunodeficiency virus (HIV) and SARS-CoV-2 coinfections.A NLR stratification was carried out to provide a unique perspective on the inflammatory events in COVID-19 patients. The NLR index has been demonstrated to predict hyper-inflammatory status in patients [24-26]. Hence, the rationale behind the NLR stratification was to reflect unresolved inflammatory responses and predict the different cytokines and cell types that confer these responses. An advantage of using this index is that it can then be used to discern the inflammatory events taking place in a low resource healthcare setting [27]. From the results, CRP expression was noted to be indicative of severe disease, since 100% of patients with a higher NLR score were CRP positive. However, CRP titres could have improved this analysis. Additionally, there were no observed significant statistical differences between the haematological parameters of patients in the high NLR group and patients the low NLR group. Patients in the high NLR group had higher platelet counts compared to low NLR group, which indicates a risk of accelerated clot formation. Accelerated clot formation may occur as a consequence of platelet-neutrophil complexation, and promote a pro-thrombic environment, hyper-inflammation and prolonged neutrophil survival [23]. Hence, there might be a need to monitor the NLR and platelet counts of COVID-19 patients.Whilst circulating basophils and immature granulocytes were significantly higher in counts, monocytes were significantly lower for patients in the high NLR group. According to these observations, immature granulocytes and basophils could be heavily involved in inflammatory responses whilst monocytes may have a protective role. These results were also emphasized by the Spearman’s correlation analysis of leucocytes. Elevated immature granulocytes reflect prolonged innate immune response in the high NLR group, which may be due to prolonged stimulation of the bone marrow [28]. These findings also point to cases of emergency myelopoiesis in patients with a high NLR. Together with neutrophils, immature granulocytes may aggravate inflammation and may eventually lead to ARDS [28]. Basophils have been neglected as a leucocyte subset possibly due to their minority and redundancy in roles with mast cells [29]. Nonetheless, basophils interact with other cells making use of basophil derived factors that can contribute to inflammatory responses and they have been noted to augment T-helper 17 responses and IL-17 production [29]. Thus, it may be important to revisit the role of basophils in inflammation, especially as a result of viral infections. Monocytes may be implicated in the hyper-inflammatory processes associated with COVID-19 infections [13]. Monocyte counts were lower in the high NLR group, which was unexpected. Thus, it may be crucial to determine the dominant immuno-phenotypes of circulating monocytes for both NLR groups.Severe cases of COVID-19 are characterised by a cytokine storm, which was earlier reported to be reminiscent of a macrophage activation syndrome [5, 30, 31]. Macrophage activation syndrome is typified by elevated levels of IFN-γ [15]. The concentration of IFN-γ in the serum of COVID-19 patients was relatively lower. Results of this study were concordant with another study that examined T-helper 1 and T-helper 2 cytokines. The study reported a prevalent expression of IL-6 and IL-10 and lower expression of TNF-α, IL-2, IL-4 and IFN-γ [11]. More importantly, high levels of IL-17A expression and relatively lower IFN-γ gamma expression suggest skewed CD4+ T-lymphocytes polarisation towards a pro-inflammatory T-helper 17 subset in the high NLR group. It appears as if the T-helper 17 responses in the high NLR group results in inflammation and unabated IL-6 and IL-10 expression. Neutrophils and IL-6 are mediators of T-helper 17 polarisation of naïve CD4+ T-lymphocytes [32]. Since severe COVID-19 is hallmarked by neutrophilia, stratification using the NLR index provided a significant perspective on the immunopathology of the disease. It is possible that as the severity of the disease progresses, prolonged and excessive neutrophil recruitment, as well as IL-6 expression among other factors, may provide a milieu that promotes T-helper 17 and IL-17 production. Although, the neutrophilia stratification did not reveal significant statistical differences in the distribution of lymphocyte subsets, patients with neutrophilia had elevated CD4+ T-lymphocyte percentages and CD4+/CD8+ T-lymphocyte ratio. These results support earlier findings that directly illustrated how neutrophils mediate the T-helper 17 promotion in COVID-19 patients [33]. Therefore, inhibiting the pivotal events that promote T-helper 