Mathematical modeling of pathogenicity of Cryptococcus neoformans
2008; Springer Nature; Volume: 4; Issue: 1 Linguagem: Inglês
10.1038/msb.2008.17
ISSN1744-4292
AutoresJacqueline García, John M. Shea, Fernando Alvarez‐Vasquez, Asfia Qureshi, Chiara Luberto, Eberhard O. Voit, Maurizio Del Poeta,
Tópico(s)Toxin Mechanisms and Immunotoxins
ResumoArticle15 April 2008Open Access Mathematical modeling of pathogenicity of Cryptococcus neoformans Jacqueline Garcia Jacqueline Garcia Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author John Shea John Shea Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Fernando Alvarez-Vasquez Fernando Alvarez-Vasquez Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Department of Biostatistic, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Asfia Qureshi Asfia Qureshi Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Chiara Luberto Chiara Luberto Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Eberhard O Voit Eberhard O Voit W.C. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA Search for more papers by this author Maurizio Del Poeta Corresponding Author Maurizio Del Poeta Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC, USA Division of Infectious Diseases, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Jacqueline Garcia Jacqueline Garcia Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author John Shea John Shea Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Fernando Alvarez-Vasquez Fernando Alvarez-Vasquez Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Department of Biostatistic, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Asfia Qureshi Asfia Qureshi Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Chiara Luberto Chiara Luberto Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Eberhard O Voit Eberhard O Voit W.C. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA Search for more papers by this author Maurizio Del Poeta Corresponding Author Maurizio Del Poeta Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC, USA Division of Infectious Diseases, Medical University of South Carolina, Charleston, SC, USA Search for more papers by this author Author Information Jacqueline Garcia1, John Shea1, Fernando Alvarez-Vasquez1,2, Asfia Qureshi1, Chiara Luberto1, Eberhard O Voit3 and Maurizio Del Poeta 1,4,5 1Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA 2Department of Biostatistic, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA 3W.C. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA 4Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC, USA 5Division of Infectious Diseases, Medical University of South Carolina, Charleston, SC, USA *Corresponding author. Department of Biochemistry and Molecular Biology, Medical University of South Carolina, 173 Ashley Avenue, BSB 503, Charleston, SC 29425, USA. Tel.: +843 792 8381; Fax: +843 792 8565; E-mail: [email protected] Molecular Systems Biology (2008)4:183https://doi.org/10.1038/msb.2008.17 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Cryptococcus neoformans (Cn) is the most common cause of fungal meningitis worldwide. In infected patients, growth of the fungus can occur within the phagolysosome of phagocytic cells, especially in non-activated macrophages of immunocompromised subjects. Since this environment is characteristically acidic, Cn must adapt to low pH to survive and efficiently cause disease. In the present work, we designed, tested, and experimentally validated a theoretical model of the sphingolipid biochemical pathway in Cn under acidic conditions. Simulations of metabolic fluxes and enzyme deletions or downregulation led to predictions that show good agreement with experimental results generated post hoc and reconcile intuitively puzzling results. This study demonstrates how biochemical modeling can yield testable predictions and aid our understanding of fungal pathogenesis through the design and computational simulation of hypothetical experiments. Synopsis Extended Synopsis Cryptococcus neoformans (Cn) is a fungal microbial pathogen that lives in the environment and in the gastrointestinal tract of several birds, pigeons in particular (Casadevall and Perfect, 1998). Upon inhalation of Cn spores or desiccated yeast cells, the fungus can grow in the extracellular space of the alveoli and in the intracellular environment of phagocytic cells, particularly alveolar macrophages (Levitz et al, 1999). Hence, Cn is considered a facultative intracellular pathogen. Thus, once in the lung, the fungus must adapt to two different environments: the extracellular space characterized by neutral/alkaline pH and the intracellular milieu of the phagolysosome characterized by acidic pH. In recent