1. Genetics and Genomics
  2. Microbiology and Infectious Disease

The RAM signaling pathway links morphology, thermotolerance, and CO2 tolerance in the global fungal pathogen Cryptococcus neoformans

  1. Benjamin J Chadwick
  2. Tuyetnhu Pham
  3. Xiaofeng Xie
  4. Laura C Ristow
  5. Damian J Krysan
  6. Xiaorong Lin  Is a corresponding author
  1. Department of Plant Biology, University of Georgia, United States
  2. Department of Microbiology, University of Georgia, United States
  3. Department of Pediatrics, Carver College of Medicine, University of Iowa, United States
  4. Department of Microbiology and Immunology, Carver College of Medicine, University of Iowa, United States
https://doi.org/10.7554/eLife.82563
Share this article
Cite this article
  1. Benjamin J Chadwick
  2. Tuyetnhu Pham
  3. Xiaofeng Xie
  4. Laura C Ristow
  5. Damian J Krysan
  6. Xiaorong Lin
(2022)
The RAM signaling pathway links morphology, thermotolerance, and CO2 tolerance in the global fungal pathogen Cryptococcus neoformans
eLife 11:e82563.
https://doi.org/10.7554/eLife.82563
  2. Download BibTeX
  3. Download .RIS

Abstract

The environmental pathogen Cryptococcus neoformans claims over 180,000 lives each year. Survival of this basidiomycete at host CO2 concentrations has only recently been considered an important virulence trait. Through screening gene knockout libraries constructed in a CO2-tolerant clinical strain, we found mutations leading to CO2 sensitivity are enriched in pathways activated by heat stress, including calcineurin, Ras1-Cdc24, cell wall integrity, and Regulator of Ace2 and Morphogenesis (RAM). Overexpression of Cbk1, the conserved terminal kinase of the RAM pathway, partially restored defects of these mutants at host CO2 or temperature levels. In ascomycetes such as Saccharomyces cerevisiae and Candida albicans, transcription factor Ace2 is an important target of Cbk1, activating genes responsible for cell separation. However, no Ace2 homolog or any downstream component of the RAM pathway has been identified in basidiomycetes. Through in vitro evolution and comparative genomics, we characterized mutations in suppressors of cbk1Δ in C. neoformans that partially rescued defects in CO2 tolerance, thermotolerance, and morphology. One suppressor is the RNA translation repressor Ssd1, which is highly conserved in ascomycetes and basidiomycetes. The other is a novel ribonuclease domain-containing protein, here named PSC1, which is present in basidiomycetes and humans but surprisingly absent in most ascomycetes. Loss of Ssd1 in cbk1Δ partially restored cryptococcal ability to survive and amplify in the inhalation and intravenous murine models of cryptococcosis. Our discoveries highlight the overlapping regulation of CO2 tolerance and thermotolerance, the essential role of the RAM pathway in cryptococcal adaptation to the host condition, and the potential importance of post-transcriptional control of virulence traits in this global pathogen.

Editor's evaluation

This paper reports the identification of molecular determinants of CO2 tolerance in the human fungal pathogen Cryptococcus neoformans. The results are important for our understanding of how the fungus adapts from the ambient atmosphere to the CO2-enriched environment in the human host, and the findings are convincing and rely on biochemical, molecular, and genetic techniques. The results should be of interest to a broad community in the life sciences including microbiologists and infectious diseases investigators.

https://doi.org/10.7554/eLife.82563.sa0

Introduction

There are over 278,000 cases of cryptococcal meningitis every year, causing over 180,000 deaths (Rajasingham et al., 2017). Cryptococcal meningitis is primarily caused by the ubiquitous environmental fungus Cryptococcus neoformans. Airborne spores or desiccated yeast cells of C. neoformans are inhaled into the lungs, where they are cleared or remain dormant until reactivation upon host immunosuppression (Casadevall and Perfect, 1998; Zhao et al., 2019).

Litvintseva et al. found that most environmental Cryptococcus isolates cannot cause fatal disease in mouse models of cryptococcosis, despite having similar genotypes and in vitro phenotypes to known virulent isolates, including thermotolerance, melanization, and capsule production (Litvintseva and Mitchell, 2009). Mukaremera et al., 2019 also observed that in vitro phenotype assays for thermotolerance, capsule production, titan cell formation, or fluconazole heteroresistance could not differentiate high-virulence strains from low-virulence strains. These observations raise the possibility that other, unidentified virulence traits are important for Cryptococcus pathogenesis. Tolerance to host levels of CO2 (~5% CO2 in the host vs. ~0.04% in ambient air) is likely a significant factor separating the potentially virulent natural isolates from the non-pathogenic environmental isolates that Litvintseva et al. tested (Krysan et al., 2019; Litvintseva and Mitchell, 2009).

The ability to adapt to host conditions is a prerequisite for cryptococcal pathogenesis. For instance, the ability of C. neoformans to replicate at human body temperature (≥37°C) has been extensively investigated. Many genes have been shown to be essential for thermotolerance (Perfect, 2006; Stempinski et al., 2021; Yang et al., 2017), including calcineurin which is currently being explored for antifungal drug development (Gobeil et al., 2021). By contrast, the underlying mechanisms or genes that play a role in CO2 tolerance have yet to be identified. Here, we set out to identify CO2-sensitive mutants and to gain the first insight into the genetic components involved in CO2 tolerance in C. neoformans.

Results

CO2 sensitivity is independent of pH

Our previous work indicates that many C. neoformans environmental strains are sensitive to 5% CO2 when grown on buffered RPMI media, commonly used for mammalian cell cultures and testing antifungal susceptibility (Krysan et al., 2019). CO2 at host concentrations also acts synergistically with the commonly used antifungal drug fluconazole in inhibiting cryptococcal growth on buffered RPMI media. Because CO2 lowers the pH of aqueous environments, it is possible that the CO2 growth inhibitory effect or its synergy with fluconazole is simply due to lower medium pH. To address this question, we tested sensitivity to fluconazole of wild-type (WT) strain H99 using E-test on buffered RPMI media of either pH 6 or pH 7, with or without 5% CO2. In this E-test, the size of halo (clearance zone) reflects fungal susceptibility to fluconazole. As shown in Figure 1A, clearance zones were much larger in 5% CO2 relative to those in ambient air at both pH 6 and pH 7, indicating that CO2 sensitizes cryptococcal susceptibility to fluconazole. Furthermore, CO2 inhibits the growth of H99 at both pH 6 and pH 7 (smaller colony size in 5% CO2 relative to that in ambient air). Additionally, growth of CO2-sensitive environmental strain A7-35-23 (Krysan et al., 2019) was severely inhibited by 5% CO2 at both pH 6 and pH 7 (Figure 1B). In general, C. neoformans grows better at acidic pH (can grow well in pH 3), and both A7-35-23 and H99 grew better at pH 6 than at pH 7 in ambient air (Figure 1B). Taken together, these results suggest that cryptococcal growth inhibition by CO2 is not simply due to lowered pH.

Figure 1
Download asset Open asset
CO2 sensitivity is not simply due to lowered medium pH.

(A) H99 cells were plated onto RPMI solid medium buffered to either pH 6 or pH 7. Fluconazole containing E-test strips were placed onto the lawn of H99 cells, and the plates were incubated at 37°C in ambient air or in 5% CO2. The larger the halo surrounding the E-strip, the more sensitive the cells are to fluconazole. The intercept value of the halo with the E-strip is the minimal inhibitory concentration. (B) Cells of the previously identified CO2-tolerant H99 and CO2-sensitive A7-35-23 were serial diluted, spotted onto RPMI media buffered to pH 6 or pH 7, and incubated at 37°C in ambient air or in 5% CO2.

Identifying genes important for CO2 tolerance

To identify genes involved in CO2 tolerance in C. neoformans, we screened gene deletion mutants constructed in the CO2-tolerant clinical reference strain H99. For large-scale screening, we used the nutrient rich yeast peptone dextrose (YPD) medium on which C. neoformans grows well. Accordingly, we tested the growth of two CO2-sensitive environmental strains and the CO2-tolerant H99 strain in different levels of CO2 when cultured on YPD. As expected, relative to H99, the CO2-sensitive strains A7-35-23 and A1-38-2 grew poorly at 5% CO2 and worse at 20% CO2 (Figure 2A). Using this approach, the following deletion mutant libraries were screened at 20% CO2 on YPD media: a set of strains previously constructed in our lab, the collections constructed by the Madhani lab, and a set generated in the Lodge Lab (Chun and Madhani, 2010). As some mutants are known to be temperature sensitive, we carried out the screens at 30°C rather than 37°C. From over 5000 gene knockout mutants screened (~7000 protein coding genes in the H99 genome), 96 were found to be sensitive to CO2 by visual observation (Supplementary file 1). We noticed that knockout mutants for multiple pathways known to be activated by heat stress are CO2 sensitive, including the Ras1-Cdc24 pathway, calcineurin, cell wall integrity (CWI), and Regulator of Ace2 and Morphogenesis (RAM). This finding indicates an overlapping nature of these two traits.

Figure 2 with 3 supplements see all
Download asset Open asset
The Regulator of Ace2 and Morphogenesis (RAM) pathway effector kinase Cbk1 is critical for CO2 tolerance.

(A). The clinical reference strain H99 and environmental strains A7-35-23 and A1-38-2 were grown overnight in yeast peptone dextrose (YPD), serially diluted, and spotted onto solid YPD media plates. Photographs were taken 2 days after incubation in the indicated condition. (B) This serial dilution spotting assay was similarly performed for H99 and the mutants indicated. Two independent overexpression transformants for each mutant background were included as biological replicates. (C) Heatmap showing normalized total RNA counts of NanoString targets in H99 and cbk1Δ cultured at either ambient or 5% CO2, red indicates higher and blue indicates lower transcript abundance. (D) Volcano plot showing significantly differentially expressed transcripts (p-value of <0.05) in the cbk1Δ compared to H99 in the 5% CO2 condition. (E) Serial dilution spotting assay of H99 and four of the mutants found in the deletion set screening to be CO2 sensitive which also correspond to significantly downregulated genes shown in the volcano plot.

We were surprised by the absence of components of adenylyl cyclase-PKA(protein kinase A) pathway from the set of hits. In Candida albicans, the adenylyl cyclase pathway is crucial for the yeast-hypha transition in response to host levels of CO2 (Klengel et al., 2005). This pathway has also been proposed to play an important role for Cryptococcus to sense CO2, and the carbonic anhydrase Can1 is required for growth at low concentrations of CO2 (Bahn et al., 2005; Mogensen et al., 2006). However, we found that adenylyl cyclase pathway mutants showed no growth defects at host levels CO2, including the adenylyl cyclase mutant cac1Δ, the adenylyl cyclase associated protein mutant aca1Δ, the alpha G protein subunit mutant gpa1Δ, and the cAMP-dependent protein kinase mutant pkr1Δ (Figure 2—figure supplement 1). This indicates that growth defects in response to host levels of CO2 are likely independent of bicarbonate activation of adenylyl cyclase. This is not unexpected given that bicarbonate is not a limiting factor under the high level of CO2 used in our screen.

