Space waste medium enables fungal growth and organic acid production as key ISRU processes for sustainable space habitats

Astronauts on the International Space Station still depend on resources brought by cost-intensive resupply missions. To enhance their independence from Earth and prepare for long-term crewed missions to the Moon or Mars, there is an urgent need to develop self-sustained habitats that rely on in situ utilization of resources, thus reducing mass, space, and environmental constraints. One promising approach involves growing filamentous fungi on locally available nutrients and utilizing the fungi’s vast bioprocessing abilities to answer the crew’s needs in situ , e.g., through production of antibiotics, crop bioremediation, or biomining of lunar and Martian regolith. However, given the limited resources that can be transported for long-term space missions, the utilization of traditional culture media to support such production systems is impractical. Thus, in this work we developed an alternative culture medium using components present in space waste resources for growth of the fungus Penicillium simplicissimum , a commonly used model species in biotechnological applications. We identified 10 promising waste components and used Design of Experiments screening and optimization strategies to test 94 media formulations, including 32 runs in the screening step and 62 in the optimization step. Validation experiments demonstrated that the optimized space waste medium (SWM), which contains cellulose, lunar regolith flow-through, synthetic saliva, urea, starch, casamino acids, and lignin, supported reproducible fungal growth of 1.68 ± 0.37 g/L (in dry biomass), equivalent to 35.22% of the amount obtained under the nutrient-rich control medium (PDB). This SWM formulation distinctly outperformed the Potato Dextrose Broth control in absolute citric acid production by 10.78% (92.5 ± 23.3 mg/L vs. 83.5 ± 42.1 mg/L, respectively) and yielded a 301.20% increase (∼3-fold higher) in biomass-normalized specific citric acid yield (58.5 ± 26.8 mg/g vs. 19.4 ± 12.3 mg/g). This notable performance implies a fundamental shift in fungal carbon allocation away from primary growth, suggesting a biochemical pivot toward secondary metabolism. Our study reports a promising space waste based medium for fungal biotechnology to promote sustainable processes, both on Earth and in future long-term space missions.

As humanity gets closer to establishing a long-term presence on the Moon and on Mars, the practicality of undertaking such far-reaching expeditions beyond Earth requires overcoming substantial challenges (Douglas et al., 2021). Although the International Space Station (ISS) is equipped with life-support systems for water, air, and waste recycling (Volpin et al., 2020; Verbeelen et al., 2021; Rai et al., 2022; Williamson et al., 2024), astronauts still rely on resupply missions for a wide range of resources (food, spacecraft materials, medical supplies, and scientific experiments), meaning that crews are still Earth-dependent (Ichimura and Yamashiki, 2025). Moreover, the transportation of such resources to the ISS incurs substantial costs, often exceeding $2k/kg, and is estimated to exceed $10,8k/kg to the Moon (Harper et al., 2016; Jones, 2023). Hence, there is a pressing need to develop cost-effective and sustainable life-support systems, that do not depend on constant resupply or resources from Earth, and that can rely on utilizing the resources already present in space habitats.

Bioregenerative Life Support Systems (BLSS) are artificially constructed closed-loop systems that mimic Earth’s natural ecosystems by incorporating producers (plants), consumers (humans/animals), and decomposers (microorganisms). These are designed to minimize the reliance on external supplies (Liu et al., 2021), by incorporating waste management strategies that can turn waste into valuable resources. These systems are often coupled with In Situ Resource Utilization (ISRU) approaches (Maiwald et al., 2021; Maiwald, 2023), that can help the efficient utilization of local materials and resources (e.g., lunar regolith) to produce supplies that would not be possible to obtain otherwise. Together, these approaches help to reduce the mass, space, and environmental constraints of long-term crewed space missions (Gumulya et al., 2022). Additionally, by harnessing the potential of microorganisms they offer a multitude of advantages (e.g., reduction in power demand and launch volume, among many others) that make microbial-based BLSS highly suitable for space exploration (Santomartino et al., 2023).

Among all the various types of microorganisms that can potentially provide valuable resources in space habitats, like pharmaceutical products, nutrients and minerals through biotechnology systems, filamentous fungi are excellent candidate organisms due to their presence on various Earth environments and extensive use in terrestrial biotechnologies (Cortesão et al., 2020b; Meyer et al., 2020; Meyer, 2021). Additionally, they exhibit remarkable resilience and adaptability to thrive in various extreme environments, including space conditions (Cortesão et al., 2020a; Simões et al., 2023; Santomartino et al., 2026). However, to our knowledge, no BLSS has intentionally incorporated fungi as active agents for organic waste degradation or plant growth promotion, only through indirect approaches. For instance, experiments related to the Lunar Palace 365 project included fungi within rhizospheric microbial communities during wheat cultivation (Sun et al., 2022; Liao et al., 2025b), though these fungi were naturally occurring rather than deliberately introduced. In the waste recycling system of Lunar Palace 365, “microbial inoculants capable of degrading plant waste” were initially reported (Fu et al., 2016), but the subsequent microbial analyses focused primarily on bacteria (Liu et al., 2020), reflecting the assumption that waste degradation was driven by bacterial consortia alone. Later studies of the compost samples from the same system revealed the presence of fungi (Liao et al., 2025a; Wang et al., 2025). These studies suggest that fungi have already appeared in BLSS systems, but mainly as incidental members of plant microbiomes or unplanned contributors to waste decomposition. This highlights a clear research gap and underscores the potential value of deliberately integrating fungi into future BLSS. Importantly, the unintended presence of fungi in closed life-support systems is also consistent with observations from actual spaceflight environments. Filamentous fungi like Aspergillus spp. and Penicillium spp. have been identified as part of the microbiome of space stations in Low Earth Orbit (LEO), such as Mir and the ISS (Ott et al., 2004; Checinska Sielaff et al., 2019; Cortesão et al., 2020b).

In this work, the filamentous fungi Penicillium simplicissimum was selected as a model organism based on its documented biotechnological versatility and potential relevance for ISRU strategies in space habitats (Amiri et al., 2011; Arshadi et al., 2022). Studies have demonstrated the capability of P. simplicissimum to mediate biomining processes through the production of organic acids such as citric acid and oxalic acid (Srichandan et al., 2019), that are considered key metabolites for fungal biotechnology. These organic acids can act as natural chelating agents, capable of dissolving solid metals by forming soluble metal complexes, and chelate ions (Dusengemungu et al., 2021). A recent study performed aboard the ISS demonstrated the capacity of this species to enhance the mobilization of palladium, platinum, and other elements from L -chondrite asteroidal material under microgravity conditions (Santomartino et al., 2026). Similar experiments involving lunar regolith demonstrate that P. simplicissimum is able to mobilize metals from the regolith into its surrounding medium (Figueira et al., 2025). Moreover, it can effectively leach zinc, nickel, copper, and aluminum from waste mobile phones and computer circuit boards (Arshadi et al., 2022; Esmaeili et al., 2022; Guillén et al., 2026). This finding holds particular significance for long-term space missions as it implies that technological waste can be recycled rather than simply discarded onto newly colonized territories.

Despite their advantages, one of the main challenges that arises in integrating fungal-based biotechnologies into BLSS is translating Earth-optimized cultivation conditions to a space habitat scenario that is severely limited in space, resources and crew time. Particularly, the impracticality and high costs of transporting resources from Earth to the Moon, makes conventional cultivation of fungal species such as P. simplicissimum unfeasible, and thus emphasizes the pressing need for alternative methods that foster fungal growth for a space biotechnology set-up (Santomartino et al., 2023). While transporting the fungi themselves is relatively simple, the challenge lies not only in germinating these spores using the limited resources available on the destination site, but also in maintaining growth and efficient production of relevant compounds. Earth-based fungal biotechnologies are very well established, utilizing large volumes of a high-nutrient culture media (e.g., in bioreactors) to maximize product yield, which is often tailored specifically to each product of interest. However, such large volumes of high-resource culture media are not feasible in a space habitat context (Meyer et al., 2020). Sustainable space habitats then rely on the successful adaptation of such effective Earth-based biotechnological systems to space. Exploring a low-resource culture medium in which fungi are able to produce a broad spectrum of products is an important step to understand how we can integrate fungal-based biotechnology systems into space exploration.

