Next Article in Journal
Seven New Species of Entoloma Subgenus Cubospora (Entolomataceae, Agaricales) from Subtropical Regions of China
Next Article in Special Issue
A New Overview of Sex Bias in Fungal Infections
Previous Article in Journal
A Fusarium verticillioides MAT1-2 Strain near Isogenic to the Sequenced FGSC7600 Strain for Producing Homozygous Multigene Mutants
Previous Article in Special Issue
Transcriptional and Post-Translational Roles of Calcineurin in Cationic Stress and Glycerol Biosynthesis in Cryptococcus neoformans
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JoF
Volume 10
Issue 8
10.3390/jof10080593
jof-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types

by
Luigi Musetta
1,
Shannon Helsper
1,
Lara Roosen
1,
Dries Maes
1,
Anca Croitor Sava
1,
Liesbeth Vanherp
1,2,
Willy Gsell
1,
Greetje Vande Velde
1,
Katrien Lagrou
3,4,
Wieland Meyer
5 and
Uwe Himmelreich
1,*
1
Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
2
µNEURO Research Centre of Excellence, University of Antwerp, 2000 Antwerp, Belgium
3
Laboratory of Clinical Microbiology, Department of Microbiology, Immunology and Transplantation, KU Leuven, 3000 Leuven, Belgium
4
Department of Laboratory Medicine, National Reference Center for Mycosis, UZ Leuven, 3000 Leuven, Belgium
5
Westerdjjk Fungal Biodiversity Institute—KNAW, 3584 CT Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
J. Fungi 2024, 10(8), 593; https://doi.org/10.3390/jof10080593
Submission received: 25 June 2024 / Revised: 13 August 2024 / Accepted: 15 August 2024 / Published: 22 August 2024
(This article belongs to the Special Issue Cryptococcus Infections and Pathogenesis)
Browse Figures
Review Reports Versions Notes

Abstract

:
The controversially discussed taxonomy of the Cryptococcus neoformans/Cryptococcus gattii species complex encompasses at least eight major molecular types. Cerebral cryptococcomas are a common manifestation of cryptococcal neurological disease. In this study, we compared neurotypical symptoms and differential neurovirulence induced by one representative isolate for each of the eight molecular types studied. We compared single focal lesions caused by the different isolates and evaluated the potential relationships between the fungal burden and properties obtained with quantitative magnetic resonance imaging (qMRI) techniques such as diffusion MRI, T2 relaxometry and magnetic resonance spectroscopy (MRS). We observed an inverse correlation between parametric data and lesion density, and we were able to monitor longitudinally biophysical properties of cryptococcomas induced by different molecular types. Because the MRI/MRS techniques are also clinically available, the same approach could be used to assess image-based biophysical properties that correlate with fungal cell density in lesions in patients to determine personalized treatments.

1. Introduction

Isolates of the Cryptococcus neoformans/Cryptococcus gattii species complexes (CN/CG) are opportunistic pathogenic yeasts found in the environment (e.g., soil, decaying wood, bird guano). The infectious particles present in the environment enter the host initially through inhalation. They can cause lung infections and disseminate with a remarkable tropism for the central nervous system (CNS) [1]. Cryptococcal meningoencephalitis is globally the most common cause of CNS fungal infection, which mainly affects immunocompromised people such as HIV-infected individuals and solid organ transplant recipients [2,3]. Once in the brain, the opportunistic yeast can lead to the development of meningoencephalitis and/or induce a typical spherical lesion in the brain parenchyma called cryptococcomas. The latter typically consists of a local accumulation of cryptococci encapsulated in a lytic globular structure composed of gelatinous mucoid material and occasionally inflammatory cells [4,5].
In 2022, the WHO (World Health Organization) published, for the first time since its foundation, a fungal priority list, ranking CN and CG in the critical and medium priority groups, respectively. In this report, there is a clear request to better understand the antifungal susceptibility of different molecular types [6,7]. The complex relationship of different isolates has resulted in repeated proposals for revisions of the taxonomy of the CN/CG species complexes and has been part of a continuous debate [8,9,10,11,12,13,14,15,16]. The consensus on multi-locus sequence typing highlighted a differential geographical distribution as well as different associations with the clinical manifestation of symptoms [17,18,19,20].
While CN and CG result in partially different symptoms and manifestations of cryptococcosis in patients [10,11], these have not been studied systematically for the major different molecular types. In addition, confounding factors like underlying diseases, geographic location and others make it difficult to assess the pathogenesis of different molecular types of CN/CG isolates. Preclinical studies in rodent models provide the opportunity to assess differences between the major molecular types in the pathogenesis of cryptococcosis in vivo under controlled conditions [21,22,23,24,25]. However, due to differences in methodology, mouse models, and the use of only very few cryptococcal isolates, a systematic comparison to evaluate differences among the eight major molecular types in vivo using animal models is lacking. Limitations of murine models can include the use of invasive methods that are restricted to single time points relying on survival curves to define virulence, which exclude fundamental information to delineate the state of progression of the disease and dissemination [26,27,28,29]. Pool et al. provided criteria to evaluate the neurotypical symptoms of cryptococcosis between genetically modified strains, with the potential to also discriminate differences in neurovirulence with disease progression across the different major molecular types of the cryptococcus species complexes [30].
Recently, our group investigated in vivo imaging techniques to assess the pathogenesis, dissemination, and treatment of cryptococcosis in murine models non-invasively and longitudinally [31,32,33,34,35,36]. Hereby, quantitative magnetic resonance imaging (qMRI) methods are of particular interest in assessing cerebral cryptococcosis due to its direct translatability to human patients. qMRI refers to non-invasive MRI techniques that allow quantitative measurements of the biophysical properties of tissues under in vivo conditions both in animal models and in the clinic. qMRI holds the remarkable potential to discriminate between different tissue types, as well as different lesions, based on their imaging properties, either complementing or replacing biopsies [5,37]. qMRI techniques, including diffusion MRI, MR relaxometry, and spectroscopy, have been used to assess cell density or related capsule size within fungal lesions of a living host, enabling repeated and hereby longitudinal assessment of physical and metabolic changes of the lesion content in vivo [34,38]. These properties correlate with the concentration of fungal metabolites like trehalose or mannitol, as well as the colony-forming units (CFUs) in cryptococcomas [5,34,39,40,41].
While differences between reference strains of CN and CG, namely H99 and R265, are frequently studied, no systematic evaluation of virulence, pathogenesis, and disease progression among the different major molecular types of the CN/CG species complexes has been performed. In this study, we characterized differences in pathogenesis, lesion formation and biophysical properties of cryptococcomas in the mouse brain in vivo using qMRI techniques and investigated potential differences between cerebral cryptococcomas caused by the eight molecular types of CN/CG (VN I-IV; VG I-IV). We investigated the evolution of fungal lesion formation over time and, in addition, investigated non-invasively how MR properties potentially characterize the major molecular types of the CN/CG species complexes. We correlated brain fungal burden and the lesion fungal density with the clinically available qMRI techniques, delineating unique lesion biophysical properties.

2. Materials and Methods

2.1. Fungal Culture and Inoculum Preparation

Cryptococcal isolates, each representing one of the eight major molecular types, were received from the Molecular Medical Mycology Research Laboratory at Westmead Institute for Medical Research, University of Sydney, Australia [17] (Table 1). The stock cultures were stored at −80 °C suspended in a 10% (w/v) milk powder solution in sterile phosphate-buffer saline (PBS; 1X, GIBCO®, Life Technologies-Invitrogen, Paisley, UK). After thawing, the isolates were plated on Sabouraud agar (2% agar, following manufacturer instructions, product number: 64644, Biorad, Hercules, CA, USA) and incubated upside-down at 30 °C for 2 to 3 days. After the incubation period, colonies were transferred and resuspended into a liquid Sabouraud growth medium (2% glucose, following manufacturer instructions, product number CM0147, Oxoid, UK). The samples were vortexed and incubated for another 2 days at 30 °C. After the incubation period, samples were centrifuged (5 min; 650× g; 20 °C), and the formed pellet was washed twice with sterile PBS and again centrifuged (5 min; 650× g; 20 °C). The final pellet was resuspended in 10 mL PBS, and 10 µL of the fungal suspension was counted using a Neubauer-improved chamber (Marienfeld, Lauda-Königshofen, Germany). The suspension was then further diluted to obtain the desired inoculum of 100 µL volume containing 104 cells/µL in sterile PBS. The viability of the cryptococci in the inoculum was confirmed by the number of CFUs in a 10-fold dilution series starting with 20 µL of the inoculum plated on Sabouraud agar. Colonies were counted manually after 3 to 4 days of incubation upside-down at room temperature.

