Intermediate monocytes expansion and homing markers expression in COVID-19 patients associate with kidney dysfunction

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We performed an immunophenotypic analysis on circulating monocytes in 19 COVID-19 patients in comparison with 11 naïve HIV-1 patients and 10 healthy subjects. Reduced frequency of classical monocytes and increased frequency of intermediate monocytes characterized COVID-19 patients with respect to both HIV naïve patients and healthy subjects. Intensity of C-C motif chemokine receptor 2 (CCR2) monocyte expression highly correlated with parameters of kidney dysfunction. Our data indicate that highly activated monocytes of COVID-19 patients may be pathogenically associated to the development of renal disease. Covid-19 SARS-CoV-2 Monocytes innate immunity C-C motif chemokine receptor 2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to different clinical pictures (from asymptomatic/pauci-symptomatic infection to moderate/severe forms of disease), suggesting that the clinical manifestations might strictly depend on the outcome of the SARS-CoV-2 immune system interaction in the patient. The factors that trigger severe illness in SARS-CoV-2 infected individuals are not completely understood. Immune system dysregulation, leading to an excessive inflammatory response to SARS- CoV-2, is thought to be a major cause of disease severity and death in patients with Coronavirus Disease (COVID-19) ( 1 ). This condition is associated with high levels of circulating cytokines as well as by substantial mononuclear cell infiltration in the lungs, heart ( 2 ), spleen, lymph nodes and kidney ( 3 , 4 ). Among mononuclear cells, a key pathogenic role for COVID-19 inflammation has been attributed to monocytes ( 5 , 6 ). In particular, expansion of CD14 + CD16 + + intermediate monocytes ( 7 ) has been observed in SARS-CoV-2 infected patients ( 8 ). This particular subset of circulating monocytes has been previously found associated with inflammation and viral diseases as HIV infection ( 7 , 9 ). In order to better characterize the phenotype of monocytes in patients with SARS-CoV-2 infection, we analyzed circulating monocytes of COVID-19 patients in comparison with corresponding cells from healthy subjects and HIV-1 naïve patients. Moreover, we searched for associations between phenotypic monocyte abnormalities in COVID-19 patients and serum clinical markers of disease, finding a strict correlation between C-C motif chemokine receptor 2 (CCR2) expression and parameters of kidney functionality. Materials And Methods Patients and healthy donors This was a descriptive observational cross-sectional clinical study. Peripheral blood was collected from 19 consecutives patients from March 2020 affected by moderate/severe COVID-19 who were enrolled at the Division of Infectious Diseases and the Internal Medicine and Clinical Immunology Unit of the Policlinic San Martino University Hospital in Genoa (Supplementary Table 1). Diagnosis of COVID-19 was confirmed in all patients by real-time reverse-transcriptase polymerase chain reaction (RT-PCR) positive from a nasal and/or throat swab. We collected also peripheral blood from 11 HIV-1 naïve patients, SARS-CoV-2 negative (Supplementary Table 2), as well as from 10 healthy donors (HD). The study was carried out in compliance with the Helsinki Declaration and approved by the Ethical Committee of the San Martino Hospital in Genoa (N. CER Liguria 114/2020 - ID 10420 and P.R.251REG2014). Monoclonal antibodies and immunofluorescence analyses Cell expression of membrane antigens was tested by immunofluorescence analysis performed with 100 µl of peripheral blood. Samples were incubated with specific fluorochrome-conjugated monoclonal antibodies (mAbs) at room temperature for 20 minutes in the dark. The following panel was used: phycoerythrin (PE) conjugated anti-CD38, Peridinin Chlorophyll Protein Complex-Cyanin 5.5 PerCP-Cy5.5conjugated anti-HLA-DR, allophycocyanin (APC) conjugated anti-CD11b, brilliant violet (BV) 421 conjugated anti-CCR2, BV605 conjugated anti-CD16, BV711 conjugated anti-CD14, BV785 conjugated anti-CD3 (Becton Dickinson, (BD) Biosciences, San Josè CA). For lysing red blood cells and fixing leukocytes following direct immunofluorescence staining of human peripheral blood, samples were resuspended in 4 ml of FacsLysing buffer (containing formaldehyde, BD) and then centrifuged and resuspended in 300 µl of FacsLysing. Following the staining and lysing procedures the cells were analyzed by a BD LRSFortessa X-20 flow cytometer (BD Biosciences) using FACS DIVA software 8.0 (BD Biosciences). Levels of expression of HLA-DR, CD38, CCR2 markers were shown as mean fluorescence intensity (MFI) on the monocyte subsets. Since HLA-DR, CD38 and CCR2 molecules resulted absent on neutrophils, we used the MFI of HLA-DR, CD38 and CCR2 molecules on this population as an internal negative control. Cytometer performances were checked weekly with CS&T beads (BD Biosciences) to determine cytometer settings and performance measurements for reproducible application. Gating strategy for monocyte identification The gating strategy to identify monocytes, as described in Supplementary Fig. 1, was the following: a) debris and dead cell exclusion in forward-scatter (FSC-Height) vs side-scatter channel (SSC-Height) plot (Panel A); b) doublet exclusion in FSC-Area vs FSC-Height plot (Panel B); c) gating for monocytes in HLA-DR vs SSC plot to select them as HLA-DR + cells with higher SSC than HLA-DR + and HLA-DR- lymphocytes; in this plot monocytes were distinguished from neutrophils based on the higher SSC and HLA-DR negativity of these latter cells (Panel C); d) confirmation of monocyte population as CD3-CD11b + in CD3 vs CD11b plot (Panel D). The differentiation between monocytes and neutrophils was corroborated by the analyses of HLA-DR (that are molecules not present on the surface of neutrophils), CD16 (brighter expression on neutrophils) and CD11b (brighter expression on monocytes). We used HLA-DR instead of CD14 as identifier marker for monocytes in order to not underestimate the subpopulation of non-classical monocytes (that exhibit CD14 low/neg expression). Then the HLA-DR + CD3-CD11b + monocyte population was analyzed by CD14 and CD16 markers to identify the three subpopulations of monocytes, as follows: classical (CD14 + + CD16-), intermediate (CD14 + + CD16+) and non-classical (CD14+/-CD16++) (Supplementary Fig. 1, Panel E). The levels of CD16 positivity within the monocyte population were discriminated through comparison with those of CD3 + T and putative B lymphocytes, NK CD16 + cells, and neutrophils, each of them references for negative, intermediate and bright expressions, respectively (Supplementary Fig. 2). The percentage of monocytes, evaluated as HLA-DR + CD11b + CD3- (Supplementary Fig. 1, Panel