years, we have found that a class of lipids, sphingolipids, represents a reservoir of molecules implicated in the regulation of fungal growth either in neutral/alkaline (Rittershaus et al, 2006; Kechichian et al, 2007) or acidic environments (Shea et al, 2006), in the modulation of Cn virulence factors, such as melanin production (Heung et al, 2004, 2005), and in the regulation of phagocytosis (Luberto et al, 2003; Mare et al, 2005; Tommasino et al, 2008). Interestingly, the shift of Cn cells from the extracellular to the intracellular compartment is particularly important because it changes the outcome of the infection in a severely immunocompromised host (Kechichian et al, 2007). Interestingly, the contribution of each Cn population (extracellular versus intracellular) to the outcome of infection is determined by the host immune status (Luberto et al, 2003). Therefore, the understanding of the mechanism(s) that regulate survival of Cn in both compartments (extracellular versus intracellular) may lead to novel therapeutic interventions based on the fine tune up of important Cn switches determined by the host immune status. In this paper, a mathematical model representing the sphingolipid metabolic pathway of Cn was developed. It was tested to simulate sphingolipid adaptation to a shift from an alkaline to an acidic pH, to mimic the phagocytosis of Cn by macrophages (Figure 1). The model was designed and analyzed within the framework of the biochemical system theory, which uses power-law representations for all enzymatic and transport processes (Savageau, 1969a, b, 1976; Torres and Voit, 2002). By coupling mathematical simulations using the model with experimental determinations, multiple factors were found to be required for adaptation of Cn to the acidic environment. In particular, experimental determinations showed that sphingolipid phytoceramide C26 significantly increases when cells are shifted from alkaline to acidic pH (Table I), and this result was predicted by the model. Interestingly, production of phytoceramide C26 is under the control of inositol phosphosphingolipid phospholipase C (Isc1) enzyme, because a Δisc1 mutant strain dramatically reduces the level of this sphingolipid. As expected, Δisc1 mutant has a growth defect at acidic pH (Supplementary Figure 1). Moreover, the use of a different mutant of the sphingolipid pathway showed that phytoceramide C26 is necessary but not sufficient for the adaptation process. In particular, downregulation of inositol phosphorylceramide synthase (Ipc1), which uses phytoceramide as a substrate, shows also a growth defect at low pH even though the levels of phytoceramide C26 are not altered. Therefore, it is proposed that other lipids regulated by Ipc1, such as complex sphingolipids and/or diacylglycerol, may also be involved in this adaptation process. Based on our simulations, the model suggests that the growth defect at low pH observed when Isc1 is deleted or Ipc1 downregulated is due to a decreased activity of the plasma membrane H+ATPase (Pma1), and the experimental findings (Table V) indeed support this prediction (Figure 1). We hypothesize that Isc1 regulates Pma1 through phytoceramide C26, whereas Ipc1 regulates Pma1 through DAG and/or complex sphingolipids. In conclusion, the mathematical model of sphingolipid metabolism helps to predict the adaptation of Cn in the host environments and contributes to a better understanding of its pathogenic traits. Introduction Cryptococcus neoformans (Cn) is a fungal pathogen that infects humans via the respiratory tract. It is an environmental microorganism particularly present in pigeon droppings or associated with eucalyptus tree but it can be isolated from soil, water, milk, fruits, horse intestinal flora, bird nests, bats, burns, and cockroaches. Once inhaled in the lung, dissemination of the infection through the bloodstream leads to the development of a life-threatening meningoencephalitis, particularly in immunocompromised patients (Casadevall and Perfect, 1998; Perfect, 2005). An important characteristic that enables the fungus to cause disease is its ability to grow in alkaline, neutral, and acidic environments of the human body. Alkaline/neutral environments are found extracellularly, such as in alveolar spaces and in the bloodstream, whereas acidic environments are characteristically found intracellularly, within the phagolysosome of host phagocytic cells (Feldmesser et al, 2001). Indeed, Cn is a facultative intracellular pathogen and can move in and out without killing host cells (Alvarez and Casadevall, 2006; Ma et al, 2006). In doing so, it constantly needs to adapt to a new environment, for instance, by changing the organization of