2.2. Stereotactic Injection in Mice

Animal experiments were performed in accordance with the national regulations and the European Directive 2010/63/EU [42] and after approval by the Animal Ethics Committee of KU Leuven under project number p197/2021. Female BALB/cAnNCrl mice (n = 4 per isolate, 32 mice total) at the age of 8 to 10 weeks (internal stock KU Leuven, Leuven, Belgium; Charles River Laboratories) were used for all experiments. The mice were housed in ventilated cages with food and water ad libitum and a maximum of four animals per cage. For surgical procedures, the animals were first anesthetized using an intraperitoneal injection of ketamine (40 to 60 mg/kg; Nimatek, Eurovet Animal Health, Bladel, The Netherlands) and medetomidine (0.05 to 0.10 mg/kg; Domitor, Orion Pharma, Espoo, Finland). The animals were then fixed on a stereotactic head frame (Stoelting, Wood Dale, IL, USA). A dosage of 30 µL Lidocaine (Xylocaine 2%, Adrenaline 1:200.00, AstraZeneca, Dilbeek, Belgium) was administered subcutaneously at skull level, the skin was disinfected with betadine solution, and the fur on the head was cut. We applied the ophthalmic gel (Vidisic gel 10 g, Bausch + Lomb Pharm SA, Bridgewater, NJ, USA) on the animals’ eyes to avoid drying during the procedure. A sterile scalpel was used to make a 0.5–1 cm incision in the middle of the scalp in the caudal direction. A Hamilton Syringe (701 RN, 30 gauge 1″ 10 µL) was cleaned with distilled water, 70% ethanol, and sterile PBS before and after each injection of cryptococci. The injection site was determined with respect to the bregma at 0.5 mm anterior and 2 mm right lateral. A 1 mm wide hole was drilled into the skull, and 1 µL, equal to 104 cryptococci, was injected 3 mm deep from the dura using the Hamilton Syringe pump (QSI, Stoelting, Wood Dale, IL, USA) (settings: 0.5 µL/min; syringe ID: 700 HML 10 µL—0.485). After 10 min, the needle was withdrawn slowly and disinfected with 70% ethanol. The animal was placed on a heating pad, and the hole in the skull was filled with UV-activated dental filling resin (NanoFIl A2 3 g, AT&M Biomaterials, Beijing, China). The incision site was sutured and disinfected with betadine. Anesthesia was reversed using intraperitoneal injection of Atipamezol (0.1 to 1 mg/kg; Antisedan, Orion Pharma, Espoo, France). Animals recovered overnight in a warmed cage (half on a 37 °C heating pad) with wet food (Ssniff, Soest, Germany). After full recovery, the animals were reintroduced into the standard-of-practice housing. This murine model is based on previously described rat and murine models [41].

2.3. Morbidity Scoring

Post injection, the animals were evaluated every 1 to 3 days for neurotypical symptoms and weighed. The animal morbidity induced by the cryptococcal infection was scored using previously established criteria by Pool et al. [30]. The ethical endpoint was defined when 75% of the group reached an average level 2 in the morbidity scoring or 50% reached level 1 (Table 2). Additionally, euthanasia was performed if the lesion reached a volume greater than 75 mm3 or the lesion was not visible (0 mm3) on anatomical scans post-day 6 over two imaging time points defined as “full recovery”. In such cases, euthanasia criteria were met regardless of the morbidity scoring, although the scoring was still noted for that time point.

2.4. Magnetic Resonance Imaging

During all MRI scans, the mice were anesthetized using 1.5–2.0% isoflurane in 100% oxygen. After induction of anesthesia, the mice were positioned on a heated mouse bed while continuously receiving anesthesia [43]. We applied ophthalmic gel (Vidisic gel 10 g, Bausch + Lomb Pharm SA, USA) on the animal’s eyes to avoid drying during the scan. The animals’ breathing rate (60–120 breaths/min) and body temperature (37 ± 1 °C) were closely monitored (Small Animal Instruments, Stony Brook, NY, USA) and maintained at physiological levels. MRI scans were acquired every 2–4 days post-injection using a 9.4 T preclinical horizontal MRI scanner (Biospec 94/20, Bartlesville, OK, USA) with a linearly polarized 1H resonator for transmission and an actively decoupled mouse brain surface coil for reception (all Bruker Biospin, Ettlingen, Germany). The manufacturers’ software platform Paravision 360 v.3.0 was used to obtain all MRI scans. Localizer images were first acquired as an anatomical reference for the positioning of subsequent scans. Two-dimensional T2-weighted MRI scans were acquired using a rapid acquisition with relaxation enhancement (RARE) sequence. Parametric T2 relaxation maps were acquired with an MSME (Multi Slice Multi Echo) protocol and apparent diffusion coefficient (ADC) maps with a diffusion-weighted pulsed gradient spin echo (PSGE) sequence [38]. Parametric maps were acquired in the same orientation and with the same slice thickness as the axial T2-weighted MR images. In addition, 1H Point Resolved Spectroscopy (PRESS) magnetic resonance spectroscopy (MRS) data of a single volume of interest centered around the cryptococcal lesion were acquired from a predefined volume of 8 mm3; in addition, one animal per group was selected to acquire MR spectra in a predefined volume of 8 mm3 in the contralateral region [34,40]. The parameters used for MRI acquisition are listed in Table 3.
Parametric maps (T2 and ADC) were calculated using the Paravision 360 v.3.0 image sequence analysis tool. Scans were then converted into NIFTI format for further analysis.

2.5. Data Analysis MRI

The NIFTI-converted images were analyzed using ITKSnap v3.8 [44]. Lesion volume (mm3), T2 (ms), and ADC (mm2/s) values were determined by manually delineating the lesion over the entirety of its volume using the adaptive brush tool (brush size: 20—granularity: 0.15—smoothness: 0.30) on the whole anatomical scan and overlaying the segmentation on the co-registered parametric scans. A standardized circular region of interest of 0.56 mm3 was selected in the contralateral region of the brain, corresponding to the left striatum approximatively opposite to the center of the lesion, as a reference and quality control.

2.6. Data Analysis MRS

1H MR spectra were analyzed (Matlab, Statistics and Machine Learning Toolbox) with principal component analysis (PCA) in the region of interest [0.5–4.2] ppm, where common metabolites display their peaks, and region [5.0–5.5] ppm, where trehalose is present. With PCA, a set of uncorrelated variables (features) called principal components (PCs) are computed [44,45] so that most of the variance in the original dataset can be explained by the first PCs. Moreover, PCA provides insight into which metabolites/metabolic regions within the MR spectra are most relevant in describing the variance between the different groups.

2.7. Brain CFU Plating

When the endpoint was reached based on criteria determined by the morbidity scores, animals were sacrificed by intraperitoneal injection of a pentobarbital overdose (1 g/kg), and brains were extracted. The brain was weighed and homogenized in 600 µL sterile PBS. The homogenizer (Tissue Master 125 Watt W/7 mm probe, OMNI International, Kennesaw, GA, USA) was cleaned and disinfected between samples by activating it for 10 s in distilled water, 70% ethanol, and PBS. A 10-fold dilution series of the brain homogenates was made up to 1/106. The samples were plated in triplicate on Sabouraud agar and incubated upside-down for 3–4 days at room temperature. The samples’ CFUs were manually counted, as described above. We later normalized the CFU through a logarithmic transformation of CFU divided by either brain weight (fungal burden) or lesion volume (lesion fungal density).

2.8. Statistical Analysis

Statistical analyses were performed by using Graphpad Prism v9.5.1. Log-rank (Mantel Cox) test was used to determine statistical differences in the endpoint curves. Mixed effects analysis with Geisser–Greenhouse correction for sphericity, followed by Dunnett’s multiple comparison tests, was used to determine longitudinal statistical differences in the morbidity scoring, lesion volume, T2, and ADC parameters. Correlations were assessed using linear regression and Pearson correlation coefficients.