Because the calcineurin, Ras1-Cdc24, CWI, and RAM pathways are all activated at host temperature and were identified in our screen for CO2-sensitive mutants, we reasoned their downstream effectors may be related or genetically interact. As the RAM pathway effector kinase mutant cbk1Δ showed the most severe defect in thermotolerance and CO2 tolerance compared to the mutants of the other pathways, we first overexpressed the gene CBK1 in the following mutants, cdc24Δ (Ras1-Cdc24), mpk1Δ (CWI), cna1Δ (Calcineurin), and the cbk1Δ mutant itself and observed their growth at host temperature and host CO2 (Figure 2B). Overexpression was achieved by placing the CBK1 open reading frame after the inducible CTR4 promoter, which is highly activated in YPD media (Ory et al., 2004; Wang et al., 2014; Wang et al., 2012). The CBK1 overexpression construct was specifically integrated into the ‘safe haven’ locus SH2 (Lin et al., 2020; Upadhya et al., 2017) in each mutant strain background to avoid complications due to positional effects. We additionally confirmed overexpression of CBK1 by RT-PCR (Figure 2—figure supplement 2). As expected, the growth defects of the cbk1Δ mutant at 37°C with and without 5% CO2 were largely restored by CBK1 overexpression. At 30°C, overexpression of CBK1 restored the growth of the mpk1Δ mutant, the cna1Δ mutant, and the cdc24Δ mutant in the CO2 condition. In terms of thermotolerance, overexpression of CBK1 restored growth of mpk1Δ but not cna1Δ, while the growth defect of cdc24Δ at 37°C was exacerbated. CBK1 overexpression failed to rescue growth of any of these mutants when both stressors were present (37°C+5% CO2). We found that overexpression of CBK1 in the WT H99 background caused a modest growth defect at 37°C+5% CO2. Thus, the detrimental effects from CBK1 overexpression under this growth condition may partially explain its inability to fully rescue growth of these tested CO2-sensitive mutants. The reciprocal overexpression of CDC24, MPK1, or CNA1 in the cbk1Δ mutant background did not restore growth under 37°C and/or 5% CO2 (Figure 2—figure supplement 3). These results support a hypothesis that Cbk1 integrates multiple stress response pathways to regulate both CO2 tolerance and thermotolerance.

To determine the extent of Cbk1’s role in CO2 tolerance, we conducted NanoString gene expression profiling of the WT H99 and cbk1Δ mutant cultured in ambient air and in 5% CO2 at 30°C (Figure 2C). Transcript levels of 118 genes were measured, and those genes were chosen based on RNA sequencing results from a separate study (Ristow et al., in preparation). In that study, these genes were differentially expressed in CO2 vs. ambient air conditions in either two CO2-sensitive or two CO2-tolerant natural strains (Source data 1). Out of these 118 CO2-associated genes, 81 were found to be significantly differentially expressed in the cbk1Δ mutant in both ambient air and in 5% CO2, indicating they are intrinsically dysregulated in the cbk1Δ mutant. 57/81 of these genes are downregulated and 24/81 upregulated compared to the WT H99 strain (Figure 2D). Interestingly, 16/57 of the downregulated genes were also hits in our deletion set screening. We picked four of these deletion mutants which showed high sensitivity in our screen, to assay their sensitivity to host CO2 conditions by spotting assay (Figure 2E). Taken together, this transcriptomic profiling shows that loss of Cbk1 significantly affects the expression of CO2-related genes.

The RAM signaling pathway is critical for normal morphology, thermotolerance, and CO2 tolerance

The RAM pathway effector kinase Cbk1 is part of the NDR/LATS family of kinases, which is conserved from yeast to humans and affects a wide range of cellular functions including cell-cycle regulation. In C. neoformans, various virulence factors are impacted by deletion of CBK1, including urease activity and thermotolerance (Lee et al., 2016). Through our genetic screen for CO2-sensitive mutants, we found that all tested Cryptococcus RAM pathway mutants are extremely sensitive to 5% CO2 and high temperature, and they show no growth at 37°C+5% CO2 (Figure 3A). In ascomycetes such as Saccharomyces cerevisiae and C. albicans, RAM pathway mutants are defective in cytokinesis and exhibit loss of polarity, resulting in enlarged round cells that cluster together (Saputo et al., 2012; Figure 3—figure supplement 1A). In contrast, though defective in cytokinesis (Magditch et al., 2012; Walton et al., 2006), Cryptococcus RAM pathway mutants are hyper-polarized and constitutively form clusters of elongated pseudohyphal cells (Figure 3B). Moreover, we found that while the C. albicans homozygous cbk1ΔΔ mutant exhibits a general growth defect compared to the WT control, it shows no apparent specific growth defect at 37°C with or without 5% CO2 (Figure 3—figure supplement 1C). These results suggest that, although the RAM pathway is conserved in its role in cytokinesis, the effects of its downstream targets are divergent between ascomycetes and basidiomycetes.

Figure 3 with 1 supplement see all
Download asset Open asset
The Regulator of Ace2 and Morphogenesis (RAM) pathway is critical for normal morphology, thermotolerance, and CO2 tolerance.

(A) Cryptococcus neoformans WT H99 and RAM pathway mutants were serially diluted, spotted onto yeast peptone dextrose (YPD) medium, and incubated for 2 days at the indicated condition. (B) The cellular morphology of C. neoformans WT H99 and RAM pathway mutants cultured in YPD medium.

Suppressors of the cbk1Δ mutant show improved growth at host conditions

In ascomycetes, Ace2 is a key downstream transcription factor of the RAM pathway (hence in the name of RAM − Regulator of Ace2 and Morphogenesis), which is important for the activation of genes responsible for cell separation as well as a large number of genes with other functions (Mulhern et al., 2006; Wakade et al., 2020). However, no homolog to Ace2 has been identified in Cryptococcus or other basidiomycetes. Furthermore, no downstream targets of the RAM pathway have been identified in any basidiomycetes. To investigate potential downstream effectors of the RAM pathway in Cryptococcus, we screened for spontaneous suppressor mutants of cbk1Δ. To do so, cbk1Δ mutant cells from an overnight culture in liquid YPD at 30°C were plated onto solid YPD media and incubated for 2 days at 37°C+5% CO2. Out of >1×108 cells plated and cultured under this condition that is inhibitory for growth of the original cbk1Δ mutant, 11 suppressor colonies were isolated for further examination and sequencing. All the suppressor isolates showed dramatically improved growth over the original cbk1Δ mutant at 37°C and modestly improved growth at 37°C+5% CO2 (Figure 4C). Based on their distinctive phenotypes, the 11 suppressors were classified into two groups: sup1 (2/11) and sup2 (9/11). Shorter chains of cells in both groups indicate a partial restoration in cytokinesis (Figure 4D). The sup2 group has slightly improved growth at 37°C+5% CO2 and forms shorter chains of cells compared to the sup1 group (Figure 4C and D). Besides of these observations, sup1 and sup2 displayed similar phenotypes in growth assays including the cryptococcal virulence traits tested, including melanin production, capsule, urease activity, and cell wall stress tolerance. (Figure 4—figure supplement 1). Both sup1 and sup2 showed no improved growth compared to the cbk1Δ mutant at pH 7.4 37°C+5% CO2. This is likely due to the detrimental combination of high temperature, CO2, and high pH, as the WT also showed significantly reduced growth in this condition. (Figure 4—figure supplement 1A). Because RAM pathway suppressor mutants were previously identified after treatment with calcineurin inhibitor FK506 and showed improved growth in FK506 and restored mating (Magditch et al., 2012), we also tested our suppressors’ growth in FK506 and their ability to mate. We found that both sup1 and sup2 failed to restore growth of the cbk1Δ on media supplemented with FK506 or restore the ability to mate with the congenic strain H99a (Figure 4—figure supplement 1).

Figure 4 with 2 supplements see all
Download asset Open asset
Natural suppressors of the Regulator of Ace2 and Morphogenesis (RAM) pathway cbk1Δ mutant restore multiple defects.

(A) Protein diagram of Psc1 showing the effects and positions of suppressor mutations in the two sup1 type natural suppressors. (B) Protein diagram of Ssd1 and the effects and positions of suppressor mutations in Ssd1 in the nine sup2 type natural suppressors. STOP indicates a non-sense mutation, MS a missense mutation, and FS a frameshift mutation. Asterisks (*) indicate mutations in the same suppressor strain. (C) Serial dilutions of H99 and the mutant strains were spotted onto yeast peptone dextrose (YPD) agar media and incubated for 2 days in the indicated condition to observe growth. (D) The cellular morphology of H99 and the mutant strains in liquid YPD cultures were examined under microscope.

Along with the original cbk1Δ mutant, we sequenced the genomes of the 11 cbk1Δ suppressors. By comparing their genome sequences with each other and with the original cbk1Δ mutant, we found that both sup1 type suppressor mutants contained a disruptive in-frame deletion at the same location in CNAG_01919, which encodes a putative Poly(A)-specific ribonuclease (PARN) domain-containing protein (Figure 4A). This domain was previously reported in S. pombe proteins (Marasovic et al., 2013). Interestingly, through a BLAST search of the PARN domain, we did not identify this domain in any protein in the genomes of S. cerevisiae, C. albicans, or other ascomycetes but found it in basidiomycetes and higher eukaryotes. The in-frame deletion results in a change of two amino acids within the predicted PARN domain, the only discernable domain present in this protein. We named this previously uncharacterized gene Partial Suppressor of cbk1Δ (PSC1). All nine sup2 isolates contained loss of function or missense mutations in the gene CNAG_03345 (Figure 4B), which encodes an RNA-binding protein homologous to S. cerevisiae Ssd1p, a known suppressor of cbk1Δ phenotypes in S. cerevisiae. ScSsd1p represses transcript translation and is negatively regulated by Cbk1p phosphorylation (Jansen et al., 2009; Wanless et al., 2014).

To confirm that the putative loss-of-function mutations in SSD1 and PSC1 are responsible for suppressing cbk1Δ phenotypes, we created cbk1Δssd1Δ and cbk1Δpsc1Δ double mutants together with the control single mutants ssd1Δ and psc1Δ. Indeed, relative to the cbk1Δ mutant, the double mutants showed reduced sensitivity to host temperature and CO2 levels (Figure 4C), similar to the natural suppressor mutants. Likewise, the morphology of the double mutants resembles that of the spontaneous suppressor mutants (Figure 4D). The deletion of SSD1 and PSC1 alone in the WT background did not yield any discernable phenotype. The results confirm that loss-of-function mutations in SSD1 and PSC1 are responsible for partial suppression of the cbk1Δ mutant’s growth defects observed in the isolated suppressor strains. Interestingly, sup2 and the cbk1Δssd1Δ mutants both grew noticeably better than sup1 and cbk1Δpsc1Δ at 37°C and 37°C+5% CO2. To test the genetic interaction between the two suppressor genes SSD1 and PSC1, we created a triple cbk1Δpsc1Δssd1Δ mutant and the control strain psc1Δssd1Δ. The psc1Δssd1Δ control strain did not exhibit any defect and grew similarly well to either single mutant or the WT (Figure 4C). The triple mutant cbk1Δpsc1Δssd1Δ grew similarly well as sup2 or cbk1Δssd1Δ at 37°C+5% CO2 (Figure 4C). However, the triple mutant displayed aberrant morphology and budding defects which are not observed in the natural suppressor mutants or the cbk1Δssd1Δ and cbk1Δpsc1Δ double mutants (Figure 4D). These results suggest that Psc1 and Ssd1 may function in the same pathway in regulating thermotolerance and CO2 tolerance, but their downstream effects on cell separation and/or polarized growth may be overlapping and distinct.