As a first step to establish a self-sustainable fungal-based ISRU system in space, this work explored the utilization of components present in space waste resources for the cultivation of the fungus P. simplicissimum. We used a Design of Experiments (DOE) approach to assay potential space waste sources and determine their optimal concentrations in high-throughput, low-volume screening, using live-cell imaging of spore germination. Our study further demonstrates the ability of P. simplicissimum to use the developed space waste medium to produce substantial biomass and citric acid, a key metabolite for a wide range of fungal biotechnologies.

Identification of potential space waste components

A literature review was conducted to identify the culture media most used for the cultivation of filamentous fungi and their respective applications. Articles published between 2008 and 2023 were selected from peer-reviewed journals. Information was compiled on the fungal species used, cultivation methods for vegetative growth and spore production, and the carbon sources, nitrogen sources, and supplementary components present in each medium.

To identify candidate nutrient sources from space-relevant waste streams, a second literature-based survey was conducted focusing on waste currently generated on the International Space Station (ISS) and waste expected to arise during future long-duration space missions. This survey included research articles, technical reports, and other relevant documentation related to waste management in spaceflight systems. Information on the composition of major waste categories, including food waste, plant-derived residues, human waste, and discarded materials, was compiled and analyzed. The references considered in this analysis were published between 2006 and 2023.

Rather than testing heterogeneous waste streams directly, this first-stage approach used representative chemical compounds associated with the identified space-relevant waste sources, allowing tighter control over medium composition and clearer interpretation of the contribution of each component. In addition to supporting component selection, the compiled literature data was used to define biologically relevant starting ranges for medium formulation. Reported concentration ranges, functional roles, and inhibitory thresholds of comparable nutrient sources in established fungal media were used as reference points to define the starting concentrations selected for screening.

Fungal strain and culture conditions

The filamentous fungus strain used in this work was P. simplicissimum (DSM 1097), obtained from the German Collection of Microorganisms and Cell Cultures (DSMZ). P. simplicissimum was selected as a promising fungal species for biotechnology applications in the space exploration context (Santomartino et al., 2023; 2026; Figueira et al., 2025). Fungal spores were collected from 7-day-old cultures grown at room temperature (22 °C) on Potato Dextrose Agar (PDA) (Sigma). Spore suspension was prepared by flooding the agar plates with 10 mL of saline solution (0.9% NaCl) and gently scraping the surface using a cotton stick to harvest the spores. The spore suspensions were then retrieved from the agar plate and then filtered through two layers of Miracloth filter (Millipore) to ensure full filtration of hyphal fragments and finally stored at 4 °C. Spore concentration was determined using a Neubauer counting chamber. All further experiments were conducted with fresh spore suspensions that were no older than 3 days.

Space waste medium development and optimization

Design of experiments (DOE)

To systematically investigate and optimize the composition of a space waste fungal medium, a DOE approach was employed. The initial screening phase was conducted using Statgraphics Centurion XVI (Version 16.1.03; 32-bits) (StatPoint, Inc., Virginia, United States) to identify the most influential medium components (factors), followed by an optimization phase using Minitab® (Version 20.3; 64-bit) (Minitab Inc., State College, PA, United States) to determine the best concentration levels of the selected components. Data visualizations were rendered in Python 3.10 using the Matplotlib library.

Culture medium - screening via fractional factorial design

A fractional factorial design (FFD) was used as an initial screening step to identify medium components influencing the growth of P. simplicissimum. Ten factors (medium components) were selected based on literature reports of their potential impact on fungal growth: (A) cellulose, (B) lunar regolith flow-through (FT), (C) synthetic saliva, (D) folic acid, (E) urea, (F) starch, (G) synthetic urine, (H) casamino acids, (I) lignin, and a (J) trace element solution. A two-level fractional factorial design (2 10−5, resolution IV) with 32 experimental runs was used to evaluate their effects. Each factor was evaluated at two concentration levels: high (+1) and low (−1). All experimental units were randomized.

Each of the 10 selected waste components was acquired synthetically (rather than retrieved from complex natural waste sources). This enabled an initial assessment of P. simplicissimum’s ability to utilize individual compounds, which can later be used to assess fungal growth in more complex waste sources, such as crop biomass or real human waste-like saliva and urine. Individual stock solutions were systematically prepared for every component evaluated, spanning both the highly soluble nutrients and those exhibiting limited aqueous solubility. All synthetic compounds utilized were research-grade reagents as supplied without intentional size-fractionation or pre-milling. Liquid stock solutions were prepared using double-distilled water (ddH 2 O). To revert gravimetric segregation effects (especially from compounds with limited aqueous solubility), stock solutions were suspended via vigorous magnetic stirring immediately prior to aliquoting.

The synthetic saliva and synthetic urine solutions were prepared following the methods described by Woo et al. (2010) and Feng and Wu (2006), respectively. The lunar regolith flow-through was obtained by mixing 5 g of EAC-1A simulant (Engelschiøn et al., 2020) with 20 mL of ddH 2 O, followed by 0.2 µm pore-size Whatman filtration to recover the soluble fraction, which has been observed to contain trace amounts of mineral elements (Figueira et al., 2025).

For the experimental setup, 96-well microplates were prepared, where each well contained 290 µL of the correspondent test medium inoculated with 10 µL of a 7.5 × 10 6 spores/mL spores suspension to set each experimental unit to a final volume of 300 µL. Stock solutions of all 10 components were prepared, and the 32 unique waste media formulations were generated by combining the required volumes from stock solutions. Each medium formulation was tested in triplicate (n = 3). To have reference values for optimal fungal growth, Potato Dextrose Broth (PDB) was prepared according to the manufacturer’s instructions and used as benchmark control for comparing fungal development under nutrient-rich conditions (n = 6). The multi-well plates were incubated at room temperature (22 °C–24 °C), and spore germination followed by hyphal formation was monitored for 48 h using the live-imaging oCelloScope™ (BioSense Solutions ApS, Farum, Denmark). Data points were collected hourly, and fungal growth was assessed using the SESAfungi algorithm, with results reported as SESA values (Aunsbjerg et al., 2015). The maximum value detected within 48 h was used as the response variable.

The effects of the 10 independent variables on maximum growth values were analyzed under a (linear) first-order polynomial model, which can be described by the following equation (Equation 1): where Y is the predicted response for “SESAfungi algorithm max growth (SESA values)”; β 0 is the intercept; β i is the linear regression coefficient for each factor; X i represents the independent variables, and ϵ is the random error.

The model fitness was evaluated based on standard deviation (SD), the regression coefficient (R 2), adjusted R 2, predicted R 2 and the F- test-based statistical significance (p- value). Model adequacy was verified by assessing the normality of residuals using the Shapiro-Wilk (W) test, supplemented by evaluations of skewness and kurtosis. The independence of observations was evaluated using the Durbin-Watson statistic to ensure the absence of autocorrelation. Visual validation was further performed using four-in-1 residual diagnostic plots, alongside a Predicted vs. Actual Response plot to confirm the absence of heteroscedasticity and identify potential outliers.

All coefficients (β) were calculated based on the Ordinary Least Squares (OLS) method. Variable significance values and OLS results were reported in the ANOVA (Analysis of variance) and Coefficients table. Pareto chart of standardized effects, and main effects plot of response means were rendered to help visualize the effects on the response variable. Only significant factors (medium components; p -values <0.05) with positive effect were selected for further steps.

To justify the transition from low-volume multi-well plate screening to the shake-flask optimization phase, a feasibility-gap analysis was conducted to assess the remaining potential performance of the waste-based medium. The theoretical maximum growth under screening conditions was obtained by solving the fitted first-order linear model equation at the factor levels that maximize the predicted response. Then, the top-performing experimental screening run was selected and compared to the linear model-predicted maximum value. The predicted maximum was also compared against the PDB screening control. These comparisons were done using one-sample t-tests, with a significance threshold set at p < 0.05. The percentages relative to these comparisons were also calculated.