3. Results

3.1. Cryptococcal Isolates of the Different Major Molecular Types Result in Variable Neurovirulence

Mice were stereotactically injected with one of the eight isolates of CN (VN I-IV) or CG (VG I-IV), each representing a different major molecular type. The morbidity scoring allowed the longitudinal assessment of neurological symptoms, highlighting differential progression and the manifestation of cryptococcoma in the brain. The neurotypical symptoms and morbidity induced by each isolate were variable between isolates (Figure 1A,B). Reaching the ethical endpoint was highly variable between molecular types, ranging between 5 and 20 days. Neurovirulence of the isolates was classified on the day of the average time needed for the morbidity score to reach level 2: “very high” (≤6 days), “high” (>6 days to ≤10 days), “medium” (>10 days to ≤15 days), “low” (≥16 days), or “non-neurovirulent” in case of full recovery (Figure 1C). We observed that CN isolates had medium to very high virulence, and CG isolates had either very high, low, or no virulence. Very high neurovirulent isolates showed generally a rapid decrease in the health score within 2–3 days after infection. On the contrary, isolates with lower neurovirulence showed variable trends with initial symptoms, such as reduced hygiene, being present only after 6–8 days post-infection. Non-neurovirulent isolates did not show any decay in the well-being of the animal. Additionally, normalized brain fungal burden calculated using the logarithm transformation of CFU divided by the brain weight at the endpoint (Figure 1D) was statistically different between CN and CG species (Supporting Figure S1).

3.2. Assessment of Cryptococcomas Using T2-Weighted Anatomical MRI

The development of cerebral cryptococcomas in this model is identified as hyperintensity and/or deformation of the right striatum in the mouse brain (Figure 2A,B). The shape, dimension, and evolution of lesion formation depended on the molecular type (Supporting Figures S3–S10A). Analysis of T2-weighted MRI showed that the lesion volume increases over time with comparable growth rates, resulting in lower virulent isolates manifesting larger lesion dimensions at the endpoint (Figure 2C,D). The differences in the lesion curves between the different molecular types were not statistically significant (Figure 2C,D).
Mixed effects statistical analysis indicated that lesion dimension is significantly related to the time post-injection rather than the neurovirulence scoring of the different isolates (Table 4), and VG III was excluded from the statistical analysis due to its non-neurovirulent property in this model.
When considering the endpoint, we observed a statistically relevant difference across isolates. Specifically, isolates with lower virulence tend to exhibit increased lesion volumes at the endpoint compared with those with higher virulence (Figure 3A,B). An additional biomarker of relative fungal burden is the image-based calculation of lesion density (LD) obtained using the logarithm transformation of CFU divided by the lesion volume at the endpoint (Figure 3C,D). When the cell densities of the major molecular types of CN in the lesions are compared with the cell densities of the major molecular types of CG, the analysis shows a higher fungal cell concentration in CN lesions (Figure 3E).

3.3. PCA Analysis of MRS Data at Endpoint

With PCA, we identify the metabolic regions of the acquired MR spectra that show variations that could be explored to discriminate between the different major molecular types of CN and CG. Due to the variable dimension of the lesion at the endpoint (Figure 3A,B), there was a variable partial volume effect, adding contribution from surrounding tissue to the lesion spectra that was ultimately identified as one of the PCs (Supporting Figure S2). We observed a better separation between CN and CG if the lesion volume is higher, and thus, the acquired MR spectra are a better representation of the fungal lesion formation. Consequently, there was a clear separation of VN I, being represented by higher lesion volume cases, with elevated peaks in the trehalose region. For VG III, due to its non-neurovirulent property in this model, the acquired MR spectra exhibited normal tissue metabolic features that group apart from all other cases.

3.4. Longitudinal Quantitative Parametric Evaluation of Cryptococcomas Show Molecular Type Dependent T2 Relaxometry and Disease Dependent Diffusivity

We tested the possibility of differentiating between different major molecular types of lesion-forming isolates of the CN/CG species complexes using qMRI. We investigated the longitudinal evolution of the fungal lesion in vivo (Figure 4A,B) and observed that T2 values were relatively consistent over time within each molecular type (Supporting Figures S3–S10B,D), indicating that the viscosity of the fungal lesion is approximatively consistent over time but different between different isolates/major molecular types (Figure 4C–E).
ADC values generally increase with the evolution of the disease (Figure 5A,B), showing higher values in CG species but similar values at the endpoint across the different major molecular types (Figure 5C–E). Compared with tissue measured in the contralateral hemisphere, water motility is higher in the cryptococcal lesions (Supporting Figures S3–S10C,E).
We further compared the qMRI parameters of the lesions at the endpoint, using the contralateral side of the brain as control. Both isolates of CN and CG show an increased T2 and water diffusivity (ADC) compared with the contralateral brain (Figure 6). We observed generally higher T2 values in the CG isolates compared with CN (Figure 6A), as well as statistically relevant differences between the major molecular types within each species complex (Figure 6B,C). We observed a similar trend of higher ADC values in the CG isolates compared with CN (Figure 6D), but on the contrary, we do not observe statistical differences between the molecular types of each species complex at the endpoint (Figure 6E,F). Both parametric values inversely correlate with lesion density, calculated using the logarithm transformation of CFU divided by lesion volume at the endpoint (Figure 6G,H). The different fungal density and molecular composition of the fungal lesion causes a change in the qMRI parameters distinguishable between CN and CG, with CN isolates having a more compact and viscous lesion environment compared with the looser structure of the CG isolates (Figure 6I,J).