To determine if the suppressor mutations restore transcript abundance of the differentially expressed genes under CO2 in cbk1Δ, we compared the profiles of cbk1Δ to the two suppressor mutants: sup1 and sup2. Overall, we found that the spontaneous suppressors do not restore transcript abundances of most differentially expressed genes in cbk1Δ to WT levels (Figure 4—figure supplement 2), suggesting that suppressors affect post-transcriptional regulation of CO2 tolerance.

Spontaneous suppressors of cbk1Δ mutant show improved ability to survive and replicate in the host

RAM mutants have previously been found to be attenuated in virulence in the invertebrate wax moth larva infection model and mouse intranasal infection models (Lee et al., 2016; Magditch et al., 2012). Occasionally, cryptococcal strains with point mutations in RAM genes cause death of mice when revertant mutations occur, which restore the function of the RAM pathway (Magditch et al., 2012). As shown above and consistent with previous literature, the cbk1Δ mutant shows a severe growth defect at host temperature and CO2 concentrations (Figure 3C). Because sup1 and sup2 both largely restored growth to the cbk1∆ mutant at 37°C but only modestly restored growth at 37°C+5% CO2, we decided to test if, and by how much, these suppressor mutations would affect the virulence of the cbk1Δ mutant. We infected mice with 1×104 cells of WT, cbk1Δ, sup1, or sup2 intranasally. In this intranasal infection model, the WT H99 strain establishes lung infection first and typically disseminates to other organs including the brain by 7–10 days post-infection (DPI). Mice infected by H99 normally become morbidly ill by 3–4 weeks post-infection and have a high fungal burden in the lungs, brain, and kidney (Chadwick and Lin, 2020; Lin et al., 2022).

As expected, all mice infected with H99 were moribund by DPI 26 (Figure 5A), while those infected with the cbk1Δ mutant survived until the experiment was terminated at DPI 60. Surprisingly, sup1 and sup2 strains did not cause any mortality either. The organ fungal burden, however, revealed differences in virulence levels between these strains. At the time of euthanasia for H99-infected mice (prior to DPI 26), the median fungal burden in the lungs, brains, and kidneys was 2.1×108, 1.4×106, and 2.4×104 colony-forming units (CFUs) per organ, respectively (Figure 5B). As expected, mice completely cleared the cbk1Δ mutant at DPI 35. Surprisingly, despite largely restored growth at 37°C, sup1 was completely cleared from the mouse lungs by DPI 35, similar to the cbk1Δ mutant. In comparison, although sup2 did not cause any death during the study period, it was able to replicate in the mouse lungs. The median lung fungal burden at DPI 35 was 8.2×104, over eightfold higher than the original inoculum. The sup2 strain maintained the same high lung fungal burden at DPI 60 (Figure 5B), indicating that it can persist in the lung tissue. The only in vitro difference observed between sup1 and sup2 was better growth of sup2 at host CO2 levels which may explain the difference in their ability to propagate and persist in the mouse lung. However, it is worth nothing that due to the complex host environment, there could be other unrecognized factors contributing to the differences in vivo.

Figure 5
Download asset Open asset
Suppressor mutants are partially restored for phagocytosis and can disseminate in the intravenous infection model of cryptococcosis.

(A) Mice were infected with 1×104 fungal cells intranasally, and their survival was monitored for 60 days post-infection. (B) At day 35 post-infection (DPI 35) and at the time of termination (DPI 60), 5 out of 10 mice per group for the cbk1Δ mutant, sup1 and sup2 groups were harvested for brains, kidneys, and lungs. For H99 infected mice, they were euthanized at their clinical end point (all before DPI 26). Tissue homogenate was serially diluted and plated onto YNB(yeast nitrogen base) medium to count the colony-forming units (CFUs) to measure the fungal burden per organ. (C) Murine macrophage J774A.1 cells were co-incubated with 2×106 cryptococcal cells opsonized with serum from naïve mouse for 2 hr. Non-adherent or phagocytosed cells were washed, and cryptococcal cells were released and then serially diluted before plating onto YNB medium for measurement of CFUs. (D) The same as above, except opsonization, was performed with serum of mice vaccinated against cryptococcosis. (E) Mice were challenged with 1×105 cryptococcal cells intravenously. At day 5 post-infection, five mice per group were sacrificed. Brains, kidneys, and lungs of euthanized mice were dissected and homogenized. Serial dilutions were plated to count CFUs for quantification of fungal burden per organ.

Although the spontaneous suppressor sup2 was able to replicate in the mouse lungs, no fungal burden was detected in the brain or the kidney at DPI 35 or 60 (no organisms were detected in any of the mice), indicating that the mutant was unable to disseminate. We considered two hypotheses: (1) inability of suppressor sup2 to disseminate from the lungs; (2) inability of suppressor sup2 to penetrate other organs from the blood. Because C. neoformans can disseminate from the lungs to other organs by a ‘Trojan Horse’ mechanism, where Cryptococcus travels within the mobile host phagocytes (Kechichian et al., 2007; Santiago-Tirado et al., 2017), we examined phagocytosis of the cbk1Δ mutant and its suppressors to test the first hypothesis. We expected that cryptococcal mutants defective in being phagocytosed by host cells might be defective in dissemination, and the cbk1Δ mutant was previously found to have a poor phagocytosis index (Lin et al., 2015). Here, we co-cultured murine macrophage JA774 cells with H99, cbk1Δ, sup1, sup2, the double mutant cbk1Δssd1Δ, or the control single mutant ssd1Δ. Because different types of opsonization can impact phagocytosis of C. neoformans, opsonization was performed using either naïve mouse serum (complement mediated phagocytosis) or serum from mice vaccinated against cryptococcosis (complement+antibody mediated phagocytosis). The serum (containing antibodies) from the vaccinated mice recognizes antigens present in the capsule of cryptococcal cells (Lin et al., 2022; Zhai et al., 2015). Consistent with our previous finding, phagocytosis of the cbk1Δ mutant was extremely low (~1% of the WT H99 level under complement mediated phagocytosis, Figure 5C). Opsonization with serum from vaccinated mice increased phagocytosis of cbk1Δ and the suppressor mutants, but the phagocytosis indexes of these mutants were still only 20% or less than that of the WT (Figure 5D). In both phagocytosis experiments, the suppressor mutants or the double mutants cbk1∆ssd1∆ and cbk1∆psc1∆ mutants showed increased phagocytosis relative to the cbk1∆ mutant. The poor phagocytosis of the cbk1Δ mutant and its suppressors may contribute to their lack of dissemination from the lungs to the other organs in the inhalation infection mouse model of cryptococcosis.

To test the second hypothesis, we infected mice intravenously with H99, cbk1Δ, sup1, sup2, the double mutant cbk1Δssd1Δ, or the control single mutant ssd1Δ. In this intravenous infection model, the barrier of the lungs is bypassed. H99 cells disseminate to the brain and other organs within hours (O’Connor et al., 2013). Because H99 rapidly disseminates in this model, infected mice typically reach moribundity after 1 week. Therefore, we euthanized mice at DPI 5 before H99-infected mice would have become moribund. As expected, H99-infected mice showed high fungal burdens in the lungs, brains, and kidneys, with the highest fungal burden in the brain (over 106 CFUs; Figure 5E). The cbk1Δ mutant failed to disseminate in this intravenous infection model as no viable cells were recovered in any organ. Similarly, we could not recover any sup1 cells from the lungs or the brain and only detected a few fungal cells in the kidney. In contrast, sup2 suppressor mutants were recovered in all three organs, albeit with reduced fungal burdens (~104 CFUs in the brain and a few hundred in lungs/kidney) compared to the WT H99 control group (Figure 5E). This finding indicates that the sup2 suppressor, once disseminated into the bloodstream, can invade other organs and replicate. Combined with the earlier observations that (1) both suppressors fully restore growth at host temperature and (2) sup2 is slightly more CO2 tolerant than sup1, the observation that only sup2 can survive, amplify, and persist in animals implicates an importance of CO2 tolerance in cryptococcal pathogenesis. Collectively, the results from phagocytosis, the inhalation infection model, and the intravenous infection model support the hypothesis that failure of the suppressor mutants to disseminate to other organs in the intranasal model is largely due to reduced phagocytosis and inability to escape the lungs. That said, other factors, such as increased systemic clearance by the immune system, could potentially contribute to the containment of the mutant in the lungs. Again, the cbk1Δssd1Δ mutant recapitulated the phenotype of the sup2 strain in intravenous infection model and other in vitro assays, demonstrating that our observed sup2 phenotypes are due to disruption of SSD1.

As mutants that are temperature sensitive have reduced virulence in the mouse model of cryptococcosis, we decided to test the virulence of these strains in the Galleria mellonella larvae infection model to remove temperature as a variable. We inoculated G. mellonella larvae with 5×104 cells of WT, cbk1Δ, sup1, or sup2 and maintained the larvae at 30°C as previously described (Magditch et al., 2012). PBS buffer inoculated larvae were included as a sham control. Infected larvae (n=20 per strain) were monitored for survival over a period of 15 days post-inoculation. All 20 of the larvae inoculated with the H99 strain died between DPI 3 and DPI 10 (Figure 6A). In comparison, only 2/20 of the cbk1Δ mutant-infected larvae died during this period. Interestingly, 5/20 sup1-infected larvae and 7/20 sup2-infected larvae died in this experiment (Figure 6A), indicating their partially restored virulence in this larva infection model. To further confirm the observed differences between these strains in this model conducted at 30°C, we infected five larvae with 5×104 cells per strain and measured their fungal burden at day 5 post-inoculation. At DPI 5, the mean fungal burden of WT-infected larvae was 1.5×107 CFUs (Figure 6B). In comparison, the mean fungal burden for the cbk1Δ-infected larvae was only 8.4×102 CFUs, which is almost 20,000-fold lower than the WT control group and about 60-fold lower than the original inoculum, indicating that most cbk1Δ cells have been cleared by this time point. The mean fungal burden of sup1-infected larvae was 9.6×103 CFUs, while the mean fungal burden sup2-infected larvae was 2.3×104 CFUs (Figure 6B). These results indicate that both sup1 and sup2 partially rescued virulence of the cbk1Δ mutant and that sup2 showed slightly better restoration of virulence compared to sup1 in this insect model, which is independent of tolerance to mammalian body temperature.

Figure 6
Download asset Open asset
Suppressor mutants partially restore virulence in the G. mellonella model.

(A) G. mellonella larvae were infected with 5×104 fungal cells of the indicated strain, and their survival was monitored for 15 days post-inoculation. The cbk1Δ group survival curve was not significantly different from sup1 (p-value=0.2) and was significantly different from the sup2 curve (p-value=0.05). (B) At day 5 post-inoculation (DPI 5), 5 out of 25 larvae per group for the H99, the cbk1Δ mutant, sup1, and sup2 groups were homogenized, serially diluted, and plated to count the colony-forming units (CFUs) and measure the fungal burden of each larva. The fungal burden of the sup1 and sup2 groups was significantly higher than the cbk1Δ group (p-value<0.001).