17 pro-inflammatory may provide a key therapeutic strategy for treating severe cases of COVID-19.The Spearman’s correlation matrix highlighted a strong and significant correlation between circulating lymphocytes and monocytes. This correlation was further investigated by analysing the correlation between lymphocyte subset counts and monocyte percentages. Correlation between lymphocyte subsets and monocytes aimed revealing the overall landscape of lymphocyte activation and expansion. CD4+ T-lymphocytes had the weakest correlation among the lymphocyte subsets, excluding natural killer (CD16+CD56+) lymphocytes. CD4+ T-lymphocytes rely largely on MHC II antigen presentation, and monocytes develop into antigen presenting cells that present antigens via the MHC II molecules [34]. Thus, indicating a dysfunctional antigen presentation.This study was limited by a number of challenges and facets. First, participating patients were recruited and their blood sampled within an identical time period, which was on admission. Since there was no subsequent sampling, only baseline observations were made. There is a possibility that some of the responses can only be observed within a time frame that was overlooked in this regard, thus making it difficult to draw a more comprehensive conclusion. While this study only took into consideration hospitalised patients, studying the entire disease spectrum including those that were not hospitalised may help derive some of the factors that confer protection. Another limitation is that the only peripheral blood was drawn from the patients. Peripheral blood mostly provides a reliable perspective on the key biomarkers and cellular populations [35]. Since COVID-19 is a respiratory disease that can manifest in different ways, it is possible that there are some key events that could be occurring at sites of infections. Sampling bronchoalveolar fluid, for example, and relating it to the NLR index may provide a holistic analysis of the mediators that contribute to the excessive neutrophil recruitment and potentially provide a rationale for drug design and therapeutic strategies.
Conclusion
Conclusively, COVID-19 patients exhibited white blood cell percentages that were skewed in favour of increased neutrophil and immature granulocyte percentages. When the patients were stratified by their NLR scores, patients categorised in the high NLR group were noted to have elevated levels of immature granulocytes and basophils, and lower monocyte counts compared to those in the low NLR group. The granulocytic and neutrophilic environment in severe COVID-19 patients was shown to promote a typical T-helper 17 response. The response was significantly marked by skewed CD4+ T-lymphocyte expansion demonstrated by relatively high IL-17A expression and low IFN-γ expression. Consequently, inflammation proceeded unabatedly pronounced by IL-10 and IL-6 expression for patients within the high NLR Group. Therefore, these results support that excessive neutrophil recruitment in COVID-19 patients may drive a T-helper 17 response as previously demonstrated by an earlier study [33]. Based on these findings, monoclonal antibodies targeting the IL-17 and IL-6 signalling pathways, targeting neutrophil activity, systemic corticosteroids and broad activity immunomodulatory drugs can help dampen the hyper-inflammatory events that are driven by neutrophils in COVID-19 patients.
Demographic information of COVID-19 patients.
(DOCX)Click here for additional data file.
Data of haematological parameters of COVID-19 patients.
(DOCX)Click here for additional data file.
White blood cell differential data of COVID-19 patients.
(DOCX)Click here for additional data file.
Data of lymphocyte subset populations of COVID-19 patients.
(DOCX)Click here for additional data file.
Data of CRP expression by COVID-19 patients.
(DOCX)Click here for additional data file.
Cytokine concentrations in COVID-19 patients’ serum.
(DOCX)Click here for additional data file.
Cytokine concentrations in control subject’s serum.
(DOCX)Click here for additional data file.
Demographic and haematological features of COVID-19 patients with respect to neutrophil lymphocyte ratio (NLR).
(DOCX)Click here for additional data file.
Column scatter plots of lymphocyte subsets of COVID-19 patients.
(PDF)Click here for additional data file.13 Apr 2022
PLOS ONE
Dear Dr. Choto,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the specific points raised by the external reviewer in a point-by-point reply.
Please submit your revised manuscript by May 28 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please include the following items when submitting your revised manuscript:
If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.