4. Discussion

In this study, we provide a systematic in vivo evaluation of differences in neurovirulence induced by eight isolates representing the eight major molecular types of the CN/CG species complex. Many studies have addressed molecular and phenotypic/molecular differences between isolates of the CN/CG species complexes in culture, resulting in various proposals for their taxonomic classification [10,12,13,14,17,18,46,47,48,49]. To our knowledge, none of them have addressed differences in disease manifestation and pathogenesis in vivo. Contrary to the most commonly used laboratory reference strains (for CN strain H99 (VN I) and for CG strain R265 (VG II)), clinical and veterinarian isolates show differences in morbidity, and as such, we observed variable degrees of neurovirulence between the herein studied isolates representing the different major molecular types. We also confirmed that, on average, the CN species cause a higher fungal burden in the brain compared with the CG isolates at the endpoint using CFU counts. While these traditional evaluation methods of animal models, including morbidity scoring, survival curves, and CFU counts, provide useful information, in vivo imaging methods like MRI enable valuable insight into neurovirulence prior to endpoint. Specifically, MRI provides quantitative and multi-parametric information on clinically relevant parameters (lesion formation), cellular content (cell density, viscosity) and metabolism in a non-invasive manner at multiple time points for individual animals or patients [5,34,35,38]. The ability to delineate variable degrees of neurovirulence caused by the different isolates in a single focal lesion murine model from initial onset through endpoint is imperative for further understanding isolate-specific disease progression and kinetics. In addition, different isolates/major molecular types need to be studied before overall conclusions concerning the differentiation of the major molecular types by MRI methods can be concluded.
Lesion formation, including growth curves, lesion dimensions, and morphological differences in the manifestation of the focal fungal disease in the mouse brain, was achieved by using conventional anatomical MRI. Distinct patterns of lesion volume increase with comparable growth rates, as well as distinct growth patterns and hyperintensities for each of the isolates, were demonstrated. Trends in lesion density were comparable to the gold standard of CFU counts and showed differences between the isolates of the different major molecular types.
So-called advanced, quantitative MRI methods allow for the further characterization of lesions based on their physical properties. For example, T2 MR relaxometry provides a measure for the mobility of molecules like water and lipids due to the different degrees of water being bound to macromolecules and various degrees of viscosity [50]. Similarly, the determination of apparent diffusion coefficients (ADCs) by using diffusion MRI provides a measure for the random motion (diffusion) of water molecules in different tissues, thus highlighting pathology-derived alterations, namely differences in cell density and intercellular space [51,52]. While T2 and ADC values are frequently used for the characterization of tumor tissue, applications for fungal lesions remain sparse. In a previous study, we correlated these values with the composition of cryptococcomas, namely cryptococcal cell density and the fungal capsule size in the frequently used reference strains of C. neoformans and C. gattii, H99 and R265, respectively [38]. It is of interest to note that T2 and ADC values for H99 and R265 in this previous study were similar to the isolates of their respective molecular types (VN I and VG II) used in this study. This is the first indication that variability between isolates of the same molecular type might be less than between isolates of different molecular types. The MR properties of cryptococcomas correspond to their typical fluid-filled, pseudocystic lesion containing a variable amount of cryptococci. Our group previously compared the unique ADC and T2 values in focal cerebral cryptococcal lesions to in vivo fluorescence imaging of the two reference strains [38]. In this multi-modal study, we correlated cellular properties, like cell dimension and cell density, obtained with in vivo microscopy with qMRI findings. Higher cell densities and smaller capsule sizes were indicated for H99 when compared to R265. Our work here supports and generalizes these previous findings, as T2 and ADC values are significantly smaller for the molecular types of CN when compared to CG, indicating less intercellular space and higher cellular density. This previous study focused on a limited selection of fluorescent isolates, whereas here, we expand on the association between multiple clinical and veterinarian isolates, their disease manifestation, and image-based biomarkers. It is of interest to note that endpoint ADC values of the CN and CG isolates were comparable, even if the disease progression for each of these isolates was different. Exceptions are VG III and VG IV, where either no brain lesions were formed (VG III) or a very small lesion was formed (VG IV). The latter results in possibly biased ADC values due to partial volume effects resulting from the small size of the lesion relative to image slice thickness. Although more isolates per molecular type should be tested in the future, the work presented here supports the necessity to investigate not only microbiological and molecular tests for classifying isolates of the CN/CG species complexes but also experimental data on pathogenesis and physical properties of different isolates in in vivo models to gain complex characteristics to guide patient-specific treatment. Although no sham-injected control group was included in this study, previous work has shown that injection of PBS or non-proliferating cells results in only minor temporary changes in MRI (hypointense contrast due to needle track), which resolve within 3 to 4 days [39,53,54].
Further investigation of additional isolates also applied to different, more physiological models such as intravenous or intranasal administration would substantiate if this observation at a species and molecular type level can be generalized, adding fundamental data for species description. We chose the intracranial model in order to (1) minimize partial volume effects during the quantification of parametric qMRI data and (2) due to the availability of reference data from the H99 and R265 isolates in this model [34,38,39]. The relevance of partial volume effects became apparent for the quantification of metabolic data retrieved from the MRS experiments, where classification attempts were hampered by substantial contributions of surrounding brain tissue in the case of small lesions. The development of a quantitative method to overcome the substantial partial volume effects of measuring metabolism within the lesion in this model is underway. Regardless, PCA has shown to be a useful metric to identify the components of fungal metabolism and brain partial volume effects.
In conclusion, we demonstrate that clinically translatable qMRI techniques provide information on differences between CN and CG non-invasively, which are also based on differences in cell density in lesions and capsule size. While differences between molecular types were observed in terms of pathogenicity and neurological manifestation, more isolates per molecular type need to be studied. This foundational work can be easily translated to the clinic, which may add quantitative image-based parameters to optimize disease follow-up and, ideally, treatment thereof, as also demonstrated in other preclinical models [23,34,55]. Thereby, the qMRI approach provides non-invasive in vivo read-outs of variability, enabling insight into the biophysical properties caused by the different molecular types of the CN/CG species complexes.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jof10080593/s1. Table S1: Characteristics of the Cryptococcus neoformans and Cryptococcus gattii isolates [17,56,57]; Figure S1. Correlation of lesion density and fungal burden. A. & B. CN and CG brain fungal burden differences across the different major molecular types. The graphs show median distribution and standard deviation. One way ANOVA with Fisher LSD test. (p-value, *** < 0.001, **** < 0.0001); C. The graph is a linear Pearson correlation between lesion density and fungal burden in the whole brain; Figure S2. Endpoint MR spectroscopy data for representative isolates of CN and CG, and principal components analysis of MRS data. MR spectra at end-point obtained from CN (A) and CG (B) representative isolates. Principal components analysis (PCA) loading plots for PC1 and PC2 are shown (C), together with their coefficients. The distribution of species related (D) and isolate related (E) in the PCA coefficient space of PC1 and PC2 is colour marked; Figure S3. VN I longitudinal qMRI assessment of lesion (right striatum) and contralateral side (left striatum). T2w (A), T2 maps (B) and ADC maps (C) from one animal acquired at the different time points. The graphs show the average and standard deviation of T2 Values (D) and ADC values (E) of the lesion and corresponding contralateral side. 2-way ANOVA with Tukey’s multiple comparison. (p-values: ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001); Figure S4. VN II longitudinal qMRI assessment of lesion (right striatum) and contralateral side (left striatum). T2w (A), T2 maps (B) and ADC maps (C) from one animal acquired at the different time points. The graphs show the average and standard deviation of T2 Values (D) and ADC values (E) of the lesion and corresponding contralateral side. 2-way ANOVA with Tukey’s multiple comparison. (p-values: ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001); Figure S5. VN III longitudinal qMRI assessment of lesion (right striatum) and contralateral side (left striatum). T2w (A), T2 maps (B) and ADC maps (C) from one animal acquired at the different time points. The graphs show the average and standard deviation of T2 Values (D) and ADC values (E) of the lesion and corresponding contralateral side. 2-way ANOVA with Tukey’s multiple comparison. (p-values: ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001); Figure S6. VN IV longitudinal qMRI assessment of lesion (right striatum) and contralateral side (left striatum). T2w (A), T2 maps (B) and ADC maps (C) from one animal acquired at the different time points. The graphs show the average and standard deviation of T2 Values (D) and ADC values (E) of the lesion and corresponding contralateral side. 2-way ANOVA with Tukey’s multiple comparison. (p-values: ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001); Figure S7. VG I longitudinal qMRI assessment of lesion (right striatum) and contralateral side (left striatum). T2w (A), T2 maps (B) and ADC maps (C) from one animal acquired at the different time points. The graphs show the average and standard deviation of T2 Values (D) and ADC values (E) of the lesion and corresponding contralateral side. 2-way ANOVA with Tukey’s multiple comparison. (p-values: ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001); Figure S8. VG II longitudinal qMRI assessment of lesion (right striatum) and contralateral side (left striatum). T2w (A), T2 maps (B) and ADC maps (C) from one animal acquired at the different time points. The graphs show the average and standard deviation of T2 Values (D) and ADC values (E) of the lesion and corresponding contralateral side. 2-way ANOVA with Tukey’s multiple comparison. (p-values: ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001); Figure S9. VG III longitudinal qMRI assessment of lesion (right striatum) and contralateral side (left striatum). T2w (A), T2 maps (B) and ADC maps (C) from one animal acquired at the different time points. The graphs show the average and standard deviation of T2 Values (D) and ADC values (E) of the lesion and corresponding contralateral side. 2-way ANOVA with Tukey’s multiple comparison. (p-values: ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001); Figure S10. VG IV longitudinal qMRI assessment of lesion (right striatum) and contralateral side (left striatum). T2w (A), T2 maps (B) and ADC maps (C) from one animal acquired at the different time points. The graphs show the average and standard deviation of T2 Values (D) and ADC values (E) of the lesion and corresponding contralateral side. 2-way ANOVA with Tukey’s multiple comparison. (p-values: ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001).

Author Contributions

Conceptualization, L.M., L.V. and U.H.; methodology, L.M., L.V., W.G. and A.C.S.; software, L.M., W.G. and A.C.S.; validation, L.M., S.H. and U.H.; formal analysis, L.M. and A.C.S.; investigation, L.M., L.R. and D.M.; resources, W.M., K.L. and U.H.; data curation, L.M. and A.C.S.; writing—original draft preparation, L.M.; writing—review and editing, S.H. and U.H.; visualization, L.M. and U.H.; supervision, G.V.V., K.L., W.M. and U.H.; project administration, U.H.; funding acquisition, U.H. All authors have read and agreed to the published version of the manuscript.

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813120. This project received additional funding from ERA-NET InfectERA project ‘CryptoVIEW’, Flemish Research Foundation (FWO) under grant agreement G067023N, and KU Leuven for research grant RUN/21/001 and C14/20/095.

