A conserved grain-associated immunosuppressive niche in Sudanese patients with mycetoma

Mohamed Osman; Helen Ashwin; Grant Calder; Peter O’Toole; Sahar M. Bakhiet; Ahmed M. Musa; Paul M. Kaye; Ahmed Hassan Fahal

doi:10.1371/journal.pntd.0014230

Abstract

Mycetoma is a neglected tropical disease caused by various bacterial and fungal pathogens that has a significant health impact across a broad geographically defined “mycetoma belt” spanning South America, Africa and Asia. Histologically, mycetoma is characterised by invasive and destructive granuloma development in the skin, deep tissues and bone, leading to tissue destruction, deformities and high morbidity. The presence of macroscopic, highly compacted pathogen microcolonies, or “grains,” is a key diagnostic feature, and the formation of grains supports pathogen persistence and disease chronicity. However, there is a paucity of information on immune responses in mycetoma patients and on the relative importance of phylogeny and/or grains in establishing the local immune landscape. Here, we used spatial proteomics to examine the distribution of 43 immune-related proteins in surgical biopsies from 11 patients with mycetoma of bacterial (actinomycetoma; Actinomadura pelletierii and Streptomyces somaliensis; n = 6) and fungal (eumycetoma; Madurella mycetomatis; n = 5) origin. Using mixed-effects modelling, an exploratory analysis across species and pathogen classes revealed few significant differences in immune marker expression. In contrast, and independently of pathogen class, the cellular infiltrate closest to grain boundaries had higher per-cell expression of CD66b⁺ neutrophils, ARG1, and VISTA. The preferential accumulation of CD66b⁺ARG1⁺VISTA⁺ cells at grain boundaries was confirmed by quantitative immunofluorescence analysis. Hence, the local tissue microenvironment surrounding the mycetoma grain represents a specialised immunosuppressive niche, with parallels to the tumour microenvironment.

Trial Registration

The study was prospectively registered on ClinicalTrials.gov NCT04401969.

Author summary

Mycetoma is a devastating disease found in tropical countries that causes high levels of tissue damage, often leading to amputation of affected limbs. Various bacterial and fungal pathogens cause the disease, but this does not affect how the disease appears. A common feature of mycetoma that helps doctors distinguish it from other diseases is the presence of “grains” in the tissues, which are discharged through the open sinuses. Under the microscope, they appear as colonies of the pathogen, together with host cell debris. It has been speculated that these grains allow the pathogen to resist immune attack and therefore contribute to the chronic nature of the infection. To understand how this may occur, we used spatial profiling and immunohistology to map the locations of multiple immune markers in the tissues of patients diagnosed with mycetoma. Our results show that phagocytic cells (neutrophils) in closest proximity to grains express molecules associated with immunosuppression. Similar neutrophils are found in close association with tumour cells in patients with cancer, where they inhibit host immunity and reduce the effectiveness of anti-tumour drugs. We speculate that these cells may play a similar role in mycetoma patients.

Citation: Osman M, Ashwin H, Calder G, O’Toole P, Bakhiet SM, Musa AM, et al. (2026) A conserved grain-associated immunosuppressive niche in Sudanese patients with mycetoma. PLoS Negl Trop Dis 20(8): e0014230. https://doi.org/10.1371/journal.pntd.0014230

Editor: Joseph M. Vinetz, Yale University, UNITED STATES OF AMERICA

Received: April 1, 2026; Accepted: August 8, 2026; Published: August 17, 2026

Copyright: © 2026 Osman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Raw data files for DSP and immunohistochemistry are included as supplementary tables in this manuscript. Code for statistical analysis and plotting are provided in a GitHub repository at https://github.com/paulkaye/Osman_Mycetoma.git.

Funding: The work was funded by a grant from the Ministry of Higher Education and Scientific Research, Sudan (to AHF), and by the UKRI Medical Research Council/Department for International Development Global Challenges Research Fund (to PMK via a QR grant from the University of York). PMK, MO and HA were also supported by a Wellcome Investigator Award (WT224290 to PMK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Mycetoma, recently recognised by the WHO as a neglected tropical disease, is a chronic, potentially disfiguring subcutaneous granulomatous inflammatory disease [1–3]. Mycetoma is reported worldwide, but notably in a geographically defined “mycetoma belt,” and affects the poorest of the poor at the individual, family, community, and health system levels [4–6]. Mycetoma is caused by >70 species of true fungi (eumycetoma) or actinomycete bacteria (actinomycetoma), but the disease presentation is broadly similar across pathogen classes [2,7,8].

Diagnostically and histologically, mycetoma is associated with the presence of “grains”, compacted microcolonies of organisms associated with host cell debris and extracellular matrix components [2,9–12]. Grain morphology and ultrastuctural characteristics can be used to classify disease by pathogen class [2,7,10,13,14], and the triad of a painless subcutaneous mass, multiple sinuses and the sero-purulent discharge that contains grains is a mycetoma characteristic [2]. The histological response has been characterised in patients with both eumycetoma and actinomycetoma and supports a histopathological picture that is associated with both myeloid (predominantly neutrophil) and lymphoid infiltration [15–19]. In eumycetoma, grading based on the presence of neutrophils, granuloma-associated giant cells, and necrosis has been proposed [16]. However, studies on specific immune cell populations and their products remain cursory, with a focus on T cell (TH1 and TH2) -associated cytokines, IL-1 family cytokines and mediators of neutrophil recruitment and activation, such as IL-17 and MMP9 [15,16,19–24]. Such tissue responses are likely shaped by responses to the pathogen and to tissue destruction, as well as by host genetics [24,25].

Presently, treatment options for eumycetoma patients are limited and characterised by low cure and high recurrence rates [26–28], despite most causative fungi being susceptible to many antifungal drugs [29–32]. For actinomycetoma, a combination of antibacterials is available, but cure requires extensive treatment regimens that limit compliance [27]. Surgical intervention is required for the treatment of most eumycetoma patients, with unpredictable recurrence rates [33]. Although it is likely that chronicity and treatment response are intimately linked through the microenvironmental control of immune responses [34], this has not been formally explored in the context of mycetoma.

Here, we conducted an exploratory study to investigate the value of applying digital spatial profiling to study the mycetoma granuloma microenvironment. Using a 43-plex protein panel that features multiple immune-oncology targets, we sought to identify cell types and phenotypic markers within the mycetoma lesions and specifically whether cell distribution and/ or phenotype was influenced by proximity to mycetoma grains.

Methods

Ethics statement

The study was conducted in accordance with the principles of the Declaration of Helsinki. It was approved by the Ethical Review Committee of Soba University Hospital, University of Khartoum and the Department of Biology Ethics Committee, University of York (Ref: MO201903). All patients provided written consent for the use of diagnostic surgical biopsies in research aimed at understanding mycetoma pathogenesis. The study protocol did not impact the standard of care.

Patients

This observational clinical study (ClinicalTrials.gov id: NCT04401969) was conducted at the Mycetoma Research Centre, Soba University Hospital, University of Khartoum, Khartoum, Sudan, with tissue analysis conducted at the University of York, UK. The study originally included 28 patients, and the diagnosis was confirmed by clinical examinations, culture of grains and histopathological examination of material obtained by a surgical biopsy. True-Cut surgical biopsies from all patients were taken as part of routine diagnostic procedures, fixed in 10% formalin for 24 hours, and processed to Formalin Fixed Paraffin Embedded (FFPE) sections. After staining with Haematoxylin and Eosin (H&E), 11 patients (six with actinomycetoma and five with eumycetoma) were selected for further study based on the ready identification of grains in FFPE sections. These patients were classified as having actinomycetoma caused by either Actinomadura pelletieri (n = 4; P1, 4, 7, and 21) or Streptomyces somaliensis (n = 2; P2 and 25), and eumycetoma caused by Madurella mycetomatis (P8, 17, 24, 27, and 28). None of the patients was HIV positive, diabetic or malnourished.

GeoMx digital spatial profiling

Spatial protein profiling on 5µm thick sections from FFPE tissue biopsies were performed using the NanoString GeoMx DSP system (Bruker, Spatial Biology, Bothwell, USA). A profiling panel of 43 immune-oncology proteins together with housekeeping proteins and isotype controls were assessed. All staining, imaging, probes hybridisation and counting were done as described in the manufacturer protocols. Using the GeoMx DSP Control Center software (v2.4, Bruker Spatial Biology) two regions of interest (ROIs) with a 300µm diameter were manually created surrounding grains (called “Near”) and two more were created 200–300µm away from any grain perimeter (called “Far”) equalling four ROIs per patient and forty-four ROIs in total. UV light was used to release identifying bar codes from the ROI area which were sequentially collected for later identification and counting using the NanoString nCounter system. Digital count data were analysed using GeoMx Data analysis Suite (v2.4) where counts are normalised to positive External RNA Controls Consortium (ERCC) controls and to nuclei counts. ROIs with abnormal levels of hybridisation or housekeeping (HK) protein expression were removed from the analysis. Target proteins were only retained for analysis if counts were 3x the geometric mean of the isotype controls in >10% of ROIs. HK proteins were also excluded from analysis as their expression may vary under inflammatory conditions. Data for the 32 proteins passing QC and thresholding were exported for further analysis in R (see Statistical Analysis) and analysed as normalised counts using linear mixed modelling, adjusting for patient ID (repeat measures), pathogen class, and position (Near vs Far).