A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.We look forward to receiving your revised manuscript.Kind regards,Paul Proost, Ph.D.Academic EditorPLOS ONEJournal Requirements:When submitting your revision, we need you to address these additional requirements.1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found athttps://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf andhttps://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf2. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.4. Thank you for stating the following in your Competing Interests section:The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Please complete your Competing Interests on the online submission form to state any Competing Interests. If you have no competing interests, please state "The authors have declared that no competing interests exist.", as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-nowThis information should be included in your cover letter; we will change the online submission form on your behalf.5. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.6. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.7. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to Questions
Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes********** 3. Have the authors made all data underlying the findings in their manuscript fully available?The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes********** 4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes********** 5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: In this manuscript, the authors provide a descriptive data-set analyzing leukocytes and some cytokines in a small cohort of hospitalized COVID-19 patients. The study is confirming results from several other studies showing lymphopenia and elevated (immature) neutrophils (and so elevated neutrophil-to-lymphocyte ratios) in blood from COVID-19 patients and its association with COVID-19 severity. Moreover, the authors discovered elevation of some cytokines in serum of COVID-19 patients, confirming the cytokine storm occurring in these patients. As such, the study is not really novel, but a confirmation of the typical findings seen in severe COVID-19. Here are my remarks:- In the abstract, the authors mentioned that “the results show that a systemic neutrophilic environment may preferentially skew CD4+ lymphocytes towards T-helper 17 and IL-17A promotion”. Although this might be true, this is NOT directly shown by the results. The authors could only speculate this based on the elevated IL-17A levels. Other studies (e.g. Parackova et al., J Leukoc Biol, 2021) really show that COVID-19 neutrophils promote the induction of Th17 cells. The authors should include this in their discussion and rephrase their abstract and conclusion.- 43 hospitalized COVID-19 patients were included in the study. Can the authors provide additional information about these patients? Were these patients admitted to intensive care units (ICUs), did they get respiratory support? If and how were these patients treated for their COVID-19 disease? Moreover, in the different experiments, not always the same number of patients is included. The authors should provide the reason for excluding some patients in some experiments. Moreover, authors should include how many healthy controls were included in the study in the methods section.- Blood samples were collected during the first 8 days of hospitalization. Can the authors do a kinetics analysis with the data? As shown by Metzemaekers et al. (Clin Transl Immunology, 2021), neutrophil function and cytokine responses could be significantly different shortly after admission to ICU compared to one week stay in ICU.- In the data analysis, the authors chose for a NLR value of 7.5 to stratify patients in two groups. What is the rationale to choose 7.5 as a cut-off?- Discussion section:o Line 452-454: Elevated immature granulocytes are linked to prolonged innate immune responses and prolonged stimulation of the bone marrow. Emergency myelopoiesis should also be indicated.o Line 461-462: It is shown in the results that monocytes are lowered, but nowhere it is shown that they have a protective role.o Line 497-500 and line 529: Too much speculation which should be removed.o Line 525: downregulated IFN-y expression: downregulated compared to what?- Revision of the tables + figures + references should be made:o In some tables median (Q1-Q3) should be added.o The NLR value could be added to table 2.o Table 6 could be moved to the Supplementary material, if necessary, as no significant differences are shown. Moreover, if the differences are not significant, authors should only talk about a trend and should not stress differences too much when discussing results. Same for Figure 4.o Table 7 could be combined with Table 5 as Table 5 alone does not contain a lot of useful information. Table 7 shows the relation of CRP with severity, which is much more useful.o The order of the panels in Figure 3 should be changed so that they are in a logical order corresponding to the text. In panel 3B IL-4 instead of IL-2 should be stated.o In the figure legends, authors should remove asterisks for significance if it is not visible in the figures.o References should be mentioned in chronological order throughout the text.- English writing could be improved + authors should carefully check for typo’s e.g. In the text for Figure 1 there is no significant correlation between immature granulocyte count and lymphocyte count (p = 0.059) as it is now incorrectly mentioned in the results. Moreover, the rs-values and p values are not corresponding to the ones found in Table 4.********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Seppe Cambier[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.