D), was referred to the total leukocytes (lymphocytes-monocytes-neutrophils) identified based on their FSC-H and SSC-H features in Panel A. The frequencies of different monocyte subsets (Supplementary Fig. 1, Panel E) were determined as percentages of the total monocyte population defined in Supplementary Fig. 1, Panel D. Multidimensional data reduction analysis To visualize the different clustering of monocyte subpopulations in the three groups of subjects (healthy donors, Covid-19 and HIV + naïve patients), flow cytometric data derived from a representative subject for each group were exported with compensated parameters to FCS express software v6.03.0011 (DeNovo Software) in order to perform a multidimensional data reduction analysis. Monocytes were defined based on their FSC vs SSC physical parameters and HLA-DR + CD11b + expression (as shown in Supplementary Fig. 1, Panel D): 144000 monocytes per subject were merged into a new FCS file. A t-dependent Stochastic Neighbor Embedding (t-SNE) map was generated, using FCS express software 119 v6.03.0011 (DeNovo Software), in the merged file among 1000 iterations with Barnes-Hut 287 approximation and 40 perplexity value for following markers: FSC-A, SSC-A, CCR2, HLA- DR, CD38. This generated 2-D plots that clustered the cells on the basis of marker expression profiles. Statistical analyses The existence of statistically significant differences between means of data was analyzed by Mann-Whitney t test for non-parametric values. The existence of statistically significant correlations between variable parameters was analyzed by Spearman test for non-parametric values. Calculation were performed by GraphPad Prism v.5 software (GraphPad Software, San Diego – California, USA). Results Monocyte phenotypic characterization COVID-19 patients showed monocyte circulating frequencies comparable to those of healthy subjects and HIV + naive patients (Fig. 1 A). However, the relative distributions of the three different monocyte subsets, namely classical (CD14 + + CD16-), intermediate (CD14 + + CD16+) and non-classical (CD14+/-CD16++) types ( 10 ), was peculiar of COVID-19 patients. In fact, the frequency of classical monocytes was decreased and that of intermediate monocytes was increased in COVID-19 patients with respect to both healthy donors and HIV + naïve patients (Figs. 1 B and 1 C). It should be noted that the frequency of intermediate monocytes was higher in COVID-19 patients than in HIV + naive patients (Fig. 1 C). No differences were observed concerning non-classical monocytes (Fig. 1 D). Interestingly, when monocyte morpho-cytometric features were comparatively analyzed in the three subgroups of subjects, again a peculiarly altered morphology, characterized by an increase in side scatter (SSC) dimension (index of cytoplasmic complexity), hallmarked the monocyte population of COVID-19 patients compared to HD and HIV patients (Fig. 2 ). The differences among the three subgroups further emerged when data, relative to monocyte expression of CD38 and HLA-DR activation markers and CCR2 homing receptor of a representative subject for each group, were merged and evaluated applying t-dependent Stochastic Neighbor Embedding (t-SNE) analysis. The t-SNE maps showed that monocytes derived from COVID-19 patient #8, HIV + naïve patient #1 and healthy donor #2 clusterized differently, accordingly with their morphologic features and expression profiles of CD38, HLA-DR and CCR2 molecules (Supplementary Fig. 3, Panels A-C). This analysis showed a different clustering of monocyte subsets among the different subjects, confirming the enrichment of intermediate monocytes in the sample derived from the COVID-19 patient with respect to the other groups (Supplementary Fig. 3, Panel B). Moreover, the t-SNE maps of mean fluorescence intensity of CD38 and HLA-DR activation markers showed their increased expression on monocytes subsets derived from both patients with respect to the healthy donor. Higher CCR2 monocyte expression was only observed in the clusters of the COVID-19 patient with respect to those of the other groups (Supplementary Fig. 3, Panel C). These differences in CD38, HLA-DR and CCR2 MFI between COVID-19 patients and both HIV + naïve patients and HD, suggested by the multidimensional analyses performed on cells from representative subjects of each group, were confirmed by conventional cytometry analyses (Supplementary Fig. 4 and Fig. 3 ). Correlations among monocyte and clinical parameters In order to verify whether monocyte expression profiles of CD38, HLA-DR and CCR2 on different monocyte subsets may have a clinical impact in COVID-19 patients, we correlated MFI of CD38, HLA-DR and CCR2 molecules with the serum levels of the following clinical indexes: creatinine, glomerular filtration rate (GFR), azotemia, troponin I, D-dimer, ferritin, fibrinogen, pro-calcitonin, C-reactive protein, lactate dehydrogenase, creatinine phosphokinase (Supplementary Table 3). We found that CCR2 expression on classic, intermediate and non classic monocyte subsets highly correlated with kidney function parameters (Fig. 4 , Box A, B and C, respectively). In fact, we observed a direct correlation between CCR2 MFI on classical, intermediate and non-classic monocytes with azotemia (Fig. 4 upper panels) and creatinemia (Fig. 4 , middle panels), and an inverse correlation with GFR (Fig. 4 , lower panels). Moreover, CD38 expression on intermediate monocyte subset showed a direct correlation with ferritin and fibrinogen serum concentrations (Supplementary Fig. 5). Discussion Collectively, the results of our study show that monocytes of COVID-19 patients are highly activated and that their distribution among the three circulating subsets of monocytes is quite peculiar, since it is different from that of both healthy donors and HIV + naive patients. This finding, together with that related to the peculiar morpho-cytometric parameters of intermediate monocytes in COVID-19 patients, suggests that SARS-CoV-2 infection induces a robust stimulation of these cells. Such stimulation selectively expands the intermediate monocytes, that constitute a cell subset provided with pro-inflammatory features and that has been associated with infective and inflammatory diseases ( 7 ). Interestingly, these cells in COVID-19 patients highly expressed CCR2, a chemokine receptor that likely drives them toward the tissue site of inflammation ( 11 , 12 ). Accordingly, elevated levels of CCL2, the chemokine specific for the CCR2, have been observed in the bronco-alveolar fluid of COVID-19 patients with pneumonia ( 13 ), thus likely explaining its rich monocyte content ( 14 ). Hence, we searched for an association between CCR2 MFI and the serum levels of several clinical indexes in our series of COVID-19 patients. We found that CCR2 MFI on intermediate monocytes correlated with all the clinical