Discussion

Detection of and adaptation to changing CO2 levels are an important trait across biological kingdoms and may play a crucial role in the pathogenicity of fungi (Bahn and Mühlschlegel, 2006; Cummins et al., 2014; Hetherington and Raven, 2005; Krysan et al., 2019). Here, we report the identification of genes required for growth at high levels of CO2 in the fungal pathogen C. neoformans. Multiple pathways important for growth at high temperature, such as the Ras1-Cdc24, CWI, Calcineurin, and RAM pathways, were found to be required for growth in high CO2 concentrations, indicating that growth in response to host CO2 may be intricately coordinated and co-regulated with response to host temperature. It is therefore likely that both host CO2 and host temperature represent stressors that cryptococcal cells infecting mammalian hosts must overcome to cause disease.

Calcineurin and RAM pathways were both identified in our screen for mutants that affect cryptococcal CO2 sensitivity. A previous study found synthetic lethality between the RAM and calcineurin pathways in C. neoformans but not in S. cerevisiae (Walton et al., 2006). This corroborates our findings of the key differences between the basidiomycete C. neoformans and the ascomycete yeasts. In C. albicans, CO2 levels are sensed through bicarbonate or cAMP-dependent activation of adenylyl cyclase to increase hyphal growth (Du et al., 2012; Hall et al., 2010). While these pathways may also be functioning to sense CO2 in Cryptococcus (Bahn et al., 2005; Mogensen et al., 2006), our results indicate that these pathways do not play a significant role in host CO2 tolerance in C. neoformans. We also found that disruption of the RAM pathway effector kinase Cbk1 caused a severe growth defect at host CO2 in C. neoformans but not in C. albicans. The vast differences between these organisms in terms of growth response to CO2 may reflect the evolutionary distance between these species and/or the distinct niches they normally occupy. Indeed, C. albicans is a human commensal and has adapted to host CO2 concentrations. S. cerevisiae is a powerful fermenter that thrives in conditions with high levels of CO2. For the environmental fungus C. neoformans, however, the ability to grow in a CO2-enriched condition does not appear to be strongly selected for in the natural environment, and the host level of CO2 (~5% CO2) is over 100-fold higher than the ambient air (~0.04% CO2).

The RAM pathway mutants were among the most sensitive mutants to host levels of CO2. Remarkably, the growth defects of cbk1Δ could be partially restored by single mutations in the genes PSC1 or SSD1. While the PARN-encoding gene PSC1 represents an uncharacterized protein, SSD1 is a known suppressor of cbk1Δ phenotypes that has been extensively characterized in ascomycete yeasts to regulate the translation of numerous and diverse mRNA transcripts (Hu et al., 2018; Jansen et al., 2009; Lee et al., 2015; Li et al., 2009b; Wanless et al., 2014). Our genetic interaction analysis indicates that Psc1 likely functions in the same pathway as Ssd1. Interestingly, in S. cerevisiae, deletion of SSD1 can suppress the lethality of the cbk1Δ mutant but not the cell separation defect, which is regulated by the transcription factor Ace2 (Kurischko et al., 2005). However, an Ace2 homolog has not been identified in Cryptococcus or any other basidiomycete (Lin et al., 2015). In C. albicans, Ssd1 plays an important role in polarized growth and hyphal initiation by negatively regulating the transcription factor Nrg1 (Lee et al., 2015). The observation that cbk1Δpsc1Δ and cbk1Δssd1Δ suppressor mutants partially rescue cell separation defects or depolarized growth suggests that C. neoformans may primarily utilize Ssd1/Psc1 rather than a potential Ace2 homolog to regulate cell separation or polarization. Differential regulation of target mRNA transcripts by Ssd1 and Psc1 may explain the functional divergence of the RAM pathway we observed between the basidiomycete Cryptococcus and the ascomycete yeasts. Our observation that the natural suppressors do not restore transcript abundances of CO2-associated genes in cbk1Δ to WT levels supports a hypothesis that disruption of Ssd1 and Psc1 suppresses the cbk1Δ mutant’s defects at a post-transcriptional level. C. neoformans has been demonstrated to use post-transcriptional regulation to adapt to various host stresses (Bloom et al., 2019; Kalem et al., 2021; Stovall et al., 2021). A temperature-sensitive environmental species of Cryptococcus, Cryptococcus amylolentus, fails to initiate host stress-induced translational reprogramming and is non-pathogenic (Bloom et al., 2019). Whether or not translatome reprogramming is initiated in C. neoformans in response to host CO2, and whether such reprogramming, if occurs, relies on Ssd1 and/or Psc1, has yet to be determined.

Materials and methods

Strains, growth conditions, and microscopy examination

Request a detailed protocol

Strains used in this study are listed in the key resources table. Unless stated otherwise, all C. neoformans cells were maintained at 30°C on YPD media or YPD + CuSO4 (25  μM) for strains transformed with PCTR4-CBK1. For morphological examination, all strains were examined under a Zeiss Imager M2 microscope, equipped with an AxioCam MRm camera. For spotting assays, the tested strains were grown overnight in liquid YPD medium at 30°C with shaking at 220 RPM. The cells were then adjusted to the same cell density of OD600=1 and serially diluted 10-fold. The cell suspensions were then spotted onto YPD agar medium and incubated at the indicated condition for 2 days. CO2 levels were controlled by a VWR CO2 incubator or by a Pro-CO2 controller (Biospherix, Lacona, NY, USA).

Genetic manipulation

Gene deletion constructs

Request a detailed protocol

To delete the gene SSD1, a deletion construct with a nourseothricin (NAT) resistance marker cassette with 5’ and 3’ homology arms to SSD1 was used. Primers Linlab7974 (gctgcctttgcgtcatctc) and Linlab7976 (ctggccgtcgttttactctcgccttccttctcctta) were used to amplify the 5’ arm from the H99 genome. The 3’ arm was amplified from H99 with primers Linlab7977 (gtcatagctgtttcctgcgattgacattgccgtcttag) and Linlab7979 (cgacctgatcaaactactcgc). The NAT marker was amplified with universal primers M13F and M13R from plasmid pPZP-NATcc. The three pieces were fused together by overlap PCR and amplified with nested primers Linlab7975 (acaatgagccactgccag) and Linlab7977 (tgcgtgttcactactgtagac). To disrupt the gene PSC1, a hygromycin (HYG) marker cassette was used to insert into the PARN domain. To generate the sgRNA for specific targeting to the SSD1 locus, the U6 promoter and sgRNA scaffold were amplified from JEC21 genomic DNA and the plasmid pDD162 using primers Linlab7980/Linlab4627 (ttgagtggggtgggtcaattaacagtataccctgccggtg and ggctcaaagagcagatcaatg) and Linlab7981/Linlab4628 (aattgacccaccccactcaagttttagagctagaaatagcaagtt and cctctgacacatgcagctcc). For sgRNA targeted mutation of PSC1, the primers Linlab8380/Linlab4627 (tagttgttttcgccgacgccaacagtataccctgccggtg and ggctcaaagagcagatcaatg) were used to amplify the U6 promoter and Linlab8381/Linlab4628 (ggcgtcggcgaaaacaactagttttagagctagaaatagcaagtt and cctctgacacatgcagctcc) to amplify the sgRNA scaffold. The U6 promoter and sgRNA scaffold were fused together by overlap PCR with primers Linlab4594/Linlab4595 (ccatcgatttgcattagaactaaaaacaaagca and ccgctcgagtaaaacaaaaaagcaccgac) to generate the final sgRNA construct as described previously (Fan and Lin, 2018; Lin et al., 2020).

Gene overexpression constructs

Request a detailed protocol

The CBK1 overexpression construct was generated by amplifying the CBK1 open reading frame with primers Linlab7005/BC (ataggccggccatgtcgtatcgcccaatccag) and Linlab7006/BC (cagcatctcgtatcgtcggaag) and cloning the fragment with FseI and PacI into the pXC plasmid backbone (Wang et al., 2012), which contains the promoter of CTR4 and neomycin resistance marker. The CTR4 promoter is highly induced on the copper limiting YPD media. The MPK1 overexpression construct was generated by amplifying the MPK1 open reading frame with primers Linlab8326/BC (ataggccggccatggacaatacccctagacac) and Linlab8327/BC (ccttaattaaggctatgataatttctgcctctcc) and cloning the fragment with FseI and AsiSI into a pUC19 plasmid backbone, containing the promoter of GPD1 and neomycin resistance marker. The CDC24 overexpression construct was generated by amplifying the CDC24 open reading frame with primers Linlab6674/BC (ataggccggccatgtctgtatccggtcccatctc) and Linlab6675/BC (ccttaattaaggataaatctctccttgtggggtacc) and cloning the fragment with FseI and PacI into a pUC19 plasmid backbone, containing the promoter of CTR4 and neomycin resistance marker. The overexpression constructs were integrated into the SH2 locus as described previously (Fan and Lin, 2018; Lin et al., 2020).

Transformation

Request a detailed protocol

Constructs for overexpression and deletion were transformed into Cryptococcus strains by the TRACE method (Fan and Lin, 2018; Lin et al., 2020), and transformants were selected on YPD medium with 100  μg/mL of NAT,100 μg/mL of neomycin (NEO), or 200 μg/mL of HYG.

Quantitative real-time PCR

Request a detailed protocol

WT H99 strain along with the cbk1Δ, CBK1OE strain were cultured by shaking at 220 RPM at 30°C overnight in liquid YPD medium containing 50 μM CuSO4 to suppress the CTR4 promoter of the CBK1OE construct. The cultures were then diluted to OD600=0.2 in fresh liquid YPD medium containing 50 μM BCS(bathocuproine disulfonate) to induce expression. After 5 hr of further incubation, cells were collected, flash frozen in liquid nitrogen, and lyophilized overnight. Three biological replicates per strain were used. Total RNA was isolated by using the PureLink RNA Mini Kit (Invitrogen), and first strand cDNA was synthesized using the GoScript Reverse Transcription System (Promega) following the manufacturer’s instructions. The Power SYBR Green system (Invitrogen) was used for RT-PCR. The following primers were used to target CBK1: Linlab9217/BC (gatgctctcactcctgattcc) and Linlab8641/BC (gtacgagtctgacttcaccga). The following primers were used to target the TEF1 housekeeping gene as an endogenous control for each sample: Linlab329/XL (cgtcaccactgaagtcaagt) and Linlab330/XL (agaagcagcctccatagg). Relative transcript level was determined using the ΔΔct method as described previously. Statistical significance was determined using a Student’s t-test.