Institutional Review Board Statement

Animal experiments were performed in accordance with the national regulations and the European Directive 2010/63/EU and after approval by the Animal Ethics Committee of KU Leuven under project number p197/2021.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data is displayed in this paper. Raw data is available on request, please contact [email protected].

Acknowledgments

We thank Rita Merckx (Laboratory of Clinical Microbiology) for isolate reception and the initial sample management.

Conflicts of Interest

K.L. received consultancy fees from Mundipharma, speaker fees from Pfizer, Gilead, Mundipharma and FUJIFILM Wako chemicals Europe GmbH, a service fee from TECOmedical and a fee for Advisory Board participation from Pfizer. The remaining 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.

References

  1. Perfect, J.R.; Casadevall, A. Cryptococcosis. Infect. Dis. Clin. N. Am. 2002, 16, 837–874. [Google Scholar] [CrossRef]
  2. Williamson, P.R.; Jarvis, J.N.; Panackal, A.A.; Fisher, M.C.; Molloy, S.F.; Loyse, A.; Harrison, T.S. Cryptococcal Meningitis: Epidemiology, Immunology, Diagnosis and Therapy. Nat. Rev. Neurol. 2016, 13, 13–24. [Google Scholar] [CrossRef] [PubMed]
  3. Schwartz, S.; Kontoyiannis, D.P.; Harrison, T.; Ruhnke, M. Advances in the Diagnosis and Treatment of Fungal Infections of the CNS. Lancet Neurol. 2018, 17, 362–372. [Google Scholar] [CrossRef]
  4. Mathews, V.P.; Alo, P.L.; Glass, J.D.; Kumar, A.J.; McArthur, J.C. AIDS-Related CNS Cryptococcosis: Radiologic-Pathologic Correlation. AJNR Am. J. Neuroradiol. 1992, 13, 1477–1486. [Google Scholar] [PubMed]
  5. Duarte, S.B.L.; Oshima, M.M.; do Mesquita, J.V.A.; do Nascimento, F.B.P.; de Azevedo, P.C.; Reis, F. Magnetic Resonance Imaging Findings in Central Nervous System Cryptococcosis: Comparison between Immunocompetent and Immunocompromised Patients. Radiol. Bras. 2017, 50, 359–365. [Google Scholar] [CrossRef]
  6. WHO. WHO Fungal Priority Pathogens List to Guide Research, Development and Public Health Action; WHO: Geneva, Switzerland, 2022. [Google Scholar]
  7. Parums, D.V. Editorial: The World Health Organization (WHO) Fungal Priority Pathogens List in Response to Emerging Fungal Pathogens during the COVID-19 Pandemic. Med. Sci. Monit. 2022, 28, e939088-1–e939088-3. [Google Scholar] [CrossRef] [PubMed]
  8. Hagen, F.; Lumbsch, H.T.; Arsic Arsenijevic, V.; Badali, H.; Bertout, S.; Billmyre, R.B.; Bragulat, M.R.; Cabañes, F.J.; Carbia, M.; Chakrabarti, A.; et al. Importance of Resolving Fungal Nomenclature: The Case of Multiple Pathogenic Species in the Cryptococcus Genus. mSphere 2017, 2, e00238-17. [Google Scholar] [CrossRef] [PubMed]
  9. Ngamskulrungroj, P.; Gilgado, F.; Faganello, J.; Litvintseva, A.P.; Leal, A.L.; Tsui, K.M.; Mitchell, T.G.; Vainstein, M.H.; Meyer, W. Genetic Diversity of the Cryptococcus Species Complex Suggests That Cryptococcus gattii Deserves to Have Varieties. PLoS ONE 2009, 4, e5862. [Google Scholar] [CrossRef]
  10. Kwon-Chung, K.J.; Bennett, J.E.; Wickes, B.L.; Meyer, W.; Cuomo, C.A.; Wollenburg, K.R.; Bicanic, T.A.; Castañeda, E.; Chang, Y.C.; Chen, J.; et al. The Case for Adopting the “Species Complex” Nomenclature for the Etiologic Agents of Cryptococcosis. mSphere 2017, 2, e00357-16. [Google Scholar] [CrossRef]
  11. Franzot, S.P.; Salkin, I.F.; Casadevall, A. Cryptococcus neoformans Var. Grubii: Separate Varietal Status for Cryptococcus neoformans Serotype A Isolates. J. Clin. Microbiol. 1999, 37, 838–840. [Google Scholar] [CrossRef]
  12. Kwon-Chung, K.J.; Varma, A. Do Major Species Concepts Support One, Two or More Species within Cryptococcus neoformans? FEMS Yeast Res. 2006, 6, 574–587. [Google Scholar] [CrossRef] [PubMed]
  13. Kwon-Chung, K.J. A New Species of Filobasidiella, the Sexual State of Cryptococcus neoformans B and C Serotypes. Mycologia 1976, 68, 942–946. [Google Scholar] [CrossRef]
  14. Kwon-Chung, K.J. Morphogenesis of Filobasidiella neoformans, the Sexual State of Cryptococcus neoformans. Mycologia 1976, 68, 821–833. [Google Scholar] [CrossRef] [PubMed]
  15. Benham, R.W. The Genus Cryptococcus. Bacteriol. Rev. 1956, 20, 189–201. [Google Scholar] [CrossRef]
  16. Benham, R.W. Cryptococcosis and Blastomycosis. Ann. N. Y. Acad. Sci. 1950, 50, 1299–1314. [Google Scholar] [CrossRef]
  17. Meyer, W.; Aanensen, D.M.; Boekhout, T.; Cogliati, M.; Diaz, M.R.; Esposto, M.C.; Fisher, M.; Gilgado, F.; Hagen, F.; Kaocharoen, S.; et al. Consensus Multi-Locus Sequence Typing Scheme for Cryptococcus neoformans and Cryptococcus gattii. Med. Mycol. 2009, 47, 561–570. [Google Scholar] [CrossRef]
  18. Cogliati, M. Global Molecular Epidemiology of Cryptococcus neoformans and Cryptococcus gattii: An Atlas of the Molecular Types. Scientifica 2013, 2013, 675213. [Google Scholar] [CrossRef]
  19. Firacative, C.; Roe, C.C.; Malik, R.; Ferreira-Paim, K.; Escandón, P.; Sykes, J.E.; Castañón-Olivares, L.R.; Contreras-Peres, C.; Samayoa, B.; Sorrell, T.C.; et al. MLST and Whole-Genome-Based Population Analysis of Cryptococcus gattii VGIII Links Clinical, Veterinary and Environmental Strains, and Reveals Divergent Serotype Specific Sub-Populations and Distant Ancestors. PLoS Negl. Trop. Dis. 2016, 10, e0004861. [Google Scholar] [CrossRef]
  20. Montoya, M.C.; Magwene, P.M.; Perfect, J.R. Associations between Cryptococcus Genotypes, Phenotypes, and Clinical Parameters of Human Disease: A Review. J. Fungi 2021, 7, 260. [Google Scholar] [CrossRef]
  21. Chrétien, F.; Lortholary, O.; Kansau, I.; Neuville, S.; Gray, F.; Dromer, F. Pathogenesis of Cerebral Cryptococcus neoformans Infection after Fungemia. J. Infect. Dis. 2002, 186, 522–530. [Google Scholar] [CrossRef]
  22. Finquelievich, J.L.; Negroni, R.; Bava, A.J.; Iovannitti, C. Treatment of Experimental Sub-Acute Cryptococcosis in the Wistar Rat. Medicina 1988, 48, 506–510. [Google Scholar]
  23. Vanherp, L.; Poelmans, J.; Hillen, A.; Janbon, G.; Brock, M.; Lagrou, K.; Vande Velde, G.; Himmelreich, U. The Added Value of Longitudinal Imaging for Preclinical in Vivo Efficacy Testing of Therapeutic Compounds against Cerebral Cryptococcosis. Antimicrob. Agents Chemother. 2020, 64, e00070-20. [Google Scholar] [CrossRef] [PubMed]
  24. Krockenberger, M.B.; Malik, R.; Ngamskulrungroj, P.; Trilles, L.; Escandon, P.; Dowd, S.; Allen, C.; Himmelreich, U.; Canfield, P.J.; Sorrell, T.C.; et al. Pathogenesis of Pulmonary Cryptococcus gattii Infection: A Rat Model. Mycopathologia 2010, 170, 315–330. [Google Scholar] [CrossRef]
  25. Wirth, F.; Staudt, K.J.; Araújo, B.V.; Ishida, K. Experimental Models for Pharmacokinetic and Pharmacodynamic Studies of Antifungals Used in Cryptococcosis Treatment. Future Microbiol. 2022, 17, 969–982. [Google Scholar] [CrossRef]
  26. Chaturvedi, S.; Ren, P.; Narasipura, S.D.; Chaturvedi, V. Selection of Optimal Host Strain for Molecular Pathogenesis Studies on Cryptococcus gattii. Mycopathologia 2005, 160, 207–215. [Google Scholar] [CrossRef]
  27. Campbell, L.T.; Simonin, A.R.; Chen, C.; Ferdous, J.; Padula, M.P.; Harry, E.; Hofer, M.; Campbell, I.L.; Carter, D.A. Cryptococcus Strains with Different Pathogenic Potentials Have Diverse Protein Secretomes. Eukaryot. Cell 2015, 14, 554–563. [Google Scholar] [CrossRef]