parameters of renal function (creatinine, GFR, azotemia). This unprecedented finding suggests that intermediate monocytes may be also pathogenically related to renal alterations and acute kidney insufficiency, clinical manifestations of SARS-CoV-2 infection observed in about 14% and 5% of patients ( 15 ), respectively. This is not surprising since SARS-CoV-2 may target renal tissues due to the presence of the ACE2 receptor on the epithelial cells of the proximal tubules ( 16 ). Moreover our data show that the intensity of CD38 expression by intermediate monocytes of COVID-19 patients correlated with biomarkers of inflammation, as ferritin and fibrinogen, whose production is dependent by IL6 ( 17 , 18 ), a cytokine highly released by monocytes ( 19 ) and found at high concentration in COVID-19 patient serum ( 20 ). Collectively, our data show that SARS-CoV-2 infection determines peculiar alterations of monocytes targeting morpho-phenotypic and maturation features, and that elevated CCR2 MFI mainly on intermediate monocytes associates with parameters of renal function. Declarations FUNDING This work was financially supported by a generous donation from Mediolanum Farmaceutici spa. CONFLICT OF INTEREST/COMPETING INTERESTS 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. Outside the submitted work: C.D. has received speaker honoraria from Angelini, Novartis, Gilead, ViiV, Shinogi, D. R. G. reports personal consultation fees from Stepstone Pharma GmbH and grants from MSD Italia and Correvio Italia. A.D.B. reports hospital grant from Gilead and consultancy for ViiV, Janssen, MSD, Gilead, Abbvie. M. B. serves on scientific advisory boards for Angelini, AstraZeneca, Bayer, Cubist, Pfizer, Menarini, MSD, Nabriva, Paratek, Roche, Shionogi, Tetraphase, The Medicine Company, and Astellas Pharma Inc, and has received funding for travel or speaker honoraria from Algorithm, Angelini, Astellas Pharma Inc, AstraZeneca, Cubist, Pfizer, MSD, Gilead Sciences, Menarini, Novartis, Ranbaxy, and Teva. AVAILABILITY OF DATA MATERIAL The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. AUTHORS’ CONTRIBUTIONS AP, DF, GF, MF, TA, were in charge of the study design. AP, DF, TA, FB carried out the phenotyping experiments and analyzed the data. AP, DF, FF, TA, FB performed laboratory work and experiments. CD, ADM, MB, ADB, AV, PP, RDP, DRG they managed the patients, their withdrawals and the clinical aspect, MG managed the data collection site. All authors contributed to the article and approved the submitted version. ETHICS APPROVAL The study was carried out in compliance with the Helsinki Declaration and approved by the Ethical Committee of the San Martino Hospital in Genoa (N. CER Liguria 114/2020 - ID 10420 and P.R.251REG2014). CONSENT TO PARTECIPATE All participants provided written informed consent to participate CONSENT FOR PUBLICATION All participants have consented to publication of their data ACKNOWLEDGMENTS We thank the GECOVID-19 Study group. References Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ; HLH Across Speciality Collaboration, UK. COVID-19: consider cytokine storm syndromes and immunosuppression. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet (2020) 395:1033–34. Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, Liu S, Zhao P, Liu H, Zhu L, Tai Y, Bai C, Gao T, Song J, Xia P, Dong J, Zhao J, Wang FS. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med (2020) 8:420–22. Feng Z, Diao B, Wang R, Wang G, Wang C, Tan Y, Liu L, Wang C, Liu Y, Liu Y, Yuan Z, Ren L, Wu Y. 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Metabolic syndrome is an inflammatory disorder: A conspiracy between adipose tissue and phagocytes. Clin Chim Acta (2019) 496:35–44. Moreira AC, Mesquita G, Gomes MS. Ferritin: An Inflammatory Player Keeping Iron at the Core of Pathogen-Host Interactions. Microorganisms (2020) 8:589. Kany S, Vollrath JT, Relja B. Cytokines in Inflammatory Disease. Int J Mol Sci (2019) 20:6008. Smetana K Jr, Brábek J. Role of Interleukin-6 in Lung Complications in Patients With COVID-19: Therapeutic Implications. In Vivo (2020) 34(3 Suppl):1589-92. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tif Supplementary Figure 1. Gating strategy for identification of monocyte subsets in peripheral blood of a healthy donor (HD #2). SupplementaryFigure2.tif Supplementary Figure 2. The figure describes the strategy to define the boundary between positive and negative cells in the analysis of the relative distribution of classical (CD16-), intermediate (CD16 dim) and non-classical (CD16 bright) monocytes of representative HD #2. Three levels of CD16 expression are shown by the cell populations of neutrophils and lymphocytes: CD3+ T lymphocytes and putative B cells are CD16- and they represent an internal negative control; putative NK cells (CD3-CD16+) and neutrophils (CD3-CD16++) represent the intermediate and high level of CD16 expression. SupplementaryFigure3.jpg Supplementary Figure 3. t-dependent Stochastic Neighbor Embedding (t-SNE) maps on circulating monocytes. t-SNE algorithm was applied among 1000 iterations with Barnes-Hut approximation and 40 perplexity values for the following parameters: FSC-A SSC-A, CD16, CD14, HLA-DR, CD38, CCR2. (A) t-SNE color plot of concatenated monocytes derived from representative HD #2 (orange), COVID-19 patient #8 (pink) and HIV+ naïve patient #1 (violet) (144000 events for sample) shows the spatial distribution of each t-SNE generated island. (B) t-SNE color plots exhibit the cluster distribution of the three different monocyte subsets identified by different colors. The left plot shows all concatenated samples, while the following ones show separately the monocyte cell clusters relative to the COVID-19 patient, the HIV+ naïve patient and the HD, respectively. (C) t-SNE maps of concatenated monocytes based on expression levels of CD38, HLA-DR and CCR2 molecules. The comparison of these plots with those of Panels A and B allows to associate each cell clusters to a subject and to a monocyte subset. The colorimetric scale of marker expression is shown (blue, yellow and red colors indicate absent, intermediate and high expression, respectively). SupFigure4.tif Supplementary Figure 4. The figure describes the strategy to define positive and negative cells in the analysis of expression profile of CCR2 chemokine receptor on classical, intermediate and non-classical monocytes in representative HD #2 and COVID-19 patient #9. SupFigure5.doc Supplementary Figure 5. Correlation between CD38 MFI on monocyte subsets and serum inflammatory markers. Box A, Box B and Box C show the correlations of serum fibrinogen (upper row) and ferritin (lower row) with CD38 MFI on classical (Box A), intermediate (Box B) and non-classical (Box C) monocyte subsets, respectively, in COVID-19 patients. SupplementaryTable1.doc Supplementary Table 1. Supplementary