NanoString RNA profiling

Request a detailed protocol

Overnight YPD cultures of H99, cbk1Δ, cbk1Δssd1Δ, and cbk1Δpsc1Δ were washed 2× in PBS and resuspended in RPMI +165 mM MOPS, pH 7.4 before quantification on an Invitrogen Countess automated cell counter. Cells were diluted to 7.5×105 cells per mL in 3 mL per well in a 6-well plate. Two wells were used for each biological replicate (n=3) and condition (ambient or 5% CO2). Plates were sealed with BreatheEasy sealing membranes (Sigma #Z380059) and incubated in a static incubator at 30°C in ambient air or 5% CO2 for 24 hr. Cells were harvested, pelleted at 3200×g for 5 min, and the supernatant was removed. The pellets were then frozen at –80°C and lyophilized overnight. Lyophilized cells were disrupted for 45 s with 0.5 mm glass beads on an MP Biomedicals FastPrep-24 benchtop homogenizer. RNA was extracted following manufacturer instructions for the Invitrogen PureLink RNA mini-kit with on-column DNAse treatment. Purified RNA was quantified on a NanoDrop OneC spectrophotometer, and a total of 100 ng per sample was combined with a custom probeset (Source data 1) from NanoString Technologies according to manufacturer instructions. Probes were hybridized at 65°C for 18 hr, then run on a NanoString nCounter SPRINT profiler according to manufacturer instructions. Data from Reporter Code Count files were extracted with nSolver software (version 4.0), and raw counts were exported to Microsoft Excel. Internal negative controls were used to subtract background from raw counts (negative control average +2 SDs). Counts were normalized across samples by total RNA counts. Probes below background were set to a value of 1. Fold change and significance were calculated in Excel after averaging biological triplicates, using a Student t-test (p<0.05). Volcano plot was generated with transformed values (−log[p-value] and log2[fold change]) in GraphPad Prism 9. Normalized total counts were used in Morpheus (https://software.broadinstitute.org/morpheus/) to generate a heat map, with hierarchical clustering, one minus Pearson correlation, average linkage method, and clustered according to rows and columns.

Bioinformatics

Request a detailed protocol

Whole genome sequencing was performed using the Illumina platform with NovaSeq 6000 at the University of California – Davis Sequencing Center, Novogene USA. A paired-end library with approximately 350 base inserts was constructed for each sample, and all libraries were multiplexed and run in one lane using a read length of 150 bases from either side.

The Illumina reads were first trimmed with Trim Galore v0.6.5 (Krueger, 2021) and then mapped to the C. neoformans H99 reference genome (FungiDB version 50) using the BWA-MEM algorithm of the BWA aligner v0.7.17 (Li, 2013). SAMtools v1.10 (Li et al., 2009a), Picard Tools v2.16.0 (Broad_Institute, 2022), and bcftools v1.13 (Danecek et al., 2021) were used for variant calling from each sample. Variants in the suppressor strains were called with the original cbk1Δ mutant as a reference.

The protein diagrams of Psc1 and Ssd1 were made with the illustrator of biological sequences software package (Liu et al., 2015).

Phagocytosis assays

Request a detailed protocol

The authenticated mouse macrophage cell line J774A.1 (ATCC TIB-67) was acquired from the American Type Culture Collection. Before being used, normal morphology, cell adhesion, and phagocytosis activity of the cell line was confirmed. Contamination by mycoplasma was not detected. Phagocytosis assays were performed using similar procedures as we described previously (Lin et al., 2015). Briefly, 1 mL of 2×105 J774A.1 macrophages (MΦ) in DMEM was seeded into a 24-well plate and incubated at 37°C with 5% CO2 for 24 hr. Cryptococcus strains with a starting OD600 of 0.2 in 3 mL of liquid YPD were cultured for 16 hr. Each strain had three technical replicates. The cells were washed three times in sterile H2O. 2×106 cryptococcal cells of each strain were opsonized in either 40 μL of 100% fetal bovine serum, naïve mouse serum, or mouse serum from LW10 vaccinated A/J mice (Lin et al., 2022; Zhai et al., 2015), for 30 min prior to co-incubation with MΦ. Old DMEM from MΦ was removed, and 1 mL of fresh DMEM with the opsonized Cryptococcus cells was added, followed by a 2 hr incubation at 37°C with 5% CO2. The co-culture was then washed six times with warm PBS to remove non-adherent Cryptococcus cells. To lyse the macrophages, the cell suspensions were washed with 1 mL of cold PBS +0.01% Triton X. Serial dilutions in PBS of the cell suspensions were then plated onto YNB agar medium and allowed to grow at 30°C for 2 days to count CFUs. Statistical analyses were performed using the program Graphpad Prism 8. A two-tailed t-test was applied to determine significance. A p-value of less than 0.05 was considered significant.

G. mellonella infection model

Request a detailed protocol

G. mellonella larvae were purchased from Best Bait (Marblehead, OH, USA). The infection was performed as described previously described (Mylonakis et al., 2005). In brief, cryptococcal strains were inoculated in 3 mL of liquid YPD medium with the initial OD600=0.2 (approximately 106 cell/mL) and incubated for 15 hr at 30°C with shaking. Prior to infection, cells were washed with sterile PBS three times and adjusted to the final concentration of 1×107 cell/mL. 5 μL of the cell suspension (5×104 cells), or PBS for the control group, were injected into the last left proleg of the larvae. The proleg was cleaned with 70% ethanol prior to injection. Infected larvae were maintained at 30°C and monitored daily for survival.

Prior to fungal burden quantification, larvae were first cleaned with 70% ethanol. The larvae were cut open with sterile scissors and vortexed in a microcentrifuge tube containing 500 μL PBS and 100 μL of 0.5 mm diameter glass beads (RPI). Larval suspensions were then serially diluted in PBS and plated onto YNB agar medium containing 50 μg/mL kanamycin and 20 μg/mL chloramphenicol and incubated at 30°C for 2 days before counting the CFUs.

Statistical analyses were performed using the program Graphpad Prism 8. The log-rank Mantel-Cox test was used to assess statistical significance of survival curves for comparison between two groups. One-way ANOVA tests were used to compare groups of three or more and for fungal burden assays.

Murine models of cryptococcosis

Intranasal infection model

Request a detailed protocol

Female Balb/C mice of 8–10 weeks old were purchased from the Jackson Labs (Bar Harbor, Maine). Cryptococcal strains were inoculated in 3 mL of liquid YPD medium with the initial OD600=0.2 (approximately 106 cell/mL) and incubated for 15 hr at 30 °C with shaking. Prior to intranasal infection, cells were washed with sterile saline three times and adjusted to the final concentration of 2×105 cell/mL. Once the mice were sedated with ketamine and xylazine via intraperitoneal injection, 50 μL of the cell suspension (1×104 cells per mouse) were inoculated intranasally as previously described (Lin et al., 2022; Zhai et al., 2012; Zhai et al., 2013; Zhao et al., 2020; Zhu et al., 2013). Mice were monitored daily for disease progression. Surviving animals were euthanized at 35 or 60 DPI, and the brain, lungs, and kidneys, were dissected.

Intravenous infection model

Request a detailed protocol

Prior to intravenous infections, cryptococcal cells were washed with sterile saline three times and adjusted to the final concentration of 1×106 cell/mL. Mice were sedated with Isoflurane. 100 μL of the cell suspension (1×105 cells per mouse) were injected intravenously as previously described (Zhai et al., 2012; Zhai et al., 2013; Zhao et al., 2020; Zhu et al., 2013). After DPI 5, animals were euthanized, and the brain, lungs, and kidneys were dissected.

For fungal burden quantifications, dissected organs were homogenized in 2 mL of cold sterile PBS using an IKA-T18 homogenizer as we described previously (Zhai et al., 2015; Zhai et al., 2012). Tissue suspensions were serially diluted in PBS and plated onto YNB agar medium and incubated at 30°C for 2 days before counting the CFUs.

Ethical statements

Request a detailed protocol

This study was performed according to the guidelines of NIH and the University of Georgia Institutional Animal Care and Use Committee (IACUC). The animal models and procedures used have been approved by the IACUC (AUP protocol numbers: A2017 08–023 and A2020 06–015).

