Effective treatment of systemic candidiasis by synergistic targeting of cell wall synthesis

Abstract

Fungal infections pose a serious threat to global human health fueled by the increase in immunosuppressive therapies, medical implants, and transplantation. The emergence of multidrug resistance with limited options of current antifungal drugs are a further constraint. There is thus a clear and unmet need to identify therapeutic targets and develop alternative classes of antifungal agents. Here, we hypothesize that dual targeting of key regulatory genes of fungal cell wall synthesis (FKS1 encoding β-1,3-glucan synthase and CHS3 encoding chitin synthase) can synergistically inhibit fungal growth. Based on iterative designs, we generate a small library of fungal-targeted nanoconstructs, and identify a lead construct (FTNx) that shows preferential accumulation in fungal cells over mammalian cells and leads to prominent antifungal effects in vitro. We further show that FTNx is highly effective in a mouse model of disseminated candidiasis, demonstrating diminished fungal growth and enhanced survival rate. This strategy appears promising as an effective treatment for fungal infections in mammalian hosts.

Fig. 1. Overview of antifungal therapy design and experimental approach.

We generated a small nanoconstruct library (Nx(X)) to condense fungal silencing oligonucleotides (fso) for synergistic targeting of fungal cell wall synthesis. The design involved the formation of nanoconstructs that selectively interact with the fungal cell wall components and induce internalization, leading to FTNx. Dual gene silencing (FKS1 and CHS3) by FTNx leads to impaired synthesis of β−1,3-glucan and chitin, resulting in prominent antifungal effects in vivo. Created in BioRender. Chung, J. (2025) https://BioRender.com/9q3cdpr.

Fig. 2. Development of fso-delivering nanoconstructs for fungal targeting.

a Preparation of fso-loaded nanoconstructs (Nx(X)) uses various cations (X) for complexation to stabilize the gold nanoparticle cores, that were screened for formulations that have preferential fungal uptake and minimal mammalian cell uptake. b DLS and c zeta potential measurements of Nx(X). Data represent mean ± s.e.m. (n = 3 biological replicates). d, e Representative confocal images of C. albicans (d, scale bar, 5 μm) and NIH-3T3 cells (e, scale bar, 50 μm) treated with Nx(X) or naked fso as the control (n = 3 biological replicates; green, FAM-labeled fso; blue, 4′,6-diamidino-2-phenylindole (DAPI)). f, g Quantification of uptake in C. albicans (f) and NIH-3T3 cells (g) obtained from (d) and (e). Data represent mean ± s.e.m. (n = 3 biological replicates). One-way ANOVA used to obtain the p-value by comparison of groups with the naked fso group. Representative data from three independent experiments. Source data are provided as a Source Data file. a created in BioRender. Chung, J. (2025) https://BioRender.com/9q3cdpr.

Fig. 3. Dual gene targeting of cell wall synthesis is an effective antifungal strategy.

a Sequences of fso targeting FKS1 and CHS3 specific to C. albicans. b, c mRNA expression levels of FKS1 (b, n = 3, no treat and naked fso; n = 4, control NP, FTNx(NT), and FTNx), and CHS3 (c, n = 5, no treat; n = 3, control NP and FTNx; n = 4, naked fso; n = 6, FTNx(NT)) in C. albicans treated with FTNx (100 nM), control NP (100 nM), naked fso (50 μM), or FTNx(NT) (100 nM), normalized to levels of ACT1 mRNA. Data represent mean ± s.e.m. from biological replicates. One-way ANOVA for comparison with ‘No treat’ control. d Schematic of the mechanism of antifungal activity by dual gene silencing of FKS1 and CHS3 in Candida spp. Targeting a single gene leads to weak to moderate effects, while dual gene targeting leads to potent antifungal effects. e–g Antifungal effect of FTNx in C. albicans measured by the WST-8 assay. e Comparison of single or dual fso with FTNx (100 nM, 24 h, n = 2). f Various time periods (100 nM, n = 3), and g different doses with incubation times (n = 3) on treating FTNx with dual fso. h Confocal microscopy of live and dead fungal cells stained with SYTO9 and PI, respectively (scale bar, 20 μm). i SEM (scale bar 10 μm), and j TEM (scale bar 400 nm) of C. albicans treated with FTNx with dual fso. k, l Fluorescence staining of cell wall components in C. albicans treated with FTNx and quantification by normalization to ‘No treat’ control. k Blue, chitin (CaFW); green, β−1,3-glucan (Dectin-488); red, mannan (ConA-Tred); scale bar, 5 μm; n = 2 for no treat, n = 3 for FTNx. l Green, chitin (WGA-488); red, β−1,3-glucan (Dectin-647); scale bar, 5 μm; n = 3. e–l Data represent mean ± s.e.m. from biological replicates (Two-way ANOVA for comparison with No treat control). Representative data from three independent experiments. Source data are provided as a Source Data file. a, d created in BioRender. Chung, J. (2025) https://BioRender.com/9q3cdpr.

