Integrated Bioprocessing of Phytoremediation-Derived Chlorella Biomass: Enzymatic Activity Profiles During Saccharification and Fermentation with Wickerhamomyces sp.

Abstract

Residual microalgal biomass generated during wastewater phytoremediation represents an underexploited resource for developing sustainable bioprocesses. This study investigated the biotechnological valorization of phytoremediation-derived Chlorella biomass through an integrated process combining α-amylase-assisted saccharification and fermentation with the non-conventional yeast Wickerhamomyces sp. UFFS-CE-3.1.2 in a stirred-tank bioreactor. Following physical pretreatment to enhance intracellular compound accessibility, fermentation was conducted for 72 h under anaerobic conditions, and temporal changes in enzymatic activities and fermentation-associated compounds were monitored by spectrophotometric assays and high-performance liquid chromatography (HPLC), respectively. The integrated process exhibited distinct temporal profiles of hydrolytic and antioxidant enzyme activities, with maximum activities of 1275.23 U/mL for catalase, 1014.59 U/mL for ascorbate peroxidase, 1291.67 U/mL for protease, and 228.75 U/mL for lipase. Amylase activity remained detectable throughout the 72 h process. Total sugars decreased from 8.88 g/L at 0 h to 0.03 g/L at 72 h. In comparison, glycerol peaked at 5.62 g/L at 18 h, and ethanol remained at approximately 1.0 g/L between 18 and 48 h. Because several enzymatic activities were already detected before yeast inoculation, the observed profiles cannot be attributed exclusively to Wickerhamomyces sp. and should instead be interpreted as characteristics of the integrated bioprocess. Overall, the results demonstrate that residual Chlorella biomass generated during wastewater phytoremediation can serve as a renewable feedstock for further biotechnological processing, supporting an extended valorization pathway within a circular bioprocessing framework.

1. Introduction

The increasing demand for food, energy, and industrial products driven by global population growth has intensified the exploitation of natural resources and increased the generation of agricultural and industrial residues, reinforcing the need for more sustainable production systems. In this context, biotechnological strategies aligned with the United Nations Sustainable Development Goal (SDG 12—Responsible Consumption and Production) have emerged as promising alternatives for converting renewable feedstocks and industrial residues into value-added products while minimizing environmental impacts [1].

Among the renewable biological feedstocks currently being explored, microalgae have attracted considerable attention due to their rapid growth, high photosynthetic efficiency, and remarkable biochemical diversity. These photosynthetic microorganisms include both prokaryotic organisms, such as cyanobacteria, and eukaryotic green algae, which predominantly inhabit freshwater and marine ecosystems [2]. Their ability to capture atmospheric CO₂ at rates substantially higher than terrestrial plants, combined with high biomass productivity, makes them attractive feedstocks for sustainable biotechnological applications [3]. Furthermore, microalgae can be cultivated under diverse environmental conditions, including wastewater streams, simultaneously promoting nutrient removal and biomass generation [4]. Consequently, microalgal biomass has been increasingly explored as a substrate for producing biofuels, enzymes, and other biotechnologically relevant products [5,6,7].

Among these microorganisms, Chlorella sp. is one of the most extensively studied species because of its high protein, carbohydrate, and lipid content, although its biochemical composition varies with cultivation conditions [8,9]. In the present study, residual Chlorella biomass generated during the phytoremediation of anaerobically digested swine wastewater, a residual stream from biogas production, was employed as the substrate for subsequent bioprocessing. Thus, unlike conventionally produced or commercially sourced microalgal biomass, the biomass evaluated here had already fulfilled a previous environmental function by contributing to nutrient recovery from a residual stream before being further valorized.

In addition to efficiently removing nitrogen, phosphorus, and organic matter from wastewater, microalgal phytoremediation generates considerable amounts of residual biomass that frequently remain underutilized despite their significant biotechnological potential [10]. Valorizing this residual biomass is an attractive strategy within the circular bioeconomy, transforming an environmental liability into a renewable feedstock for industrial bioprocesses. Within this context, integrated microalgal biorefineries have emerged as a promising approach to maximize biomass utilization by converting residual biomass into multiple value-added products rather than a single commodity.

Despite these advantages, the rigid, complex structure of the microalgal cell wall limits access to intracellular compounds, reducing substrate availability during microbial fermentation. Consequently, pretreatment strategies such as autoclaving and freeze–thaw cycles have been successfully employed to disrupt the cell wall and enhance the release of intracellular components [6,11]. Following cell disruption, intracellular carbohydrates become accessible for enzymatic hydrolysis, thereby increasing the availability of fermentable sugars for microbial metabolism. Among these compounds, starch accumulated within chloroplasts represents an important carbon reserve that requires enzymatic hydrolysis before microbial assimilation. In this context, enzymatic saccharification with α-amylase is an efficient and economically attractive strategy because of its high catalytic efficiency, commercial availability, and widespread industrial use, particularly in the brewing industry [8,12,13].