Immunohistology

4µm sections from FFPE blocks were used for all analyses. Slides were heat fixed at 50 ^°C for 10 minutes in an oven. Paraffin was removed from the sections by sequential washes: 2x 5 minutes in Xylene, 3 minutes in 95% alcohol, 3 minutes in 70% alcohol, and then water for rehydration. Antigens were unmasked under high pressure in citrate buffer using a high-pressure cooker for 15 minutes. Following three washes with PBST (PBS, 0.05% Tween 20), Slides were blocked with the background suppressor TrueBlack IF Background suppressor 23012A (Biotium, Fremont, USA) for 30 min at room temperature. Primary antibodies were diluted in blocking buffer(TrueBlack IF Blocking buffer 23012B, Biotium), and incubated overnight at 4 ^°C. Sections were then probed with the following antibodies; CD68 (Abcam, Cambridge, UK; ab955, 1:100) detected by Goat anti-Mouse AF546 (1:500), CD4 (Abcam; ab133616, 1:100) detected by Goat anti-rabbit AF488 (1:400) or Donkey anti-rabbit Dy650 (1:500), CD8 (Biolegend, San Diego, USA; 372902, 1:100) detected by Goat anti-Mouse AF546 (1:500), CD3 (Abcam, Ab5690, 1:100) detected by Goat anti-rabbit AF488 (Invitrogen, Paisley, UK 1:400) or Donkey anti-rabbit Dy650 (Invitrogen, 1:500), Arg1 (Invitrogen H1D5, 1:200) detected by Goat anti mouse F(ab)2 AF555 (Invitrogen; A21425,1:500), CD66b AF647 (Biolegend 392912, 1:50) detected by Mouse IgG1 AF647 (Biolegend 400130, 1:500), V-domain Ig suppressor of T cell activation (VISTA; Cell Signaling Technology, Leiden, Netherlands; 64953S; 1:100), CD15 (Invitrogen, 756-0158-94) and Siglec-8 (Abcam, ab198690) detected by Donkey anti-Rabbit CF750 (Biotium 20298, 1:500), CD20 Dy650 (Novus NBP-47840C, 1:100), CD56 Dy650 (Novus, Abingdon, UK; NBP-33132C, 1:100), and CD66b AF647 (Biolegend 392912, 1:20). Intermediate blocking using Mouse IgG (Abcam, ab37355, 1:400) was included where necessary. Sections were counterstained with DAPI and mounted in FluoromountG mounting medium (ThermoFisher, Waltham, USA). Images were acquired using Zeiss AxioScan.Z1 slide scanner (Zeiss, Jena, Germany). Identical exposure times and threshold settings were used for each channel on all sections of similar experiments. Quantifications of cell types based on marker expression were performed using StrataQuest Analysis Software (TissueGnostics, Vienna, Austria) within manually curated ROIs extending in 100um steps from grain perimeters (<100um, 101–200um, and 201–300um).

Statistical analysis

DSP protein expression data were analysed using linear models implemented in the limma framework, using log2-transformed normalised counts (log2[counts + 1]). To account for repeated measurements per patient, patient identity was modeled as a blocking factor and within-patient correlation was estimated using duplicateCorrelation [35]. All models included sampling position (Near vs Far) as a covariate. Species-level effects were first assessed using a global moderated F-test to identify species-associated heterogeneity, followed by exploratory pairwise comparisons between species. Additional analysis grouped samples by pathogen class to determine differences associated with each class. For class-based analyses, differential expression between pathogen classes was tested using a moderated t-test within the repeated-measures framework. For all analyses, p-values were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate (FDR), and proteins with FDR < 0.05 were considered statistically significant. All analyses were conducted in R 4.4.2 using the tidyverse [36], limma [37], and ggplot2 [38] packages. Package versions are recorded via renv [39].

Immunohistochemistry data were collected as the number of marker-positive cells per unit area (mm²) using StrataQuest software. Cell counts in the three position ranges (0–100 µm, 101–200 µm, and ≥201 µm) were converted to sample-level proportions to account for differences in total counts. Repeated measures were handled using mixed-effects models. Proportional data were analysed as compositional data using additive log-ratio transformations, with the ≥ 201 µm range as the reference. A pseudocount of 0.5 was added to facilitate modelling. Linear mixed-effects models using the lme4 [40] package in R were fitted for each log-ratio outcome, with class as a fixed effect and patient as a random intercept. Class effects were assessed using likelihood-ratio tests, and a global test of compositional differences was obtained by combining log-ratio tests using Fisher’s method. Model-estimated mean proportions were obtained using the emmeans [41] package by back-transformation for visualisation and interpretation. Data were graphically represented using the ggplot2 package.

Results

Study population

The overall study design is shown in Fig 1A. Of the 28 patients initially recruited to the study, 11 were selected for this analysis based on the ease of grain identification in H&E sections. Their demographic, clinical, and histological characteristics, and causal organisms are shown in Table 1. None of the included patients had prior medical or surgical treatment. Representative histopathology (H&E) images are provided in S1 Fig. Causative organisms were Actinomadura pelletieri (P1, 4, 7, and 23), Streptomyces somaliensis (P2, 25) and Madurella mycetomatis (P8, 17, 24, 27, and 28).

Download:

Table 1. Details the clinical characteristics of the eumycetoma and actinomycetoma patients.

https://doi.org/10.1371/journal.pntd.0014230.t001

Download:

Fig 1. Immune microenvironment of mycetoma lesions.

A. Experimental workflow for spatial profiling mycetoma lesions using Digital Spatial Profiling GeoMx assay (DSP), with validation by immunohistology. B. Representative section showing placement of ROIs for DSP analysis. Grain indicated by a star. C. Violin plots showing expression of CD127, CD163, IDO1 and PD-L2 in patients with mycetoma caused by S. somaliensis, A. pelletieri and M. mycetomatis. FDR for comparisons is shown in the Figure. Points represent means value for each patient. D. Violin plot showing CD163 expression by mycetoma pathogen class. In C and D, significance was determined using a moderated t-test with correction for multiple testing and repeated measures. Significant FDRs are shown in each panel. Panel A was created in BioRender. Kaye, P. (2026) https://BioRender.com/ga0aq4s.

https://doi.org/10.1371/journal.pntd.0014230.g001

Spatial profiling of mycetoma surgical biopsies

For these 11 patients, we performed spatial protein profiling. For each patient, we examined 4 regions of interest (ROIs) based on their positions relative to grains: two surrounding grains (“Near”) and two in tissue areas devoid of grains (“Far”) (Fig 1B). From an initial protein panel comprising 43 immune protein targets, 32 were expressed, passed QC and thresholding (see Methods) and were used for further analysis (S2 Fig and S1 Table). Protein counts were normalised to nuclei, allowing estimates of per-cell protein expression independent of the extent of cellular infiltration.

As a first exploratory analysis, we assessed per-cell expression of immune proteins based on causative species and independently of position. Protein expression data were analysed using linear models that accounted for repeated measures within each patient and sampling position. For all 32 proteins, there was evidence of an overall species effect (FDR < 0.05), suggesting species-associated immune response heterogeneity (S3 Fig). However, when direct pairwise comparisons were performed between individual species, only four proteins showed statistically significant species-related effects (CD127, CD163, IDO1, PD-L2; Fig 1C). This result is consistent with limited power due to the small sample size and unequal patient numbers (n = 2, 4, and 5).

To partially mitigate these limitations, and because these species belong to the two major classes of pathogens (shaping potentially different immune responses), we grouped samples into actimomycetoma (S. somaliensis and A. pellettieri; n = 6) and eumycetoma (M. mycetomatis; n = 5) to examine response differences at this broader taxonomic level. Using a repeated-measures model similarly adjusted for position, only CD163 expression was significantly different, with greater per-cell expression in eumycetoma patients compared to actinomycetoma patients (FDR = 0.033; Fig 1D). CD163 is a high-affinity scavenger receptor commonly associated with M2 macrophage activation, immunosuppression and resistance to immunotherapy [42].