Fig. 4. Development and validation of universal fso targeting multiple Candida species.

a Design of candidate fsos targeting FKS1, FKS2, and CHS3 to universally target various Candida spp. b–e Antifungal effects upon treatment of FTNx to C. albicans measured by the WST-8 assay. b Screening of the designed candidates by treating single fso via FTNx, and subsequently with micafungin (8 μg/ml). c Effect of dual fso delivery via FTNx, targeting either FKS1 or FKS2, alongside CHS3. Heatmap depicts values of fungal growth. d, e Treatment of FTNx (F.48 + C.57) to C. albicans for various time periods (d); and at different concentrations of FTNx (e). f, g Treatment of FTNx (F.48 + C.57) to various Candida spp. and representative confocal images of fungal uptake (f, 4 h; green, FAM-labeled fso; blue, DAPI; scale bar, 20 μm), and fungal growth (g, 24 h). h Antifungal effect upon treating FTNx with gapmer or unmodified single fso candidate to C. albicans, followed by treatment with micafungin (8 μg/ml). FTNx treated at 100 nM unless otherwise specified. b–e, g, h Data represent mean ± s.e.m. from experiments of biological replicates (n = 3). One-way ANOVA for comparison of treatment groups with ‘No treat’ control group. Representative data from three independent experiments. Source data are provided as a Source Data file.

Fig. 5. In vivo antifungal activity of FTNx.

a Biodistribution studies in uninfected mice. b Fluorescence imaging of excised tissues after intraperitoneal injection of FTNx or naked fso (6–24 h). c Quantification of signals (Cy5.5-labeled fso) from (b). Data represent mean ± s.e.m. (n = 4 mice per group). d Antifungal efficacy in a disseminated candidiasis mouse model. e Sequences of fso for dual delivery. f, g Fungal cell burden (logCFU) in infected mice after treatment. f Various doses of FTNx (F.Ca + C.Ca) (PBS, n = 8; FTNx (NT): 3X, n = 5; 5X, n = 4; FTNx (F.Ca+C.Ca): 1X,3X,5X, each n = 5 mice per group). g FTNx (3X, F.54 + C.Ca. and F.57 + C.Ca) compared with micafungin (10 mg/kg) and FTNx (NT) as controls (PBS, n = 9; micafungin, n = 8; FTNx (NT) 3X, n = 4; FTNx (F.54 + C.Ca), n = 8; FTNx (F.57 + C.Ca), n = 9 mice per group). h PAS and H&E staining of kidney tissues of treated mice. i Survival rate of mice in a lethal infection model treated twice with FTNx (3X, F.57 + C.Ca) at 24 h time interval (n = 7 mice). Arrowheads indicate injection time points. j Body weight changes of uninfected mice after FTNx treatment (n = 5 mice per group). k Efficacy of FTNx in a dermal candidiasis model. l PAS and H&E staining of mouse skin tissues after treatment of FTNx (3X, F.57 + C.Ca) (n = 4 mice per group). m Examination scores for fungal pseudohyphae, inflammation, and antifungal effect from (l). n, o Biochemical analysis of blood serum from uninfected mice treated with FTNx (3X, F.57 + C.Ca). Dotted lines mark normal ranges of ALT and BUN levels (n = 4, except n = 3 for FTNx, 48 h (n) and FTNx, 24 h (o)). b, h, l Representative images shown from each group (scale bar, 100 μm). f, g, i, j, n, o Data represent mean ± s.e.m. One-way ANOVA (f, g), two-way ANOVA (j), and log-rank (Mantel-Cox) test (i) for comparison with PBS control group. Source data are provided as a Source Data file. a, d, e, k created in BioRender. Chung, J. (2025) https://BioRender.com/9q3cdpr.