The non-conventional yeast Wickerhamomyces sp. has recently emerged as a promising microorganism for biotechnological applications owing to its metabolic versatility and adaptability under different fermentation conditions. The strain UFFS-CE-3.1.2, originally isolated from decaying wood, has previously demonstrated the ability to produce ethanol from lignocellulosic substrates [14]. More recently, its biotechnological potential was further demonstrated using commercially sourced Chlorella biomass as a fermentation substrate, with detection of multiple enzymatic activities and fermentative metabolites [15]. However, the present study addresses a fundamentally different feedstock and bioprocessing context: the Chlorella biomass evaluated here was generated during the phytoremediation of anaerobically digested swine wastewater derived from biogas production and, therefore, had already fulfilled an environmental function through nutrient recovery from a residual stream before its subsequent valorization. This distinction is particularly relevant because microalgal biomass composition is strongly influenced by cultivation conditions and nutrient availability, which may affect its accessibility and behavior during subsequent saccharification and fermentation. Accordingly, the scientific advance of the present work lies in evaluating whether this phytoremediation-derived residual biomass can be further incorporated into a sequential valorization pathway integrating enzymatic saccharification with α-amylase and fermentation by Wickerhamomyces sp. UFFS-CE-3.1.2 under controlled stirred-tank bioreactor conditions. Therefore, this study investigated the integrated bioprocessing of phytoremediation-derived Chlorella biomass, with emphasis on the temporal profiles of enzymatic activities and fermentative metabolites throughout the process. By linking anaerobic digestion, microalgal-based nutrient recovery, biomass saccharification, and subsequent fermentation, this approach explores a cascaded bioprocessing strategy to extend the value chain of residual streams within a circular bioeconomy.

2. Materials and Methods

2.1. Biomass

The microalgal biomass used in this study consisted of Chlorella sp. cultivated during the phytoremediation of anaerobically digested swine wastewater at the swine and poultry facilities of EMBRAPA (Concórdia, Santa Catarina, Brazil) [16]. The biomass composition comprised 56.1% protein, 34.7% carbohydrates, 1.7% lipids, and 7.8% minerals [17]. After harvesting, the biomass was stored at −80 °C until use. Before fermentation, the moisture content of the frozen biomass was determined using a moisture analyzer to standardize biomass concentration on a wet-weight basis [15].

2.2. Microorganism and Inoculum Preparation

Fermentations were carried out using Wickerhamomyces sp. UFFS-CE-3.1.2, previously isolated from decaying wood [14]. The strain was maintained on YPD agar slants containing 1% (w/v) yeast extract, 2% (w/v) peptone, 2% (w/v) glucose, and 2% (w/v) agar. For inoculum preparation, the yeast was transferred to liquid YPD medium and incubated at 30 °C and 120 rpm for 24 h in an orbital shaker [18]. The resulting inoculum was used to inoculate the fermentation medium at 10% (v/v), following the procedure described by Zanivan et al. [19].

2.3. Biomass Pretreatment and Fermentation in a Stirred-Tank Bioreactor

Before fermentation, the frozen microalgal biomass was thawed to facilitate the initial disruption of cellular structures [20]. A total of 200 g of biomass, corrected according to its moisture content, was suspended in 3 L of 0.2 M sodium phosphate buffer (pH 5.5), following the biomass proportion previously reported by Kubeneck et al. [21].

The fermentation medium was prepared directly in a stirred-tank bioreactor (BIO TEC FLEX, Tecnal, Piracicaba, Brazil) and sterilized by autoclaving at 121 °C and 1 atm for 15 min. In addition to ensuring sterilization, this thermal treatment partially disrupted the microalgal cell wall, enhancing access to intracellular compounds for subsequent enzymatic hydrolysis [8].

After sterilization, the medium pH was adjusted to 5.0 using 2 M NaOH. Commercial α-amylase (30 mL) was then added under the optimum conditions previously established by a Central Composite Rotatable Design (CCRD) [15], with proportional adjustment according to the biomass concentration used in the present study.

Following a 1 h saccharification period, the medium was inoculated with Wickerhamomyces sp. UFFS-CE-3.1.2 at 10% (v/v), using the 24 h inoculum described in Section 2.2, standardized to a cell concentration of 10⁶ cells/mL. Fermentation was conducted for 72 h at a working volume of 3 L, 30 °C, and 80 rpm under anaerobic conditions, without external aeration. Two Rushton turbine impellers mounted on the same shaft provided mixing. The initial pH was adjusted to 5.0 before inoculation and was not subsequently controlled during fermentation. Dissolved oxygen was monitored using a sensor coupled to the bioreactor, and measurements showed that anaerobic conditions were reached within approximately 30 min after the start of fermentation and maintained thereafter.

Samples were collected immediately after α-amylase addition, immediately before yeast inoculation (“yeast inoculation” sampling point), immediately after inoculation (“0 h”, corresponding to the beginning of fermentation), and after 18, 24, 48, and 72 h of fermentation for subsequent enzymatic and chromatographic analyses.

2.4. Enzymatic Activity Analysis

At each sampling time, the collected fermentation samples were manually filtered by pressing through a synthetic fabric to separate the liquid fraction from the residual solids. The retained solid fraction was sterilized and discarded. In contrast, the liquid permeate was centrifuged (NT 815, Nova Técnica, São Paulo, Brazil) at 2000 rpm and 4 °C for 30 min. We collected the resulting supernatant and used it as the enzymatic extract for subsequent enzymatic activity assays. No additional cell-disruption step was performed during extract preparation. The enzymatic extract was analyzed immediately after preparation, without storage. We used it directly in the enzymatic assays whenever possible; when required to keep the analytical response within the appropriate range for the respective assay, we diluted the extract according to the specific enzymatic activity methodology. We accounted for the corresponding dilution factor in the final activity calculation.