In contrast to these observed species- and pathogen class-associated effects, sampling position (i.e., Near vs Far) was more robustly associated with differences in protein expression across all modelling strategies (Fig 2A). Near ROIs showed significantly greater expression of leucocyte (CD45) and lineage (CD3, CD8, CD56, CD66b, panCK) markers, as well as leucocyte-associated activation and immunoregulatory proteins (CD45RO, ARG1, CTLA4, IDO1, and VISTA). In contrast to this enrichment in immune-related proteins in near ROIs, far ROIs were enriched for SMA, Fibronection and CD34, indicative of a more stromal-rich environment (Fig 2A). Pairwise correlation analysis for the subset of proteins significantly associated with near ROIs identified distinct peri-grain niches, notably with high per cell expression of i) CD45, CD3 and CD8 (Fig 2B) and ii) CD66b, ARG1 and VISTA (Fig 2B-2D). These niches were less evident in the analysis of the same protein set in far ROIs, though strong correlations between CD3 and CD8 and between CD66b and ARG1 were maintained (S4A- S4C Fig). VISTA remained correlated with CD66b, but was more closely correlated with proteins defining a lymphoid niche (S4A Fig). Given that ARG1 and VISTA have been identified in recent years as major contributors to immunosuppression within the tumour microenvironment [43–45], these data indicate that the microenvironment near grains can be characterised as “immune hot”, with evidence of an immunosuppressive microenvironment.

Download:

Fig 2. Per cell protein expression in near and far ROIs.

A. Violin plots showing protein expression for 14 targets showing significant differences in expression between near and far ROIs. Significance was determined using a moderated t-test with correction for multiple testing and repeated measures. B. Spearman correlation of 11 near-associated proteins in near ROIs. C, D. Correlation between CD66b and ARG1 (C) and VISTA (D) in near ROIs, indicating individual patients by colour.

https://doi.org/10.1371/journal.pntd.0014230.g002

CD66b⁺ cells accumulate near grains

CD66b⁺ cells expressing ARG1 and/or VISTA have previously been associated with the generation of an immunosuppressive tumour microenvironment [44,46]. To confirm that CD66b⁺ cells were preferentially localised near grains, we stained tissue sections with antibodies directed to CD3, CD68 and CD66b. This analysis excluded P21, where material was limited, but included two additional actimomycetoma samples (P12 and P23) where grains were eventually found in FFPE sections. Qualitative assessment indicated that CD66b⁺ as well as CD3⁺ cells were more commonly observed in close proximity to grains compared to other regions of the tissue, whereas CD68⁺ macrophages were more diffusely located (Fig 3A, top panels). To distinguish between eosinophils and other myeloid cell subsets, we stained for CD15 and Siglec-8 (S5 Fig). Siglec-8⁺ cells were sparse and somewhat randomly dispersed in these biopsies and did not express CD15, likely reflecting tissue eospinophils with down regulated CD15 [47]. In contrast, CD15⁺Siglec-8^- cells were abundant and accumulated around grains, in a similar manner to CD66b⁺ cells. Thus, eosinophils are not a major contributor to the grain-proximal niche.

Download:

Fig 3. Distribution and expression of ARG1 and VISTA on CD66b⁺ cells.

A. Representative immunostaining for P1 and P8 showing in top panels DAPI (blue) CD3 (green), CD68 (orange) and CD66b (red) and in bottom panels DAPI (blue) ARG1 (purple), VISTA (yellow) and CD66b (red). The grain boundary is located to the left of the image (orange dotted line) with 0–100 µm and 101–200 µm zone boundaries marked by blue and green dotted lines, respectively. Scale bar = 100 µm. B. Representative image to show zone based analysis in Stratquest. Scale bar = 100 µm. C. Stacked bar plots show patient-level mean proportions of counts in three zones (0–100 µm, yellow; 101–200 µm, blue; ≥ 201 µm, green), averaged across repeated samples within each patient. Bars are faceted by class. Mixed-effects compositional analysis (see Methods). D, E. Phenotypic composition of CD66b⁺ cells by zone, shown for all patients (D) and individually by patient (E). Legend as for C. Global test, Fisher’s method: χ² = 47.6, p = 1.4 × 10 ⁻⁸.

https://doi.org/10.1371/journal.pntd.0014230.g003

To quantify the distribution of CD66b⁺ cells relative to grain perimeters, we created ROIs (zones) extending 0–100 μm, 101–200 μm, and 200 + μm from grain boundaries (Fig 3A, 3B), To assess the spatial distribution of CD66b⁺ cells across zones, cell densities (cells/mm²) were converted to proportions within each sample and analysed as compositional data using additive log-ratio transformation and mixed-effects modelling with patient as a random effect. This analysis indicated that CD66b⁺ cells were preferentially distributed closest to grains but this was not significantly different between eumycetoma and actinomycetoma patients (p = 0.79, χ² = 1.68; Global Fisher’s test; Fig 3C). Back transformed model-estimated compositions indicated that CD66b⁺ cells were preferentially distributed towards the 0–100 µm and 101–200 µm zones relative to the 200 + µm zone, with estimated enrichments of 6.8 and 4.1-fold and 2.3- and 2.1-fold, for actinomycetoma and eumycetoma respectively.

As both ARG1 and VISTA can be expressed by CD66b⁺ cells as well as tumour-associated macrophages [48] and some T cells [49], we co-stained sections for CD66b, ARG1, and VISTA to confirm whether these molecules were co-expressed (Fig 3A, bottom panels). CD66b⁺ cell phenotype composition was significantly affected by spatial position (p = 1.4 x10^-8, χ² = 47.6; Global Fisher’s test; Fig 3D, 3E). In the most proximal zone to grains, almost all CD66b⁺ cells expressed ARG1 and ~80% coexpressed ARG1 and VISTA. No significant compositional difference was observed between 0–100 µm and 101–200 µm ranges (p = 0.28) whereas both proximal zones were significantly different from the 200 + µm zone (p < 0.0001 and p < 0.0005, respectively). These changes were driven by relative enrichment of CD66b⁺ARG1⁺VISTA⁺ cells. Collectively, these results indicate that spatial proximity to a grain is associated with a distinct CD66b⁺ cell phenotype characterised by co-expression of ARG1 and VISTA. The concordance between DSP protein expression (Fig 2A), marker correlation (Fig 2B-2D), and single-cell immunophenotyping (Fig 3) supports the presence of a spatially organised, immunoregulatory CD66b⁺ARG1⁺VISTA⁺ cell niche that may contribute to local immunosuppression.

Discussion

Mycetoma is a chronic progressive granulomatous inflammatory disease caused by phylogenetically dissimilar bacterial (actinomycetoma) and fungal (eumycetoma) pathogens. Actinomycetoma is generally considered more aggressive, inflammatory, destructive, and invasive, yet it responds better to a combination of antibiotics. Eumycetoma has a gradual onset and a slow progression. It is more localised and less destructive than actinomycetoma. [2,27,33]. The data reported here suggest a common pathway in mycetoma linked to the recruitment and retention of immunosuppressive CD66b⁺ cells within a peri-grain niche that bears similarities to the tumour microenvironment.

Our analysis at the species and class level indicated minimal differences in expression of immune oncology targets between these two forms of disease. Weakly significant differences in protein expression were observed across all targets, but most did not meet the FDR threshold when comparing species or classes pairwise. The exception was CD163, a marker associated with resident non-migratory M2-like macrophages in the tumour microenvironment [42,50], which was more highly expressed in eumycetoma patients. Our data emphasise the need for larger cohort studies to ascertain species and class-level differences in immune marker expression. For example, a cohort of 100 patients was used to identify species- and class-specific differences in MMP9 and IL-17A expression [23]. However, such sample sizes are beyond what is practical or generally affordable when employing highly multiplexed spatial technologies.

CD66b is a GPI-anchored glycoprotein constitutively expressed by human neutrophils and other granulocytes, that is upregulated during activation-induced exocytosis of neutrophil granules [51,52]. CD66b⁺ neutrophils have been identified in the tumour microenvironment where they play an immunoregulatory role [44,46]. Our finding of increased numbers of leucocytes and, in particular, CD66b⁺ cells near to mycetoma grains is in keeping with previous reports [15,16,22], but the first to co-localise expression of the immunosuppressive molecules ARG1 and VISTA to CD66b⁺ cells. Although commonly used as a marker of activated neutrophils, CD66b is also highly expressed on myeloid-derived suppressor cells (MDSCs) and eosinophils. Although our data rule out eosinophils as major contributors to the grain-proximal niche, our protein panels do not distinguish amongst other CD66b⁺ myeloid cells, e.g., neutrophils and MDSCs. Hence, we cannot currently say whether ARG1 and VISTA are co-expressed only on neutrophils, on MDSC, or on both. Further studies are therefore required to dissect the functionality and plasticity of mycetoma-associated CD66b⁺ cells and to define the roles of neutrophils and MDSC subpopulations in mycetoma.