Table 1 shows the demographic and clinical features of COVID-19 patients. SupplementaryTable2.doc Supplementary Table 2. Clinical features of naïve HIV+ patients SupplementaryTable3.doc Supplementary Table 3. Laboratory clinical parameters of COVID-19 patients. SupplementaryTable4.doc Supplementary Table 4. Blood count data of COVID-19 and HIV+ naïve patients Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 16 Sep, 2022 Reviews received at journal 16 Sep, 2022 Reviewers agreed at journal 12 Sep, 2022 Reviewers invited by journal 30 May, 2022 Submission checks completed at journal 27 May, 2022 Editor assigned by journal 27 May, 2022 First submitted to journal 25 May, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1693973","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":109253659,"identity":"cd859fcf-1fd3-4d78-b180-e614758a1d51","order_by":0,"name":"Chiara 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Martino","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bianca","middleName":"","lastName":"Bruzzone","suffix":""},{"id":109253670,"identity":"efcb9e36-7615-459c-a537-aa5053fe2566","order_by":11,"name":"Mauro Giacomini","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mauro","middleName":"","lastName":"Giacomini","suffix":""},{"id":109253671,"identity":"224e1043-c1df-4ee9-ac15-7ab62c769d97","order_by":12,"name":"Daniele Roberto Giacobbe","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"Roberto","lastName":"Giacobbe","suffix":""},{"id":109253672,"identity":"85bc4f4e-05fc-4478-89e1-c3248730b336","order_by":13,"name":"Paolo Pelosi","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Pelosi","suffix":""},{"id":109253673,"identity":"883045b8-4edb-49b4-a67e-c5f5686ecca3","order_by":14,"name":"Andrea De Maria","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"De Maria","suffix":""},{"id":109253674,"identity":"4690be65-76a7-4688-8e20-51dc2f8c02b4","order_by":15,"name":"Matteo Bassetti","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Bassetti","suffix":""},{"id":109253675,"identity":"92dbf635-babd-4a0b-9eaf-aa775459759e","order_by":16,"name":"Raffaele De Palma","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Raffaele","middleName":"","lastName":"De Palma","suffix":""},{"id":109253676,"identity":"66f51308-a34a-4149-aaa0-f2b9cf1cf07f","order_by":17,"name":"Gilberto Filaci","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gilberto","middleName":"","lastName":"Filaci","suffix":""}],"badges":[],"createdAt":"2022-05-25 19:59:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1693973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1693973/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22086401,"identity":"0647555c-69d1-4d8a-b35a-e1f181088b46","added_by":"auto","created_at":"2022-05-31 16:42:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of circulating monocytes\u003c/strong\u003e. The frequency of total monocytes out of circulating leucocytes (A), and those of classical (B), intermediate (C) and non-classical (D) monocyte subsets were comparatively analyzed in COVID-19 patients, in HIV+\u0026nbsp;naïve patients and in healthy donors.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/7120868ea9550c4c40be081e.png"},{"id":22086403,"identity":"37789e8c-da16-4fab-90e6-b805fc097880","added_by":"auto","created_at":"2022-05-31 16:42:31","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1054588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytometric features of circulating monocytes.\u003c/strong\u003e The figure shows the morpho-cytometric characteristics (upper and middle rows) and the relative distribution among the three monocytes subsets (classical, intermediate and non-classical monocytes) (lower panels) of circulating monocytes on representative samples derived from COVID-19 patient # 8 (middle column), healthy donor #2 (HD, left column) and HIV+ naïve patient #1 (right column).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/4d193844bc850449dff3fba8.jpeg"},{"id":22086963,"identity":"e0a45ecb-68de-4c25-82bc-af8702a783ed","added_by":"auto","created_at":"2022-05-31 16:47:31","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":587168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of HLA-DR, CD38 and CCR2 MFI on monocyte subsets derived\u0026nbsp;from healthy donors (HD), COVID-19 and HIV+ naïve patients\u003c/strong\u003e. Box A, Box B and Box C show the comparison of HLA-DR, CD38 and CCR2 MFI (upper row, middle row, lower row, respectively) on classical monocytes (Box A), intermediate monocytes (Box B) and non-classical monocyte (Box C) derived from healthy donors (HD), COVID-19 patients and HIV+ naïve patients, respectively.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/18d09bc55a43316366ea1f6c.jpeg"},{"id":22086961,"identity":"df299e41-5392-498e-b01f-7e43dafae985","added_by":"auto","created_at":"2022-05-31 16:47:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42499,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between CCR2 MFI on circulating monocyte subsets and parameters of renal function in COVID-19 patients.\u003c/strong\u003e Box A, Box B and Box C show the correlations of serum azotemia (upper row), creatinine (middle row) and GFR (lower row) with CCR2 MFI on classical (Box A), intermediate (Box B) and non-classical monocytes (Box C), respectively, in COVID-19 patients.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/41d956eb5de771ff271877ce.png"},{"id":22087917,"identity":"5898ae21-ea0c-4ef7-9aff-0dfe995ff5af","added_by":"auto","created_at":"2022-05-31 16:57:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":712995,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/9f947a64-8112-4811-8e69-cfc146e9f9cf.pdf"},{"id":22086402,"identity":"9033aa90-1566-43e4-85e5-a7a52ba77609","added_by":"auto","created_at":"2022-05-31 16:42:31","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":248696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e Gating strategy for identification of monocyte subsets in peripheral blood of a healthy donor (HD #2).\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/82f396786b0426dab2abe19f.tif"},{"id":22086399,"identity":"9d61194c-45c2-4925-bb41-ae90675863d8","added_by":"auto","created_at":"2022-05-31 16:42:31","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":133232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2. \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003efigure describes the strategy to define the boundary between positive and negative cells in the analysis of the relative distribution of classical (CD16-), intermediate (CD16 dim) and non-classical (CD16 bright) monocytes of representative HD #2. Three levels of CD16 expression are shown by the cell populations of neutrophils and lymphocytes: CD3+ T lymphocytes and putative B cells are CD16- and they represent an internal negative control; \u0026nbsp;putative NK cells (CD3-CD16+) and neutrophils (CD3-CD16++) represent the intermediate and high level of CD16 expression.