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Cryptococcus neoformans KN99α, matα)WT strain: H99Nielsen et al., 2003
Genetic reagent (C. neoformans KN99α, matα)WT strain: H99aNielsen et al., 2003
Genetic reagent (C. neoformans KN99α, matα)cbk1ΔWalton et al., 2006FJW9H99alpha, CBK1::NATr
Genetic reagent (C. neoformans KN99α, matα)mob2ΔWalton et al., 2006FJW10H99alpha, MOB2::NATr
Genetic reagent (C. neoformans KN99α, matα)kic1ΔWalton et al., 2006FJW8H99alpha, KIC1::NATr
Genetic reagent (C. neoformans KN99α, matα)tao3ΔWalton et al., 2006AI136MATalpha, TAO3:: NATr
Genetic reagent (C. neoformans KN99α, matα)sog2ΔWalton et al., 2006AI131MATalpha, SOG2:: NATr
Genetic Reagent (Candida albicans, SN250)SN250Wakade et al., 2020SN250HIS-, LEU-, ARG-
Genetic Reagent (C. albicans, SN250)cbk1ΔΔWakade et al., 2020ΔΔcbk1SN250, ΔΔcbk1: HIS-, LEU-, ARG-
Genetic reagent (C. neoformans KN99α, matα)cna1ΔFGSC deletion set Plate 46 Well E12cna1ΔH99alpha CNA1::NATr
Genetic reagent (C. neoformans KN99α, matα)cdc24ΔFGSC deletion set Plate 33 Well C4cdc24ΔH99alpha, CDC24::NATr
Genetic reagent (C. neoformans KN99α, matα)mpk1ΔFGSC deletion set Plate 11 Well A5mpk1ΔH99alpha, MPK1::NATr
Genetic reagent (C. neoformans KN99α, matα)CBK1OEThis study.BC1449H99alpha, PCTR4-CBK1-mCherry- NEOr
Genetic reagent (C. neoformans KN99α, matα)cdc24Δ, CBK1OEThis study.BC1281H99alpha, CDC24::NATr, PCTR4-CBK1-mCherry- NEOr
Genetic reagent (C. neoformans KN99α, matα)cdc24Δ, CBK1OEThis study.BC1282H99alpha, CDC24::NATr, PCTR4-CBK1-mCherry- NEOr
Genetic reagent (C. neoformans KN99α, matα)cna1Δ, CBK1OEThis study.BC1283H99alpha, CNA1::NATr, PCTR4-CBK1-mCherry- NEOr
Genetic reagent (C. neoformans KN99α, matα)cna1Δ, CBK1OEThis study.BC1284H99alpha, CNA1::NATr, PCTR4-CBK1-mCherry- NEOr
Genetic reagent (C. neoformans KN99α, matα)mpk1Δ, CBK1OEThis study.BC1285H99alpha, MPK1::NATr, PCTR4-CBK1-mCherry- NEOr
Genetic reagent (C. neoformans KN99α, matα)mpk1Δ, CBK1OEThis study.BC1286H99alpha, MPK1::NATr, PCTR4-CBK1-mCherry- NEOr
Genetic reagent (C. neoformans KN99α, matα)cdc24Δ, CDC24OEThis study.BC650H99alpha, CDC24::NATr, PCTR4-mNeonGreen-CDC24-NEOr
Genetic reagent (C. neoformans KN99α, matα)cbk1Δ, CBK1OEThis study.BC669H99alpha, CBK1::NATr, PCTR4-CBK1-mCherry- NEOr
Genetic reagent (C. neoformans KN99α, matα)mpk1Δ, MPK1OEThis study.BC1356H99alpha, MPK1::NATr, PGPD1-mNeonGreen-MPK1-NEOr
Genetic reagent (C. neoformans KN99α, matα)cna1Δ, CNA1OEKozubowski et al., 2011LK214H99a, CNA1::NEOr, PH3-GFP-CNA1-NATr
Genetic reagent (C. neoformans KN99α, matα)cbk1Δ, CDC24OEThis study.BC1357H99alpha, CBK1::NATr, PCTR4-mNeonGreen-CDC24-NEOr
Genetic reagent (C. neoformans KN99α, matα)cbk1Δ, MPK1OEThis study.BC1358H99alpha, CBK1::NATr, PGPD1-mNeonGreen-MPK1-NEOr
Genetic reagent (C. neoformans KN99α, matα)cbk1Δ, CNA1OEThis study.BC1359H99alpha, CBK1::NATr, PH3-GFP-CNA1-NATr
Genetic reagent (C. neoformans KN99α, matα)sup1This study.BC1068H99alpha, CBK1::NATr,SUP1
Genetic reagent (C. neoformans KN99α, matα)sup2This study.BC1076H99alpha, CBK1::NATr,SUP2
Genetic reagent (C. neoformans KN99α, matα)cbk1Δssd1ΔThis study.BC1239H99alpha, CBK1::NATr, SSD1::NEOr
Genetic reagent (C. neoformans KN99α, matα)ssd1ΔThis study.BC1241H99alpha, SSD1::NEOr
Genetic reagent (C. neoformans KN99α, matα)cbk1Δpsc1ΔThis study.BC1369H99alpha, CBK1::NATr, PSC1::HYGr
Genetic reagent (C. neoformans KN99α, matα)psc1ΔThis study.BC1393H99alpha, PSC1::HYGr
Genetic reagent (C. neoformans KN99α, matα)cac1ΔBahn et al., 2006YSB42H99alpha, CAC1::NATr STM#159
Genetic reagent (C. neoformans KN99α, matα)aca1ΔFGSC deletion set Plate 32 Well H6aca1ΔH99alpha, ACA1::NATr
Genetic reagent (C. neoformans KN99α, matα)pkr1ΔFGSC deletion set Plate 33 Well H7pkr1ΔH99alpha, PKR1::NATr
Genetic reagent (C. neoformans KN99α, matα)pde1ΔFGSC deletion set Plate 10 Well A12pde1ΔH99alpha, PDR1::NATr
Genetic reagent (C. neoformans KN99α, matα)pde2ΔFGSC deletion set Plate 11 Well H7pde2ΔH99alpha, PDR2::NATr
Genetic reagent (C. neoformans KN99α, matα)gpa1ΔBahn et al., 2005YSB83H99alpha, GPA1::NATr STM#5
Genetic reagent (C. neoformans KN99α, matα)cln1ΔThis study.BZ36H99alpha, CLN1::NATr
Genetic reagent (C. neoformans KN99α, matα)ecm2201ΔFGSC deletion set Plate 2 Well A10ecm2201ΔH99alpha, ECM2201::NATr
Genetic reagent (C. neoformans KN99α, matα)kin4ΔBahn Kinase Deletion Set Plate 4 Well G8 (Lee et al., 2016)kin4ΔH99alpha, KIN4::NATr
Recombinant DNA reagentpPZP-NATccDickinson et al., 2013pPZP-NATcc
Recombinant DNA reagentpDD162Walton et al., 2006pDD162
Recombinant DNA reagentpFZ1-CDC24This study.PCTR4-2-mNeonGreen-CDC24(H99)-NEOr
Recombinant DNA reagentpXC-CBK1-mChThis study.PCTR4-2-CDC24(H99)-mCherry-NEOr
Recombinant DNA reagentLKB61Kozubowski et al., 2011PGPD1-mCherry-CNA1-HYGr
Recombinant DNA reagentpUC19-MPK1-mNGThis study.PGPD1-MPK1-mNeonGreen-NEOr
Cell line
(Mus. Musculus, macrophage cell line J774A.1)		J774A.1American Type Culture CollectionATCC TIB-67
Chemical Compound and drugHygromycinResearch Products InternationalCat. NO.: H75000
Chemical Compound and drugG418Research Products InternationalCat. NO.: G64000
Chemical Compound and drugNourseothricinJena BioscienceCat. NO.: AB-102–25 G
Software and algorithmGraphpad Prism 9Graphpad
Software and algorithmnSolver software version 4.0NanoString Technologies
Software and algorithmTrim Galore v0.6.5Krueger, 2021
Software and algorithmBWA aligner v0.7.17Li, 2013
Software and algorithmSAMtools v1.10Li et al., 2009a
Software and algorithmPicard Tools v2.16.0Broad_Institute, 2022
Software and algorithmbcftools v1.13Danecek et al., 2021
Software and algorithmIllustrator of biological sequences (IBS)Liu et al., 2015

Data availability

Sequences generated from this research has been deposited to the Sequence Read Archive (SRA) under project accession number: PRJNA791949.

The following data sets were generated
    1. Lin X
    (2022) NCBI BioProject
    ID PRJNA791949. Whole genome sequencing of C. neoformans H99 suppressor strains of the RAM pathway downstream kinase Cbk1 knockout mutant.
    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA791949

References

    1. Gobeil SMC
    2. Bobay BG
    3. Juvvadi PR
    4. Cole DC
    5. Heitman J
    6. Steinbach WJ
    7. Spicer LD
    (2021) Leveraging fungal and human calcineurin-inhibitor structures
    Biophysical Data, and Dynamics To Design Selective and Nonimmunosuppressive FK506 Analogs. MBio 12:e0300021.
    https://doi.org/10.1128/mBio.03000-21

Decision letter

  1. Arturo Casadevall
    Senior and Reviewing Editor; Johns Hopkins Bloomberg School of Public Health, United States
  2. Ella Jacobs
    Reviewer; Johns Hopkins Bloomberg School of Public Health, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "The RAM signaling pathway links morphology, thermotolerance, and CO2 tolerance in the global fungal pathogen Cryptococcus neoformans" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Arturo Casadevall as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Ella Jacobs (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Need to address the comments from all three reviewers on clarifications on the text as indicated in their recommendations to authors

2) Need to address concerns about strain The cbk1Δ strain raised by reviewer 2.

3) Consider reviewer 3 recommendation to add an additional experiment testing survival after H2O2 stress.

Reviewer #1 (Recommendations for the authors):

Some points for consideration are as follows.

Lines 20-21: does it matter in the abstract that no ACE2 homology is known? The logical discovery from the suppressor screen would be its equivalent but that did not happen. Therefore recommend deleting those two sentences.

Lines 43-44: could delete "Krysan and Lin laboratories demonstrated that." Also on line 44, to "in the host vs. ~00.04% in".

Carbonic anhydrase has been studied in C. neoformans, so there is some research on the role of CO2 on this fungus. This might influence the text on lines 345-346 about bicarbonate sensing.

General: use a consistent style for pH; probably pH # (with the space) is the better option than pH#. One example of the mix in styles is seen in the labels on figure 1.

Lines 85-86: what was the rationale, other than convenience, of using a "simplified medium" [although note YPD is a complex undefined medium].

Line 133: as commented in the "Public Review" section, details are missing here about gene choice. For example, what are the "results from a separate study"?

Figure 2A: not sure this panel is needed.

Lines 169-170 and 343-355: are likely an extrapolation. It is worth considering two aspects of C. neoformans biology. (a) H99 is non-representative of serotype A, featuring a chromosomal rearrangement and gene loss. (b) A RAM pathway mutant in a serotype D background does not show strong temperature sensitivity (see Magditch et al. PLoS Pathogens 2012 figure 1). It may be wise to explore the CO2 impact in other background as well before drawing too many conclusions about differences between fungal phyla. Likewise, the CO2 phenotypes seen here for the cdc24 mutant are stronger than those observed by Chang et al. PLoS Genetics 2014. What is the consequence on O2 levels of increasing CO2 levels, e.g. do they reduce leading towards hypoxia?

Suppressor phenotypes and figure 4: Magditch et al. 2012 screened for suppressors using the sensitivity of RAM pathway mutants to calcineurin inhibition. Do the PSC1 and SSD1 suppressors grow in the presence of FK506? Likewise, do they restore the mating defects seen in RAM pathway mutants?

Line 270: perhaps change "all zeros in" to "no CFUs isolated from".

Line 278: "to have a poor phagocytosis".

Line 281: it would be good to expand on the rationale for using the vaccinated mouse material, and what the vaccine was.

Line 298: "detected a few fungal".

Lines 471, 477: delete "media" [the last M in "DMEM"].

Line 475: micro symbol for "ul".

Reference list: check the formatting is consistent, e.g. subscript 2 in CO2, italics on species names, in case the journal does not do this.

Figure S3 legend: italics on "cbk1".

Table S1 could be more informative, e.g. one more column to highlight possible functions or links. For example, it was not immediately clear which were the RAM pathway mutants or their gene names. How many of these mutants are also temperature sensitive or have other phenotypes? Do these CNAG_##### genes have other names from previous studies that could orient readers better?

Reviewer #2 (Recommendations for the authors):

1. The authors have screened for suppressor mutants of cbk1Δ using the assay described in the text (lines 183-188). A total of 11 suppressor colonies were further examined and the genomes were sequenced. Based on the sequencing result, the suppressor colonies were categorized into two groups: two colonies in the sup1 group, both of which contained an in-frame deletion in the PSC1 gene, and 9 colonies in the sup2 group that contain mutations (any of the missense, frame-shift, or premature STOP) in the SSD1 ORF. The authors have performed in vitro as well in vivo experiments in a mice model with a single suppressor colony from each group. While the nature of mutations varies in sup2 colonies, the authors have shown that the cbk1Δssd1Δ strain behaves similarly to the sup2 group. While it is unclear from the text the nature of the mutation in the sup2 colony, the authors took forward the mutants for subsequent experiments (whether missense, frameshift, or premature STOP). It may be assumed that a missense mutation found in sup2 and a frameshift mutation found in sup1, which the authors reported, lead to a suppressor phenotype similar to respective gene deletion in the cbk1Δ background. It is suggestive of the critical role(s) of the individual residues in both the proteins, mutations of which lead to complete loss of activity. We recommend the authors generate the respective point mutants in those two proteins and recapitulate the sup1 and sup2 phenotypes.

2. It is understood from the in vitro results that:

a. The cbk1Δ strain is nearly inviable (Figure 4C) at 37oC irrespective of ambient or +5% CO2.

b. The sup1, cbk1Δsup1Δ exhibit partial rescue at 37oC alone but very poor rescue at 37oC+5% CO2

c. Similarly, the sup2, cbk1Δssd1Δ strains exhibit partial rescue, slightly better than sup1.

Thus, at 37oC, in presence of ambient or 5% CO2, those strains exhibit a significant loss of viability compared to H99. However, the authors used the same number of cells as inoculum of all strains for the mice experiment ignoring the difference in the viability of the above strains. Hence, the conclusion that the sup1 and sup2 strains fail to cause any mortality (lines 255-258) is questionable. We recommend that the authors normalize the sup1 and sup2 inoculum such that viable numbers, not the total numbers, of cells, are comparable to those in H99 at the host condition. Authors may consider using an alternate model system to validate those virulence traits by taking into account the above factors.

3. The authors may consider showing the overexpression of Cbk1 using Western blots (Figure 2).

4. Adding scale bars to Figure 4D will help in understanding the morphological changes better.

5. Line 93: Adding the information on the total number of ORFs in the C. neoformans genome will help the reader understand what percentage of the genome was represented by the deletion mutant collection.