Culture medium - optimization via response surface methodology

Following the screening phase, seven key factors; (A) cellulose, (B) lunar regolith FT, (C) synthetic saliva, (E) urea, (F) starch, (H) casamino acids and (I) lignin, were identified as positively significant and therefore selected for further optimization using a Response Surface Methodology (RSM) approach. A three-level Box–Behnken design (BBD) was employed, resulting in 62 experimental runs, including six central points. The selected factors were tested at three concentration levels: high (+1), intermediate (0) and low (−1). Concentration levels for the center points (0) were derived from the high levels (+1) used in the screening phase to target the area of maximum response.

RSM-BBD optimization experiments were conducted in 100 mL shake flasks containing 40 mL of culture medium. Each medium formulation was prepared according to the experimental design matrix and inoculated to a final fungal spore concentration of 1 × 10 6 spores/mL. Cultures were incubated at room temperature (22 °C–24 °C) with continuous agitation at 120 rpm for 7 days. Fungal growth was quantitatively assessed by measuring the dried biomass collected at the end of the incubation. The dry biomass yield (in grams per liter) was used as the response variable to analyze the optimization model. Positive control cultures containing 40 mL of PDB were run in parallel (n = 6).

A full quadratic regression model was fitted to the collected data from testing the seven variables. The behavior of the system was explained by the following second-order polynomial equation (Equation 2): where Y is the response variable “Dry Biomass Yield” in grams per liter (g/L); β 0 is the intercept, β i, β ii, and β ij are the regression coefficients of the linear, quadratic and interaction parameters, respectively; and X i X j represent the independent variables, and ϵ the residual error.

The quality of the fitted polynomial model was judged statistically by its SD, R 2, adjusted R 2, predicted R 2 and both Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The statistical significance of the model was verified by Fisher’s F- test and its associated probability (p- value). A reduced model was generated by backward elimination of non-significant terms (p > 0.05) and its quality was also tested for fitness and statistical significance. To select the most appropriate model for the subsequent analysis, both models were compared based on their predictive power and reliability. The validity of the selected model was confirmed by verifying the normality of the residuals via the Shapiro-Wilk test, skewness, and kurtosis. The Durbin-Watson statistics were used to confirm the absence of autocorrelation. Visual validation was conducted using the four-in-1 residual diagnostics and Predicted vs. Actual Response plots.

ANOVA was performed to determine the significance of each factor (medium components) and its interactions on fungal biomass production. The Lack-of-Fit test was employed to ensure that the model effectively captured the experimental data (p > 0.05). The coefficient estimates from the regression model were analyzed to assess their individual and combined effects. The effect of significant interactions was reported graphically through Contour and 3D Surface plots.

A regression equation derived from the model was used to predict optimal values from the medium components for a theoretically optimized Space Waste Medium (SWM) capable of maximizing biomass production.

Validation of the space waste medium

Two different media were tested for the validation processes in 100 mL Erlenmeyer flasks containing 40 mL of culture medium and inoculated at a final concentration of 1 × 10 6 spores/mL. Specifically, the theoretically optimized Space Waste Medium (SWM): cellulose (factor A; 0.43 g/L), lunar regolith FT (factor B; 0.11% v/v), synthetic saliva (factor C; 26.39% v/v), urea (factor E; 2.59 g/L), starch (factor F; 0.05 g/L), casamino acids (factor H; 0.53 g/L), and lignin (factor I; 1.05 g/L); and Potato Dextrose Broth (PDB) as a reference medium. The cultures were maintained under continuous agitation at 120 rpm for a duration of 7 days at room temperature (22 °C–24 °C). To ensure that mechanical variables did not influence fungal development as confounding noise, the cultivation settings, including flask geometry, working volume (40 mL in 100 mL flasks), and agitation rate (120 rpm), were held identical across all the parallel SWM and PDB runs, isolating the medium-specific chemical composition as the only main effect on the biological responses.

Validation experiments were designed to evaluate biomass production, citric acid biosynthesis, and pH variations throughout incubation. For biomass yield and citric acid quantification, a sample size of n = 4 biological replicates were used for each medium. To strengthen characterization of the chemical environment, pH variation was analyzed using n = 6 biological replicates per medium across two independent experimental repetitions.

Dry biomass, pH, and citric acid quantification

For the quantification of dried biomass, the content of the shake-flasks was initially transferred to Falcon tubes through two layers of a previously weighed Miracloth filter (Millipore), to ensure the retrieval of all fungal biomass. Each double-filter carrying the fungal biomass was submitted to a desiccation process at 60 °C for 24 h. The weight of the filters with the fungal biomass was measured after desiccation with a high precision analytical scale (Sartorius). To eliminate gravimetric bias, dry weight measurements were corrected by subtracting the theoretical mass of insoluble medium components - estimated solids in medium (e.s.m). These background solids were estimated based on the solubility constants of reagents in neutral water after autoclaving (121 °C) and its respective concentration scaled to the 40 mL volume. The fungal dried biomass was calculated in accordance with the following equation (Equation 3):

Where W a f t e r is the filter and biomass weight after desiccation, W b e f o r e is the weight of only the filter before desiccation and W e. s. m. are the calculated theoretical insoluble mass from each tested reagents combination.

To assess changes in pH over time and compare differences across media, 200 μL aliquots were collected at four separate time points (Days 1, 2, 5, and 7) during the incubation period. The pH was then measured using indicator strips (Sigma; pH range 4.0–10.0).

Citric acid production was quantified after 7 days of incubation using an enzymatic assay kit (Megazyme®) according to the manufacturer’s instructions (measured via colorimetry assay and convertible to mg/L). Metabolic efficiency was assessed by calculating the specific citric acid yield (Y P / X) for both the optimized SWM and the PDB control (Equation 4):

Statistical analysis

Welch’s t-tests were used for all between-group comparisons to account for potential unequal variances. All statistical analyses were conducted using Python 3.10 with Statsmodels and SciPy packages, and significance was defined at p < 0.05.

Statistical validation of the optimized SWM

To evaluate experimental stability across phases, the dry biomass yields of the PDB controls obtained during the optimization and validation stages were compared using Welch’s t-test. To statistically validate SWM optimization, a one-sample t-test was used to compare the dry biomass yields obtained from the SWM validation experiment against the maximum yield predicted by the reduced quadratic model. One-sample t-tests were also used to individually compare the same predicted maximum against the dry biomass yields of the PDB controls from the optimization and validation stages. Relative performance percentages were additionally calculated for all relevant benchmark comparisons.

Comparison of optimized SWM and PDB control performance

Differences in pH between SWM and PDB were evaluated independently for Days 1, 2, 5, and 7 using Welch’s t-tests.

Repeated measures analysis of variance (RM-ANOVA) was used to analyze overall pH changes throughout the incubation period separately for SWM and PDB. Sequential paired t-tests, adjusted using the Holm–Bonferroni correction, were then performed to evaluate progressive pH shifts between consecutive time points within each medium (Days 1–2, 2–5, and 5–7).

Differences in biomass, citric acid production, and specific citric acid yield between the optimized SWM and the validation-stage PDB control were also evaluated using Welch’s t-tests. Relative performance percentages were likewise calculated for these comparisons.

To compare fungal morphology between the optimized SWM and PDB control during validation, representative observations were recorded at two developmental stages. Early hyphal growth was imaged 24 h after inoculation by withdrawing aliquots from shake-flask cultures for oCelloScope™ observation, whereas final pellet morphology was documented photographically after 7 days. Comparisons were performed qualitatively in terms of hyphal expansion, branching pattern, pellet size, pellet regularity, and aggregation behavior.

Selection of space waste components for fungal growth

To establish a nutritional baseline for space-based fungal biotechnology, commonly used culture media for biotechnologically relevant filamentous fungi were analyzed through an extensive literature review. This analysis focused on the carbon (C) and nitrogen (N) sources, as well as supplementary growth-promoting components, reported across different fungal cultivation systems (Supplementary Table S3).

The compiled dataset showed that glucose (35%) and potato infusion (23%) were the most frequently used carbon sources in terrestrial fungal media, followed by sucrose (17%) and malt extract (13%). With respect to nitrogen nutrition, potato infusion also represented the most frequent nitrogen-containing component (33%), reflecting its dual role in conventional media, followed by sodium nitrate (24%) and soya peptone (16%). Additional compounds with dual carbon and nitrogen functionality, such as uridine (5%) and acetamide (3%), were also identified. The survey further highlighted the recurrent inclusion of mineral supplements, particularly potassium (34%), magnesium (23%), and phosphorus (16%), as common components supporting fungal growth (Supplementary Figure S1).