Arginase-1 (ARG1) and V-domain Ig Suppressor of T cell Activation (VISTA; PD-1H) have been shown to negatively influence immune effector responses at the tumour site, acting respectively as metabolic and cell membrane-expressed immune checkpoint molecules. In the tumour microenvironment, ARG1 overexpression can inhibit T cell proliferation by depleting arginine [53,54]. VISTA is a B7-family immune checkpoint molecule constitutively expressed on myeloid cells that ligates T cell-expressed P-selectin glycoprotein ligand-1 (PSGL-1) [55] and leucine-rich repeats and immunoglobulin-like domains 1 (LRIG1) to deliver an inhibitory signal. Both ARG1 and VISTA are expressed in the Mycobacterium tuberculosis granuloma [56,57], and ARG1 expression associated with macrophage polarisation has been observed in the Schistosoma mansoni granuloma [58]. Further research into the expression and function of these immunosuppressive molecules in the broader context of granulomatous inflammation is clearly warranted, as are studies to directly address whether the expression of these molecules is influenced directly by grain/ pathogen-derived products or by other microenvironmental cues.

The identification of immune checkpoint molecules in the grain microenvironment opens the possibility of targeted adjunct therapy. Novel therapeutics have been identified that target arginase [44,46,59–61] and VISTA [43,62–64]. In cancer immunotherapy, numerous clinical trials have assessed the efficacy of combination chemotherapy with checkpoint inhibitors, aiming to achieve greater therapeutic benefit by reactivating local T cell immunity. Numerous studies have also identified synergies between immune effector mechanisms and antimicrobials, involving both innate (e.g., MAIT cells [65]) and conventional lymphocyte responses [34]. In the case of mycetoma, therefore, there is a compelling argument that alleviating local immunosuppression may also improve the efficacy of antimicrobials and/ or limit their duration of use, thereby reducing antimicrobial resistance. This may be particularly true for anti-fungal agents, given the poor prognosis of eumycetoma patients. Hence, whilst our study remains exploratory, our data suggests that a more comprehensive analysis of the immunosuppressive grain-associated microenvironment could lead to the development of novel combination therapies for mycetoma. Such intervention, however, would need to take into account the possibility that the role of this immunosuppressive niche is to limit host pathology due to excessive inflammation.

Our study has clear limitations. The sample size was small, limiting our analysis to largely exploratory findings. ROI selection was based on the ability to readily observe grains in H&E images and hence may have led to some selection bias in the choice of patients and ROIs for analysis. Many of the protein markers in the IO panel were not expressed at sufficiently high levels to be evaluated. Whilst not an uncommon finding [66], this limited the characterisation of the grain-associated microenvironment, notably with regard T cell phenotypes. Improved multiplexed protein profiling approaches and/ or single cell spatial transcriptomics may overcome this issue for future studies. Finally, although our ROIs for spatial profiling were relatively large and captured protein expression from multiple cell types, this limitation was mitigated by orthogonal validation at single cell level by quantitative immunohistology.

In conclusion, this exploratory study showed similar immune protein expression patterns and cellular infiltrates in the mycetoma-immune microenvironment for both eumycetoma and actinomycetoma patients, with a clear grain-associated microenvironment characterised by the enhanced presence of CD66b⁺ARG1⁺VISTA⁺ myeloid cells. Given the clinical challenge associated with conventional antimicrobial treatment, further exploration of the therapeutic potential of modulators of the grain-associated microenvironment could be pursued.

Supporting information

S1 Table. DSP raw data file.

https://doi.org/10.1371/journal.pntd.0014230.s001

(XLSX)

S1 Fig. Representative histology of mycetoma patients (H&E).

https://doi.org/10.1371/journal.pntd.0014230.s002

(DOCX)

S2 Fig. GeoMx protein expression across all ROIs.

https://doi.org/10.1371/journal.pntd.0014230.s003

(DOCX)

S3 Fig. Protein expression by species.

https://doi.org/10.1371/journal.pntd.0014230.s004

(PDF)

S4 Fig. Correlations plots for Far ROIs.

https://doi.org/10.1371/journal.pntd.0014230.s005

(DOCX)

S5 Fig. Expression of CD15 and Siglec-8 in mycetoma biopsies.

https://doi.org/10.1371/journal.pntd.0014230.s006

(DOCX)

Acknowledgments

The authors thank Salma Ramadan and Emanwell Master for technical assistance and the patients and their families for providing samples for this study.