\u0026nbsp;\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/6779c4a539c296acf241dcf9.tif"},{"id":22087173,"identity":"6449a1d5-9f13-4b0b-bbcf-3a7b1460e626","added_by":"auto","created_at":"2022-05-31 16:52:31","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":942617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3. t-dependent Stochastic Neighbor Embedding (t-SNE) maps on circulating monocytes. \u003c/strong\u003e\u0026nbsp;t-SNE algorithm was applied among 1000 iterations with Barnes-Hut approximation and 40 perplexity values for the following parameters: FSC-A SSC-A, CD16, CD14, HLA-DR, CD38,\u0026nbsp;CCR2. (A) t-SNE color plot of concatenated monocytes derived from representative HD #2 (orange), COVID-19 patient #8 (pink) and HIV+ naïve patient #1 (violet) (144000 events for sample) shows the\u0026nbsp;spatial distribution of each t-SNE generated island. \u0026nbsp;(B) t-SNE color plots exhibit \u0026nbsp;the cluster distribution of the three different monocyte subsets identified by different colors. The left plot shows all concatenated samples, while the following ones show separately the monocyte cell clusters relative to the COVID-19 patient, the HIV+ naïve patient and the HD, respectively. (C) t-SNE maps of concatenated monocytes based on expression levels of CD38, HLA-DR and CCR2 molecules. The comparison of these plots with those of Panels A and B allows to associate each cell clusters to a subject and to a monocyte subset. The colorimetric scale of marker expression is shown (blue, yellow and red colors indicate absent, intermediate and high expression, respectively).\u0026nbsp;\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/62230a1b1006a2d8d10dfbc2.jpg"},{"id":22086409,"identity":"bc6ec92e-5ed7-40ba-a2a2-b33fc378f089","added_by":"auto","created_at":"2022-05-31 16:42:31","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":265312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4. \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003efigure describes the strategy to define positive and negative cells in the analysis of expression profile of CCR2 chemokine receptor on classical, intermediate and non-classical monocytes in representative\u0026nbsp;HD #2 and COVID-19 patient #9.\u003c/p\u003e","description":"","filename":"SupFigure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/a52bcbfde56471abb6c1ff77.tif"},{"id":22086962,"identity":"17cb11af-77e2-4573-8e4c-5e7e3cd4d028","added_by":"auto","created_at":"2022-05-31 16:47:31","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2083328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5.\u003c/strong\u003e \u003cstrong\u003eCorrelation between CD38 MFI on monocyte subsets and serum inflammatory markers.\u003c/strong\u003e Box A, Box B and Box C show the correlations of serum fibrinogen (upper row) and ferritin (lower row) with CD38 MFI on classical (Box A), intermediate (Box B) and non-classical (Box C) monocyte subsets, respectively, in COVID-19 patients.\u003c/p\u003e","description":"","filename":"SupFigure5.doc","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/5cdebb7f8fc2f34c035a2927.doc"},{"id":22087916,"identity":"23701aec-c47c-4c05-86c6-3cfad1d30ca1","added_by":"auto","created_at":"2022-05-31 16:57:31","extension":"doc","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":41472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. \u003c/strong\u003eSupplementary Table 1 shows the demographic and clinical features of COVID-19 patients.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.doc","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/73dc359e87838c10272c2890.doc"},{"id":22086405,"identity":"b6e3fc77-a5a2-4cbb-be81-a6591e87567a","added_by":"auto","created_at":"2022-05-31 16:42:31","extension":"doc","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":38912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2. \u003c/strong\u003eClinical features of naïve HIV+ patients\u003c/p\u003e","description":"","filename":"SupplementaryTable2.doc","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/58f01d9b8b1d4d0cecba11de.doc"},{"id":22086966,"identity":"eea9ff5b-0964-45df-b7e2-1de8ec69bbcf","added_by":"auto","created_at":"2022-05-31 16:47:31","extension":"doc","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":33792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3. \u003c/strong\u003eLaboratory clinical parameters of COVID-19 patients.\u003c/p\u003e","description":"","filename":"SupplementaryTable3.doc","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/120617ad4974b58f09464afe.doc"},{"id":22086967,"identity":"5664587e-244f-4f2a-b320-d9049598a6eb","added_by":"auto","created_at":"2022-05-31 16:47:31","extension":"doc","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":62976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 4.\u003c/strong\u003e Blood count data of COVID-19 and HIV+ naïve patients\u003c/p\u003e","description":"","filename":"SupplementaryTable4.doc","url":"https://assets-eu.researchsquare.com/files/rs-1693973/v1/d99db96c30dc04c15003aa96.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intermediate monocytes expansion and homing markers expression in COVID-19 patients associate with kidney dysfunction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to different clinical pictures (from asymptomatic/pauci-symptomatic infection to moderate/severe forms of disease), suggesting that the clinical manifestations might strictly depend on the outcome of the SARS-CoV-2 immune system interaction in the patient. The factors that trigger severe illness in SARS-CoV-2 infected individuals are not completely understood. Immune system dysregulation, leading to an excessive inflammatory response to SARS- CoV-2, is thought to be a major cause of disease severity and death in patients with Coronavirus Disease (COVID-19) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This condition is associated with high levels of circulating cytokines as well as by substantial mononuclear cell infiltration in the lungs, heart (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), spleen, lymph nodes and kidney (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Among mononuclear cells, a key pathogenic role for COVID-19 inflammation has been attributed to monocytes (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In particular, expansion of CD14\u0026thinsp;+\u0026thinsp;CD16\u0026thinsp;+\u0026thinsp;+\u0026thinsp;intermediate monocytes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) has been observed in SARS-CoV-2 infected patients (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This particular subset of circulating monocytes has been previously found associated with inflammation and viral diseases as HIV infection (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In order to better characterize the phenotype of monocytes in patients with SARS-CoV-2 infection, we analyzed circulating monocytes of COVID-19 patients in comparison with corresponding cells from healthy subjects and HIV-1 na\u0026iuml;ve patients. Moreover, we searched for associations between phenotypic monocyte abnormalities in COVID-19 patients and serum clinical markers of disease, finding a strict correlation between C-C motif chemokine receptor 2 (CCR2) expression and parameters of kidney functionality.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and healthy donors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a descriptive observational cross-sectional clinical study. Peripheral blood was collected from 19 consecutives patients from March 2020 affected by moderate/severe COVID-19 who were enrolled at the Division of Infectious Diseases and the Internal Medicine and Clinical Immunology Unit of the Policlinic San Martino University Hospital in Genoa (Supplementary Table 1). Diagnosis of COVID-19 was confirmed in all patients by real-time reverse-transcriptase polymerase chain reaction (RT-PCR) positive from a nasal and/or throat swab. We collected also peripheral blood from 11 HIV-1 na\u0026iuml;ve patients, SARS-CoV-2 negative (Supplementary Table 2), as well as from 10 healthy donors (HD). The study was carried out in compliance with the Helsinki Declaration and approved by the Ethical Committee of the San Martino Hospital in Genoa (N. CER Liguria 114/2020 - ID 10420 and P.R.251REG2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMonoclonal antibodies and immunofluorescence analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell expression of membrane antigens was tested by immunofluorescence analysis performed with 100 \u0026micro;l of peripheral blood. Samples were incubated with specific fluorochrome-conjugated monoclonal antibodies (mAbs) at room temperature for 20 minutes in the dark. The following panel was used: phycoerythrin (PE) conjugated anti-CD38, Peridinin Chlorophyll Protein Complex-Cyanin 5.5 PerCP-Cy5.5conjugated anti-HLA-DR, allophycocyanin (APC) conjugated anti-CD11b, brilliant violet (BV) 421 conjugated anti-CCR2, BV605 conjugated anti-CD16, BV711 conjugated anti-CD14, BV785 conjugated anti-CD3 (Becton Dickinson, (BD) Biosciences, San Jos\u0026egrave; CA). For lysing red blood cells and fixing leukocytes following direct immunofluorescence staining of human peripheral blood, samples were resuspended in 4 ml of FacsLysing buffer (containing formaldehyde, BD) and then centrifuged and resuspended in 300 \u0026micro;l of FacsLysing. Following the staining and lysing procedures the cells were analyzed by a BD LRSFortessa X-20 flow cytometer (BD Biosciences) using FACS DIVA software 8.0 (BD Biosciences). Levels of expression of HLA-DR, CD38, CCR2 markers were shown as mean fluorescence intensity (MFI) on the monocyte subsets. Since HLA-DR, CD38 and CCR2 molecules resulted absent on neutrophils, we used the MFI of HLA-DR, CD38 and CCR2 molecules on this population as an internal negative control. Cytometer performances were checked weekly with CS\u0026amp;T beads (BD Biosciences) to determine cytometer settings and performance measurements for reproducible application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGating strategy for monocyte identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gating strategy to identify monocytes, as described in Supplementary Fig.\u0026nbsp;1, was the following: a) debris and dead cell exclusion in forward-scatter (FSC-Height) vs side-scatter channel (SSC-Height) plot (Panel A); b) doublet exclusion in FSC-Area vs FSC-Height plot (Panel B); c) gating for monocytes in HLA-DR vs SSC plot to select them as HLA-DR\u0026thinsp;+\u0026thinsp;cells with higher SSC than HLA-DR\u0026thinsp;+\u0026thinsp;and HLA-DR- lymphocytes; in this plot monocytes were distinguished from neutrophils based on the higher SSC and HLA-DR negativity of these latter cells (Panel C); d) confirmation of monocyte population as CD3-CD11b\u0026thinsp;+\u0026thinsp;in CD3 vs CD11b plot (Panel D). The differentiation between monocytes and neutrophils was corroborated by the analyses of HLA-DR (that are molecules not present on the surface of neutrophils), CD16 (brighter expression on neutrophils) and CD11b (brighter expression on monocytes).\u003c/p\u003e\n\u003cp\u003eWe used HLA-DR instead of CD14 as identifier marker for monocytes in order to not underestimate the subpopulation of non-classical monocytes (that exhibit CD14 low/neg expression). Then the HLA-DR\u0026thinsp;+\u0026thinsp;CD3-CD11b\u0026thinsp;+\u0026thinsp;monocyte population was analyzed by CD14 and CD16 markers to identify the three subpopulations of monocytes, as follows: classical (CD14\u0026thinsp;+\u0026thinsp;+\u0026thinsp;CD16-), intermediate (CD14\u0026thinsp;+\u0026thinsp;+\u0026thinsp;CD16+) and non-classical (CD14+/-CD16++) (Supplementary Fig.\u0026nbsp;1, Panel E). The levels of CD16 positivity within the monocyte population were discriminated through comparison with those of CD3\u0026thinsp;+\u0026thinsp;T and putative B lymphocytes, NK CD16\u0026thinsp;+\u0026thinsp;cells, and neutrophils, each of them references for negative, intermediate and bright expressions, respectively (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003eThe percentage of monocytes, evaluated as HLA-DR\u0026thinsp;+\u0026thinsp;CD11b\u0026thinsp;+\u0026thinsp;CD3- (Supplementary Fig.\u0026nbsp;1, Panel D), was referred to the total leukocytes (lymphocytes-monocytes-neutrophils) identified based on their FSC-H and SSC-H features in Panel A. The frequencies of different monocyte subsets (Supplementary Fig.\u0026nbsp;1, Panel E) were determined as percentages of the total monocyte population defined in Supplementary Fig.\u0026nbsp;1, Panel D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultidimensional data reduction analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo visualize the different clustering of monocyte subpopulations in the three groups of subjects (healthy donors, Covid-19 and HIV\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve patients), flow cytometric data derived from a representative subject for each group were exported with compensated parameters to FCS express software v6.03.0011 (DeNovo Software) in order to perform a multidimensional data reduction analysis. Monocytes were defined based on their FSC vs SSC physical parameters and HLA-DR\u0026thinsp;+\u0026thinsp;CD11b\u0026thinsp;+\u0026thinsp;expression (as shown in Supplementary Fig.