Reviewer #3 (Recommendations for the authors):

This manuscript would be strengthened by an experiment comparing the suppressor strain's survival after H2O2 exposure or another general stress test to bolster the claim that sup2's increased growth at host CO2 levels at pH 6 is a solid explanation for the differences in establishment and persistence of infection in the absence of mouse mortality.

The acknowledgement that other virulence factors are also impacted by this kinase should be made more apparent in the text and/or included in a main figure.

Additionally, in SI Appendix Figure 4, the slightly increased growth of sup2 during CO2 exposure is only seen at pH 6, while at pH 7.4 both strains appear similarly negatively impacted by CO2 with growth reduced to cbk1∆ levels. Further commentary on why the change in pH appears to greatly impact the sup1 and sup2 strains in pH 7.4 compared to pH 6 would be helpful.

https://doi.org/10.7554/eLife.82563.sa1

Author response

Reviewer #1 (Recommendations for the authors):

Some points for consideration are as follows.

Lines 20-21: does it matter in the abstract that no ACE2 homology is known? The logical discovery from the suppressor screen would be its equivalent but that did not happen. Therefore recommend deleting those two sentences.

Although it will not matter to the design of our gene deletion screen, the suppressor screen, or the main findings of the study, we believe that exclusion of the statement about the lack of ACE2 homolog in Cryptococcus will likely raise more questions in readers’ mind because Ace2 plays such an important role in the RAM pathway in the well‐studied Saccharomyces and Candida species. Furthermore, our findings indicate that in the absence of an Ace2 homolog in C. neoformans, the genes we identified here play a similar role in cell separation and morphology as Ace2 does in these ascomycete yeasts.

Lines 43-44: could delete "Krysan and Lin laboratories demonstrated that." Also on line 44, to "in the host vs. ~00.04% in".

Changed.

Carbonic anhydrase has been studied in C. neoformans, so there is some research on the role of CO2 on this fungus. This might influence the text on lines 345-346 about bicarbonate sensing.

We thank the reviewer for pointing this out. Yes, there are studies on genes important for growth in ambient air (low levels of CO2) but dispensable for growth in high concentrations of CO2. Carbonic anhydrase Can1 and Can2 of C. neoformans are such factors and therefore they are dispensable for cryptococcal virulence in the host due to restored growth by high concentrations of CO2. Our study here focuses on genes important for tolerance of CO2 at high concentrations. That said, we agree with the reviewer that it is on a related topic. We have included a reference to a study on Cryptococcus on carbonic anhydrases in the second Results section (lines 99‐102), and we have modified the Discussion section on lines 439‐443. “In C. albicans, CO2 levels are sensed through bicarbonate or cAMPdependent activation of adenylyl cyclase to increase hyphal growth (Du et al., 2012; Hall et al., 2010).

While these pathways may also be functioning to sense CO2 in Cryptococcus (Bahn, Cox, Perfect, and Heitman, 2005; Mogensen et al., 2006), our results indicate that these pathways do not play a significant role in host CO2 tolerance in C. neoformans.”

General: use a consistent style for pH; probably pH # (with the space) is the better option than pH#. One example of the mix in styles is seen in the labels on figure 1.

We thank the reviewer for finding this inconsistency. We have changed the style of all to “pH #”.

Lines 85-86: what was the rationale, other than convenience, of using a "simplified medium" [although note YPD is a complex undefined medium].

We modified lines 84‐85 to “For large‐scale screening, we used the nutrient rich YPD medium on which C. neoformans grows well”. Besides convenience, the pH and nutrient content of YPD is also beneficial for Cryptococcus growth and therefore limits any potential detrimental effects that other media might have caused.

Line 133: as commented in the "Public Review" section, details are missing here about gene choice. For example, what are the "results from a separate study"?

We have modified this section to make the details of the separate transcriptomics study more clear on lines 133‐135. “Transcript levels of 118 genes were measured and those genes were chosen based on RNA sequencing results of four different natural isolates in a separate study (Krysan et al., in preparation). In that study, these genes were differentially expressed in CO2 vs ambient air conditions in either two CO2‐sensitive or two CO2‐tolerant natural strains (Source data 1). The list of the 118 probes is provided in this manuscript as Source data 1.

Figure 2A: not sure this panel is needed.

Figure 2A is included to show that the previously identified CO2‐sensitive strains, based on growth on the nutrient‐limiting mammalian cell culture RPMI media (Krysan et al., 2019), are also sensitive on rich YPD media that is favorable for fungal growth. The growth defects become more severe when the CO2 concentration is increased to 20%, which is the concentration used in our deletion library screen. The CO2 effect on cryptococcal growth on YPD media has not been reported previously and we believe that it is important to include the result in this study.

Lines 169-170 and 343-355: are likely an extrapolation. It is worth considering two aspects of C. neoformans biology. (a) H99 is non-representative of serotype A, featuring a chromosomal rearrangement and gene loss. (b) A RAM pathway mutant in a serotype D background does not show strong temperature sensitivity (see Magditch et al. PLoS Pathogens 2012 figure 1). It may be wise to explore the CO2 impact in other background as well before drawing too many conclusions about differences between fungal phyla. Likewise, the CO2 phenotypes seen here for the cdc24 mutant are stronger than those observed by Chang et al. PLoS Genetics 2014. What is the consequence on O2 levels of increasing CO2 levels, e.g. do they reduce leading towards hypoxia?

We thank the reviewer for pointing out these observations to us. Based on the results of Magditch et al. (Magditch et al., 2012), the RAM pathway mutant phenotype in different strain backgrounds of Cryptococcus is similar to our mutants in the H99 background in terms of morphology and temperature sensitivity (their Figure 1 vs. our Figure 3). However, the RAM mutant phenotype is distinct from ascomycete yeasts such as Saccharomyces cerevisiae and Candida albicans (the later shown in Figure 3‐figure supplement 1), which is not temperature sensitive and exhibits depolarized growth as round yeast cells.

Our CO2 incubator controls CO2 directly but not the O2 levels. It is possible that increasing CO2 from 0.04% to 5% will accordingly lower other gases in the air including O2 by 95%, which means that the O2 levels should be around 21%*0.95 = 19.95%. This is hardly considered a hypoxic condition for Cryptococcus. Thus, our gas conditions should be similar to their 20%O25%CO2. However, we took pictures of our colonies after 2 days of incubation whereas they took pictures after 3 days of incubation. The prolonged incubation in their setting might have diminished the differences in growth that could be observed at earlier time points. This and other possible differences in incubators, such as humidity, may also contribute to the variation in phenotype of our cdc24Δ mutant here vs. in the publication by Chang et al. PLoS Genetics 2014. That said, there is a discernable growth defect of the cdc24Δ mutant in 20%O25%CO2 based on their Figure 1A, which is consistent with our observation.

Suppressor phenotypes and figure 4: Magditch et al. 2012 screened for suppressors using the sensitivity of RAM pathway mutants to calcineurin inhibition. Do the PSC1 and SSD1 suppressors grow in the presence of FK506? Likewise, do they restore the mating defects seen in RAM pathway mutants?

We thank the reviewer for bringing this to our attention. We have now tested these two phenotypes and added them to the Figure 4‐figure supplement 1. The suppressor mutants are unable to rescue FK506 growth inhibition or the mating defects of the cbk1Δ mutant. However, psc1 and ssd1 suppressors restored many of the cbk1Δ mutant defects, including growth on Congo red and H2O2, in addition to thermotolerance and CO2 tolerance. The growth rescue is consistent with the selective condition used to isolate these suppressor mutants.

Line 270: perhaps change "all zeros in" to "no CFUs isolated from".

Changed.

Line 278: "to have a poor phagocytosis".

Changed.

Line 281: it would be good to expand on the rationale for using the vaccinated mouse material, and what the vaccine was.

We have modified lines 344‐348 to describe the rationale: “Because different types of opsonization can impact phagocytosis of C. neoformans, we decided to opsonize the fungus using either naïve mouse serum (complement mediated phagocytosis) or serum from mice vaccinated against cryptococcosis (complement + antibody mediated phagocytosis). The serum (containing antibodies) from the vaccinated mice recognizes antigens present in the capsule of cryptococcal cells (Lin et al., 2022; Zhai et al., 2015).”

Line 298: "detected a few fungal".

Changed.

Lines 471, 477: delete "media" [the last M in "DMEM"].

Changed.

Line 475: micro symbol for "ul".

Changed.

Reference list: check the formatting is consistent, e.g. subscript 2 in CO2, italics on species names, in case the journal does not do this.

Changed.

Figure S3 legend: italics on "cbk1".

Changed.

Table S1 could be more informative, e.g. one more column to highlight possible functions or links. For example, it was not immediately clear which were the RAM pathway mutants or their gene names. How many of these mutants are also temperature sensitive or have other phenotypes? Do these CNAG_##### genes have other names from previous studies that could orient readers better?

We thank the reviewer for this suggestion. We have added the gene name from the Saccharomyces genome database (SGD) for each gene ID which has a homolog in S. cerevisiae to Supplementary file 1 (formerly Table S1). Fortunately, many of the genes discussed in the manuscript have homologues in Saccharomyces cerevisiae, including the RAM pathway gene names.

Reviewer #2 (Recommendations for the authors):

1. The authors have screened for suppressor mutants of cbk1Δ using the assay described in the text (lines 183-188). A total of 11 suppressor colonies were further examined and the genomes were sequenced. Based on the sequencing result, the suppressor colonies were categorized into two groups: two colonies in the sup1 group, both of which contained an in-frame deletion in the PSC1 gene, and 9 colonies in the sup2 group that contain mutations (any of the missense, frame-shift, or premature STOP) in the SSD1 ORF. The authors have performed in vitro as well in vivo experiments in a mice model with a single suppressor colony from each group. While the nature of mutations varies in sup2 colonies, the authors have shown that the cbk1Δssd1Δ strain behaves similarly to the sup2 group. While it is unclear from the text the nature of the mutation in the sup2 colony, the authors took forward the mutants for subsequent experiments (whether missense, frameshift, or premature STOP). It may be assumed that a missense mutation found in sup2 and a frameshift mutation found in sup1, which the authors reported, lead to a suppressor phenotype similar to respective gene deletion in the cbk1Δ background. It is suggestive of the critical role(s) of the individual residues in both the proteins, mutations of which lead to complete loss of activity. We recommend the authors generate the respective point mutants in those two proteins and recapitulate the sup1 and sup2 phenotypes.

The sup1 group contained amino acid deletion mutations in the PARN domain of Psc1, and the sup2 group contained frameshifts or gain of stop codons in Ssd1. In one suppressor mutant that contained a frameshift mutation in Ssd1, it also contains a missense mutation in Ssd1. We apologize that we mistakenly left out the asterisk that was referenced in the Figure 4 legend in the original manuscript. We have added an asterisk in Figure 4 near “MS” and a description in the figure legend in this revised version to show that this mutation was found together with a frameshift mutation in the same suppressor mutant. Based on the evidence we have gathered, we conclude that the suppressor mutants were generated by loss‐of‐function mutations in Ssd1 or Psc1 for the following reasons: (1) Based on our DNA sequencing results, we only identified high impact mutations in either Psc1 or Ssd1 in the suppressor mutants; (2) Each suppressor mutant contained a high impact polymorphism or indel in Psc1 or Ssd1; (3) The double deletion mutants recapitulate the phenotypes of the natural suppressor mutants.