We determined hydrolytic and oxidative enzyme activities using previously established methodologies specific to each enzyme. In all assays, we included appropriate reaction blanks to correct for background absorbance and potential interference from reaction components. Depending on the analytical protocol, blanks consisted of reaction mixtures prepared with the appropriate buffer and substrate, omitting the enzymatic extract or adding the stopping reagent before incubation.

We calculated enzymatic activities according to the equations and unit definitions established in the respective referenced methods, considering reaction time, extract volume, and, when applicable, the corresponding dilution factor. One enzyme unit (U) is explicitly defined below for each assay. Depending on the assay’s analytical principle, activity calculations were based on changes in absorbance using the corresponding relationships and extinction coefficients, product quantification using an appropriate calibration curve, or percentage inhibition, as specified in the respective referenced methods.

2.4.1. Laccase

Laccase activity was determined using ABTS as the chromogenic substrate according to Hou et al. [22]. The reaction mixture consisted of 0.4 mL of 10 mM ABTS, 0.2 mL of enzyme extract, and 3.4 mL of 50 mM acetate buffer (pH 5.0). The reaction was incubated at 40 °C for 5 min, and absorbance was measured at 420 nm. Laccase activity was calculated from ABTS oxidation, following Hou et al. [22]. One unit (U) of laccase activity was defined as the amount of enzyme required to oxidize 1 μmol of ABTS per minute under the assay conditions. Activity was expressed as U/mL of enzymatic extract.

2.4.2. Lipase

Lipase activity was determined according to Treichel et al. [23]. The reaction mixture consisted of 9 mL of an emulsion containing 5% gum arabic, 10% olive oil, and 100 mM phosphate buffer (pH 6.0), and 1 mL of enzyme extract. Samples were incubated at 35 °C and 165 rpm for 32 min, then stopped by adding 10 mL of an acetone–ethanol solution (1:1, v/v). Blank assays were prepared by adding the stopping solution before incubation, as described by Kempka et al. [24]. Free fatty acids released during the reaction were quantified by titration with 0.049 M NaOH until pH 11, and lipase activity was calculated based on the amount of NaOH required to neutralize the released fatty acids. One unit (U) of lipase activity was defined as the amount of enzyme required to release 1 μmol of free fatty acids per minute under the assay conditions. Lipase activity was expressed as U/mL of enzymatic extract.

2.4.3. Protease

Protease activity was determined as described by Waghmare et al. [25]. The reaction mixture consisted of 1 mL casein solution, 1 mL enzyme extract, and 0.5 mL 50 mM glycine–NaOH buffer (pH 9.0). After incubation at 40 °C for 30 min, we terminated the reaction by adding 0.5 mL of trichloroacetic acid (TCA). Subsequently, 0.5 mL aliquots were mixed with 2.5 mL of 50 mM sodium carbonate buffer (pH 8.23) and 0.5 mL of 0.1 M Folin reagent. We prepared blank assays according to Cupp-Enyard [26]. Following incubation at room temperature for 25 min, absorbance was measured at 660 nm. One unit (U) of protease activity was defined as the amount of enzyme required to release 1 μmol of tyrosine equivalents per minute under the assay conditions. Protease activity was expressed as U/mL of enzymatic extract.

2.4.4. Peroxidase

Peroxidase activity was determined according to Khan and Robinson [27] and Devaiah and Shetty [28]. The reaction mixture consisted of 1.5 mL of 5 mM phosphate buffer (pH 5.0), 2 mL of distilled water, 0.5 mL of 1% guaiacol, and 1 mL of 8% hydrogen peroxide. After stabilizing at 35 °C for 15 min, add 1 mL of enzyme extract and immediately measure absorbance at 470 nm. One unit (U) of peroxidase activity was defined as the amount of enzyme producing a change of 0.001 in absorbance per minute at 470 nm under the assay conditions. Peroxidase activity was expressed as U/mL of enzymatic extract.

2.4.5. Catalase

Catalase activity was determined according to Havir and McHale [29] and Hasan et al. [30]. The reaction mixture consisted of 1.5 mL of 0.05 M potassium phosphate buffer (pH 6.8), 0.9 mL of distilled water, and 0.5 mL of 0.0125 M hydrogen peroxide. After incubation at 25 °C for 2 min, add 0.1 mL of enzyme extract and record absorbance at 240 nm for 3 min at 30 s intervals, following the method described by Aebi [31]. One unit (U) of catalase activity was defined as the amount of enzyme required to decompose 1 μmol of H₂O₂ per minute under the assay conditions. Catalase activity was expressed as U/mL of enzymatic extract.

2.4.6. Ascorbate Peroxidase

Ascorbate peroxidase (APX) activity was determined according to Nakano and Asada [32]. The reaction mixture consisted of 1.5 mL of 0.05 M potassium phosphate buffer (pH 6.0), 0.86 mL of distilled water, 0.24 mL of 0.008 M ascorbic acid, and 0.3 mL of 0.001 M hydrogen peroxide. After incubating at 25 °C for 2 min, add 0.1 mL of enzyme extract and record absorbance at 290 nm for 1 min at 15 s intervals. One unit (U) of APX activity was defined as the amount of enzyme required to oxidize 1 μmol of ascorbate per minute under the assay conditions. APX activity was expressed as U/mL of enzymatic extract.