  28. Barcellos, V.A.; Martins, L.M.S.; Fontes, A.C.L.; Reuwsaat, J.C.V.; Squizani, E.D.; de Sousa Araújo, G.R.; Frases, S.; Staats, C.C.; Schrank, A.; Kmetzsch, L.; et al. Genotypic and Phenotypic Diversity of Cryptococcus gattii VGII Clinical Isolates and Its Impact on Virulence. Front. Microbiol. 2018, 9, 132. [Google Scholar] [CrossRef]
  29. Zang, X.; Ke, W.; Wang, L.; Wu, H.; Huang, Y.; Deng, H.; Zhou, M.; Wu, N.; Xue, X.; Shen, D. Molecular Epidemiology and Microbiological Characteristics of Cryptococcus gattii VGII Isolates from China. PLoS Negl. Trop. Dis. 2022, 16, e0010078. [Google Scholar] [CrossRef] [PubMed]
  30. Pool, A.; Lowder, L.; Wu, Y.; Forrester, K.; Rumbaugh, J. Neurovirulence of Cryptococcus neoformans Determined by Time Course of Capsule Accumulation and Total Volume of Capsule in the Brain. J. Neurovirol. 2013, 19, 228–238. [Google Scholar] [CrossRef]
  31. Vande Velde, G.; Poelmans, J.; De Langhe, E.; Hillen, A.; Vanoirbeek, J.; Himmelreich, U.; Lories, R.J. Longitudinal Micro-CT Provides Biomarkers of Lung Disease That Can Be Used to Assess the Effect of Therapy in Preclinical Mouse Models, and Reveal Compensatory Changes in Lung Volume. DMM Dis. Models Mech. 2016, 9, 91–98. [Google Scholar] [CrossRef] [PubMed]
  32. Vanherp, L.; Poelmans, J.; Hillen, A.; Govaerts, K.; Belderbos, S.; Buelens, T.; Lagrou, K.; Himmelreich, U.; Vande Velde, G. Bronchoscopic Fibered Confocal Fluorescence Microscopy for Longitudinal in Vivo Assessment of Pulmonary Fungal Infections in Free-Breathing Mice. Sci. Rep. 2018, 8, 3009. [Google Scholar] [CrossRef] [PubMed]
  33. Vanherp, L.; Ristani, A.; Poelmans, J.; Hillen, A.; Lagrou, K.; Janbon, G.; Brock, M.; Himmelreich, U.; Vande Velde, G. Sensitive Bioluminescence Imaging of Fungal Dissemination to the Brain in Mouse Models of Cryptococcosis. Dis. Model. Mech. 2019, 12, dmm039123. [Google Scholar] [CrossRef] [PubMed]
  34. Vanherp, L.; Poelmans, J.; Weerasekera, A.; Hillen, A.; Croitor-Sava, A.R.; Sorrell, T.C.; Lagrou, K.; Vande Velde, G.; Himmelreich, U. Trehalose as Quantitative Biomarker for in Vivo Diagnosis and Treatment Follow-up in Cryptococcomas. Transl. Res. 2021, 230, 111–122. [Google Scholar] [CrossRef] [PubMed]
  35. Anjum, S.H.; Bennett, J.E.; Dean, O.; Marr, K.A.; Hammoud, D.A.; Williamson, P.R. Neuroimaging of Cryptococcal Meningitis in Patients without Human Immunodeficiency Virus: Data from a Multi-Center Cohort Study. J. Fungi 2023, 9, 594. [Google Scholar] [CrossRef] [PubMed]
  36. Roosen, L.; Maes, D.; Musetta, L.; Himmelreich, U. Preclinical Models for Cryptococcosis of the CNS and Their Characterization Using In Vivo Imaging Techniques. J. Fungi 2024, 10, 146. [Google Scholar] [CrossRef]
  37. Keenan, K.E.; Biller, J.R.; Delfino, J.G.; Boss, M.A.; Does, M.D.; Evelhoch, J.L.; Griswold, M.A.; Gunter, J.L.; Hinks, R.S.; Hoffman, S.W.; et al. Recommendations towards Standards for Quantitative MRI (QMRI) and Outstanding Needs. J. Magn. Reson. Imaging 2019, 49, e26–e39. [Google Scholar] [CrossRef]
  38. Vanherp, L.; Poelmans, J.; Govaerts, K.; Hillen, A.; Lagrou, K.; Vande Velde, G.; Himmelreich, U. In Vivo Assessment of Differences in Fungal Cell Density in Cerebral Cryptococcomas of Mice Infected with Cryptococcus neoformans or Cryptococcus gattii. Microbes Infect. 2023, 25, 105127. [Google Scholar] [CrossRef]
  39. Vanherp, L.; Govaerts, K.; Riva, M.; Poelmans, J.; Coosemans, A.; Lagrou, K.; Gsell, W.; Vande Velde, G.; Himmelreich, U. CryptoCEST: A Promising Tool for Spatially Resolved Identification of Fungal Brain Lesions and Their Differentiation from Brain Tumors with MRI. Neuroimage Clin. 2021, 31, 102737. [Google Scholar] [CrossRef]
  40. Ngamskulrungroj, P.; Himmelreich, U.; Breger, J.A.; Wilson, C.; Chayakulkeeree, M.; Krockenberger, M.B.; Malik, R.; Daniel, H.M.; Toffaletti, D.; Djordjevic, J.T.; et al. The Trehalose Synthesis Pathway Is an Integral Part of the Virulence Composite for Cryptococcus gattii. Infect. Immun. 2009, 77, 4584–4596. [Google Scholar] [CrossRef]
  41. Himmelreich, U.; Dzendrowskyj, T.E.; Allen, C.; Dowd, S.; Malik, R.; Shehan, B.P.; Russell, P.; Mountford, C.E.; Sorrell, T.C. Cryptococcomas Distinguished from Gliomas with MR Spectroscopy: An Experimental Rat and Cell Culture Study. Radiology 2001, 220, 122–128. [Google Scholar] [CrossRef]
  42. Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes Text with EEA relevance. Off. J. EU 2010, 276, 33–79.
  43. Navarro, K.L.; Huss, M.; Smith, J.C.; Sharp, P.; Marx, J.O.; Pacharinsak, C. Mouse Anesthesia: The Art and Science. ILAR J. 2021, 62, 238–273. [Google Scholar] [CrossRef]
  44. Pearson, K. On Lines and Planes of Closest Fit to Systems of Points in Space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 1901, 2, 559–572. [Google Scholar] [CrossRef]
  45. Jolliffe, I.T.; Cadima, J. Principal Component Analysis: A Review and Recent Developments. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2016, 374, 20150202. [Google Scholar] [CrossRef]
  46. Kwon-Chung, K.J.; Fraser, J.A.; Doering, T.L.; Wang, Z.A.; Janbon, G.; Idnurm, A.; Bahn, Y.-S. Cryptococcus neoformans and Cryptococcus gattii, the Etiologic Agents of Cryptococcosis. Cold Spring Harb. Perspect. Med. 2014, 4, a019760. [Google Scholar] [CrossRef] [PubMed]
  47. Lin, X.; Heitman, J. The Biology of the Cryptococcus neoformans Species Complex. Annu. Rev. Microbiol. 2006, 60, 69–105. [Google Scholar] [CrossRef] [PubMed]
  48. Hagen, F.; Khayhan, K.; Theelen, B.; Kolecka, A.; Polacheck, I.; Sionov, E.; Falk, R.; Parnmen, S.; Lumbsch, H.T.; Boekhout, T. Recognition of Seven Species in the Cryptococcus gattii/Cryptococcus neoformans Species Complex. Fungal Genet. Biol. 2015, 78, 16–48. [Google Scholar] [CrossRef]
  49. Saidykhan, L.; Onyishi, C.U.; May, R.C. The Cryptococcus gattii Species Complex: Unique Pathogenic Yeasts with Understudied Virulence Mechanisms. PLoS Negl. Trop. Dis. 2022, 16, e0010916. [Google Scholar] [CrossRef]
  50. Margaret Cheng, H.L.; Stikov, N.; Ghugre, N.R.; Wright, G.A. Practical Medical Applications of Quantitative MR Relaxometry. J. Magn. Reson. Imaging 2012, 36, 805–824. [Google Scholar] [CrossRef]
  51. Drake-Pérez, M.; Boto, J.; Fitsiori, A.; Lovblad, K.; Vargas, M.I. Clinical Applications of Diffusion Weighted Imaging in Neuroradiology. Insights Imaging 2018, 9, 535–547. [Google Scholar] [CrossRef]
  52. Patterson, D.M.; Padhani, A.R.; Collins, D.J. Technology Insight: Water Diffusion MRI—A Potential New Biomarker of Response to Cancer Therapy. Nat. Clin. Pract. Oncol. 2008, 5, 220–233. [Google Scholar] [CrossRef] [PubMed]
  53. Van der Perren, A.; Toelen, J.; Casteels, C.; Macchi, F.; Van Rompuy, A.S.; Sarre, S.; Casadei, N.; Nuber, S.; Himmelreich, U.; Osorio Garcia, M.I.; et al. Longitudinal Follow-up and Characterization of a Robust Rat Model for Parkinson’s Disease Based on Overexpression of Alpha-Synuclein with Adeno-Associated Viral Vectors. Neurobiol. Aging 2015, 36, 1543–1558. [Google Scholar] [CrossRef] [PubMed]