Based on these terrestrial benchmarks and on the anticipated waste streams generated on the International Space Station (ISS) and during future long-duration missions (Supplementary Table S1), ten components were selected as candidate inputs for the development of the Space Waste Medium (SWM). As a controlled first approach, these inputs were not tested as raw waste streams, but rather as defined representative compounds associated with those sources (Supplementary Table S2). To ensure absolute batch uniformity and enable precise volumetric delivery across all experimental phases, these components were prepared as individual stock solutions (Supplementary Table S4). These components were chosen to fulfill complementary nutritional roles within a circular BLSS and were grouped into four functional categories (Supplementary Figure S2).

Accordingly, starch, cellulose, and lignin were selected as carbon sources because they represent major storage and structural components of plant-derived residues expected in closed agricultural systems (Pfister and Zeeman, 2016; MacNeill et al., 2017; Andlar et al., 2018). Casamino acids were included as an organic nitrogen source (Yang et al., 2022). Folic acid, urea, synthetic urine, and synthetic saliva were selected as dual carbon and nitrogen inputs representing soluble fractions associated with human-derived waste streams (Sprenger et al., 2003; Feng and Wu, 2006; Veverka et al., 2007; Woo et al., 2010). Lunar regolith flow-through (FT) and a trace element solution were included as mineral sources, consistent with an in situ resource utilization (ISRU) approach and the requirement for micronutrient supplementation during fungal growth (Figueira et al., 2025). Together, these ten components established a component-level framework for formulating a fungal medium from closed-habitat-derived byproducts.

Space waste medium development and optimization

The overall experimental workflow, progressing from the initial screening of waste components to response surface optimization and final experimental validation, is visually summarized in Figure 1.

Component level selection and screening design

Starting concentrations for the selected components were established to provide a robust nutritional baseline while remaining within biologically relevant and experimentally compatible limits. These values were based on concentrations reported for established fungal media (Supplementary Table S3; Supplementary Figure S1), reported inhibitory thresholds for the selected compounds (Supplementary Table S2), and, where possible, the carbon-to-nitrogen balance described for fungal growth (Cooke, 1968).

Urea was set at 2.5 g/L, within the range commonly used in fungal enzyme production media (0.3–4.0 g/L) (Prasetyo et al., 2010), while remaining below concentrations associated with inhibitory ammonia toxicity (Veverka et al., 2007; Navarathna et al., 2010). Casamino acids were included at 0.5 g/L to provide a moderate organic nitrogen input, approximating the amino-nitrogen contribution of crop residues (Yang et al., 2022).

Cellulose, lignin, and starch were set at relatively low concentrations compared with those used in industrial submerged cultures, because higher loadings increase turbidity and viscosity and could interfere with oCelloScope™ SESAfungi measurements. Accordingly, cellulose was set at 0.4 g/L and starch at 0.05 g/L to provide a detectable nutritional stimulus while preserving optical clarity, whereas lignin was set at 1.0 g/L as a level compatible with both ligninolytic induction and the optical constraints of the screening platform (Janusz et al., 2017; Saini and Sharma, 2021; Winters et al., 2022).

Synthetic saliva and synthetic urine were used at 25% v/v to provide representative soluble mineral and organic contributions without the excessive osmotic load associated with full-strength formulations (Feng and Wu, 2006; Woo et al., 2010). Folic acid was set at 0.02 g/L (20 mg/L) to avoid vitamin limitation during screening (Sprenger et al., 2003). Lunar regolith FT and the trace element solution were each included at 0.1% v/v as low-level mineral supplements intended to provide micronutrients without introducing excessive particulate matter (Figueira et al., 2025).

Overall, these concentrations provided a scientifically grounded baseline for the screening phase while maintaining the optical transparency required for reliable live imaging of spore germination and hyphal extension. Hence, the high-level (+1) concentrations selected for the screening phase were: cellulose (factor A; 0.4 g/L), lunar regolith FT and trace elements solution (factor B and J respectively; 0.1% v/v), synthetic saliva and synthetic urine (factor C and G respectively; 25% v/v), folic acid (factor D; 0.02 g/L), urea (factor E; 2.5 g/L), starch (factor F; 0.05 g/L), casamino acids (factor H; 0.5 g/L) and lignin (factor I; 1 g/L). Low levels (−1) corresponded to 0 g/L or 0% v/v (Table 1).

By employing a resolution IV fractional factorial design, the 10 candidate components were screened using only 32 unique experimental runs (Supplementary Table S5). This represents a 96.8% reduction in experimental effort compared to the 1,024 runs required for a traditional two-level full-factorial design (2 10), allowing for a rapid and efficient identification of significant growth factors (Montgomery, 2013).

After obtaining the max growth SESA values from the oCelloScope™, the resulting dataset was fitted to a first order (linear) model, to evaluate the impact of the 10 candidate components on the growth of P. simplicissimum. The first-order model demonstrated strong statistical fitness and high reliability, with a Standard Deviation (SD) of 0.094, an R 2 of 76.82%, an adjusted R 2 of 73.47%, and a predicted R 2 of 68.99%. The F- value of 22.93 and its highly significant p- value (1.79 × 10 −21) confirmed the robustness of the screening data (Supplementary Table S6; Supplementary Figure S3).

Diagnostic testing verified that the model satisfied all necessary assumptions for linear regression. The residuals followed a normal distribution determined by the Shapiro-Wilk test (W = 0.978, p = 0.742), supported by a skewness of −0.343 and kurtosis of 2.443. The Durbin-Watson statistic of 1.807 confirmed the independence of observations. Visual inspection of the 4-in-1 diagnostic plots showed no systematic patterns or heteroscedasticity, validating the model for factor selection (Supplementary Table S7; Supplementary Figure S4).

Statistical analysis using a Pareto chart of standardized effects, main effects plots, and ANOVA identified 7 factors with a significant positive influence on growth (p < 0.05): cellulose (factor A; β = 0.06994), starch (factor F; β = 0.0745), casamino acids (factor H; β = 0.05809), lignin (factor I; β = 0.06673), urea (factor E; β = 0.05312), synthetic saliva (factor C; β = 0.03788), and lunar regolith FT (factor B; β = 0.02915). In contrast, folic acid (factor D; p = 0.002) and synthetic urine (factor G; p = 0.039) exhibited significant negative effects, while the trace element solution (factor J; p = 0.062) was not statistically significant (Figure 2; Supplementary Figure S5; Supplementary Tables S8, S9).

The influence of these factors is described by the following first-order polynomial equation (Equation 5):

A feasibility analysis was conducted to compare the best-performance screened media formulation with the model-predicted maximum growth response and to determine whether further optimization was possible. The best experimental run (#21) resulted in an average value of 0.675 ± 0.014, which was significantly lower than the theoretical maximum predicted by the first-order model (0.7217; p = 0.0282) (Table 2). This meant the best achieved run was about 93.53% of the theoretical maximum. This significant gap strongly suggested that the truly optimal factor levels had not been reached, justifying the transition to a higher-order response surface methodology to capture potential interactions and nonlinear effects (Myers et al., 2016). Under the same screening conditions, the model-predicted maximum represented only 80.06% of the growth observed in the PDB control (0.9014 ± 0.072) and remained significantly lower (p = 0.0017).

Based on these results, the 7 positively contributing factors: cellulose, lunar regolith FT, synthetic saliva, urea, starch, casamino acids and lignin, were selected for subsequent optimization.

Medium optimization

The 7 significant factors were optimized using a Box-Behnken Design (BBD) (Table 3). This optimization step was completed in 62 experimental runs (including six central points) (Supplementary Table S10), effectively navigating a seven-factor, three-level landscape that would have theoretically required 2,187 unique formulations (3 7) under a full-factorial approach (Montgomery, 2013; Myers et al., 2016).

A full quadratic polynomial model was initially fitted to describe the relationship between the components and dry biomass yield after 7 days (SD = 0.220). To ensure model parsimony, a reduced quadratic model was generated via backward elimination of non-significant terms (p > 0.05). The reduced model (SD = 0.215) outperformed the full model across all metrics, showing a lower AIC (1.484 vs. 6.241) and BIC (44.026 vs. 82.818), as well as high adjusted R 2 (88.50%) and predicted R 2 (73.92%) values. Therefore, the reduced model was selected as the best fit. The selected model was highly significant (F = 25.71, p = 3.01 × 10 −17) and explained approximately 92.08% of the observed biomass variability (Table 4).

Normality of the optimization data was verified via the Shapiro-Wilk test (W = 0.984, p = 0.612), with a skewness of 0.502 and kurtosis of 3.901. The Durbin-Watson statistic of 1.936 indicated high independence of experimental runs. Model performance was further evidenced by the strong alignment in the Predicted vs. Actual Response plot and the consistency of the four-in-1 residual diagnostics plots (Supplementary Table S11; Supplementary Figures S6, S7).

Model adequacy was rigorously confirmed by a non-significant Lack-of-Fit test (F = 1.27, p = 0.436), indicating the reduced quadratic model was highly adequate for describing the experimental data. The ANOVA for the reduced model revealed that linear, quadratic, and interaction terms all significantly influenced biomass production (p < 0.001). All seven linear terms were positive and significant, with cellulose (factor A; F = 10.14), starch (factor F; F = 9.37) and casamino acids (factor H; F = 9.72) showing the strongest individual influence on yield. All quadratic terms exhibited negative coefficients (e.g., (A 2) cellulose × cellulose, β = −0.6503), indicating a parabolic relationship and the presence of a clearly defined optimal peak for each component within the explored concentration range (Supplementary Tables S12, S13; Supplementary Figure S8).

Analysis of the response surfaces revealed critical interactions, particularly between nitrogen and carbon sources (Supplementary Figure S9). The interaction between Urea and Lignin (E:I) was found to be the most statistically significant (F = 19.14, p < 0.001) (Figure 3A). Other significant interactions showed synergistic effects where minerals might optimize the metabolic environment for complex polymer utilization, i.e., Cellulose × Lunar Regolith FT (A:B) (Figure 3B). A detailed investigation into the biochemical nature of these interactions will be the subject of future studies.

The regression equation for dry biomass yield was determined as follows (Equation 6):

By solving the second-order regression equation, the optimal factor levels were determined: cellulose (factor A; 0.43 g/L), lunar regolith FT (factor B; 0.11% v/v), synthetic saliva (factor C; 26.39% v/v), urea (factor E; 2.59 g/L), starch (factor F; 0.05 g/L), casamino acids (factor H; 0.53 g/L), and lignin (factor I; 1.05 g/L). Under these conditions, the model predicted a maximum dry biomass yield of 2.21417 g/L (Figure 4; Supplementary Table S14).

Validation of the space waste medium

The optimized SWM was validated through independent experimental runs and compared with cultures grown in PDB. First, the experimental reliability across phases was confirmed by comparing the PDB benchmarks from the shake flasks experiments. Specifically, the mean dry biomass yield of the validation-stage PDB was 98.37% of the yield obtained during the optimization stage, indicating no significant difference between the two (p = 0.9011) (Table 5).

The tested SWM formulation produced a biomass yield of 1.6797 ± 0.3696 g/L. A one-sample t-test comparing the validation runs with the model-predicted maximum of 2.2142 g/L resulted in a p -value of 0.0629, indicating that the experimental SWM biomass yield was not significantly different from the predicted value. The experimental yield captured 75.86% of the model’s theoretical maximum (Table 5).

The model-predicted maximum under shake-flask conditions corresponded to 45.67% of the PDB biomass observed during the optimization stage (4.8479 ± 0.6526 g/L; p = 0.0001) and 46.43% of that observed during the validation stage (4.7687 ± 1.0798 g/L; p = 0.0179) (Table 5).

Comparison of optimized SWM and PDB: biomass, metabolic profile, and morphology

Regarding pH measurements, Welch’s t-tests at each time point demonstrated that the pH levels of SWM and PDB were not significantly different at the start of incubation (Day 1; p = 0.116), but became significantly divergent by the second day (p = 0.025). The pH gap between the two media increased exponentially over time (Figure 5A; Supplementary Table S15).

RM-ANOVA confirmed highly significant pH changes throughout the incubation period for both media (p < 0.0001) (Supplementary Table S16). In SWM, the growth of P. simplicissimum exhibited a steady and incremental alkalinization trajectory. Sequential paired t-tests (Holm–Bonferroni adjusted) identified a consistent upward trend at every sampling interval (Day 1–2, 2–5, and 5–7). Although the individual day-to-day increments followed a gradual progression (p = 0.0751 for all intervals), the cumulative effect resulted in a robust transition toward neutrality, reaching a final pH of 6.57 ± 0.46 (Figure 5B; Supplementary Table S17). In contrast, the PDB control was characterized by an initial period of stability followed by a rapid acidification phase. No significant change in pH was observed during the first 24 h of incubation (Day 1–2; p = 0.0501). However, this was immediately followed by a strong acidification change (p = 0.0037) between Day 2 (pH = 5.33 ± 0.52) and Day 5 (pH = 3.50 ± 0.45). This highly acidic environment remained stable until the final 48 h, ending at a terminal pH of 3.42 ± 0.20 (Figure 5C; Supplementary Table S17).

Biomass production in the PDB validation control (4.7687 ± 1.0798 g/L) remained significantly higher than in the optimized SWM (p = 0.0071), and the optimized SWM achieved 35.22% of the biomass obtained in this nutrient-rich reference (Figure 6A; Supplementary Table S18).

Although numerically different, the final citric acid concentrations in SWM and PDB showed no statistically significant difference (p = 0.7247). SWM yielded 92.5 ± 23.3 mg/L, slightly exceeding the 83.5 ± 42.1 mg/L achieved with PDB (Figure 6B; Supplementary Table S18). Despite the lower total biomass, SWM outperformed PDB by 10.78% in absolute citric acid production. When normalized to biomass, the specific yield (Y P/X) of citric acid in SWM (58.5 ± 26.8 mg/g) was three times higher (301.20%) than in PDB (19.4 ± 12.3 mg/g), although the difference was not statistically significant (p = 0.0539) (Figure 6C; Supplementary Table S18).

Figure 7 illustrates the morphological differences of P. simplicissimum grown in optimized SWM and PDB during the validation experiments. After 7 days of cultivation, PDB formed larger and more regular fungal pellets, whereas SWM produced smaller and less uniform aggregates. Representative oCelloScope™ images obtained from shake-flask aliquots at 24 h of incubation further showed denser and more interconnected hyphal growth in PDB, while SWM displayed a more spatially restricted pattern of development. Together, these observations indicate slower and less extensive morphological progression in SWM than in the nutrient-rich control.

Potato-based media such as Potato Dextrose Broth (PDB) and Potato Dextrose Agar (PDA) are widely used on Earth because they reliably support rapid fungal germination, biomass accumulation, and metabolite production (US Food and Drug Administration, 1998; Atlas, 2010; Sigma-Aldrich, 2018; Pitt and Hocking, 2022). Although potato cultivation has been proposed for controlled environments relevant to space exploration (Mortley et al., 2000; Wheeler, 2006; Ramírez et al., 2019; Duri et al., 2022; Caporale et al., 2023), direct use of potato-derived media during long-duration missions would be impractical, since diverting edible crops toward microbial media preparation would be difficult to justify in a resource-limited BLSS (Santomartino et al., 2023). There is therefore a need for culture media that support fungal biotechnology using locally available and recyclable inputs compatible with BLSS and ISRU strategies. In this context, the present study designed and validated a Space Waste Medium (SWM) capable of supporting the growth of P. simplicissimum using waste-derived and regolith-derived components. Importantly, this first-stage formulation did not use heterogeneous waste streams directly but instead relied on defined representative compounds associated with the identified space-relevant waste sources.

Medium component selection

The component selection process was guided by two complementary criteria: prior evidence that P. simplicissimum can utilize specific carbon, nitrogen, and mineral sources, and the likelihood that these nutrients could be supplied by space-habitat waste streams. In addition to supporting fungal growth, the aim was to formulate a medium capable of sustaining organic acid production, given the central role of fungal organic acids in biotechnology, including pharmaceutical production, biodegradable polymer processing, and bioremediation (Karaffa and Kubicek, 2003; 2019; Papagianni, 2007; Kuivanen et al., 2016; Meyer et al., 2016; Andlar et al., 2018; Meyer, 2021; Zhuang et al., 2021; Cairns et al., 2021; di Luccio et al., 2004; Mary Sheela et al., 2021). On this basis, ten components were selected for the initial screening: cellulose, lignin, and starch as carbon sources; urea, synthetic urine, and synthetic saliva as dual carbon and nitrogen sources; lunar regolith flow-through and trace elements as mineral inputs; and casamino acids as an organic nitrogen source.

These choices are consistent with both fungal physiology and the space-habitat context. Cellulose, starch, and lignin represent plant-derived carbon sources that could arise from inedible crop residues (Pfister and Zeeman, 2016; MacNeill et al., 2017; Andlar et al., 2018; Wheeler et al., 2008). Likewise, urea and saliva represent recoverable human-derived waste fractions that can contribute nitrogen and soluble organics, thereby linking crew waste recycling directly with microbial cultivation (Clauwaert et al., 2017; Volpin et al., 2020; Rai et al., 2022). Casamino acids strengthen the nitrogen supply and may reflect amino acid-rich residues from edible biomass processing, such as from rice, tomatoes or lettuce crops grown on board (Karahara et al., 2020; Khodadad et al., 2020; Moyses et al., 2021; Yang et al., 2022; Zabel et al., 2019). The positive contribution of lunar regolith flow-through is also notable, because it suggests that water-extractable mineral fractions from regolith simulants may improve fungal performance, even if the exact responsible compounds remain unresolved (Gadd, 2010; Mapelli et al., 2012; Figueira, 2022; Figueira et al., 2025). Together, these findings support a broader ISRU strategy in which fungal media are assembled from low-value byproducts.

A further point that merits clarification is the inclusion of starch in SWM. Although this may appear to bring the formulation closer to potato-based media such as PDB/PDA, the distinction is that PDB/PDA still depends on extracting nutrients (mainly starch) from an edible crop and on relatively standardized, food-derived inputs (US Food and Drug Administration, 1998; Atlas, 2010), whereas SWM was conceived around low-value, non-edible, or unavoidable side streams generated within a closed habitat. In this context, starch should not be interpreted as a requirement for purified food-grade supplementation, since starch is also present in non-edible and residual plant tissues, including stems, leaves, roots, immature fruits, peels, and bagasse (Pfister and Zeeman, 2016; MacNeill et al., 2017; Weligama Thuppahige et al., 2023). Moreover, the optimized concentration identified here (0.05 g/L) is several orders of magnitude lower than the carbohydrate load of conventional potato dextrose media, suggesting that starch in SWM likely acts as a minor readily accessible carbon source or metabolic primer rather than as the dominant substrate (Saini and Sharma, 2021; Kerkaert and Huberman, 2023; Xu et al., 2016). Thus, instead of mimicking PDB/PDA, SWM supports a more mission-relevant and ISRU-oriented strategy in which fungal growth is sustained through the valorization of waste-derived carbon, nitrogen, and mineral inputs, avoiding the reliance on edible crops, refined media components, and Earth-supplied consumables.

Methodological approach: DOE versus OFAT

A major strength of this study was the use of a Design of Experiments (DOE) workflow. The formulation of a culture medium from chemically diverse waste streams is inherently a multivariable problem, making traditional one-factor-at-a-time (OFAT) approaches poorly suited for efficient optimization (Galbraith et al., 2018). OFAT is labor-intensive and, more importantly, cannot capture dependencies and interactions among medium components (Box and Wilson, 1951; Box and Behnken, 1960; Montgomery, 2013; Myers et al., 2016). In contrast, the DOE-based screening and optimization strategy used here achieved a statistically robust and experimentally validated formulation in only 94 tested formulations, including 32 runs in the screening step and 62 in the optimization step. A traditional OFAT exploration of the initial 10-factor and subsequent 7-factor design spaces would have required more than 3,200 unique combinations (Montgomery, 2013). This highlights the value of DOE for reducing experimental burden and reagent consumption, particularly in future space experiments where time, consumables, and analytical capacity are inherently limited.

The screening model successfully identified dominant positive and negative contributors in a statistically efficient manner: seven components showed significant positive effects, whereas folic acid and synthetic urine were detrimental under the tested conditions and the trace element solution was not significant. Both the screening and optimization models were highly significant and showed strong agreement between predicted and observed values. Moreover, the Durbin–Watson statistics for both models were close to 2, confirming that the randomized design effectively minimized autocorrelation and that the results were not biased by experimental order.

The feasibility-gap analysis further supported the transition from screening to optimization. The best-performing screened formulation (#21) achieved a mean growth value of 0.675 ± 0.014, corresponding to 93.53% of the theoretical maximum predicted by the first-order model (0.7217), and this difference was statistically significant (p = 0.0282). This result indicated that, although the screening design identified a high-performing formulation, the explored factor levels had not yet reached optimal conditions. This statistically significant gap provided direct evidence that further optimization was both feasible and necessary, justifying the transition to response surface methodology rather than continued empirical adjustment (Box and Wilson, 1951; Myers et al., 2016). Thus, the screening stage did not merely identify promising components; it also demonstrated that their tested concentration space still contained unrealized performance potential.

The response surface optimization then showed that fungal biomass production was governed not only by the main effects of individual components but also by curvature and interaction effects. This is unsurprising for filamentous fungi, whose growth depends on coupled nutritional, physicochemical, and morphological processes (Papagianni, 2004; Wucherpfennig et al., 2010; Kerkaert and Huberman, 2023; Grimm et al., 2005; Kossen, 2000). The reduced quadratic model captured these relationships more effectively than the full model, indicating that a simpler representation was sufficient to explain most of the variability in biomass response. This is one of the clearest advantages of DOE over OFAT: it reveals synergistic and antagonistic effects that would otherwise remain hidden (Montgomery, 2013; Galbraith et al., 2018).

In the present study, the interaction between urea and lignin was the most statistically significant interaction affecting growth, highlighting that nutrient utilization in SWM is not simply additive. A plausible explanation is that urea supplied readily available nitrogen for the synthesis of ligninolytic enzymes or other extracellular proteins required to exploit more recalcitrant carbon fractions (Kaal et al., 1995; Ikeda et al., 2007; Davis et al., 2014; Janusz et al., 2017). At the same time, the negative quadratic coefficients observed in the model indicate that these components must remain carefully balanced, since excessive urea can lead to inhibitory ammonia accumulation and excessive lignin can limit transfer processes or reduce substrate accessibility (Veverka et al., 2007; Navarathna et al., 2010; Strope et al., 2011; Yuan et al., 2021). Likewise, the significant interaction between cellulose and lunar regolith flow-through suggests that mineral ions released from the simulant may act as cofactors that improve cellulolytic performance (Mandels and Reese, 1957; Gadd, 2010; Mapelli et al., 2012; Figueira et al., 2025). Altogether, these findings emphasize the importance of viewing culture media as integrated nutritional systems rather than simple collections of independent ingredients.

Space-relevant nutrient sources support fungal growth

A central outcome of this work is that fungal growth could be sustained using a simplified formulation composed of seven components likely to be available in a space-habitat scenario. The final SWM combined cellulose (0.43 g/L), lunar regolith flow-through (0.11% v/v), synthetic saliva (26.39% v/v), urea (2.59 g/L), starch (0.05 g/L), casamino acids (0.53 g/L), and lignin (1.05 g/L). This formulation enabled the growth of P. simplicissimum, demonstrating that a targeted combination of carbon, nitrogen, and mineral sources can produce a functional fungal medium even under strongly resource-constrained conditions.

The validation experiments confirmed that the optimized SWM can reproducibly support biomass formation in shake-flask cultures, although at a lower level than PDB.

The optimized medium yielded 1.68 ± 0.37 g/L of dry biomass, which was not significantly different from the model-predicted maximum of 2.2142 g/L (p = 0.0629), indicating strong agreement between experimental results and the reduced quadratic model. This suggests that the optimization procedure successfully identified near-optimal factor levels within the tested design space (Myers et al., 2016).