2.4.7. Superoxide Dismutase

Superoxide dismutase (SOD) activity was determined as described by Hasan et al. [30]. The reaction mixture consisted of 1.5 mL of 50 mM sodium phosphate buffer (pH 7.8), 0.78 mL of 0.013 M methionine, 0.225 mL of 75 μM nitroblue tetrazolium (NBT), 0.345 mL of distilled water, and 30 μL of enzyme extract. Samples were exposed to a 15 W fluorescent lamp for 5 min. In contrast, blank samples were maintained in the dark, following Gupta et al. [33]. Absorbance was recorded at 560 nm for 1 min with readings taken at 15 s intervals, according to Ukeda et al. [34]. One unit (U) of SOD activity was defined as the amount of enzyme required to cause 50% inhibition of NBT photoreduction under the assay conditions. SOD activity was expressed as U/mL of enzymatic extract.

2.4.8. Amylase

Amylase activity was determined according to Fuwa [35] and Pongsawasdi and Yagisawa [36]. Starch diluted in 100 mM acetate buffer (pH 5.0) at a 1:100 (m/v) ratio was used as the substrate. The reaction mixture consisted of 1 mL of substrate solution and 1 mL of enzyme extract and was incubated at 38 °C for 10 min. Enzymatic activity was determined by quantifying reducing sugars using the 3,5-dinitrosalicylic acid (DNS) method, with absorbance measured at 540 nm according to Miller [37] and Kubeneck et al. [21]. One unit (U) of amylase activity was defined as the amount of enzyme required to release 1 μmol of reducing sugars, expressed as glucose equivalents, per minute under the assay conditions. Amylase activity was expressed as U/mL of enzymatic extract.

2.4.9. Cellulase

Cellulase activity was determined using a method adapted from Ghose [38]. The reaction mixture consisted of 50 mg of Whatman No. 1 filter paper, 2 mL of 0.2 M acetate buffer (pH 5.5), and 1 mL of enzyme extract. Samples were incubated at 50 °C for 1 h, and cellulase activity was determined by quantifying reducing sugars using the DNS method, with absorbance measured at 540 nm. One unit (U) of cellulase activity was defined as the amount of enzyme required to release 1 μmol of reducing sugars (expressed as glucose equivalents) per minute under the assay conditions. Cellulase activity was expressed as U/mL of enzymatic extract.

2.5. High-Performance Liquid Chromatography (HPLC)

The concentrations of cellobiose, glucose, arabinose, xylitol, fructose, glycerol, ethanol, citric acid, formic acid, and acetic acid were determined by high-performance liquid chromatography (HPLC). Prior to analysis, samples were diluted in 0.005 M sulfuric acid, vacuum-filtered through a 0.45 μm Millipore^® membrane (Merck Millipore, Burlington, MA, USA), and degassed in an ultrasonic bath for 15 min [39]. Total sugars were calculated as the sum of the concentrations of the individual sugars quantified by HPLC, with glucose representing the predominant sugar throughout the process.

Chromatographic analyses were performed using a Shimadzu HPLC system equipped with a refractive index detector (RID-10A) and an Aminex HPX-87H ion-exclusion column (Bio-Rad Laboratories, Hercules, CA, USA). The mobile phase consisted of 0.005 M H₂SO₄ delivered at 0.6 mL min⁻¹, and the column temperature was maintained at 45 °C throughout the analyses [14,15].

Compounds were identified by comparison of their retention times with those of the corresponding analytical standards and quantified using external calibration curves, following the analytical procedures described by Bazoti et al. [14] and Longo et al. [15]. We incorporated sample dilution factors into the final concentration calculations.

2.6. Statistical Analysis

Three independent fermentation batches were performed under identical operating conditions, representing three biological replicates (n = 3). Each independent batch was sampled at all predefined process stages and fermentation times; thus, the temporal measurements represent observations obtained sequentially from the same three independent fermentation batches. For each batch and sampling point, enzymatic activity assays were performed in analytical triplicate, and these analytical triplicates were averaged to obtain a single value for each independent batch at each sampling point. Statistical comparisons among sampling points were therefore based on the three independent fermentation batches (n = 3), and analytical replicates were not treated as independent observations, thereby avoiding pseudoreplication. Enzymatic activity results are expressed as mean ± standard deviation of the three independent fermentation batches. Chromatographic measurements were performed as single determinations at each sampling time and are therefore presented descriptively, without inferential statistical analysis.

3. Results and Discussion

The integrated bioprocessing of phytoremediation-derived Chlorella biomass through α-amylase-assisted saccharification followed by fermentation with Wickerhamomyces sp. UFFS-CE-3.1.2 resulted in a dynamic profile of hydrolytic and antioxidant enzyme activities throughout the process (

Table 1. Time-course of enzymatic activities during the integrated processing of phytoremediation-derived Chlorella biomass, including α-amylase-assisted saccharification and subsequent fermentation with Wickerhamomyces sp. UFFS-CE-3.1.2.