  54. Leten, C.; Roobrouck, V.D.; Struys, T.; Burns, T.C.; Dresselaers, T.; Vande Velde, G.; Santermans, J.; Lo Nigro, A.; Ibrahimi, A.; Gijsbers, R.; et al. Controlling and Monitoring Stem Cell Safety in Vivo in an Experimental Rodent Model. Stem Cells 2014, 32, 2833–2844. [Google Scholar] [CrossRef]
  55. Poelmans, J.; Himmelreich, U.; Vanherp, L.; Zhai, L.; Hillen, A.; Holvoet, B.; Belderbos, S.; Brock, M.; Maertens, J.; Vande Velde, G.; et al. A Multimodal Imaging Approach Enables In Vivo Assessment of Antifungal Treatment in a Mouse Model of Invasive Pulmonary Aspergillosis. Antimicrob. Agents Chemother. 2018, 62, e00240-18. [Google Scholar] [CrossRef]
  56. Engelthaler, D.M.; Hicks, N.D.; Gillece, J.D.; Roe, C.C.; Schupp, J.M.; Driebe, E.M.; Gilgado, F.; Carriconde, F.; Trilles, L.; Firacative, C.; et al. Cryptococcus gattii in North American Pacific Northwest: Whole-Population Genome Analysis Provides Insights into Species Evolution and Dispersal. mBio 2014, 5, e01464-14. [Google Scholar] [CrossRef]
  57. Fernandes, K.E.; Dwyer, C.; Campbell, L.T.; Carter, D.A. Species in the Cryptococcus gattii Complex Differ in Capsule and Cell Size following Growth under Capsule-Inducing Conditions. mSphere 2016, 1, e00350-16. [Google Scholar] [CrossRef] [PubMed]
Jof 10 00593 g001
Figure 1. Neurovirulence evaluation of BALB/cAnNCrl mice infected by stereotactic injection with cryptococcal isolates of the different major molecular types. (A,B): The graphs show a longitudinal assessment of neurotypical symptoms in mice stereotactically infected with various CN (VN I–IV) or CG (VG I–IV) molecular types. Each graph shows the morbidity scoring per mouse group with standard deviation (p-values: *** < 0.001, **** < 0.0001). (C): The isolates were classified on the day of the average time needed for the morbidity score to reach a level of 2. “very high” (≤6 days), “high” (>6 days to ≤10 days), “medium” (>10 days to ≤15 days), “low” (≥16 days), or “non-neurovirulent” due to full recovery. (D): normalized brain fungal burden (Colony forming units divided by brain weight) shows a higher fungal burden in the CN species compared to the CG, Unpaired Welch’s t test (p-value = 0.0059 **).
Figure 1. Neurovirulence evaluation of BALB/cAnNCrl mice infected by stereotactic injection with cryptococcal isolates of the different major molecular types. (A,B): The graphs show a longitudinal assessment of neurotypical symptoms in mice stereotactically infected with various CN (VN I–IV) or CG (VG I–IV) molecular types. Each graph shows the morbidity scoring per mouse group with standard deviation (p-values: *** < 0.001, **** < 0.0001). (C): The isolates were classified on the day of the average time needed for the morbidity score to reach a level of 2. “very high” (≤6 days), “high” (>6 days to ≤10 days), “medium” (>10 days to ≤15 days), “low” (≥16 days), or “non-neurovirulent” due to full recovery. (D): normalized brain fungal burden (Colony forming units divided by brain weight) shows a higher fungal burden in the CN species compared to the CG, Unpaired Welch’s t test (p-value = 0.0059 **).
Jof 10 00593 g001
Jof 10 00593 g002
Figure 2. Longitudinal T2–weighted MR images of two representative CN/CG isolates and assessment of cryptococcomas lesion volume induced by isolates of the different major molecular types. (A). Longitudinal T2–weighted MR images in the axial orientation of a representative animal infected with the VN I isolate. (B). Longitudinal T2–weighted MR images in the axial orientation of a representative animal infected with the VG I isolate. The graphs in (C,D) show the longitudinal evaluation of the lesion volume induced by the different CN (VN I–IV) or CG (VG I–IV) molecular types. Each time point shows the average lesion volume with standard deviation (n = 4).
Figure 2. Longitudinal T2–weighted MR images of two representative CN/CG isolates and assessment of cryptococcomas lesion volume induced by isolates of the different major molecular types. (A). Longitudinal T2–weighted MR images in the axial orientation of a representative animal infected with the VN I isolate. (B). Longitudinal T2–weighted MR images in the axial orientation of a representative animal infected with the VG I isolate. The graphs in (C,D) show the longitudinal evaluation of the lesion volume induced by the different CN (VN I–IV) or CG (VG I–IV) molecular types. Each time point shows the average lesion volume with standard deviation (n = 4).
Jof 10 00593 g002
Jof 10 00593 g003
Figure 3. Cryptococcal lesion volume and density at endpoint induced by different molecular types. (A,B) boxplots show endpoint median lesion volume distribution, standard deviation and statistical differences of CN and CG isolates, respectively (p-values: *** < 0.001). The graphs (C,D) show lesion density (LD), calculated by the logarithm of CFU/lesion volume. One-way ANOVA with Fisher LSD test. (E) Comparison of lesion density (LD) by grouping all CN and all CG isolates. Unpaired Welch’s t test. (p-values: *** < 0.001, **** < 0.0001). VG III is shown for reference in graphs (B,D); however, since it is a non-neurovirulent isolate, it was not included in (E) or any statistical analysis.
Figure 3. Cryptococcal lesion volume and density at endpoint induced by different molecular types. (A,B) boxplots show endpoint median lesion volume distribution, standard deviation and statistical differences of CN and CG isolates, respectively (p-values: *** < 0.001). The graphs (C,D) show lesion density (LD), calculated by the logarithm of CFU/lesion volume. One-way ANOVA with Fisher LSD test. (E) Comparison of lesion density (LD) by grouping all CN and all CG isolates. Unpaired Welch’s t test. (p-values: *** < 0.001, **** < 0.0001). VG III is shown for reference in graphs (B,D); however, since it is a non-neurovirulent isolate, it was not included in (E) or any statistical analysis.
Jof 10 00593 g003
Jof 10 00593 g004
Figure 4. Evaluation of T2 values in the lesion material and in the contralateral hemisphere in a single brain lesion model of cryptococcosis. (A,B) Longitudinal T2 maps of lesions representing one CN and one CG–relevant molecular type. (C,D) Longitudinal assessment of T2 values in the lesion and in the contralateral brain as reference for isolates of CN and CG species complexes. (E) Correlation matrix of T2 values shows predicted mean difference between all isolates and relative statistical difference expressed in p–value in stars. Statistical analysis was performed with mixed effect analysis 2–way ANOVA with Tukey’s multiple comparison. (p–values: * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001).
Figure 4. Evaluation of T2 values in the lesion material and in the contralateral hemisphere in a single brain lesion model of cryptococcosis. (A,B) Longitudinal T2 maps of lesions representing one CN and one CG–relevant molecular type. (C,D) Longitudinal assessment of T2 values in the lesion and in the contralateral brain as reference for isolates of CN and CG species complexes. (E) Correlation matrix of T2 values shows predicted mean difference between all isolates and relative statistical difference expressed in p–value in stars. Statistical analysis was performed with mixed effect analysis 2–way ANOVA with Tukey’s multiple comparison. (p–values: * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001).