References

1. Fahal AH. Mycetoma: A global medical and socio-economic dilemma. PLoS Negl Trop Dis. 2017;11(4):e0005509. pmid:28426654
- View Article
- PubMed/NCBI
- Google Scholar
2. Fahal AH, Suliman SH, Hay R. Mycetoma: The Spectrum of Clinical Presentation. Trop Med Infect Dis. 2018;3(3):97. pmid:30274493
- View Article
- PubMed/NCBI
- Google Scholar
3. Hay RJ, Fahal AH. Mycetoma: an old and still neglected tropical disease. Trans R Soc Trop Med Hyg. 2015;109(3):169–70. pmid:25667234
- View Article
- PubMed/NCBI
- Google Scholar
4. Ahmed AOA, van Leeuwen W, Fahal A, van de Sande W, Verbrugh H, van Belkum A. Mycetoma caused by Madurella mycetomatis: a neglected infectious burden. Lancet Infect Dis. 2004;4(9):566–74. pmid:15336224
- View Article
- PubMed/NCBI
- Google Scholar
5. López-Martínez R, Méndez-Tovar LJ, Bonifaz A, Arenas R, Mayorga J, Welsh O, et al. Update on the epidemiology of mycetoma in Mexico. A review of 3933 cases. Gac Med Mex. 2013;149(5):586–92. pmid:24108347
- View Article
- PubMed/NCBI
- Google Scholar
6. van de Sande WWJ. Global burden of human mycetoma: a systematic review and meta-analysis. PLoS Negl Trop Dis. 2013;7(11):e2550. pmid:24244780
- View Article
- PubMed/NCBI
- Google Scholar
7. de Hoog GS, van Diepeningen AD, Mahgoub E-S, van de Sande WWJ. New species of Madurella, causative agents of black-grain mycetoma. J Clin Microbiol. 2012;50(3):988–94. pmid:22205798
- View Article
- PubMed/NCBI
- Google Scholar
8. van Belkum A, Fahal A, van de Sande WWJ. Mycetoma caused by Madurella mycetomatis: a completely neglected medico-social dilemma. Adv Exp Med Biol. 2013;764:179–89. pmid:23654067
- View Article
- PubMed/NCBI
- Google Scholar
9. Fahal AH. Mycetoma: a thorn in the flesh. Trans R Soc Trop Med Hyg. 2004;98(1):3–11. pmid:14702833
- View Article
- PubMed/NCBI
- Google Scholar
10. Sheehan G, Konings M, Lim W, Fahal A, Kavanagh K, van de Sande WWJ. Proteomic analysis of the processes leading to Madurella mycetomatis grain formation in Galleria mellonella larvae. PLoS Negl Trop Dis. 2020;14(4):e0008190. pmid:32267851
- View Article
- PubMed/NCBI
- Google Scholar
11. van de Sande WWJ, Fahal AH, Goodfellow M, Mahgoub ES, Welsh O, Zijlstra EE. Merits and pitfalls of currently used diagnostic tools in mycetoma. PLoS Negl Trop Dis. 2014;8(7):e2918. pmid:24992636
- View Article
- PubMed/NCBI
- Google Scholar
12. Zijlstra EE, van de Sande WWJ, Welsh O, Mahgoub ES, Goodfellow M, Fahal AH. Mycetoma: a unique neglected tropical disease. Lancet Infect Dis. 2016;16(1):100–12. pmid:26738840
- View Article
- PubMed/NCBI
- Google Scholar
13. Ahmed SA, van den Ende BHGG, Fahal AH, van de Sande WWJ, de Hoog GS. Rapid identification of black grain eumycetoma causative agents using rolling circle amplification. PLoS Negl Trop Dis. 2014;8(12):e3368. pmid:25474355
- View Article
- PubMed/NCBI
- Google Scholar
14. ELbadawi HS, Mahgoub E, Mahmoud N, Fahal AH. Use of immunoblotting in testing Madurella mycetomatis specific antigen. Trans R Soc Trop Med Hyg. 2016;110(5):312–6. pmid:27198216
- View Article
- PubMed/NCBI
- Google Scholar
15. el Hassan AM, Fahal AH, Ahmed AO, Ismail A, Veress B. The immunopathology of actinomycetoma lesions caused by Streptomyces somaliensis. Trans R Soc Trop Med Hyg. 2001;95(1):89–92. pmid:11280076
- View Article
- PubMed/NCBI
- Google Scholar
16. Fahal AH, el Toum EA, el Hassan AM, Mahgoub ES, Gumaa SA. The host tissue reaction to Madurella mycetomatis: new classification. J Med Vet Mycol. 1995;33(1):15–7. pmid:7650573
- View Article
- PubMed/NCBI
- Google Scholar
17. Hoeksema MA, Scicluna BP, Boshuizen MCS, van der Velden S, Neele AE, Van den Bossche J, et al. IFN-γ priming of macrophages represses a part of the inflammatory program and attenuates neutrophil recruitment. J Immunol. 2015;194(8):3909–16. pmid:25750432
- View Article
- PubMed/NCBI
- Google Scholar
18. Ibrahim AI, El Hassan AM, Fahal A, van de Sande WW. A histopathological exploration of the Madurella mycetomatis grain. PLoS One. 2013;8(3):e57774. pmid:23483927
- View Article
- PubMed/NCBI
- Google Scholar
19. Nasr A, Abushouk A, Hamza A, Siddig E, Fahal AH. Th-1, Th-2 Cytokines Profile among Madurella mycetomatis Eumycetoma Patients. PLoS Negl Trop Dis. 2016;10(7):e0004862. pmid:27434108
- View Article
- PubMed/NCBI
- Google Scholar
20. Abushouk A, Nasr A, Masuadi E, Allam G, Siddig EE, Fahal AH. The Role of Interleukin-1 cytokine family (IL-1β, IL-37) and interleukin-12 cytokine family (IL-12, IL-35) in eumycetoma infection pathogenesis. PLoS Negl Trop Dis. 2019;13(4):e0007098. pmid:30946748
- View Article
- PubMed/NCBI
- Google Scholar
21. Fahal A, Mahgoub ES, El Hassan AM, Abdel-Rahman ME, Alshambaty Y, Hashim A, et al. A new model for management of mycetoma in the Sudan. PLoS Negl Trop Dis. 2014;8(10):e3271. pmid:25356640
- View Article
- PubMed/NCBI
- Google Scholar
22. Guimarães CC, Castro LGM, Sotto MN. Lymphocyte subsets, macrophages and Langerhans cells in actinomycetoma and eumycetoma tissue reaction. Acta Trop. 2003;87(3):377–84. pmid:12875932
- View Article
- PubMed/NCBI
- Google Scholar
23. Siddig EE, Mohammed Edris AM, Bakhiet SM, van de Sande WWJ, Fahal AH. Interleukin-17 and matrix metalloprotease-9 expression in the mycetoma granuloma. PLoS Negl Trop Dis. 2019;13(7):e0007351. pmid:31295246
- View Article
- PubMed/NCBI
- Google Scholar
24. Mhmoud NA, Fahal AH, van de Sande WWJ. The association between the interleukin-10 cytokine and CC chemokine ligand 5 polymorphisms and mycetoma granuloma formation. Med Mycol. 2013;51(5):527–33. pmid:23210681
- View Article
- PubMed/NCBI
- Google Scholar
25. Ali RS, Newport MJ, Bakhiet SM, Ibrahim ME, Fahal AH. Host genetic susceptibility to mycetoma. PLoS Negl Trop Dis. 2020;14(4):e0008053. pmid:32352976
- View Article
- PubMed/NCBI
- Google Scholar
26. Wadal A, Elhassan TA, Zein HA, Abdel-Rahman ME, Fahal AH. Predictors of Post-operative Mycetoma Recurrence Using Machine-Learning Algorithms: The Mycetoma Research Center Experience. PLoS Negl Trop Dis. 2016;10(10):e0005007. pmid:27798643
- View Article
- PubMed/NCBI
- Google Scholar
27. Welsh O, Al-Abdely HM, Salinas-Carmona MC, Fahal AH. Mycetoma medical therapy. PLoS Negl Trop Dis. 2014;8(10):e3218. pmid:25330342
- View Article
- PubMed/NCBI
- Google Scholar
28. Zein HAM, Fahal AH, Mahgoub ES, El Hassan TA, Abdel-Rahman ME. Predictors of cure, amputation and follow-up dropout among patients with mycetoma seen at the Mycetoma Research Centre, University of Khartoum, Sudan. Trans R Soc Trop Med Hyg. 2012;106(11):639–44. pmid:22854685
- View Article
- PubMed/NCBI
- Google Scholar
29. Kloezen W, Meis JF, Curfs-Breuker I, Fahal AH, van de Sande WWJ. In vitro antifungal activity of isavuconazole against Madurella mycetomatis. Antimicrob Agents Chemother. 2012;56(11):6054–6. pmid:22964246
- View Article
- PubMed/NCBI
- Google Scholar
30. van Belkum A, Fahal AH, van de Sande WWJ. In vitro susceptibility of Madurella mycetomatis to posaconazole and terbinafine. Antimicrob Agents Chemother. 2011;55(4):1771–3. pmid:21263050
- View Article
- PubMed/NCBI
- Google Scholar
31. van de Sande WWJ, de Kat J, Coppens J, Ahmed AOA, Fahal A, Verbrugh H, et al. Melanin biosynthesis in Madurella mycetomatis and its effect on susceptibility to itraconazole and ketoconazole. Microbes Infect. 2007;9(9):1114–23. pmid:17644456
- View Article
- PubMed/NCBI
- Google Scholar
32. van de Sande WWJ, Fahal AH, Bakker-Woudenberg IAJM, van Belkum A. Madurella mycetomatis is not susceptible to the echinocandin class of antifungal agents. Antimicrob Agents Chemother. 2010;54(6):2738–40. pmid:20350944
- View Article
- PubMed/NCBI
- Google Scholar
33. Suleiman SH, Wadaella ES, Fahal AH. The Surgical Treatment of Mycetoma. PLoS Negl Trop Dis. 2016;10(6):e0004690. pmid:27336736
- View Article
- PubMed/NCBI
- Google Scholar
34. Margolis E, Rosch JW. Fitness Landscape of the Immune Compromised Favors the Emergence of Antibiotic Resistance. ACS Infect Dis. 2018;4(9):1275–7. pmid:30070470
- View Article
- PubMed/NCBI
- Google Scholar
35. Smyth GK, Michaud J, Scott HS. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics. 2005;21(9):2067–75. pmid:15657102
- View Article
- PubMed/NCBI
- Google Scholar
36. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. JOSS. 2019;4(43):1686.
- View Article
- Google Scholar
37. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. pmid:25605792
- View Article
- PubMed/NCBI
- Google Scholar
38. Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer-Verlag New York. 2016.
39. Ushey K, Wickham H. renv: Project Environments. Boston, MA: RStudio. 2026.
40. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67(1):1–48.
- View Article
- Google Scholar
41. Lenth RV, Piaskowski J. Emmeans: Estimated Marginal Means, aka Least-Squares Means. 2026.
42. van Elsas MJ, Labrie C, Etzerodt A, Charoentong P, van Stigt Thans JJC, Van Hall T, et al. Invasive margin tissue-resident macrophages of high CD163 expression impede responses to T cell-based immunotherapy. J Immunother Cancer. 2023;11(3):e006433. pmid:36914207
- View Article
- PubMed/NCBI
- Google Scholar
43. Gao Y, He Y, Tang Y, Chen Z-S, Qu M. VISTA: A Novel Checkpoint for Cancer Immunotherapy. Drug Discov Today. 2024;29(7):104045. pmid:38797321
- View Article
- PubMed/NCBI
- Google Scholar
44. Tyrinova TV, Batorov EV, Aristova TA, Ushakova GY, Sizikova SA, Denisova VV. Expression of inhibitory molecules (arginase-1, IDO, and PD-L1) by myeloid-derived suppressor cells in multiple myeloma patients in remission. Bull Exp Biol Med. 2022;174(1):71–5. pmid:36437327
- View Article
- PubMed/NCBI
- Google Scholar
45. Sandström Gerdtsson A, Knulst M, Botling J, Mezheyeuski A, Micke P, Ek S. Phenotypic characterization of spatial immune infiltration niches in non-small cell lung cancer. Oncoimmunology. 2023;12(1):2206725. pmid:37139184
- View Article
- PubMed/NCBI
- Google Scholar
46. Miret JJ, Kirschmeier P, Koyama S, Zhu M, Li YY, Naito Y, et al. Suppression of Myeloid Cell Arginase Activity leads to Therapeutic Response in a NSCLC Mouse Model by Activating Anti-Tumor Immunity. J Immunother Cancer. 2019;7(1):32. pmid:30728077
- View Article
- PubMed/NCBI
- Google Scholar
47. Curto E, Mateus-Medina ÉF, Crespo-Lessmann A, Osuna-Gómez R, Ujaldón-Miró C, García-Moral A, et al. Identification of Two Eosinophil Subsets in Induced Sputum from Patients with Allergic Asthma According to CD15 and CD66b Expression. Int J Environ Res Public Health. 2022;19(20):13400. pmid:36293979
- View Article
- PubMed/NCBI
- Google Scholar
48. Pasupuleti SK, Ramdas B, Yang K, Zhang C, Stieglitz E, Kapur R. Targeting M2-Tumor Associated Macrophages By Arginase-1 and PD-L1 in Regulating Juvenile Myelomonocytic Leukemia (JMML) Development and Relapse. Blood. 2021;138:1471.
- View Article
- Google Scholar
49. West EE, Merle NS, Kaminski MM, Palacios G, Kumar D, Wang L, et al. Loss of CD4(+) T cell-intrinsic arginase 1 accelerates Th1 response kinetics and reduces lung pathology during influenza infection. Immunity. 2023;56(9):2036–53 e12. Epub 20230811. pmid:37572656
- View Article
- PubMed/NCBI
- Google Scholar
50. Jayasingam SD, Citartan M, Thang TH, Mat Zin AA, Ang KC, Ch’ng ES. Evaluating the Polarization of Tumor-Associated Macrophages Into M1 and M2 Phenotypes in Human Cancer Tissue: Technicalities and Challenges in Routine Clinical Practice. Front Oncol. 2020;9:1512. pmid:32039007
- View Article
- PubMed/NCBI
- Google Scholar
51. Lakschevitz FS, Hassanpour S, Rubin A, Fine N, Sun C, Glogauer M. Identification of neutrophil surface marker changes in health and inflammation using high-throughput screening flow cytometry. Exp Cell Res. 2016;342(2):200–9. pmid:26970376
- View Article
- PubMed/NCBI
- Google Scholar
52. Schmidt T, Zündorf J, Grüger T, Brandenburg K, Reiners A-L, Zinserling J, et al. CD66b overexpression and homotypic aggregation of human peripheral blood neutrophils after activation by a gram-positive stimulus. J Leukoc Biol. 2012;91(5):791–802. pmid:22319104
- View Article
- PubMed/NCBI
- Google Scholar
53. Czystowska-Kuzmicz M, Sosnowska A, Nowis D, Ramji K, Szajnik M, Chlebowska-Tuz J, et al. Small extracellular vesicles containing arginase-1 suppress T-cell responses and promote tumor growth in ovarian carcinoma. Nat Commun. 2019;10(1):3000. pmid:31278254