Enzyme	Enzyme Addition	Yeast Inoculation	0 h	18 h	24 h	48 h	72 h
Amylase	3.89 c ± 0.29	28.22 a ± 4.16	22.31 b ± 1.98	27.44 a ± 1.03	24.92 b ± 1.54	26.66 a ± 2.96	28.22 a ± 3.99
Ascorbate peroxidase	227.57 b ± 3.47	20.44 e ± 4.74	63.66 c ± 5.19	57.56 c ± 7.26	43.96 d ± 6.09	52.56 c ± 5.20	1014.59 a ± 80.84
Catalase	N.D.	N.D.	N.D.	N.D.	584.86 b ± 3.98	1275.23 a ± 5.34	153.67 c ± 3.87
Peroxidase	38.33 b ± 2.89	N.D.	1.67 e ± 0.01	38.89 b ± 5.36	18.33 c ± 0.01	85.00 a ± 9.28	6.11 d ± 2.55
Lipase	N.D.	225.50 a ± 4.85	228.75 a ± 4.40	48.75 b ± 6.22	N.D.	N.D.	N.D.
Protease	1291.67 a ± 20.43	1033.33 b ± 86.96	890.00 c ± 61.94	1007.78 b ± 16.58	890.56 c ± 50.24	627.78 d ± 96.05	571.11 d ± 85.09
Superoxide dismutase	N.D.	80.29 c ± 2.31	20.34 d ± 2.17	N.D.	77.58 c ± 0.56	92.53 b ± 0.68	100.00 a ± 0.01
Laccase	0.03 b ± 0.01	N.D.	0.01 b ± 0.01	0.05 b ± 0.01	0.02 b ± 0.01	0.09 a ± 0.01	0.002 c ± 0.01
Cellulase	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.	N.D.

Values are expressed as mean ± standard deviation (n = 3). Different lowercase letters within the same row indicate significant differences among sampling times according to Tukey’s test (p < 0.05). N.D.: not detected.

). The detection of multiple enzymatic activities is consistent with the complex biochemical composition of the residual microalgal biomass, particularly its high protein and carbohydrate contents, which provide potentially available substrates and nutrients during bioconversion [10,21]. In addition, we incorporated enzymatic saccharification with commercial α-amylase to increase carbohydrate accessibility before yeast fermentation. Together, these characteristics established a nutrient-rich and metabolically dynamic system in which distinct enzymatic activity profiles were observed over the 72 h fermentation period.

An important consideration when interpreting these enzymatic profiles is that the measured activities cannot be unequivocally attributed to a single biological source. The system comprised phytoremediation-derived Chlorella biomass, commercially added α-amylase, and, after inoculation, Wickerhamomyces sp. UFFS-CE-3.1.2. Consequently, the enzymatic activities detected throughout the process may reflect contributions from different sources. Activities detected before yeast inoculation indicate contributions independent of Wickerhamomyces metabolism, potentially including enzymes associated with the microalgal biomass and/or preceding biomass processing steps. In contrast, changes observed after inoculation may additionally involve yeast metabolism. For amylolytic activity specifically, the contribution of the commercially added α-amylase must also be considered. Therefore, temporal increases or decreases in enzymatic activity are interpreted here as changes occurring within the integrated bioprocess rather than as direct evidence of enzyme production by Wickerhamomyces sp. Because we did not determine the enzyme-specific origin, gene expression, intracellular versus extracellular localization, or direct indicators of oxidative stress, we cannot resolve the relative contribution of each source or the mechanisms underlying the temporal changes in enzymatic activity from the present data. Accordingly, the mechanistic explanations discussed below should be regarded as biologically plausible interpretations supported by the previous literature rather than as mechanisms directly demonstrated in the present study.

Among the enzymatic activities detected, catalase reached the highest value during fermentation (1275.23 U/mL at 48 h), followed by ascorbate peroxidase (1014.59 U/mL at 72 h), protease (1007.78 U/mL at 18 h), and lipase (228.75 U/mL at 0 h) (Table 1). These enzymes are relevant to several industrial sectors, including food, pharmaceutical, cosmetic, textile, and environmental applications [40]. The simultaneous occurrence of hydrolytic and antioxidant enzyme activities within the same bioprocess, regardless of their individual biological origin, highlights the system’s biochemical complexity and supports the potential of phytoremediation-derived microalgal biomass for further biotechnological valorization.

The temporal profiles showed markedly different patterns among the enzymes evaluated, indicating that enzymatic activity varied with the stage of the bioprocess. Hydrolytic enzymes such as protease and lipase were predominantly detected during the initial stages. In contrast, antioxidant enzymes exhibited distinct time-dependent profiles, with catalase reaching its maximum activity at 48 h and ascorbate peroxidase and superoxide dismutase reaching their highest activities at 72 h. Rather than indicating a uniform enzymatic pattern, these contrasting profiles are consistent with a dynamic biochemical environment in which substrate availability, enzymatic saccharification, components associated with the microalgal biomass, and yeast metabolism after inoculation may all contribute to the observed activities. However, the present experimental design cannot determine the relative contribution of these factors. The temporal behavior of each enzyme is therefore discussed below in relation to the different stages of the bioprocess and to biologically plausible mechanisms reported in the literature, without implying that these mechanisms were directly demonstrated in the present study.

Following inoculation with Wickerhamomyces sp. UFFS-CE-3.1.2, marked changes in enzymatic activities were observed throughout fermentation (Table 1). These temporal variations occurred within a biochemically complex system comprising phytoremediation-derived Chlorella biomass, whose high protein and carbohydrate contents provide potentially available nutrients for microbial metabolism [10,21], and α-amylase-mediated saccharification, which was employed to increase carbohydrate accessibility before fermentation. The resulting enzymatic profiles therefore reflect the biochemical dynamics of the integrated system rather than the activity of a single component. The present data cannot resolve the relative contributions of microalgal biomass, process-associated enzymes, and yeast metabolism.