Jof 10 00593 g004
Jof 10 00593 g005
Figure 5. Evaluation of ADC values in the lesion material and contralateral hemisphere in a single lesion brain model of cryptococcosis. (A,B) Longitudinal ADC maps of lesions representing one CN and one CG (B) relevant molecular type. (C,D) Longitudinal assessment of ADC values in the lesion and in the contralateral brain for reference for isolates of CN and CG species complexes. (E) Correlation matrix of ADC values shows predicted mean difference between all isolates and relative statistical difference expressed in p–value in stars. Statistical analysis was performed with mixed effect analysis 2–way ANOVA with Tukey’s multiple comparison. (p–values: * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001).
Figure 5. Evaluation of ADC values in the lesion material and contralateral hemisphere in a single lesion brain model of cryptococcosis. (A,B) Longitudinal ADC maps of lesions representing one CN and one CG (B) relevant molecular type. (C,D) Longitudinal assessment of ADC values in the lesion and in the contralateral brain for reference for isolates of CN and CG species complexes. (E) Correlation matrix of ADC values shows predicted mean difference between all isolates and relative statistical difference expressed in p–value in stars. Statistical analysis was performed with mixed effect analysis 2–way ANOVA with Tukey’s multiple comparison. (p–values: * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001).
Jof 10 00593 g005
Jof 10 00593 g006
Figure 6. qMRI analysis (T2 and ADC values) of the lesions at endpoint induced by isolates of different molecular types and correlation with lesion density (LD). (A). Comparison between average CN, CG, and contralateral brain T2 values. (B,C). T2 values of CN and CG at endpoint show statistical differences between isolates. (D). Comparison between ADC values of CN, CG, and contralateral side. Statistical differences were calculated with one–way ANOVA with Welch’s multiple comparison test (p–values: * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001). VG III is shown for reference but is not included in the statistical analysis. (E,F). ADC values of CN and CG at endpoint with statistical differences between isolates. (G,H). Graphs show Pearson linear correlation (r) of T2 and ADC values to fungal lesion density (LD). (I). India ink stainings confirm a reduction in cell density within CG compared to CN, supporting the association between the increased capsule size and T2/ADC values. (J). Lesion cell structure model that links decreased cell density in CG compared to CN with increased capsule size and T2/ADC values as proposed by Vanherp et al. [38].
Figure 6. qMRI analysis (T2 and ADC values) of the lesions at endpoint induced by isolates of different molecular types and correlation with lesion density (LD). (A). Comparison between average CN, CG, and contralateral brain T2 values. (B,C). T2 values of CN and CG at endpoint show statistical differences between isolates. (D). Comparison between ADC values of CN, CG, and contralateral side. Statistical differences were calculated with one–way ANOVA with Welch’s multiple comparison test (p–values: * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001). VG III is shown for reference but is not included in the statistical analysis. (E,F). ADC values of CN and CG at endpoint with statistical differences between isolates. (G,H). Graphs show Pearson linear correlation (r) of T2 and ADC values to fungal lesion density (LD). (I). India ink stainings confirm a reduction in cell density within CG compared to CN, supporting the association between the increased capsule size and T2/ADC values. (J). Lesion cell structure model that links decreased cell density in CG compared to CN with increased capsule size and T2/ADC values as proposed by Vanherp et al. [38].
Jof 10 00593 g006
Table 1. List of CN/CG molecular types used for in vivo murine experiments.
Table 1. List of CN/CG molecular types used for in vivo murine experiments.
SpeciesIsolateMolecular TypeOrigin
CNWM 148VN IClinical
CNWM 626VN IIClinical
CNWM 628VN IIIClinical
CNWM 629VN IVClinical
CGWM 179VG IClinical
CGWM 04.71VG IIVeterinarian
CGWM 183VG IIIClinical
CGWM 779VG IVVeterinarian
Table 2. Morbidity scoring and co-respective behavior. Adapted from Pool et al. [30].
Table 2. Morbidity scoring and co-respective behavior. Adapted from Pool et al. [30].
ScoreConditionRelated Behavior
5AlertRearing, normal gait, normal exploration, nesting
4LethargyReduced grooming, but active/responsive/exploring
3Reduced activityMovement only when prodded, poor hygiene
2MoribundHunchback, irregular or unstable gait, eye discharge together with photophobia, hydrocephaly
1MorbidRespiratory distress, inactive, immobile, unable to right self, >20% weight loss
Table 3. MRI acquisition protocol and relative parameters.
Table 3. MRI acquisition protocol and relative parameters.
T2-Weighted AnatomicalT2-Weighted AnatomicalT2 MappingADC Mapping1H MRS
OrientationCoronalAxialAxialAxial3D
MR sequenceRARERAREMSMEPGSEPRESS
RARE factor88
TR/TE effective (ms)2500/423000/31.23000/…1000/302000/20
Number of slices92424241
Slice thickness (mm)0.50.50.50.52
FOV (cm)2 × 22 × 22 × 2 2 × 20.2 × 0.2 × 0.2
In-plane resolution (mm2)200200200200/
Acquisition time45 s2 min 30 s6 min 40 s8 min 56 s8 min 54 s
Sequence-specific details 10 TEs from
10 to 100 ms
with 10 ms
increments		3 b-values:
150, 500 and 1250 s/mm2		256 averages,
VAPOR water-suppression
Table 4. Mixed effects statistical analysis of lesion volume comparison with Geisser–Greenhouse correction.
Table 4. Mixed effects statistical analysis of lesion volume comparison with Geisser–Greenhouse correction.
Fixed Effects (Type III)p Valuep Value SummaryStatistically Significant (p < 0.05)?F (DFn, DFd)
Time<0.0001****YesF (9, 60) = 19.30
Molecular type0.1959nsNoF (6, 21) = 1.602
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Musetta, L.; Helsper, S.; Roosen, L.; Maes, D.; Croitor Sava, A.; Vanherp, L.; Gsell, W.; Vande Velde, G.; Lagrou, K.; Meyer, W.; et al. Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types. J. Fungi 2024, 10, 593. https://doi.org/10.3390/jof10080593

AMA Style

Musetta L, Helsper S, Roosen L, Maes D, Croitor Sava A, Vanherp L, Gsell W, Vande Velde G, Lagrou K, Meyer W, et al. Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types. Journal of Fungi. 2024; 10(8):593. https://doi.org/10.3390/jof10080593

Chicago/Turabian Style

Musetta, Luigi, Shannon Helsper, Lara Roosen, Dries Maes, Anca Croitor Sava, Liesbeth Vanherp, Willy Gsell, Greetje Vande Velde, Katrien Lagrou, Wieland Meyer, and et al. 2024. "Quantitative MRI of a Cerebral Cryptococcoma Mouse Model for In Vivo Distinction between Different Cryptococcal Molecular Types" Journal of Fungi 10, no. 8: 593. https://doi.org/10.3390/jof10080593

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop