Deficient Phagocytosis in Circulating Monocytes from Patients with COVID-19-Associated Mucormycosis

ABSTRACT Cases of rhino-orbital mucormycosis in patients suffering from severe coronavirus disease 2019 (COVID-19) were reported in different parts of the world, especially in India. However, specific immune mechanisms that are linked to susceptibility to COVID-19-associated mucormycosis (CAM) remain largely unexplored. We aimed to explore whether the differential regulation of circulating cytokines in CAM patients had any potential pathogenic links with myeloid phagocyte function and susceptibility to mucormycosis. A small cohort of Indian patients suffering from CAM (N = 9) as well as COVID-19 patients with no evidence of mucormycosis (N = 5) were recruited in the study. Venous blood was collected from the patients as well as from healthy volunteers (N = 8). Peripheral blood mononuclear cells and plasma were isolated. Plasma samples were used to measure a panel of 48 cytokines. CD14+ monocytes were isolated and used for a flow cytometric phagocytosis assay as well as a global transcriptome analysis via RNA-sequencing. A multiplex cytokine analysis of the plasma samples revealed reduction in a subset of cytokines in CAM patients, which is known to potentiate the activation, migration, or phagocytic activity of myeloid cells, compared to the COVID-19 patients who did not contract mucormycosis. Compared to monocytes from healthy individuals, peripheral blood CD14+ monocytes from CAM patients were significantly deficient in phagocytic function. The monocyte transcriptome also revealed that pathways related to endocytic pathways, phagosome maturation, and the cytoskeletal regulation of phagocytosis were significantly downregulated in CAM patients. Thus, the study reports a significant deficiency in the phagocytic activity of monocytes, which is a critical effector mechanism for the antifungal host defense, in patients with CAM. This result is in concordance with results regarding the specific cytokine signature and monocyte transcriptome.

Fraction of the purified monocytes was used to check for phagocytic function, while another fraction was cryostored for RNA isolation. Flow cytometric assay for phagocytosis was done using 1µm carboxylate-modified polystyrene yellow green latex beads (Sigma). After incubation with beads (2.5% stock aqueous suspension diluted 1:50 in PBS) with 10 5 monocytes at 37⁰C with 5% CO2 for 3 hrs cells were washed before flow cytometry. For assessing phagocytosis of fungal spores UV-inactivated conidia from a wild type strain Rhizopus delemar 99-880, from a brain isolate obtained from the University of Texas Health Science Center at San Antonio, were used (references in main text: 17, 18).
The strain was grown on Yeast extract agar glucose agar plates for 3 days at 37 °C. Fungal conidia (spores) were harvested by gentle shaking in the presence of sterile 0.1% Tween-20 in phosphatebuffered saline (PBS), washed twice with PBS, filtered through a 40 μm pore size cell strainer (Falcon) to separate conidia from contaminating mycelium, counted by a hemocytometer, and suspended at a concentration of 10 7 and 10 8 spores/ml. Inactivation of Rhizopus conidia was done by exposure to UV light (1 h at room temperature). For fluorescence labeling, 10 6 conidia were stained in 100 µl PBS containing 100 µg/ml Fluorescent Brightener 28 (Sigma-Aldrich, cat no. 475300) and 0.1 M NaHCO3 for 30 min protected from light in a bench-top rotator. Then the labeled conidia were washed three times with PBS and the concentration was adjusted to 10 7 or 10 8 conidia/ml. Flow cytometric assay for phagocytosis was done by incubation with spores (stock suspension diluted 1:50 in PBS) with 10 5 monocytes at 37⁰C with 5% CO2 for 3 hrs cells were washed before flow cytometry.

Monocyte transcriptome
RNA sequencing on the total cellular RNA from purified CD14 + monocytes was done on Nextseq 2000 using P2 flowcell at 2x151 read length and loading concentration of 650 pM. The raw sequencing reads were quality checked using Fastqc (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) which were filtered and trimmed with Trimmomatic (v.0.39) to remove low quality bases 19 .
The filtered reads were then aligned to the reference Human genome (assembly GRCh38.104) using Salmon (v1.4.0) 20 . The quantification generated from Salmon were then imported to R environment using tximport package and differential gene identification was performed using DESeq2 Differentially expressed genes with p value <0.05 and log2 fold change of 1.5 were called as significant.
Functional enrichment of DEGs was performed using Enrichr against Gene Ontology (GO) database and statistical significance was calculated using Fisher's Exact test 22 . Pathways related to infection and with a p value < 0.01 were considered. The pathways were plotted using the ggplot2 (Wickham, H., 2006. An introduction to ggplot: An implementation of the grammar of graphics in R. Statistics, pp.1-8.) R package and rawgraphs (https://app.rawgraphs.io/).

Statistics
All statistical analyses for functional studies were done in GraphPad Prism software and the tests done are indicated in the figure legends. Statistical analyses for the RNA-seq data were done as described above.
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