Among the antioxidant enzymes, superoxide dismutase (SOD) displayed a marked time-dependent profile. Activity was detected at yeast inoculation (80.29 U/mL), decreased to 20.34 U/mL at 0 h, was not detected at 18 h, and then increased from 77.58 U/mL at 24 h to 92.53 U/mL at 48 h, reaching a maximum of 100.00 U/mL at 72 h. SOD plays an important role in antioxidant defense by catalyzing the dismutation of superoxide radicals into hydrogen peroxide (H₂O₂) [41]. Accordingly, the increase in SOD activity observed during the later stages of the process may reflect changes in oxidative conditions within the system. However, because reactive oxygen species and other oxidative stress markers were not directly measured, this interpretation remains a biologically plausible explanation based on the established function of SOD rather than direct evidence of an oxidative stress response in the present system.

Catalase showed a distinct temporal pattern. No detectable activity occurred through 18 h, whereas activity increased sharply to 584.86 U/mL at 24 h and reached a maximum of 1275.23 U/mL at 48 h, followed by a significant decrease to 153.67 U/mL at 72 h. Catalase catalyzes the decomposition of H₂O₂ into water and oxygen without requiring an external reducing substrate, providing an efficient mechanism for peroxide removal [29]. The pronounced increase between 24 and 48 h, along with the concurrent increase in SOD activity, may reflect changes in the system’s oxidative conditions during the intermediate-to-late stages of fermentation. Similar associations between changes in cellular metabolism and oxidative responses have been described in biological systems exposed to metabolic and environmental stress [42]. However, because H₂O₂, reactive oxygen species, and other oxidative stress markers were not directly measured, the observed catalase profile cannot be taken as direct evidence of an oxidative stress response.

Peroxidase showed a different temporal profile. After relatively low or moderate activities during the initial stages, its activity reached a maximum of 85.00 U/mL at 48 h, coinciding with the catalase maximum, before decreasing to 6.11 U/mL at 72 h. Unlike catalase, peroxidases use H₂O₂ as an oxidizing agent and require electron-donating substrates, contributing to peroxide removal and redox regulation through the oxidation of organic compounds [43]. The coincident maxima of catalase and peroxidase at 48 h are therefore consistent with the simultaneous occurrence of two enzymatic activities associated with H₂O₂ metabolism. Nevertheless, without direct measurements of H₂O₂ or other oxidative stress indicators, these activity profiles do not establish the extent or biological origin of peroxide-scavenging processes in the system.

Ascorbate peroxidase (APX) exhibited a markedly different profile. Activity was already detected before yeast inoculation (227.57 U/mL), demonstrating that its occurrence cannot be attributed exclusively to yeast metabolism. Following inoculation, APX activity remained comparatively low through 48 h, ranging from 20.44 to 63.66 U/mL, before increasing sharply to 1014.59 U/mL at 72 h. APX catalyzes H₂O₂ reduction using ascorbate as an electron donor and constitutes an important component of antioxidant metabolism [32]. Given its established biochemical function, the pronounced late-stage increase in APX activity may reflect changes in the system’s oxidative conditions at 72 h; however, this mechanism was not directly evaluated in the present study. Moreover, because APX activity was already present before yeast inoculation and the residual Chlorella biomass may contribute antioxidant enzymatic activity, the observed temporal profile should be interpreted as a property of the integrated bioprocess rather than as a response attributable exclusively to Wickerhamomyces sp.

Hydrolytic enzymes also displayed distinct temporal profiles. Lipase was not detected before enzyme addition but showed high activity at yeast inoculation (225.50 U/mL) and at 0 h (228.75 U/mL), with no significant difference between these values. Activity subsequently decreased to 48.75 U/mL at 18 h and was not detected from 24 to 72 h. Lipases catalyze the hydrolysis of triacylglycerols into free fatty acids and glycerol. Carbon and nitrogen availability, pH, temperature, and oxygen transfer strongly affect their activity [44,45]. The predominance of lipase activity during the initial stages therefore indicates that this enzymatic activity was transient under the conditions of the present bioprocess. Although changes in substrate availability and physicochemical conditions may have contributed to its subsequent decline, the specific factors responsible for this temporal profile were not directly investigated.

Protease activity remained high throughout the bioprocess, although it declined significantly over time. The highest activity (1291.67 U/mL) was detected before yeast inoculation, followed by 1033.33 U/mL at inoculation and 890.00 U/mL at 0 h. A transient increase to 1007.78 U/mL occurred at 18 h, after which activity decreased to 890.56, 627.78, and 571.11 U/mL at 24, 48, and 72 h, respectively (Table 1). The substantial proteolytic activity detected before inoculation demonstrates that this activity cannot be attributed exclusively to Wickerhamomyces sp. metabolism. Considering the high protein content of the residual Chlorella biomass, endogenous microalgal enzymes and/or proteolytic activity associated with biomass processing may have contributed to the initial activity. During fermentation, changes in protease activity may additionally have been influenced by variations in substrate availability and physicochemical conditions within the medium. Proteolytic activity is influenced by nutrient composition and microbial physiological state [46], while metabolites generated during fermentation may affect enzyme stability and catalytic performance [47,48]. However, the present study did not evaluate these factors independently. They should therefore be regarded as possible explanations for the observed temporal profile rather than demonstrated mechanisms.