- View Article
- PubMed/NCBI
- Google Scholar
54. Maisonneuve C, Tsang DKL, Foerster EG, Robert LM, Mukherjee T, Prescott D, et al. Nod1 promotes colorectal carcinogenesis by regulating the immunosuppressive functions of tumor-infiltrating myeloid cells. Cell Rep. 2021;34(4):108677. pmid:33503439
- View Article
- PubMed/NCBI
- Google Scholar
55. Johnston RJ, Su LJ, Pinckney J, Critton D, Boyer E, Krishnakumar A, et al. VISTA is an acidic pH-selective ligand for PSGL-1. Nature. 2019;574(7779):565–70. pmid:31645726
- View Article
- PubMed/NCBI
- Google Scholar
56. Ashenafi S, Muvva JR, Mily A, Snäll J, Zewdie M, Chanyalew M, et al. Immunosuppressive Features of the Microenvironment in Lymph Nodes Granulomas from Tuberculosis and HIV-Co-Infected Patients. Am J Pathol. 2022;192(4):653–70. pmid:35092727
- View Article
- PubMed/NCBI
- Google Scholar
57. Peyster EG, Smith D, Bittermann T, Bravo PE, Margulies KB. Cardiac sarcoidosis: new insights beyond the granuloma using spatial proteomics. Eur Heart J. 2026;47(24):3174–88. pmid:41431931
- View Article
- PubMed/NCBI
- Google Scholar
58. Ye Z, Huang S, Zhang Y, Mei X, Zheng H, Li M, et al. Galectins, eosinophiles, and macrophages may contribute to Schistosoma japonicum egg-induced immunopathology in a mouse model. Frontiers in Immunology. 2020;11:146. pmid:32231658
- View Article
- PubMed/NCBI
- Google Scholar
59. Steggerda SM, Bennett MK, Chen J, Emberley E, Huang T, Janes JR, et al. Inhibition of arginase by CB-1158 blocks myeloid cell-mediated immune suppression in the tumor microenvironment. J Immunother Cancer. 2017;5(1):101. pmid:29254508
- View Article
- PubMed/NCBI
- Google Scholar
60. Naing A, Papadopoulos KP, Pishvaian MJ, Rahma O, Hanna GJ, Garralda E, et al. First-in-human phase 1 study of the arginase inhibitor INCB001158 alone or combined with pembrolizumab in patients with advanced or metastatic solid tumours. BMJ Oncol. 2024;3(1):e000249. pmid:39886141
- View Article
- PubMed/NCBI
- Google Scholar
61. Niu F, Yu Y, Li Z, Ren Y, Li Z, Ye Q, et al. Arginase: An emerging and promising therapeutic target for cancer treatment. Biomed Pharmacother. 2022;149:112840. pmid:35316752
- View Article
- PubMed/NCBI
- Google Scholar
62. Marin-Acevedo JA, Dholaria B, Soyano AE, Knutson KL, Chumsri S, Lou Y. Next generation of immune checkpoint therapy in cancer: new developments and challenges. J Hematol Oncol. 2018;11(1):39. pmid:29544515
- View Article
- PubMed/NCBI
- Google Scholar
63. Thisted T, Smith FD, Mukherjee A, Kleschenko Y, Feng F, Jiang Z-G, et al. VISTA checkpoint inhibition by pH-selective antibody SNS-101 with optimized safety and pharmacokinetic profiles enhances PD-1 response. Nat Commun. 2024;15(1):2917. pmid:38575562
- View Article
- PubMed/NCBI
- Google Scholar
64. Martin AS, Molloy M, Ugolkov A, von Roemeling RW, Noelle RJ, Lewis LD, et al. VISTA expression and patient selection for immune-based anticancer therapy. Front Immunol. 2023;14:1086102. pmid:36891296
- View Article
- PubMed/NCBI
- Google Scholar
65. Leeansyah E, Boulouis C, Kwa ALH, Sandberg JK. Emerging Role for MAIT Cells in Control of Antimicrobial Resistance. Trends Microbiol. 2021;29(6):504–16. pmid:33353796
- View Article
- PubMed/NCBI
- Google Scholar
66. Ashwin H, Milross L, Wilson J, Majo J, Hang Lee JT, Calder G, et al. Identification of a protein expression signature distinguishing early from organising diffuse alveolar damage in COVID-19 patients. J Clin Pathol. 2023;76(8):561–5. pmid:36894313
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Fahal AH. Mycetoma: A global medical and socio-economic dilemma. PLoS Negl Trop Dis. 2017;11(4):e0005509. pmid:28426654
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Fahal AH, Suliman SH, Hay R. Mycetoma: The Spectrum of Clinical Presentation. Trop Med Infect Dis. 2018;3(3):97. pmid:30274493
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Hay RJ, Fahal AH. Mycetoma: an old and still neglected tropical disease. Trans R Soc Trop Med Hyg. 2015;109(3):169–70. pmid:25667234
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Ahmed AOA, van Leeuwen W, Fahal A, van de Sande W, Verbrugh H, van Belkum A. Mycetoma caused by Madurella mycetomatis: a neglected infectious burden. Lancet Infect Dis. 2004;4(9):566–74. pmid:15336224
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. López-Martínez R, Méndez-Tovar LJ, Bonifaz A, Arenas R, Mayorga J, Welsh O, et al. Update on the epidemiology of mycetoma in Mexico. A review of 3933 cases. Gac Med Mex. 2013;149(5):586–92. pmid:24108347
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. van de Sande WWJ. Global burden of human mycetoma: a systematic review and meta-analysis. PLoS Negl Trop Dis. 2013;7(11):e2550. pmid:24244780
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. de Hoog GS, van Diepeningen AD, Mahgoub E-S, van de Sande WWJ. New species of Madurella, causative agents of black-grain mycetoma. J Clin Microbiol. 2012;50(3):988–94. pmid:22205798
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. van Belkum A, Fahal A, van de Sande WWJ. Mycetoma caused by Madurella mycetomatis: a completely neglected medico-social dilemma. Adv Exp Med Biol. 2013;764:179–89. pmid:23654067
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Fahal AH. Mycetoma: a thorn in the flesh. Trans R Soc Trop Med Hyg. 2004;98(1):3–11. pmid:14702833
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Sheehan G, Konings M, Lim W, Fahal A, Kavanagh K, van de Sande WWJ. Proteomic analysis of the processes leading to Madurella mycetomatis grain formation in Galleria mellonella larvae. PLoS Negl Trop Dis. 2020;14(4):e0008190. pmid:32267851
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. van de Sande WWJ, Fahal AH, Goodfellow M, Mahgoub ES, Welsh O, Zijlstra EE. Merits and pitfalls of currently used diagnostic tools in mycetoma. PLoS Negl Trop Dis. 2014;8(7):e2918. pmid:24992636
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Zijlstra EE, van de Sande WWJ, Welsh O, Mahgoub ES, Goodfellow M, Fahal AH. Mycetoma: a unique neglected tropical disease. Lancet Infect Dis. 2016;16(1):100–12. pmid:26738840
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Ahmed SA, van den Ende BHGG, Fahal AH, van de Sande WWJ, de Hoog GS. Rapid identification of black grain eumycetoma causative agents using rolling circle amplification. PLoS Negl Trop Dis. 2014;8(12):e3368. pmid:25474355
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. ELbadawi HS, Mahgoub E, Mahmoud N, Fahal AH. Use of immunoblotting in testing Madurella mycetomatis specific antigen. Trans R Soc Trop Med Hyg. 2016;110(5):312–6. pmid:27198216
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. el Hassan AM, Fahal AH, Ahmed AO, Ismail A, Veress B. The immunopathology of actinomycetoma lesions caused by Streptomyces somaliensis. Trans R Soc Trop Med Hyg. 2001;95(1):89–92. pmid:11280076
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Fahal AH, el Toum EA, el Hassan AM, Mahgoub ES, Gumaa SA. The host tissue reaction to Madurella mycetomatis: new classification. J Med Vet Mycol. 1995;33(1):15–7. pmid:7650573
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. Hoeksema MA, Scicluna BP, Boshuizen MCS, van der Velden S, Neele AE, Van den Bossche J, et al. IFN-γ priming of macrophages represses a part of the inflammatory program and attenuates neutrophil recruitment. J Immunol. 2015;194(8):3909–16. pmid:25750432
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref18] 18. Ibrahim AI, El Hassan AM, Fahal A, van de Sande WW. A histopathological exploration of the Madurella mycetomatis grain. PLoS One. 2013;8(3):e57774. pmid:23483927
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref19] 19. Nasr A, Abushouk A, Hamza A, Siddig E, Fahal AH. Th-1, Th-2 Cytokines Profile among Madurella mycetomatis Eumycetoma Patients. PLoS Negl Trop Dis. 2016;10(7):e0004862. pmid:27434108
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref20] 20. Abushouk A, Nasr A, Masuadi E, Allam G, Siddig EE, Fahal AH. The Role of Interleukin-1 cytokine family (IL-1β, IL-37) and interleukin-12 cytokine family (IL-12, IL-35) in eumycetoma infection pathogenesis. PLoS Negl Trop Dis. 2019;13(4):e0007098. pmid:30946748
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref21] 21. Fahal A, Mahgoub ES, El Hassan AM, Abdel-Rahman ME, Alshambaty Y, Hashim A, et al. A new model for management of mycetoma in the Sudan. PLoS Negl Trop Dis. 2014;8(10):e3271. pmid:25356640
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref22] 22. Guimarães CC, Castro LGM, Sotto MN. Lymphocyte subsets, macrophages and Langerhans cells in actinomycetoma and eumycetoma tissue reaction. Acta Trop. 2003;87(3):377–84. pmid:12875932
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref23] 23. Siddig EE, Mohammed Edris AM, Bakhiet SM, van de Sande WWJ, Fahal AH. Interleukin-17 and matrix metalloprotease-9 expression in the mycetoma granuloma. PLoS Negl Trop Dis. 2019;13(7):e0007351. pmid:31295246
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref24] 24. Mhmoud NA, Fahal AH, van de Sande WWJ. The association between the interleukin-10 cytokine and CC chemokine ligand 5 polymorphisms and mycetoma granuloma formation. Med Mycol. 2013;51(5):527–33. pmid:23210681
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref25] 25. Ali RS, Newport MJ, Bakhiet SM, Ibrahim ME, Fahal AH. Host genetic susceptibility to mycetoma. PLoS Negl Trop Dis. 2020;14(4):e0008053. pmid:32352976
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref26] 26. Wadal A, Elhassan TA, Zein HA, Abdel-Rahman ME, Fahal AH. Predictors of Post-operative Mycetoma Recurrence Using Machine-Learning Algorithms: The Mycetoma Research Center Experience. PLoS Negl Trop Dis. 2016;10(10):e0005007. pmid:27798643
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref27] 27. Welsh O, Al-Abdely HM, Salinas-Carmona MC, Fahal AH. Mycetoma medical therapy. PLoS Negl Trop Dis. 2014;8(10):e3218. pmid:25330342
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref28] 28. Zein HAM, Fahal AH, Mahgoub ES, El Hassan TA, Abdel-Rahman ME. Predictors of cure, amputation and follow-up dropout among patients with mycetoma seen at the Mycetoma Research Centre, University of Khartoum, Sudan. Trans R Soc Trop Med Hyg. 2012;106(11):639–44. pmid:22854685
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref29] 29. Kloezen W, Meis JF, Curfs-Breuker I, Fahal AH, van de Sande WWJ. In vitro antifungal activity of isavuconazole against Madurella mycetomatis. Antimicrob Agents Chemother. 2012;56(11):6054–6. pmid:22964246
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref30] 30. van Belkum A, Fahal AH, van de Sande WWJ. In vitro susceptibility of Madurella mycetomatis to posaconazole and terbinafine. Antimicrob Agents Chemother. 2011;55(4):1771–3. pmid:21263050
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref31] 31. van de Sande WWJ, de Kat J, Coppens J, Ahmed AOA, Fahal A, Verbrugh H, et al. Melanin biosynthesis in Madurella mycetomatis and its effect on susceptibility to itraconazole and ketoconazole. Microbes Infect. 2007;9(9):1114–23. pmid:17644456
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref32] 32. van de Sande WWJ, Fahal AH, Bakker-Woudenberg IAJM, van Belkum A. Madurella mycetomatis is not susceptible to the echinocandin class of antifungal agents. Antimicrob Agents Chemother. 2010;54(6):2738–40. pmid:20350944
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref33] 33. Suleiman SH, Wadaella ES, Fahal AH. The Surgical Treatment of Mycetoma. PLoS Negl Trop Dis. 2016;10(6):e0004690. pmid:27336736
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref34] 34. Margolis E, Rosch JW. Fitness Landscape of the Immune Compromised Favors the Emergence of Antibiotic Resistance. ACS Infect Dis. 2018;4(9):1275–7. pmid:30070470
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref35] 35. Smyth GK, Michaud J, Scott HS. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics. 2005;21(9):2067–75. pmid:15657102
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref36] 36. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. JOSS. 2019;4(43):1686.
View Article
Google Scholar