\u0026nbsp;1, Panel D): 144000 monocytes per subject were merged into a new FCS file. A t-dependent Stochastic Neighbor Embedding (t-SNE) map was generated, using FCS express software 119 v6.03.0011 (DeNovo Software), in the merged file among 1000 iterations with Barnes-Hut 287 approximation and 40 perplexity value for following markers: FSC-A, SSC-A, CCR2, HLA- DR, CD38. This generated 2-D plots that clustered the cells on the basis of marker expression profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe existence of statistically significant differences between means of data was analyzed by Mann-Whitney t test for non-parametric values. The existence of statistically significant correlations between variable parameters was analyzed by Spearman test for non-parametric values. Calculation were performed by GraphPad Prism v.5 software (GraphPad Software, San Diego \u0026ndash; California, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eMonocyte phenotypic characterization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOVID-19 patients showed monocyte circulating frequencies comparable to those of healthy subjects and HIV\u0026thinsp;+\u0026thinsp;naive patients (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, the relative distributions of the three different monocyte subsets, namely classical (CD14\u0026thinsp;+\u0026thinsp;+\u0026thinsp;CD16-), intermediate (CD14\u0026thinsp;+\u0026thinsp;+\u0026thinsp;CD16+) and non-classical (CD14+/-CD16++) types (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e), was peculiar of COVID-19 patients. In fact, the frequency of classical monocytes was decreased and that of intermediate monocytes was increased in COVID-19 patients with respect to both healthy donors and HIV\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve patients (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). It should be noted that the frequency of intermediate monocytes was higher in COVID-19 patients than in HIV\u0026thinsp;+\u0026thinsp;naive patients (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). No differences were observed concerning non-classical monocytes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Interestingly, when monocyte morpho-cytometric features were comparatively analyzed in the three subgroups of subjects, again a peculiarly altered morphology, characterized by an increase in side scatter (SSC) dimension (index of cytoplasmic complexity), hallmarked the monocyte population of COVID-19 patients compared to HD and HIV patients (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The differences among the three subgroups further emerged when data, relative to monocyte expression of CD38 and HLA-DR activation markers and CCR2 homing receptor of a representative subject for each group, were merged and evaluated applying t-dependent Stochastic Neighbor Embedding (t-SNE) analysis. The t-SNE maps showed that monocytes derived from COVID-19 patient #8, HIV\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve patient #1 and healthy donor #2 clusterized differently, accordingly with their morphologic features and expression profiles of CD38, HLA-DR and CCR2 molecules (Supplementary Fig. 3, Panels A-C). This analysis showed a different clustering of monocyte subsets among the different subjects, confirming the enrichment of intermediate monocytes in the sample derived from the COVID-19 patient with respect to the other groups (Supplementary Fig. 3, Panel B). Moreover, the t-SNE maps of mean fluorescence intensity of CD38 and HLA-DR activation markers showed their increased expression on monocytes subsets derived from both patients with respect to the healthy donor. Higher CCR2 monocyte expression was only observed in the clusters of the COVID-19 patient with respect to those of the other groups (Supplementary Fig. 3, Panel C). These differences in CD38, HLA-DR and CCR2 MFI between COVID-19 patients and both HIV\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve patients and HD, suggested by the multidimensional analyses performed on cells from representative subjects of each group, were confirmed by conventional cytometry analyses (Supplementary Fig. 4 and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations among monocyte and clinical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to verify whether monocyte expression profiles of CD38, HLA-DR and CCR2 on different monocyte subsets may have a clinical impact in COVID-19 patients, we correlated MFI of CD38, HLA-DR and CCR2 molecules with the serum levels of the following clinical indexes: creatinine, glomerular filtration rate (GFR), azotemia, troponin I, D-dimer, ferritin, fibrinogen, pro-calcitonin, C-reactive protein, lactate dehydrogenase, creatinine phosphokinase (Supplementary Table 3). We found that CCR2 expression on classic, intermediate and non classic monocyte subsets highly correlated with kidney function parameters (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Box A, B and C, respectively). In fact, we observed a direct correlation between CCR2 MFI on classical, intermediate and non-classic monocytes with azotemia (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e upper panels) and creatinemia (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, middle panels), and an inverse correlation with GFR (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, lower panels). Moreover, CD38 expression on intermediate monocyte subset showed a direct correlation with ferritin and fibrinogen serum concentrations (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCollectively, the results of our study show that monocytes of COVID-19 patients are highly activated and that their distribution among the three circulating subsets of monocytes is quite peculiar, since it is different from that of both healthy donors and HIV\u0026thinsp;+\u0026thinsp;naive patients. This finding, together with that related to the peculiar morpho-cytometric parameters of intermediate monocytes in COVID-19 patients, suggests that SARS-CoV-2 infection induces a robust stimulation of these cells. Such stimulation selectively expands the intermediate monocytes, that constitute a cell subset provided with pro-inflammatory features and that has been associated with infective and inflammatory diseases (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Interestingly, these cells in COVID-19 patients highly expressed CCR2, a chemokine receptor that likely drives them toward the tissue site of inflammation (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Accordingly, elevated levels of CCL2, the chemokine specific for the CCR2, have been observed in the bronco-alveolar fluid of COVID-19 patients with pneumonia (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), thus likely explaining its rich monocyte content (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Hence, we searched for an association between CCR2 MFI and the serum levels of several clinical indexes in our series of COVID-19 patients. We found that CCR2 MFI on intermediate monocytes