We incorporated commercial α-amylase into the bioprocess to promote hydrolysis of starch reserves and increase carbohydrate accessibility before and during fermentation. Amylase activity increased significantly from 3.89 U/mL before enzyme addition to 28.22 U/mL following α-amylase addition. After yeast inoculation, activity varied within a relatively narrow range (22.31 to 28.22 U/mL), although significant differences were observed among sampling times (Table 1). Importantly, measurable amylase activity persisted throughout the 72 h fermentation period, demonstrating that amylolytic activity was retained under the process conditions. Given that commercial α-amylase was deliberately added to the system, this persistent activity is expected to include a substantial contribution from the exogenous enzyme. It should not be attributed to Wickerhamomyces sp. Persistent amylolytic activity may have supported continued starch hydrolysis and carbohydrate availability during fermentation, consistent with the use of exogenous amylases to improve starch accessibility in fermentative systems [49]. However, the specific contribution of persistent α-amylase activity to carbohydrate release over time was not independently quantified.

Despite persistent amylase activity throughout fermentation, the concentration of total reducing sugars remained relatively low. This pattern may reflect the combined influence of carbohydrate release and utilization within the fermentation system. Carbohydrates made available during saccharification may have been assimilated by Wickerhamomyces sp. for cellular metabolism or contributed to metabolite formation, consistent with the detection of fermentation products in the chromatographic analyses. In addition, thermal processing of carbohydrate-rich biomass can promote sugar degradation reactions and the formation of furan derivatives such as 5-hydroxymethylfurfural (5-HMF), particularly under acidic conditions [50,51]. However, because the present study did not quantify 5-HMF, its possible formation during thermal pretreatment remains hypothetical. It cannot be established as a factor contributing to the observed sugar concentrations. Overall, the limited accumulation of reducing sugars may reflect the combined influence of enzymatic release, microbial utilization, metabolite formation, and other unmeasured transformations; however, the present data cannot resolve the relative contribution of these processes.

HPLC analyses revealed marked temporal changes in sugar concentrations and fermentative metabolites throughout the process. Total sugar concentration, calculated as the sum of the individual sugars quantified by HPLC and predominantly represented by glucose, progressively decreased from 8.88 g/L at 0 h to 7.32 g/L at 18 h, 4.33 g/L at 24 h, 0.92 g/L at 48 h, and 0.03 g/L at 72 h. Glycerol increased from 2.34 g/L at 0 h to a maximum of 5.62 g/L at 18 h, followed by a decrease to 3.44 g/L at 24 h, 2.75 g/L at 48 h, and 0.02 g/L at 72 h. Ethanol increased from 0.012 g/L at 0 h to 0.97 g/L at 18 h. It remained at similar concentrations at 24 and 48 h (0.99 and 1.02 g/L, respectively), before decreasing to 0.001 g/L at 72 h. Acetic acid also progressively decreased from 1.0827 g/L at 0 h to 0.6737, 0.2692, 0.0026, and 0.00001 g/L at 18, 24, 48, and 72 h, respectively. In contrast, citric acid was not detected at any of the sampling times.

These temporal profiles indicate substantial changes in the fermentation medium’s chemical composition. The progressive decrease in total sugars, together with the transient accumulation of glycerol and ethanol, is consistent with fermentative metabolism; however, the available data do not allow direct quantitative relationships between sugar consumption and metabolite formation. Glycerol formation in yeast fermentation is commonly associated with mechanisms involved in intracellular redox balance and osmotic homeostasis. In contrast, ethanol formation is consistent with fermentative utilization of available carbohydrates [49]. These established metabolic roles may explain the observed profiles but were not specifically investigated in the present system. Moreover, because the chromatographic measurements were performed as single determinations and a complete carbon mass balance was not conducted, these data should be interpreted descriptively. Accordingly, the conversion of individual carbon sources into specific metabolites, as well as the overall carbon distribution throughout the process, cannot be determined from the present dataset.

In contrast, laccase remained at very low levels throughout the process, with a maximum activity of only 0.09 U/mL at 48 h. In contrast, cellulase activity was not detected at any sampling time (Table 1). The limited laccase activity may relate to the composition of the fermentation medium, since fungal laccase activity and expression can be influenced by aromatic and phenolic compounds [52]. However, the present study did not evaluate potential inducing compounds and laccase expression; therefore, this interpretation remains tentative. Similarly, the absence of detectable cellulase activity may relate to the carbohydrate composition reported for Chlorella biomass, whose structural polysaccharides can contain substantial proportions of non-cellulosic sugars. At the same time, starch represents an important intracellular carbon reserve [53]. Nevertheless, substrate-specific induction and cellulase expression were not directly assessed; therefore, the absence of detectable activity cannot be conclusively attributed to insufficient substrate induction.

Comparison with previous microalgae-based bioprocesses further contextualizes the enzymatic activities observed in the present study. In our previous work using commercially sourced Chlorella biomass and the same Wickerhamomyces sp. UFFS-CE-3.1.2 strain, maximum activities of approximately 560 U/mL for protease, 3381 U/mL for catalase, and 277 U/mL for peroxidase were reported [15]. In the present system based on phytoremediation-derived Chlorella biomass, protease activity reached 1291.67 U/mL before yeast inoculation and 1007.78 U/mL during fermentation. In contrast, maximum catalase and peroxidase activities reached 1275.23 and 85.00 U/mL, respectively. The present system therefore showed higher measured proteolytic activities. In contrast, maximum catalase and peroxidase activities were lower than those reported for the commercial-biomass system. Importantly, these differences should not be interpreted solely as effects of the microalgal biomass source, because differences in process conditions and the unresolved biological origin of the detected activities may also contribute to the observed profiles.