[142] View Article

[143] Google Scholar

[ref37] 37. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. pmid:25605792
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref38] 38. Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer-Verlag New York. 2016.

[ref39] 39. Ushey K, Wickham H. renv: Project Environments. Boston, MA: RStudio. 2026.

[ref40] 40. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67(1):1–48.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref41] 41. Lenth RV, Piaskowski J. Emmeans: Estimated Marginal Means, aka Least-Squares Means. 2026.

[ref42] 42. van Elsas MJ, Labrie C, Etzerodt A, Charoentong P, van Stigt Thans JJC, Van Hall T, et al. Invasive margin tissue-resident macrophages of high CD163 expression impede responses to T cell-based immunotherapy. J Immunother Cancer. 2023;11(3):e006433. pmid:36914207
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref43] 43. Gao Y, He Y, Tang Y, Chen Z-S, Qu M. VISTA: A Novel Checkpoint for Cancer Immunotherapy. Drug Discov Today. 2024;29(7):104045. pmid:38797321
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref44] 44. Tyrinova TV, Batorov EV, Aristova TA, Ushakova GY, Sizikova SA, Denisova VV. Expression of inhibitory molecules (arginase-1, IDO, and PD-L1) by myeloid-derived suppressor cells in multiple myeloma patients in remission. Bull Exp Biol Med. 2022;174(1):71–5. pmid:36437327
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref45] 45. Sandström Gerdtsson A, Knulst M, Botling J, Mezheyeuski A, Micke P, Ek S. Phenotypic characterization of spatial immune infiltration niches in non-small cell lung cancer. Oncoimmunology. 2023;12(1):2206725. pmid:37139184
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref46] 46. Miret JJ, Kirschmeier P, Koyama S, Zhu M, Li YY, Naito Y, et al. Suppression of Myeloid Cell Arginase Activity leads to Therapeutic Response in a NSCLC Mouse Model by Activating Anti-Tumor Immunity. J Immunother Cancer. 2019;7(1):32. pmid:30728077
View Article
PubMed/NCBI
Google Scholar