23 Jun 2022Reviewer: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdfResponse: The manuscript and title authors’ affiliations have been revised to meet style requirements.Reviewer: Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.Response: This study was conducted after the protocol was reviewed and approved by the Medical Research Council of Zimbabwe approval MRCZ/A/2602. Permission to conduct the study was also obtained from the Joint Research Ethics Committee of Parirenyatwa Group of Hospitals and the University of Zimbabwe College of Health Sciences. After having thorough discussion on the procedures and purpose of the study and before commencement of data collection, written consent was obtained from the participants. Patients requiring ICU management were not included in the study. These could not give informed consent.An additional ethics statement section has been included in the methods section mentioning how the consent was obtained.Reviewer: We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.Response: This has been corrected online to match the information provided in the manuscript.Reviewer: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Please complete your Competing Interests on the online submission form to state any Competing Interests. If you have no competing interests, please state "The authors have declared that no competing interests exist.” as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now this information should be included in your cover letter; we will change the online submission form on your behalf.Response: The information has been provided on the online submission.Reviewer: We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.Response: All the data has been provided as Supplementary Information files.Reviewer: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.Response: Supporting Information file captions have been added within the manuscript file (at the end). The in-text citations have been updated accordingly.Reviewer: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction noticeResponse: The reference list has been reviewed. It is complete and correct. All papers that were cited are valid, and none of them have been retracted. Reference 33 has been added, to reference the findings supported by this manuscript.Reviewer: In the abstract, the authors mentioned that “the results show that a systemic neutrophilic environment may preferentially skew CD4+ lymphocytes towards T-helper 17 and IL-17A promotion”. Although this might be true, this is NOT directly shown by the results. The authors could only speculate this based on the elevated IL-17A levels. Other studies (e.g. Parackova et al., J Leukoc Biol, 2021) really show that COVID-19 neutrophils promote the induction of Th17 cells. The authors should include this in their discussion and rephrase their abstract and conclusion.Response: The sentence in the abstract has been changed to - “Results from this study suggest that a systemic neutrophilic environment may preferentially skew CD4+ lymphocytes towards T-helper 17 and IL-17A promotion, thus, aggravating inflammation.”Earlier finding by Parackova et al., 2021, has been cited in the discussion - “Additionally, these results support earlier findings that directly illustrated how neutrophils mediate the T-helper 17 promotion in COVID-19 patients [33].”The sentence in the conclusion has been revised to - “Therefore, these results suggest that excessive neutrophil recruitment in COVID-19 patients may drive a T-helper 17 response as previously demonstrated by an earlier study [33]”Reviewer: 43 hospitalized COVID-19 patients were included in the study. Can the authors provide additional information about these patients? Were these patients admitted to intensive care units (ICUs), did they get respiratory support? If and how were these patients treated for their COVID-19 disease? Moreover, in the different experiments, not always the same number of patients is included. The authors should provide the reason for excluding some patients in some experiments. Moreover, authors should include how many healthy controls were included in the study in the methods section.Response: The patients were admitted according to the hospital protocols. The hospital designates an isolation section for COVID-19 patients, where the patients were recruited for the study. Hence, ICU patients were not recruited into the study. Information regarding treatment and respiratory support was not provided.For some patients, the volume of blood that was drawn was not sufficient enough for all the possible tests. As a result, a few of them had missing results for tests like C-reactive protein test.The number of controls has been mentioned in the methods section as advised.Reviewer: Blood samples were collected during the first 8 days of hospitalization. Can the authors do a kinetics analysis with the data? As shown by Metzemaekers et al. (Clin Transl Immunology, 2021), neutrophil function and cytokine responses could be significantly different shortly after admission to ICU compared to one week stay in ICU.Response: The blood samples were collected only once, therefore no follow-up data is available to perform a kinetics analysis as suggested. However, this limitation was emphasised in the discussion - “First, participating patients were recruited and sampled within an identical time period, which was during the first 8 days of admission. Since there was no subsequent sampling which was carried out, only baseline observations were made. There is a possibility that some of the responses can only be observed within a time frame that was overlooked in this regard, thus making it difficult to draw a more comprehensive conclusion.” Please note that it is emphasized that no ICU patients were included in the study due to complication of their conditions and difficult to obtain consent.Reviewer: In the data analysis, the authors chose for a NLR value of 7.5 to stratify patients in two groups. What is the rationale to choose 7.5 as a cut-off?Response: The rationale behind 7.5 as the cut-off has been added to the methods section - “The rationale behind the 7.5 