Other microalgae-based fermentation systems also show that enzymatic performance depends strongly on the substrate, fermenting microorganism, and process configuration. For example, Camargo et al. [54] evaluated Trichoderma koningiopsis fermentation in an airlift bioreactor using microalgal biomass derived from the phytoremediation of swine wastewater. They reported a multienzymatic profile including amylase, cellulase, laccase, lipase, and peroxidase activities. Although this system provides a relevant comparison with the present study, direct quantitative comparison is constrained by differences in the fermenting microorganism, reactor configuration, process conditions, enzyme assays, and activity definitions. Collectively, these comparisons indicate that the principal advantage of the present approach should not be interpreted solely as maximizing individual enzyme activities. Rather, its relevance lies in demonstrating that phytoremediation-derived Chlorella biomass, after fulfilling a previous function in nutrient recovery from a residual stream, can sustain multiple enzymatic activities and fermentative metabolism during subsequent bioprocessing. This extends the biomass valorization pathway and distinguishes the proposed system from processes based on conventionally produced or commercially sourced microalgal feedstocks.

From a process perspective, the present study should be regarded as a laboratory-scale evaluation conducted under a single set of operating conditions rather than as an optimized production process. The maximum activities detected for catalase (1275.23 U/mL), ascorbate peroxidase (1014.59 U/mL), protease (1007.78 U/mL during fermentation), and lipase (228.75 U/mL), together with glycerol and ethanol formation, provide a quantitative indication of the biotechnological outputs obtained from phytoremediation-derived microalgal biomass under the conditions evaluated. However, the present dataset cannot rigorously establish process productivity and enzyme yields normalized to microalgal biomass, particularly because the biological origin of the detected enzymatic activities cannot be resolved. The process was not designed as an optimized enzyme-production system. Accordingly, the reported volumetric activities should not be interpreted as optimized enzyme yields.

Scaling this approach will require optimizing and validating several process parameters, including biomass loading, enzyme dosage, inoculum concentration, pH, agitation and aeration conditions, fermentation time, and the relationship between saccharification and microbial metabolism. At larger scales, mixing and mass transfer may become particularly relevant because residual microalgal biomass can influence medium heterogeneity and rheological behavior. In addition, practical feasibility will depend on the stability and reproducibility of the observed enzymatic profiles, downstream recovery and concentration of target products, the contribution of commercial α-amylase to process costs, and variability in the composition of phytoremediation-derived biomass. These aspects, along with process optimization, scale-up validation, and techno-economic assessment, must be systematically addressed before the industrial applicability of the proposed approach can be established.

Overall, the markedly different temporal profiles of hydrolytic and antioxidant enzyme activities, together with glycerol and ethanol formation, highlight the biochemical complexity of integrated processing of phytoremediation-derived Chlorella biomass. Rather than being attributable to a single biological component, the observed profiles may reflect the combined influence of the biochemical composition of the residual microalgal biomass, α-amylase-assisted saccharification, process-associated enzymatic activities, and subsequent fermentation with Wickerhamomyces sp. UFFS-CE-3.1.2, although the relative contribution of these factors cannot be resolved from the present data. The occurrence of multiple hydrolytic and antioxidant enzymatic activities, together with the detection of fermentative metabolites, highlights the biotechnological potential of further processing this residual microalgal biomass. These findings support further valorization of phytoremediation-derived biomass as a renewable feedstock within a circular bioprocessing framework, while providing a basis for future studies to elucidate the origin of the detected enzymatic activities and optimize process performance.

4. Conclusions

This study demonstrated the potential of integrating phytoremediation-derived Chlorella biomass, α-amylase-assisted saccharification, and fermentation with Wickerhamomyces sp. UFFS-CE-3.1.2 for the further biotechnological valorization of residual microalgal biomass generated during the treatment of anaerobically digested swine wastewater. The integrated process showed distinct temporal profiles of hydrolytic and antioxidant enzyme activities, with particularly high catalase, ascorbate peroxidase, and protease activities. In contrast, amylase activity persisted throughout the 72 h fermentation period. Temporal changes in total sugars, glycerol, ethanol, and acetic acid further indicated substantial biochemical changes within the fermentation system.

The contrasting enzymatic profiles observed throughout the process highlight the biochemical complexity of the interactions among phytoremediation-derived Chlorella biomass, enzymatic saccharification, and yeast fermentation. Importantly, because several enzymatic activities were already detected before yeast inoculation, their biological origin cannot be attributed exclusively to Wickerhamomyces sp. Rather, the observed profiles may reflect contributions from the residual microalgal biomass, the commercial α-amylase treatment, process-associated enzymatic activities, and yeast metabolism after inoculation. Accordingly, the present findings should be interpreted as evidence of dynamic enzymatic activities within the integrated system rather than as direct evidence of enzyme production by the yeast.

Overall, the proposed approach extends the valorization pathway of phytoremediation-derived microalgal biomass by integrating biomass saccharification and subsequent fermentation within a circular bioprocessing framework. The results show that biomass previously generated during wastewater treatment can serve as a substrate for further biotechnological processing, without assuming the specific biological origin, production yield, or recoverability of the detected enzymatic activities. Further studies incorporating source-specific controls and complementary molecular analyses are needed to elucidate the origin of these activities. Process optimization, replicated chromatographic analyses, comprehensive carbon mass balance, determination of product yields and productivities, downstream processing studies, scale-up validation, and techno-economic assessment will also be necessary before the production performance and industrial applicability of the proposed approach can be established.