different cellular components, gene expression, protein activities, or arrangements of lipids within membranes. It might be impossible to fully understand this adaptation using a purely reductionistic approach because of continual changes in the production and degradation of key cellular components and because of the complex interplay among these components during switches in the environment. Instead, the use of systems biological methods might offer an opportunity to complement reductionistic insights by explaining complex, systemic behaviors, such as cellular adaptation to environments, through the simultaneous investigation of stimuli and responses of several enzymes and metabolites in terms of space, time, and context. The spatial aspect is important because it accounts for compartmentalization and the topographic relationships among the components; the need to consider time is evident, given the dramatic dynamic changes in the molecular characteristics during adaptation; and addressing the cellular and environmental context at each time point accounts for the interdependencies between all components partaking in the adaptation process (Ahn et al, 2006a, 2006b). Thus, system biology has a chance of shedding light on the complexities that govern microbial metabolic pathways, and their regulation and adaptations may ultimately allow us to predict cellular or biological phenotypes without the need for large-scale wet experimentation. Specifically for the case of Cn, the application of systems biological concepts may provide significant insights into the mechanisms of fungal pathogenesis and into the complex interplay between the fungus and the host immunity in chronic fungal diseases. In all eukaryotic cells, the main intracellular pH regulator is the plasma membrane H+ATPase (Pma1) pump, whose activation results in proton extrusion, maintaining the intracellular pH neutral (Serrano et al, 1986; Serrano, 1988) even if the extracellular pH is acidic (Perona et al, 1990; Portillo et al, 1991). In Saccharomyces cerevisiae (Sc) and in Cn, Pma1 is essential for cell viability (Soteropoulos et al, 2000), suggesting that Pma1 has a key role in regulating the intracellular pH. Many studies in Sc proposed a role for sphingolipids in the regulation of Pma1 function in the endoplasmic reticulum (ER) and at the plasma membrane (Patton et al, 1992; Lee et al, 2002; Wang and Chang, 2002; Gaigg et al, 2005, 2006; Toulmay and Schneiter, 2007). The basic structural component of sphingolipids is a long-chain sphingoid base backbone (e.g., sphingosine or phytosphingosine (PHS)). The linkage of a fatty acid to the two-amino group of this backbone through an amide bond yields ceramide or phytoceramide. Complex sphingolipids are formed with the addition of a polar group to ceramide (or phytoceramide) via an ester bond at the C-1 position. The synthesis of sphingolipid occurs in all eukaryotic cells and, in addition to being common components of membranes, they have been recognized to function as signaling molecules in a variety of signaling pathways (reviewed in Futerman and Hannun, 2004). In mammalian cells, sphingolipids such as ceramide, ceramide-1-phosphate, and sphingosine-1-phosphate have key roles in the regulation of cellular proliferation, stress responses, cell cycle, apoptosis, inflammation, and immune response (reviewed in Luberto and Hannun, 1999; Hannun and Luberto, 2000; McQuiston et al, 2006). In Cn, sphingolipids have emerged as important molecules required for growth and signaling in alkaline/neutral (Liu et al, 2005; Rittershaus et al, 2006; Saito et al, 2006) and acidic (Buede et al, 1991; Berne et al, 2005; Shea et al, 2006) environments. Thus, we hypothesize that when Cn shifts from a neutral/alkaline environment, such as extracellular alveolar spaces or the bloodstream, to an acidic environment, such as the phagolysosome of phagocytic cells, it should promote a biochemical response associated with its sphingolipid pathway, with a consequent metabolic adaptation to the new environment. In this study, we thus developed a mathematical model of sphingolipid metabolism in the pathogenic fungus Cn and made reliable predictions on its biochemical sphingolipid adaptation to a shift from an alkaline to an acidic pH, mimicking the internalization of the fungus by phagocytic cells. The model was designed and analyzed within the framework of Biochemical System Theory (BST), which uses power-law representations for all enzymatic and transport processes (Savageau, 1969a, 1969b, 1976; Torres and Voit, 2002). Results The results of our studies fall into three categories. The first set describes experimental studies that identified inositol phosphorylceramide