[171] View Article

[172] PubMed/NCBI

[173] Google Scholar

[ref47] 47. Curto E, Mateus-Medina ÉF, Crespo-Lessmann A, Osuna-Gómez R, Ujaldón-Miró C, García-Moral A, et al. Identification of Two Eosinophil Subsets in Induced Sputum from Patients with Allergic Asthma According to CD15 and CD66b Expression. Int J Environ Res Public Health. 2022;19(20):13400. pmid:36293979
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref48] 48. Pasupuleti SK, Ramdas B, Yang K, Zhang C, Stieglitz E, Kapur R. Targeting M2-Tumor Associated Macrophages By Arginase-1 and PD-L1 in Regulating Juvenile Myelomonocytic Leukemia (JMML) Development and Relapse. Blood. 2021;138:1471.
View Article
Google Scholar

[179] View Article

[180] Google Scholar

[ref49] 49. West EE, Merle NS, Kaminski MM, Palacios G, Kumar D, Wang L, et al. Loss of CD4(+) T cell-intrinsic arginase 1 accelerates Th1 response kinetics and reduces lung pathology during influenza infection. Immunity. 2023;56(9):2036–53 e12. Epub 20230811. pmid:37572656
View Article
PubMed/NCBI
Google Scholar

[182] View Article

[183] PubMed/NCBI

[184] Google Scholar

[ref50] 50. Jayasingam SD, Citartan M, Thang TH, Mat Zin AA, Ang KC, Ch’ng ES. Evaluating the Polarization of Tumor-Associated Macrophages Into M1 and M2 Phenotypes in Human Cancer Tissue: Technicalities and Challenges in Routine Clinical Practice. Front Oncol. 2020;9:1512. pmid:32039007
View Article
PubMed/NCBI
Google Scholar

[186] View Article

[187] PubMed/NCBI

[188] Google Scholar

[ref51] 51. Lakschevitz FS, Hassanpour S, Rubin A, Fine N, Sun C, Glogauer M. Identification of neutrophil surface marker changes in health and inflammation using high-throughput screening flow cytometry. Exp Cell Res. 2016;342(2):200–9. pmid:26970376
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

[ref52] 52. Schmidt T, Zündorf J, Grüger T, Brandenburg K, Reiners A-L, Zinserling J, et al. CD66b overexpression and homotypic aggregation of human peripheral blood neutrophils after activation by a gram-positive stimulus. J Leukoc Biol. 2012;91(5):791–802. pmid:22319104
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref53] 53. Czystowska-Kuzmicz M, Sosnowska A, Nowis D, Ramji K, Szajnik M, Chlebowska-Tuz J, et al. Small extracellular vesicles containing arginase-1 suppress T-cell responses and promote tumor growth in ovarian carcinoma. Nat Commun. 2019;10(1):3000. pmid:31278254
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref54] 54. Maisonneuve C, Tsang DKL, Foerster EG, Robert LM, Mukherjee T, Prescott D, et al. Nod1 promotes colorectal carcinogenesis by regulating the immunosuppressive functions of tumor-infiltrating myeloid cells. Cell Rep. 2021;34(4):108677. pmid:33503439
View Article
PubMed/NCBI
Google Scholar

[202] View Article

[203] PubMed/NCBI

[204] Google Scholar

[ref55] 55. Johnston RJ, Su LJ, Pinckney J, Critton D, Boyer E, Krishnakumar A, et al. VISTA is an acidic pH-selective ligand for PSGL-1. Nature. 2019;574(7779):565–70. pmid:31645726
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref56] 56. Ashenafi S, Muvva JR, Mily A, Snäll J, Zewdie M, Chanyalew M, et al. Immunosuppressive Features of the Microenvironment in Lymph Nodes Granulomas from Tuberculosis and HIV-Co-Infected Patients. Am J Pathol. 2022;192(4):653–70. pmid:35092727
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref57] 57. Peyster EG, Smith D, Bittermann T, Bravo PE, Margulies KB. Cardiac sarcoidosis: new insights beyond the granuloma using spatial proteomics. Eur Heart J. 2026;47(24):3174–88. pmid:41431931
View Article
PubMed/NCBI
Google Scholar

[214] View Article

[215] PubMed/NCBI

[216] Google Scholar

[ref58] 58. Ye Z, Huang S, Zhang Y, Mei X, Zheng H, Li M, et al. Galectins, eosinophiles, and macrophages may contribute to Schistosoma japonicum egg-induced immunopathology in a mouse model. Frontiers in Immunology. 2020;11:146. pmid:32231658
View Article
PubMed/NCBI
Google Scholar

[218] View Article

[219] PubMed/NCBI

[220] Google Scholar

[ref59] 59. Steggerda SM, Bennett MK, Chen J, Emberley E, Huang T, Janes JR, et al. Inhibition of arginase by CB-1158 blocks myeloid cell-mediated immune suppression in the tumor microenvironment. J Immunother Cancer. 2017;5(1):101. pmid:29254508
View Article
PubMed/NCBI
Google Scholar

[222] View Article

[223] PubMed/NCBI

[224] Google Scholar

[ref60] 60. Naing A, Papadopoulos KP, Pishvaian MJ, Rahma O, Hanna GJ, Garralda E, et al. First-in-human phase 1 study of the arginase inhibitor INCB001158 alone or combined with pembrolizumab in patients with advanced or metastatic solid tumours. BMJ Oncol. 2024;3(1):e000249. pmid:39886141
View Article
PubMed/NCBI
Google Scholar

[226] View Article

[227] PubMed/NCBI

[228] Google Scholar

[ref61] 61. Niu F, Yu Y, Li Z, Ren Y, Li Z, Ye Q, et al. Arginase: An emerging and promising therapeutic target for cancer treatment. Biomed Pharmacother. 2022;149:112840. pmid:35316752
View Article
PubMed/NCBI
Google Scholar

[230] View Article

[231] PubMed/NCBI

[232] Google Scholar

[ref62] 62. Marin-Acevedo JA, Dholaria B, Soyano AE, Knutson KL, Chumsri S, Lou Y. Next generation of immune checkpoint therapy in cancer: new developments and challenges. J Hematol Oncol. 2018;11(1):39. pmid:29544515
View Article
PubMed/NCBI
Google Scholar

[234] View Article

[235] PubMed/NCBI

[236] Google Scholar

[ref63] 63. Thisted T, Smith FD, Mukherjee A, Kleschenko Y, Feng F, Jiang Z-G, et al. VISTA checkpoint inhibition by pH-selective antibody SNS-101 with optimized safety and pharmacokinetic profiles enhances PD-1 response. Nat Commun. 2024;15(1):2917. pmid:38575562
View Article
PubMed/NCBI
Google Scholar

[238] View Article

[239] PubMed/NCBI

[240] Google Scholar

[ref64] 64. Martin AS, Molloy M, Ugolkov A, von Roemeling RW, Noelle RJ, Lewis LD, et al. VISTA expression and patient selection for immune-based anticancer therapy. Front Immunol. 2023;14:1086102. pmid:36891296
View Article
PubMed/NCBI
Google Scholar

[242] View Article

[243] PubMed/NCBI

[244] Google Scholar

[ref65] 65. Leeansyah E, Boulouis C, Kwa ALH, Sandberg JK. Emerging Role for MAIT Cells in Control of Antimicrobial Resistance. Trends Microbiol. 2021;29(6):504–16. pmid:33353796
View Article
PubMed/NCBI
Google Scholar

[246] View Article

[247] PubMed/NCBI

[248] Google Scholar

[ref66] 66. Ashwin H, Milross L, Wilson J, Majo J, Hang Lee JT, Calder G, et al. Identification of a protein expression signature distinguishing early from organising diffuse alveolar damage in COVID-19 patients. J Clin Pathol. 2023;76(8):561–5. pmid:36894313
View Article
PubMed/NCBI
Google Scholar

[250] View Article

[251] PubMed/NCBI

[252] Google Scholar

Figures

Abstract

Author summary

Introduction

Methods

Ethics statement

Patients

GeoMx digital spatial profiling

Immunohistology

Statistical analysis

Results

Study population

Spatial profiling of mycetoma surgical biopsies

CD66b+ cells accumulate near grains

Discussion

Supporting information

S1 Table. DSP raw data file.

S1 Fig. Representative histology of mycetoma patients (H&E).

S2 Fig. GeoMx protein expression across all ROIs.

S3 Fig. Protein expression by species.

S4 Fig. Correlations plots for Far ROIs.

S5 Fig. Expression of CD15 and Siglec-8 in mycetoma biopsies.

Acknowledgments

References

CD66b⁺ cells accumulate near grains