2. It is understood from the in vitro results that:

a. The cbk1Δ strain is nearly inviable (Figure 4C) at 37oC irrespective of ambient or +5% CO2.

b. The sup1, cbk1Δsup1Δ exhibit partial rescue at 37oC alone but very poor rescue at 37oC+5% CO2

c. Similarly, the sup2, cbk1Δssd1Δ strains exhibit partial rescue, slightly better than sup1.

Thus, at 37oC, in presence of ambient or 5% CO2, those strains exhibit a significant loss of viability compared to H99. However, the authors used the same number of cells as inoculum of all strains for the mice experiment ignoring the difference in the viability of the above strains. Hence, the conclusion that the sup1 and sup2 strains fail to cause any mortality (lines 255-258) is questionable. We recommend that the authors normalize the sup1 and sup2 inoculum such that viable numbers, not the total numbers, of cells, are comparable to those in H99 at the host condition. Authors may consider using an alternate model system to validate those virulence traits by taking into account the above factors.

We thank the reviewer for bringing up the complexity of virulence assessment in mammalian animal models. All the cells for virulence assays were cultured in vitro at 30oC and not at 37oC. Furthermore, the viable CFUs used in inoculum were confirmed. That said, once inoculated into mice, fungal cells have to live at the host body temperature and the temperature‐sensitive mutants will fare worse in the host compared to the wild type control. Unfortunately, there is no good way to determine an appropriate inoculum for each strain for comparison of multiple strains in animal models because many different factors work together to determine the outcome of infections and temperature is just one of the factors. Furthermore, depending on the time of examination, different number of viable cells of the same strain will be recovered from the animals. To compound the issue further, variation in the number of fungal cells used in inoculation could cause different host immune responses even if the cells are heat‐killed prior to inoculation. Therefore, there has been no better way to compare virulence of different strains in mice than the current standard of using the same inoculum across different strains.

To remove the temperature sensitivity variable in a virulence assay, we have added experiments with the Galleria mellonella larva infection model. This model is commonly used to assay virulence at lower temperatures as the body temperature of G. mellonella is the same as the incubation environment. In this G. mellonella infection model conducted at 30°C, we observed similar virulence pattern among these strains compared to that in the mouse infection model. This is now described in the Results section 5 and included in Figure 6.

3. The authors may consider showing the overexpression of Cbk1 using Western blots (Figure 2).

Our overexpression construct was integrated into the safe haven “SH2” region for all mutants to minimize any potential positional effect in our comparison. Our overexpression of CBK1 is able to complement the cbk1Δ mutant phenotypes (Figure 2B), confirming its expression and functionality. Unfortunately, we do not have an antibody against Cbk1 for western blot analysis. Instead, we carried out a real‐time PCR experiment and confirmed that CBK1 is indeed overexpressed. The result is now included in the Figure 2‐figure supplement 2.

4. Adding scale bars to Figure 4D will help in understanding the morphological changes better.

We thank the reviewer for pointing this out. We have added a scalebar to this figure.

5. Line 93: Adding the information on the total number of ORFs in the C. neoformans genome will help the reader understand what percentage of the genome was represented by the deletion mutant collection.

We have added the total number of protein coding genes in the C. neoformans H99 genome on line 92.

Reviewer #3 (Recommendations for the authors):

This manuscript would be strengthened by an experiment comparing the suppressor strain's survival after H2O2 exposure or another general stress test to bolster the claim that sup2's increased growth at host CO2 levels at pH 6 is a solid explanation for the differences in establishment and persistence of infection in the absence of mouse mortality.

We agree with this reviewer that testing additional stresses could help differentiate sup1 and sup2 phenotypes. Other reviewers also suggested testing different phenotypes of the suppressor mutants. We have added these additional phenotypical assays along with an H2O2 stress spotting assay in the Figure 4‐figure supplement 1. As mentioned earlier, although the differences between the suppressor mutants, the cbk1∆ mutant, and the wildtype strain are dramatic in these stress assays, there is no obvious difference in growth in these phenotypical assays between sup1 and sup2 except thermotolerance and CO2 tolerance.

The acknowledgement that other virulence factors are also impacted by this kinase should be made more apparent in the text and/or included in a main figure.

We have added to our section 3 results “In C. neoformans, various virulence factors are impacted by deletion of CBK1, including urease activity and thermotolerance (K. T. Lee et al., 2016)” (lines 177-179). We have also changed our wording in the manuscript to make it clear that better tolerance of CO2 contributes to better survival of the sup2 mutant is only our hypothesis and there could be other unrecognized contributing factors. “The only in vitro difference observed between sup1 and sup2 was better growth of sup2 at host CO2 levels which may explain the difference in their ability to propagate and persist in the mouse lungs. However, it is worth nothing that due to the complex host environment, there could be other unrecognized factors contributing to the differences in vivo.” (Lines 330‐332).

Additionally, in SI Appendix Figure 4, the slightly increased growth of sup2 during CO2 exposure is only seen at pH 6, while at pH 7.4 both strains appear similarly negatively impacted by CO2 with growth reduced to cbk1∆ levels. Further commentary on why the change in pH appears to greatly impact the sup1 and sup2 strains in pH 7.4 compared to pH 6 would be helpful.

We have added an explanation for this result, “Both sup1 and sup2 showed no improved growth compared to the cbk1∆ mutant at pH 7.4 37°C + 5% CO2. This is likely due to the detrimental combination of high temperature, CO2, and high pH, as the WT also showed significantly reduced growth in this condition. (Figure 4—figure supplement 1A).” (Lines 226-229)

https://doi.org/10.7554/eLife.82563.sa2

Article and author information

Author details

  1. Benjamin J Chadwick

    Department of Plant Biology, University of Georgia, Athens, United States
    Contribution
    Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8244-6190

  2. Tuyetnhu Pham

    Department of Plant Biology, University of Georgia, Athens, United States
    Contribution
    Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared

  3. Xiaofeng Xie

    Department of Microbiology, University of Georgia, Athens, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared

  4. Laura C Ristow

    Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, United States
    Contribution
    Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared

  5. Damian J Krysan

    1. Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, United States
    2. Department of Microbiology and Immunology, Carver College of Medicine, University of Iowa, Iowa City, United States
    Contribution
    Resources, Funding acquisition, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared

  6. Xiaorong Lin

    1. Department of Plant Biology, University of Georgia, Athens, United States
    2. Department of Microbiology, University of Georgia, Athens, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing
    For correspondence
    Xiaorong.Lin@uga.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3390-8387

Funding

National Institutes of Health (R01AI147541)

  • Damian J Krysan
  • Xiaorong Lin

National Institutes of Health (R01AI140719)

  • Xiaorong Lin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by National Institutes of Health (http://www.niaid.nih.gov) (R01AI147541 to DJK and XL, and R01AI140719 to XL). The funder had no role in study design, data collection, and interpretation, or the decision to submit the work for publication. We thank all Lin lab members for their helpful suggestions. We thank Dr. Fanglin Zheng for the plasmid pFZ1, and Dr. Lukasz Kozubowski for the plasmid LKB61.

Ethics

This study was performed according to the guidelines of NIH and the University of Georgia Institutional Animal Care and Use Committee (IACUC). The animal models and procedures used have been approved by the IACUC (AUP protocol numbers: A2017 08-023 and A2020 06-015).

Senior and Reviewing Editor

  1. Arturo Casadevall, Johns Hopkins Bloomberg School of Public Health, United States

Reviewer

  1. Ella Jacobs, Johns Hopkins Bloomberg School of Public Health, United States

Publication history

  1. Received: August 9, 2022
  2. Preprint posted: August 14, 2022 (view preprint)
  3. Accepted: November 22, 2022
  4. Accepted Manuscript published: November 23, 2022 (version 1)
  5. Version of Record published: November 29, 2022 (version 2)

Copyright

© 2022, Chadwick et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 434
    Page views
  • 85
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Benjamin J Chadwick
  2. Tuyetnhu Pham
  3. Xiaofeng Xie
  4. Laura C Ristow
  5. Damian J Krysan
  6. Xiaorong Lin
(2022)
The RAM signaling pathway links morphology, thermotolerance, and CO2 tolerance in the global fungal pathogen Cryptococcus neoformans
eLife 11:e82563.
https://doi.org/10.7554/eLife.82563

Categories and tags

Further reading

    1. Developmental Biology
    2. Genetics and Genomics

    A timer gene network is spatially regulated by the terminal system in the Drosophila embryo

    Erik Clark, Margherita Battistara, Matthew A Benton
    Research Article Updated

    In insect embryos, anteroposterior patterning is coordinated by the sequential expression of the ‘timer’ genes caudal, Dichaete, and odd-paired, whose expression dynamics correlate with the mode of segmentation. In Drosophila, the timer genes are expressed broadly across much of the blastoderm, which segments simultaneously, but their expression is delayed in a small ‘tail’ region, just anterior to the hindgut, which segments during germband extension. Specification of the tail and the hindgut depends on the terminal gap gene tailless, but beyond this the regulation of the timer genes is poorly understood. We used a combination of multiplexed imaging, mutant analysis, and gene network modelling to resolve the regulation of the timer genes, identifying 11 new regulatory interactions and clarifying the mechanism of posterior terminal patterning. We propose that a dynamic Tailless expression gradient modulates the intrinsic dynamics of a timer gene cross-regulatory module, delineating the tail region and delaying its developmental maturation.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases

    Aaron W Jurrjens, Marcus M Seldin ... Anna C Calkin
    Review Article

    Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

    1. Genetics and Genomics
    2. Medicine

    Phenome-wide Mendelian randomization study of plasma triglyceride levels and 2,600 disease traits

    Joshua K Park, Shantanu Bafna ... Ron Do
    Research Article

    Background: Causality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems.

    Methods: Here, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2,600 disease traits in the European ancestry population of UK Biobank. For replication, we externally tested 221 nominally significant associations (p < 0.05) in an independent cohort from FinnGen. To account for potential horizontal pleiotropy and the influence of invalid instrumental variables, we performed sensitivity analyses using MR-Egger regression, weighted median estimator, and MR-PRESSO. Finally, we used multivariable MR controlling for correlated lipid fractions to distinguish the independent effect of plasma TG levels.

    Results: Our results identified 7 disease traits reaching Bonferroni-corrected significance in both the discovery (p < 1.92 × 10-5) and replication analyses (p < 2.26 × 10-4), suggesting a causal relationship between plasma TG levels and ASCVDs, including coronary artery disease (OR 1.33, 95% CI 1.24-1.43, p = 2.47 × 10-13). We also identified 12 disease traits that were Bonferroni-significant in the discovery or replication analysis and at least nominally significant in the other analysis (p < 0.05), identifying plasma TG levels as a novel potential risk factor for 9 non-ASCVD diseases, including uterine leiomyoma (OR 1.19, 95% CI 1.10-1.29, p = 1.17 × 10-5).

    Conclusions: Taking a phenome-wide, two-sample MR approach, we identified causal associations between plasma TG levels and 19 disease traits across organ systems. Our findings suggest unrealized drug repurposing opportunities or adverse effects related to approved and emerging TG-lowering agents, as well as mechanistic insights for future studies.

    Funding: RD is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).