synthase (Ipc1), inositol phosphosphingolipid phospholipase C (Isc1), and phytoceramides of different lengths as important contributors to the response of Cn to H+ATPase pump (Pma1) and ATP-mediated shifts in pH. Especially the putative roles of the enzymes Ipc1 and Isc1 seem puzzling, because these enzymes catalyze opposite directions of the reversible reaction between phytoceramide and inositol phosphoryl ceramide (IPC). The second set of studies used a mathematical systems model to elucidate how the different contributors might lead to an appropriate stress response. This model in turn made predictions regarding Pma1 and the role of ATP, which we investigated experimentally in the third set of studies as model validation. Experimental studies identifying Ipc1, Isc1, and phytoceramide as drivers of pH response Effect of Ipc1 downregulation and Isc1 deletion on growth of Cn in acidic environments Inositol phosphoryl ceramide synthase 1 (Ipc1) is a fungal enzyme localized in the Golgi apparatus in Sc (Levine et al, 2000) and Cn (M Del Poeta, unpublished data). It transfers inositol phosphate from phosphatidylinositol (PI) to phytoceramide, producing IPC and 1,2-sn-diacylglycerol (DAG) (Kuroda et al, 1999; Heidler and Radding, 2000). Isc1 is an enzyme localized in the ER and breaks down IPC, mannosyl-IPC (MIPC), and mannosyl diphosphoryl ceramide (M(IP)2C) to phytoceramide, inositol phosphate (MIP), and M(IP)2, respectively (Dickson and Lester, 1999). We tested a mutant in which Ipc1 is downregulated, GAL7:IPC1, grown on glucose medium, and found that it fails to adapt with sufficient speed to an acidic environment and, as a consequence, its growth is significantly retarded (Supplementary Figure 1A) (Luberto et al, 2001). Similarly, a mutant strain in which Isc1 was deleted, Δisc1, showed a delay in the adaptation to a low-pH environment (Supplementary Figure 1B) (Buede et al, 1991; Berne et al, 2005; Shea et al, 2006). Thus, two enzymes with 'opposite' biochemical activities (Ipc1 uses phytoceramide as a substrate and produces IPC, whereas Isc1 uses IPC as a substrate and produces phytoceramide) have the same effect on viability in changing pH environments. This puzzling finding was quite intriguing and warranted further investigations at the systems level (see 'Computational studies'). Figure 1.Model diagram of sphingolipid metabolism in Cn. Metabolites in boxes represent dependent variables that are defined through differential equations and are numbered from X1 to X19. Independent variables are numbered from X100 to X136. Solid arrows show flow of material. Plus signs associated with dotted arrows represent activation. The acylation state is coded as (1) C26-CoA, (2) C18-CoA, and (3) C24-CoA; these are substrates for the DH-Cer synthase reaction or for the enzyme P-Cer synthase (see main text and Supplementary information for details). Dependent variables: Pal-CoA (X1), palmitoyl-CoA; serine (X2); KDHS (X3), 3-ketodihydrosphingosine; DHS (X4), dihydrosphingosine; dihydro-C24 (X5), dihydroceramide C24; dihydro-C26 (X6), dihydroceramide C26; dihydro-C18 (X7), dihydroceramide C18; PHS (X8), phytosphingosine; phyto-C26 (X9), phytoceramide C26; phyto-C24 (X10), phytoceramide C24; phyto-C18 (X11), phytoceramide C18; Pma1 (X12), newly synthesized Pma1; IPC-C26 (X13), inositol phosphorylceramide C26; IPC-C24 (X14), inositol phosphorylceramide C24; IPC-C18 (X15), inositol phosphorylceramide C18; intracellular protons (X16); ATP (X17), adenosine-5′-triphosphate; palmitate (X18); DAG (X19), sn-1,2-diacylglycerol. Independent variables: palmitate ext (X100), palmitate external; serine ext (X101), serine external; palmitate transport (X102); serine transport (X103); Ac-CoA (X104), acetyl CoA; C26-CoA (X105), very long-chain fatty acid (C26-CoA); C18-CoA (X106), fatty acid (C18-CoA); C24-CoA (X107), fatty acid (C24-CoA); serine palmitoyltransferase (X108); ADP, adenosine biphosphate (X109); dihydro-CDase (X110), dihydroceramide ceramidase; KDHS reductase (X111), 3-ketodihydrosphingosine reductase; DH-Cer synthase (X112), dihydroceramide synthase; phyto-CDase (X113), phytoceramidase; hydroxylase (X114); hydroxylase (X115); P-Cer synthase (X116), phytoceramide synthase; Pma1p (X117), newly synthesized Pma1 in the ER; Sec61 (X118), Sec61 as probable ER insertion protein; Isc1 (X119), inositol phosphosphingolipid phospholipase C; PI (X120), phosphatidylinositol; Ipc1 (X121), inositol phosphorylceramide synthase; alternative respiration (X122); NADHm (X123), nicotinamide adenine dinucleotide; oxygen (X124); Pma1-H+ATPase (X125), synthesized plasma membrane H+-ATPase; H+ (X126), protons external; ER–Golgi transport (X127); H+ transport (X128), proton transport; SHMT (X129) serine hydroxymethyl transferase; Golgi membrane (X130); Pal-CoA synthase (X131), palmitoyl-CoA synthase; ATP total (X132); AMP (X133), adenosine monophosphate; Golgi–ER transport (X134); F0F1-ATPase (X135), F0F1-ATP synthase; H+ m (X136), mitochondrial protons. Download figure Download PowerPoint Measurement of phytoceramide subspecies in Cn wild type during growth at alkaline/neutral and acidic pH Fungal cells produce variant species of phytoceramide that differ principally in the lengths of their acyl chains and their hydroxylation states (Vaena de Avalos et al, 2004, 2005). This variability led to the question of whether phytoceramide subspecies levels would change when cells are shifted from a neutral to an acidic environment. Targeted experiments indeed revealed that in Cn cells exposed to a low-pH environment, the total level of non-hydroxylated phytoceramide decreases to half the level in a neutral environment (from 3862.7±6.34 to 1588.7±6.32 pmol/pmol Pi; P<0.05), whereas the total level of hydroxylated phytoceramides increases significantly compared to that in neutral pH (from 1244.1±3.2 to 2147.2±3.5 pmol/pmol Pi) (Table I). Analysis of the different phytoceramide subspecies revealed that very long-chain phytoceramides (both C26 hydroxylated and non-hydroxylated forms) were significantly elevated at low compared to neutral pH, whereas short-chain non-hydroxylated phytoceramides (C14, C16, and C18) were significantly decreased at low compared to neutral pH (P<0.05). Phytoceramide measurements were also performed at alkaline pH (7.4). No differences were found in phytoceramide levels or phytoceramide subspecies between pH 7.0 and 7.4 (data not shown). These results suggest that Cn changes the metabolism of certain phytoceramide subspecies when shifted from a neutral/alkaline to an acidic environment. Table 1. Identification of ceramide species at 48 h of growth Ceramide species (pmol/pmol Pi) Phyto-C18 Phyto-C18;1 Phyto-C20 Phyto-C24 Phyto-C24:1 Phyto-C26 Phyto-C26:1 α-OH-phyto-C18 α-OH-phyto-C18:1 α-OH-phyto-C24 α-OH-phyto-C26 α-OH-phyto-C26:1 pH 7 406.6 230 60 2260 640.1 170 96 3.1 1.04 570 360 310 pH 4 215.3 180 4.5* 396* 400 222.5 170.4* 4.2 3 810 710* 620 * P<0.05 (pH 4 versus 7). Mass spectrometric analysis of different phytoceramide and alpha hydroxyl phytoceramide species during late-log phase in a Cn H99 WT strain. Determinations are to neutral pH or acidic pH. The mass of each species was normalized to phosphorous levels of each sample. Results are the means of three separate experiments. The concentrations are reported as pmol/pmol Pi for phyto- (phytoceramide) and α-OH-phyto (alpha hydroxyl phytoceramide) with different length fatty acid chains. Ebselen causes elevated inhibition in Isc1 mutant As the plasma membrane proton pump H+ATPase (Pma1) is one of the major regulators of intracellular H+ (Serrano et al, 1986; Serrano, 1988), we wondered whether the absence of Isc1 or downregulation of Ipc1 would affect the susceptibility of yeast cells to Pma1 inhibitors. To address this question, we examined the minimal inhibitory concentration (MIC) and minimal fungicidal concentration (MFC) of ebselen, a well-established inhibitor of fungal Pma1 (Perlin et al, 1997). We found that loss of Isc1 or downregulation of Ipc1 significantly increases the susceptibility of yeast cells to ebselen (Table II), suggesting that both Isc1 and Ipc1 may potentially regulate Pma1 function. Table 2. Loss of Isc1 sensitizes Cn to the Pma1 inhibitor ebselena Strain pH 7 (μM) pH 4 (μM) WT 3.12 2.48 Δisc1 3.12 0.78* Δisc1REC 3.12 3.12 GAL7:IPC1-glucose 3.12 0.39* * P<0.05 (pH 4 versus 7). a Ebselen is fungicidal to WT, Δisc1, Δisc1REC, and GAL7:IPC1 strains at an MFC of 3.12 μM at a neutral pH. The Δisc1 mutant and GAL7:IPC1 strain grown on glucose (Ipc1 downregulated) showed MFCs of 0.78 and 0.39 μM, respectively. Results are the means of three separate experiments. Computational studies The diagram of all reaction steps that were deemed important in the sphingolipid pathway of Cn and on which the model is based is presented in Figure 1. Most analyses reflect the late-log phase of growth at acidic pH. We adapted an earlier sphingolipid model for yeast (Alvarez-Vasquez et al, 2004, 2005) to the peculiarities of Cn. Importantly, we incorporated several organelles that separate the physical synthesis of metabolites. Also the new model was developed during late-log phase and it contains several newly determined metabolite levels and enzyme activities in wild-type (WT) Cn grown at acidic pH. In addition to allowing for ceramides with different fatty acid chain lengths, the new model accounts for mechanisms of transporting protons between the cytosol