To ensure that this agreement was not influenced by variability in the experimental system, the stability of the reference controls through the experimental phases was also evaluated. Biomass yields obtained in PDB during the optimization and validation stages (4.8479 ± 0.6526 g/L and 4.7687 ± 1.0798 g/L, respectively) showed no significant difference (p = 0.9011), with a 98.37% agreement between both datasets. This indicates that the observed SWM performance reflects intrinsic properties of the medium rather than experimental drift or batch-to-batch variation.

Benchmarking against the PDB controls revealed a clear scale-up effect driven by cultivation time and metabolic strategy. During the 48 h microvolume screening phase, the model-predicted maximum corresponded to approximately 80.1% of the growth observed in PDB. Under shake-flask conditions, the reduced quadratic model predicted that the optimized SWM would reach approximately 45% of the PDB biomass benchmark (45.67% relative to the optimization-stage PDB and 46.43% relative to the validation-stage PDB), whereas the experimentally validated SWM ultimately achieved 35.2% of the biomass obtained in the parallel PDB control. This progressive divergence indicates that the relative performance of SWM decreases as cultivation proceeds from early germination and hyphal expansion to prolonged biomass accumulation. This shift likely reflects the fact that each experimental stage captured different aspects of fungal development and therefore imposed different nutritional and metabolic demands. The screening 96-well platform, based on optical monitoring, primarily reflects early germination and hyphal growth. Under these conditions, SWM provides sufficient nutrients to support rapid initial development relatively close to the PDB benchmark. By contrast, extended cultivation in shake flasks captures total biomass accumulation, which is strongly dependent on substrate accessibility and metabolic cost (Papagianni, 2004; Wucherpfennig et al., 2010). In PDB, the high concentration of readily metabolizable glucose supports rapid biomass accumulation and is associated with the characteristic acidification-driven growth regime (Papagianni et al., 1999; Liaud et al., 2014; Dörsam et al., 2017; Narisetty et al., 2023). In contrast, SWM relies on complex structural polymers such as cellulose and lignin, which require the synthesis and secretion of extracellular enzymes prior to assimilation (Lynd et al., 2002; Papagianni, 2004; Janusz et al., 2017; Saini and Sharma, 2021; Kerkaert and Huberman, 2023).

More importantly, the reduction in biomass in SWM is accompanied by a shift in metabolic activity, suggesting that the observed scale-up effect reflects a redistribution of metabolic resources. This supports the interpretation that SWM sustains early fungal development effectively but imposes increasing metabolic constraints over time due to the lower accessibility of its carbon sources and the energetic cost associated with their utilization (Saini and Sharma, 2021; Kerkaert and Huberman, 2023). This outcome is also consistent with broader fungal biomass benchmarks. In submerged culture, filamentous fungi show a wide range of dry biomass yields depending on species, substrate complexity, cultivation conditions, oxygen transfer, and rheology (Papagianni, 2004). Non-optimized submerged fermentations using complex or low-value substrates frequently produce relatively modest biomass yields, whereas highly optimized fed-batch systems can achieve much greater titers (Gibbs et al., 2000; Wucherpfennig et al., 2010).

Therefore, the performance of SWM should be interpreted within a broader context: a first-generation medium containing no added simple sugars and relying on complex polymeric carbon sources, can sustain a biomass yield corresponding to 35% of that obtained in the nutrient-rich PDB control. This represents a significant milestone, particularly given that SWM formulation was designed for BLSS/ISRU compatibility rather than maximal productivity.

Morphology, pH dynamics, and citric acid reveal distinct physiological regimes

The morphological differences between SWM and PDB provide additional support for the interpretation that SWM imposes stronger physiological constraints. In filamentous fungi, medium composition, pH, oxygen availability, and shear interactively shape pellet formation and mycelial architecture; these properties in turn influence mass transfer, internal diffusion, and secretion (Cui et al., 1998; Papagianni, 2004; Wucherpfennig et al., 2010; Zhang and Zhang, 2016; Veiter et al., 2018; Cairns et al., 2019a; 2019b; Schmideder et al., 2019). In our validation experiments, early oCelloScope™ observations (24 h) showed denser and more interconnected hyphal networks in PDB compared with spatially restricted hyphal development in SWM, and by Day 7, PDB formed larger and more regular pellets while SWM produced smaller, more irregular aggregates. Such morphology is consistent with reduced nutrient accessibility and/or altered pH trajectories in SWM compared with the nutrient-rich benchmark (Cui et al., 1998; Papagianni, 2004; Gomes et al., 2023). One of the most revealing outcomes of the validation step was the contrasting pH trajectory between SWM and PDB. In PDB, the culture underwent the expected acidification during the early growth phase, followed by stabilization at low pH. This pattern is consistent with the well-established behavior of filamentous fungi in glucose-rich, poorly buffered media (Amiri et al., 2011; Narisetty et al., 2023). PDB is essentially an unbuffered carbon-rich environment, and therefore the fungal metabolism itself becomes the primary driver of pH change. During exponential growth, rapid glucose uptake, proton extrusion, and secretion of organic acids can drive the pH sharply downward. In unbuffered PDB, these changes are expressed directly in the bulk medium, making pH a useful external marker of the fungus’s internal metabolic state (Karaffa and Kubicek, 2003). The low pH values observed during the later part of incubation are therefore consistent with the characteristic acidification trench described for filamentous fungi growing in glucose-rich liquid media (Papagianni, 2004; Liaud et al., 2014; Dörsam et al., 2017; Narisetty et al., 2023).

By contrast, the SWM exhibited a progressive alkalinization across the incubation period, reaching a near-neutral final pH. This divergence likely reflects a different metabolic regime driven by the distinct nutritional composition of SWM. Unlike PDB, the SWM was not dominated by a high load of readily fermentable glucose but instead contained more complex polymeric carbon sources together with nitrogen-bearing substrates such as urea, saliva-derived compounds, and amino acid mixtures. Under such conditions, the culture may have shifted toward nitrogen metabolism and ammonia-generating reactions (Gabelle et al., 2012). Urea is particularly relevant here, as Penicillium species are known to produce urease (Petruccioli et al., 1988), which hydrolyzes urea into carbon dioxide and ammonia. In aqueous media, the resulting ammonia can contribute to alkalinization and counteract acid accumulation, representing one plausible mechanism among others driving the observed pH increase (Strope et al., 2011; Sazanova et al., 2023). Thus, the steadily increasing pH in SWM likely reflects both a lower pressure for glucose-driven acidification and a contribution from nitrogen catabolism.

The pH dynamics reflect not only the metabolic activity of the fungus but also feed back into its physiology, influencing growth, morphology, and metabolite production (Peñalva and Arst, 2002; Li et al., 2022). In filamentous fungi, pH influences nutrient transport, enzyme activity, pellet formation and cell wall remodeling, and the regulation of genes linked to extracellular metabolism (Bodizs et al., 2007). A shift toward more neutral pH can alter the balance between primary growth and secondary metabolism and may also change which extracellular enzymes are favored (Cui et al., 1998). Thus, the divergence between SWM and PDB reflects distinct nutrient compositions that, in turn, create self-generated biochemical environments that shape subsequent growth. This makes pH a key design variable for future SWM refinement. Controlled pH adjustment or buffering may help disentangle whether the current alkalinization is beneficial, neutral, or restrictive for biomass productivity under space-relevant conditions.

Despite SWM yielding only ∼35% of PDB biomass, it produced similar absolute citric acid levels and the biomass-normalized citric acid yield was ∼3-fold higher in SWM. Although this difference was not statistically significant, the magnitude and direction of the effect are biologically meaningful. This result suggests a metabolic pivot in which the SWM redirected fungal carbon flux away from bulk biomass accumulation and toward extracellular metabolite formation more efficiently than the nutrient-rich control. This observation aligns with the existing literature on filamentous fungi, which frequently links the overproduction of organic acids to nutritional imbalances, environmental stress, and generally suboptimal growth conditions, rather than to conditions that are entirely favorable for growth. Consequently, when growth is constrained by factors other than carbon availability, carbon can be diverted away from biomass production and into the excretion of these acids (Karaffa and Kubicek, 2003; Papagianni, 2007).