as the cut-off value was to target neutrophilia patients (neutrophil count > 7.5 cells x 109/L) and/or lymphopenia patients (lymphocyte count > 7.5 cells x 109/L) as severe COVID-19 patients. Therefore, a ratio of 7.5 and above was deemed as a suitable cut-off value.”Reviewer: Discussion Line 452-454: Elevated immature granulocytes are linked to prolonged innate immune responses and prolonged stimulation of the bone marrow. Emergency myelopoiesis should also be indicated.Response: Indicated as advised - “These results also indicate cases of emergency myelopoiesis for patients in the high NLR group.Reviewer: Discussion Line 461-462: It is shown in the results that monocytes are lowered, but nowhere is it shown that they have a protective role.Response: The claim is derived from speculation. Since a significant difference was observed in the distribution of monocytes, with patients in the high NLR group (patients with less severe COVID-19) having lower monocyte counts, they could be linked to a protective role that this study could reveal. Since this claim was made out of speculation, it has been revised to - “Monocytes have been shown to be lower for the high NLR group, which was not as expected. Thus, it may be crucial to determine the dominant immuno-phenotypes of circulating monocytes for both NLR groups.”Reviewer: Discussion Line 497-500 and line 529: Too much speculation which should be removed.Response: Removed as advised.Reviewer: Discussion Line 525: downregulated IFN-y expression: downregulated compared to what?Response: Downregulated has been omitted for lower – “The response was significantly marked by skewed CD4+ T-lymphocyte expansion demonstrated by relatively high IL-17A expression and low IFN-γ expression.”Reviewer: In some tables median (Q1-Q3) should be added.Response: Median (Q1, Q3) are now indicated in all tables.Reviewer: The NLR value could be added to table 2.Response: Neutrophil to lymphocyte ratio (NLR) summary has been added to Table 2.Reviewer: Table 6 could be moved to the Supplementary material, if necessary, as no significant differences are shown. Moreover, if the differences are not significant, authors should only talk about a trend and should not stress differences too much when discussing results. Same for Figure 4.Response: Table 6 and Figure 4 were moved to the supplementary.Reviewer: Table 7 could be combined with Table 5 as Table 5 alone does not contain a lot of useful information. Table 7 shows the relation of CRP with severity, which is much more useful.Response: Table 7 was combined with Table 5, to give a single set of C-reactive protein results showing an association between CRP and severity.Reviewer: The order of the panels in Figure 3 should be changed so that they are in a logical order corresponding to the text. In panel 3B IL-4 instead of IL-2 should be stated.Response: The order has been revised as advised, in a logical order of the cytokines (alphabetically).Reviewer: In the figure legends, authors should remove asterisks for significance if it is not visible in the figures.Response: The asterisks were removed in all the figures and their corresponding legends, since they were not visible.Reviewer: References should be mentioned in chronological order throughout the text.Response: References have been reviewed and the references are mentioned in a chronological order.Reviewer: English writing could be improved + authors should carefully check for typo’s e.g. in the text for Figure 1 there is no significant correlation between immature granulocyte count and lymphocyte count (p = 0.059) as it is now incorrectly mentioned in the results. Moreover, the rs-values and p values are not corresponding to the ones found in Table 4.Response: English and grammar have been reviewed as advised. The text for Figure 1 has been corrected as suggested.Reviewer: Please upload a copy of Figure 5 which you refer to in your text on page 17. Or if the figure is no longer to be included as part of the submission please remove all reference to it within the text.Response: The figure referencing error has been corrected. Figure 4 is now referred to in the text as it is given.Reviewer: Please ensure that you refer to Figure 4 in your text as, if accepted, production will need this reference to link the reader to the figure.Response: The figure referencing error has been corrected. Figure 4 is now referred to in the text as it is given.Submitted filename: Response to Reviewers.docxClick here for additional data file.4 Aug 2022Excessive Neutrophil Recruitment Promotes Typical T-helper 17 Responses in Coronavirus Disease 2019 PatientsPONE-D-22-00109R1Dear Dr. Choto,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.Kind regards,George VousdenStaff EditorPLOS ONEAdditional Editor Comments (optional):Reviewers' comments:Reviewer's Responses to Questions
Comments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed********** 2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: (No Response)********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: (No Response)********** 4. Have the authors made all data underlying the findings in their manuscript fully available?The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: (No Response)********** 5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: (No Response)********** 6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: (No Response)********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No**********9 Aug 2022PONE-D-22-00109R1Excessive Neutrophil Recruitment Promotes Typical T-helper 17 Responses in Coronavirus Disease 2019 PatientsDear Dr. Choto:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.If we can help with anything else, please email us at plosone@plos.org.Thank you for submitting your work to PLOS ONE and supporting open access.Kind regards,PLOS ONE Editorial Office Staffon behalf ofDr. George VousdenStaff EditorPLOS ONE
Authors: Gerwyn Morris; Chiara C Bortolasci; Basant K Puri; Lisa Olive; Wolfgang Marx; Adrienne O'Neil; Eugene Athan; Andre F Carvalho; Michael Maes; Ken Walder; Michael Berk Journal: Life Sci Date: 2020-07-31 Impact factor: 5.037
Fig 1
Fig 2
Fig 3
Fig 4