correlated with all the clinical parameters of renal function (creatinine, GFR, azotemia). This unprecedented finding suggests that intermediate monocytes may be also pathogenically related to renal alterations and acute kidney insufficiency, clinical manifestations of SARS-CoV-2 infection observed in about 14% and 5% of patients (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), respectively. This is not surprising since SARS-CoV-2 may target renal tissues due to the presence of the ACE2 receptor on the epithelial cells of the proximal tubules (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Moreover our data show that the intensity of CD38 expression by intermediate monocytes of COVID-19 patients correlated with biomarkers of inflammation, as ferritin and fibrinogen, whose production is dependent by IL6 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), a cytokine highly released by monocytes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and found at high concentration in COVID-19 patient serum (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCollectively, our data show that SARS-CoV-2 infection determines peculiar alterations of monocytes targeting morpho-phenotypic and maturation features, and that elevated CCR2 MFI mainly on intermediate monocytes associates with parameters of renal function.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by a generous donation from Mediolanum Farmaceutici spa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST/COMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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. Outside the submitted work: C.D. has received speaker honoraria from Angelini, Novartis, Gilead, ViiV, Shinogi, D. R. G. reports personal consultation fees from Stepstone Pharma GmbH and grants from MSD Italia and Correvio Italia. A.D.B. reports hospital grant from Gilead and consultancy for ViiV, Janssen, MSD, Gilead, Abbvie. M. B. serves on scientific advisory boards for Angelini, AstraZeneca, Bayer, Cubist, Pfizer, Menarini, MSD, Nabriva, Paratek, Roche, Shionogi, Tetraphase, The Medicine Company, and Astellas Pharma Inc, and has received funding for travel or speaker honoraria from Algorithm, Angelini, Astellas Pharma Inc, AstraZeneca, Cubist, Pfizer, MSD, Gilead Sciences, Menarini, Novartis, Ranbaxy, and Teva.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA MATERIAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026rsquo; CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAP, DF, GF, MF, TA, were in charge of the study design. AP, DF, TA, FB carried out the phenotyping experiments and analyzed the data. AP, DF, FF, TA, FB performed laboratory work and experiments. CD, ADM, MB, ADB, AV, PP, RDP, DRG they managed the patients, their withdrawals and the clinical aspect, MG managed the data collection site. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was carried out in compliance with the Helsinki Declaration and approved by the Ethical Committee of the San Martino Hospital in Genoa (N. CER Liguria 114/2020 - ID 10420 and P.R.251REG2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PARTECIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent to participate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants have consented to publication of their data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the GECOVID-19 Study group.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ; HLH Across Speciality Collaboration, UK. COVID-19: consider cytokine storm syndromes and immunosuppression. COVID-19: consider cytokine storm syndromes and immunosuppression. 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Role of Interleukin-6 in Lung Complications in Patients With COVID-19: Therapeutic Implications. \u003cem\u003eIn Vivo\u003c/em\u003e (2020) 34(3 Suppl):1589-92.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-and-experimental-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clem","sideBox":"Learn more about [Clinical and Experimental Medicine](https://www.springer.com/journal/10238)","snPcode":"10238","submissionUrl":"https://submission.nature.com/new-submission/10238/3","title":"Clinical and Experimental Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Covid-19, SARS-CoV-2, Monocytes, innate immunity, C-C motif chemokine receptor 2","lastPublishedDoi":"10.21203/rs.3.rs-1693973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1693973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePatients with severe SARS-CoV-2 infection have an overwhelming inflammatory response characterized by remarkable organs monocyte infiltration. We performed an immunophenotypic analysis on circulating monocytes in 19 COVID-19 patients in comparison with 11 na\u0026iuml;ve HIV-1 patients and 10 healthy subjects. Reduced frequency of classical monocytes and increased frequency of intermediate monocytes characterized COVID-19 patients with respect to both HIV na\u0026iuml;ve patients and healthy subjects. Intensity of C-C motif chemokine receptor 2 (CCR2) monocyte expression highly correlated with parameters of kidney dysfunction. Our data indicate that highly activated monocytes of COVID-19 patients may be pathogenically associated to the development of renal disease.\u003c/p\u003e","manuscriptTitle":"Intermediate monocytes expansion and homing markers expression in COVID-19 patients associate with kidney dysfunction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-31 16:42:29","doi":"10.21203/rs.3.rs-1693973/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-09-16T15:33:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-09-16T14:25:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e933293a-6695-4d22-bd3f-24bc4da65405","date":"2022-09-12T11:03:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-05-30T09:54:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-05-27T12:00:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-27T12:00:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical and Experimental Medicine","date":"2022-05-25T19:51:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-and-experimental-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clem","sideBox":"Learn more about [Clinical and Experimental Medicine](https://www.springer.com/journal/10238)","snPcode":"10238","submissionUrl":"https://submission.nature.com/new-submission/10238/3","title":"Clinical and Experimental Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4a50d074-9dd1-4413-951c-336bb5503a6b","owner":[],"postedDate":"May 31st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-10-14T15:59:18+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-31 16:42:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1693973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1693973","identity":"rs-1693973","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}