and the phagolysosome, which are crucial for the survival of Cn. Since Pma1 trafficking (Gaigg et al, 2005) and function (Lee et al, 2002) are controlled by very long-chain phytoceramides, the mathematical model of Cn sphingolipid pathway proposed here pays particular attention to the dynamics of Pma1, as well as to short-chain and very long-chain phytoceramides and the enzymes that directly and indirectly regulate their production. The model analyses fall into two categories. As it is standard in BST, we first diagnosed the model by thoroughly investigating its stability and robustness and evaluating sensitivities and gains, especially with respect to enzyme activities. Most of the results of these analyses are unremarkable and are therefore presented in Supplementary information. The gains provided us with some insights into which metabolites are most affected by changes in particular enzymes and transport steps. However, they did not reveal compelling explanations of how Cn coordinates responses to shifts in pH. By contrast, exploration of the consequences of alterations in Ipc1 and Isc1 suggested interesting mechanisms of handling protons. All analyses were executed with the free software PLAS© (Ferreira, 2000). The cellular compartments involved in the model are as follows. Cytoplasm We include the transport of palmitate and serine, required for the first step of sphingolipid biosynthesis. In acidic conditions, as they exist inside macrophages, protons move into the cell. The inclusion of proton transport is important for the relationships with H+ATPase (Pma1) and cytoplasmic pH. Proton import into the fungal cytosol is modeled as a simple (first order) transport process, whereas the export mechanism depends highly on Pma1. A simple transport process implies direct proportionality between proton influx (inside the fungus) and the external proton concentration (outside the fungus but inside the phagolysosome). Our model considers the fungal ATP synthesis and degradation (Supplementary Table S7) and includes the F0F1-ATPase in the fungal mitochondria (equation 17). Our model assumes that the fungus is inside the phagolysosome (its physiological environment once internalized by host macrophages) with no direct contact with the host mitochondria. For instance, we refer to a simple transport process when the proton influx depends directly on the electrochemical gradient. By contrast, Pma1 (X125) utilizes energy obtained directly from ATP hydrolysis to transport protons against the electrochemical differences across the plasma membrane. Endoplasmic reticulum The first steps of the sphingolipid pathway take place in the ER. The condensation of serine and the acyl group transferred from palmitoyl-CoA form 3-ketodihydrosphingosine (KDHS). This reaction is catalyzed by serine palmitoyltransferase (Buede et al, 1991). KDHS is rapidly converted into dihydrosphingosine (DHS). In the model, DHS can be converted by a hydroxylase to PHS. At the same time, under the action of a specific ceramide synthase (sphingoid base N-acyl transferase), DHS and a long-chain fatty acid CoA may form dihydroceramides C18, C24, and C26. PHS may combine with long-chain fatty acid CoA to form phytoceramides C18, C24, and C26. Both dihydroceramides C18, C24, and C26 and phytoceramides C18, C24, and C26 may undergo hydrolytic reactions to form DHS and PHS, respectively, which are catalyzed by specific ceramidases (Mao and Obeid, 2000; Mao et al, 2000). In the model, dihydroceramide C26 may be hydroxylated directly to phytoceramide C26. Once produced, the transport of phytoceramides C18, C24, and C26 from ER to Golgi apparatus follows a first-order process. The model considers that the biosynthesis of phytoceramide and Pma1 occurs in the ER. The Pma1 in the ER compartment has an activity in response to pH signals similar to the Pma1 inserted in the plasma membrane, as previously suggested (Portillo et al, 1989). The model predicts that Pma1 is stabilized by phytoceramide C26 produced by Isc1 (Lee et al, 2002). The model also considers Sec61, which was found to be upregulated during murine macrophage infection of Cn (Fan et al, 2005), a secretory factor that could mediate the insertion of Pma1 into vesicles. Pma1 travels from the ER to the Golgi apparatus and finally to the plasma membrane where it is inserted (Chang and Slayman, 1991). Golgi apparatus In the Golgi apparatus, phytoceramides C18, C24, and C26 are substrates for Ipc1. Ipc1 transfers inositol phosphate from PI to phytoceramide(s), producing IPC (IPC-C18, -C24, and -C26) and DAG. Ipc1 is localized in the Golgi apparatus, implying that phytoceramides are transport
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