This metabolic shift reinforces the interpretation that the reduced biomass observed after scale-up is not indicative of poor medium performance, but rather of a reallocation of carbon flux from primary growth toward secondary metabolite production, a behavior that could be advantageous for resource-efficient bioprocessing in closed-loop systems. In PDB, the large glucose load and associated acidification dynamics favored rapid accumulation of bulk biomass, whereas in SWM, the energetic cost of processing complex carbon fractions likely limited biomass accumulation while increasing specific citric acid yield. The lower biomass observed after scale-up is therefore better explained as evidence of a different metabolic allocation strategy than as a failure of the waste-based medium to support growth.

At the same time, this metabolic interpretation should remain cautious, as this study only quantified citric acid using a biochemical kit at day seven. Given the strong pH divergence between media, it is plausible that the organic acid profiles were more complex and temporally dynamic than captured here. PDB is known to support glucose-driven acidification that may include not only citric acid but also gluconic, oxalic, or other acids depending on species and conditions (Manzoor et al., 2019). Likewise, in SWM, the final pH increase does not exclude earlier acidification or shifts in the timing of acid synthesis and re-consumption. Future analyses using high-performance liquid chromatography (HPLC) across multiple time points would be valuable to resolve the full organic acid spectrum of SWM.

It is important to note that our automated oCelloScope™ metrics and macro-photographic observations serve as descriptive characterizations of the morphological divergence between cultivation environments. The decrease in biomass accumulation and the altered pellet shape is likely interconnected, representing a metabolic reallocation of carbon toward organic acid secretion under nutrient limitation. Additionally, the 40 mL loading volume within a 100 mL shake flask imposes constraints on the oxygen transfer rate (OTR), which likely impacts both total biomass accumulation and mycelial fragmentation. Because gas exchange and aeration present inherent engineering challenges in spaceflight bioprocessing, this restricted-oxygen condition serves as a realistic operational baseline. Future experimental designs should therefore isolate nutritional constraints from oxygen mass-transfer limitations by evaluating various fill-volume ratios and utilizing specialized cultivation hardware.

Implications for fungal biotechnology in BLSS

This study intentionally used synthetic or individually prepared components rather than authentic heterogeneous waste streams. By isolating inputs, the study could determine which classes of compounds are inherently supportive, inhibitory, or neutral for P. simplicissimum growth. However, actual habitat waste streams will be chemically more complex and more variable. For instance, human waste, crop residues, food scraps, and process waters contain insoluble matter, inhibitors, fluctuating nutrient ratios, and microbial contaminants (Clauwaert et al., 2017; Volpin et al., 2020; Rai et al., 2022). Therefore, the present SWM should be interpreted as a component-level proof of concept rather than a direct operational recipe. Next steps should test increasingly realistic waste-derived mixtures while preserving the experimental insight gained here.

From a BLSS/ISRU perspective, the optimized SWM represents more than a substitute for PDB. It demonstrates that human-derived waste fractions, plant residues, and lunar regolith simulants can be integrated into a functional fungal culture system with measurable productivity. The medium also appears to promote a self-generated chemical environment distinct from that of conventional glucose-rich media, with reduced acidification and enhanced product formation per unit biomass. SWM’s distinct pH behavior, altered pellet morphology, and comparatively higher specific citric acid yield indicate a different physiological response from the fungus that, although not maximized for biomass productivity, supports growth using substrates far more realistic for a BLSS context. In that sense, the present study represents an important first step toward designing fungal biotechnology culture systems compatible with long-term space habitation. In particular, P. simplicissimum’s ability to produce citric acid in SWM is appealing for space applications, as citric acid is extensively used in multiple industries as a non-toxic and adaptable compound, being used as an antioxidant, preservative, acidulant, pH-regulator, and flavor enhancer in food, beverages, pharmaceuticals, and cosmetics (Papagianni, 2007; Cairns et al., 2018).

The core focus of this study was based on submerged liquid culture since it provides the most scalable format for space-based biomanufacturing. However, future research should evaluate solid-state SWM variations to investigate distinct physiological growth modes. Expanding this framework to solid matrices will build fundamental knowledge on hyphal extension, nutrient uptake, and metabolic dynamics across waste-related cultivation substrates. Furthermore, testing sequential repeated-batch cycles, such as re-inoculating fresh SWM using spores or mycelial carryover from prior runs, will rigorously verify long-term genetic and metabolic stability under waste-driven constraints.

While demonstrating chemical viability on Earth is a necessary first step, spaceflight conditions introduce fundamental physical variables. In microgravity, the absence of buoyancy-driven convection and gravitational settling restricts mass transfer, significantly impacting fungal morphology and biofilm architecture (Cortesão et al., 2022). Because SWM already imposes a nutrient-restricted regime, a microgravity environment could amplify this physiological stress. This environmental shift has been observed to drive a metabolic pivot towards secondary metabolism, enhancing organic acid secretion in Aspergillus species (Jiang et al., 2022; Show et al., 2015) and increasing carboxylic acid production by P. simplicissimum aboard the International Space Station (Santomartino et al., 2026). Therefore, spaceflight conditions could drive an even stronger metabolic response in SWM, further enhancing specific citric acid yields. With the proven chemical feasibility and biotechnological utility of SWM on the ground, testing this formulation under simulated or actual microgravity is a crucial next step.

This study demonstrates the feasibility of formulating a functional fungal culture medium from space-relevant waste for the cultivation of P. simplicissimum. By integrating human-derived waste simulants, plant-based residue compounds, and a mineral input derived from lunar regolith flow-through, we established a Space Waste Medium (SWM) that supported both fungal biomass formation and citric acid production under resource-limited conditions. These findings address a central challenge in space biotechnology: how to sustain microbial processes without relying on conventional nutrient-rich media that would be impractical to supply during long-term missions (Clauwaert et al., 2017; Santomartino et al., 2023).

A major contribution of this work is the Design of Experiments (DOE) approach, which enabled efficient exploration of a complex multivariable nutritional landscape and reduced the experimental burden by 97.1% compared with traditional exhaustive strategies. This workflow not only identified the components that most strongly supported fungal growth, but also revealed critical interaction effects that would have been difficult to detect using conventional one-factor-at-a-time approaches (Box and Wilson, 1951; Box and Behnken, 1960; Montgomery, 2013; Myers et al., 2016; Galbraith et al., 2018). Importantly, this study does not only demonstrate feasibility, but also provides a defined SWM formulation for follow-up studies: cellulose (0.43 g/L), lunar regolith flow-through (0.11% v/v), synthetic saliva (26.39% v/v), urea (2.59 g/L), starch (0.05 g/L), casamino acids (0.53 g/L), and lignin (1.05 g/L).

Validation showed that optimized SWM supported reproducible fungal growth (1.68 ± 0.37 g/L dry biomass), equivalent to ∼35% of the biomass obtained in PDB, while exhibiting distinct physiology characterized by alkalinization and higher biomass-normalized citric acid yield. These observations suggest that carbon allocation in SWM was partly redirected away from maximal biomass accumulation and toward extracellular metabolite production under constrained substrate accessibility and distinct nitrogen/pH conditions (Karaffa and Kubicek, 2003; Papagianni, 2004; 2007). In this respect, SWM functioned not only as a growth substrate, but also as a formulation capable of promoting a metabolic state relevant for resource-efficient bioprocessing in closed-loop systems.

Taken together, these findings provide a proof of concept for incorporating filamentous fungal biotechnology into BLSS and ISRU-oriented workflows. Given the altered morphological trajectories observed in liquid culture, evaluating reproductive fitness, spore viability, and multi-generational conidial health on SWM represents a crucial next step for long-term operational feasibility. Furthermore, future work should evaluate increasingly realistic heterogeneous waste mixtures, resolve the full organic acid profile, elucidate metabolic mechanisms, and determine fungal performance under partial gravity conditions.

These next steps will be essential for advancing fungal cultivation from laboratory feasibility toward operational use in space biomanufacturing systems. Ultimately, by pairing fractional factorial screenings with response surface methodology, the DOE workflow demonstrated here provides a strategic advantage, as it can be easily transposed to optimize the cultivation of any other space-relevant organism or target product.