{"paper_id":"42799e4b-02cc-4366-8a85-32d009e9adb8","body_text":"Performance Evaluation of Lab-Scale Fungal Batch Reactor and Genomic Insights of a Newly Isolated Aspergillus niger AZ2 for Black Liquor Treatment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance Evaluation of Lab-Scale Fungal Batch Reactor and Genomic Insights of a Newly Isolated Aspergillus niger AZ2 for Black Liquor Treatment Alam Zeb Khan, Salah Ud Din, Zhi Zhou, Muhammad Yasir Akbar, Sanam Islam Khan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9055675/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The pulp and paper industry generates large volumes of black liquor, a lignin-rich effluent requiring effective treatment before disposal. In this study, a lab-scale fungal batch reactor (FBR) was designed and operated using Aspergillus niger AZ2 as a lignin-degrading biocatalyst. The reactor achieved 54% lignin reduction, 66% reduction in chemical oxygen demand (COD), 57% phenol removal, and 62% color reduction, demonstrating strong treatment efficiency. Phytotoxicity tests showed a significant improvement in seed germination. These results highlight the potential of the fungal-based reactor as a simple, cost-effective, and eco-friendly strategy for black liquor treatment under alkaline conditions. The genome-based functional analysis of A. niger revealed a diverse repertoire of lignin-degrading enzymes, including laccase, peroxidases, oxalate peroxidases, and pyranose oxidases. Pathway mapping further highlighted genes associated with the breakdown of aromatic compounds in auxiliary metabolic processes supporting efficient lignin mineralization. CAZymes profiling revealed a strong lignin-degrading potential, with the genome encoding abundant glycoside hydrolases (GHs) (≈500) and auxiliary activity enzymes (AAs) (≈220), alongside a substantial number of glycosyltransferases (GTs) (≈195). An unusually low GH/AA ratio (~2.3:1) indicates enhanced oxidative capacity and lignin-modifying potential in this non-white-rot A. niger strain. Fewer carbohydrate esterase (CEs), polysaccharide lyases (PLs), and carbohydrate-binding modules (CBMs) were identified. This enzyme repertoire highlights the organism's ability to degrade polysaccharides and lignin efficiently. The findings demonstrate the strong ligninolytic potential of A. niger AZ2, highlighting its suitability for black liquor treatment and bioremediation. Aspergillus niger AZ2 Black liquor Lignin Fungal reactor Phenol Phytotoxicity Genome annotation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Research Highlights A lab-scale batch reactor was designed for black liquor treatment using Aspergillus niger AZ2. Treatment achieved 66% COD, 54% lignin, 57% phenol, and 62% color reduction, increasing seed germination from 40% to 90%. Genome analysis identified ligninolytic enzymes and aromatic compound degradation pathways. CAZymes profiling indicated strong lignocellulolytic potential dominated by GHs and AAs. Introduction The pulp and paper industries play an important role in modern society, providing paper, packaging, and hygiene products, creating jobs, and contributing to socio-economic growth. However, with the passage of time, rapid industrialization has increased environmental damage, as the paper and pulp industries are major contributors to water pollution (Gupta and Gupta 2019 ). The paper and pulp industries are among the most water-intensive, consuming approximately 250 to 300 m 3 of water per ton of paper produced across stages such as pulping, bleaching, and papermaking (Chaudhry and Paliwal 2018 ). This results in a large volume of wastewater containing over 250 chemical compounds that are discharged into the environment (Kumar et al. 2018 ). The addition of various chemicals generates highly polluted and toxic effluent known as black liquor (Kujur 2017 ; Younas et al. 2020 ). This liquor contains a variety of xenobiotic compounds, including high lignin, chlorinated phenols, metal ions, and organic acids (Chaudhry and Paliwal 2018 ). This dark-colored effluent poses a risk to the aquatic environment by blocking sunlight, altering pH, and reducing dissolved oxygen. It can also induce clastogenic, mutagenic, carcinogenic, and endocrine disruption in aquatic flora and fauna (Bai and Acharya 2019 ; Tripathy et al. 2022 ). These untreated wastewater discharge causes loss of water quality, ecological imbalance, and increase soil salinity, ultimately contributing to economic and environmental instability (Hossain and Ismail 2015 ; Patel and Dudhagara 2020 ). To address the severe environmental impacts caused by the discharge of highly polluted pulp and paper mill effluents, various treatment methods are used conventionally for the treatment of pulp and paper mill wastewater, including coagulation, flocculation, adsorption, membrane separation, advanced oxidation process, electro-oxidation, and bio-reactor-based systems (Patel et al. 2021 ). However, many of these conventional treatment approaches are energy-intensive, costly, and the chemicals used can cause secondary pollution. Consequently, bioremediation has gained attention due to its low cost and eco-friendly approach for the treatment of paper and pulp mill effluent (Mir-Tutusaus et al. 2018 ). This approach uses the metabolic abilities of microbes such as bacteria, fungi, and algae to break down the complex organic pollutants with the help of different processes like depolymerization and transformation, making it suitable for treating pulp and paper mill effluent that is rich in lignin and other organic pollutants (Kameshwar and Qin 2016 ; Baghel et al. 2020 ). Filamentous fungi, particularly ligninolytic species, are well recognized for their ability to degrade lignocellulosic biomass through extracellular oxidative enzymes. White rot fungi are considered the most effective due to their superior enzyme production for black liquor treatment and their tolerance to extreme conditions (Civzele and Mezule 2024 ). Several species, including Schizophyllum commune , Trametes versicolor , and Phanerochaete chrysosporium , have been reported in the literature for lignin degradation and effluent decolorization due to their strong ligninolytic system (Baghel et al. 2020 ). These white rot fungi have been used in different bioreactors to assess their utilization, including fluidized bed bioreactors, airlift reactors, and trickling filters (Bajpai 2018 ). A bioreactor can be designed to operate under anaerobic or aerobic, microaerobic, and environments with configurations such as membrane systems, airlift columns, and agitation, providing efficient oxygen and stable growth conditions (Chaudhry and Paliwal 2018 ; Brink et al. 2018 ). Their modular and flexible design allows easy optimization for the remediation process, making them suitable for sustainable wastewater treatment (Azubuike et al. 2016 ). Recent genome analyses of ligninolytic and biomass-degrading fungi have demonstrated that sequencing and annotation can reveal extensive enzyme repertoires essential for aromatic compound degradation (Ma et al. 2024 ). Novel wood-degrading enzymes of fungal genomes studies demonstrate ecological significance and provide adaptive advantages in natural habitats (Janusz et al. 2017 ). Comparative genomics of Penicillium funiculosum and Aspergillus terreus have revealed abundant CAZymes and auxiliary activity (AA) enzymes crucial for lignin degradation (Pasari et al. 2023 ). Aspergillus sydowii C6d encodes over 140 carbohydrate-active enzymes linked to lignocellulosic biomass degradation, indicating strong biotechnological potential (Tulsani et al. 2022 ). Aspergillus ochraceus DY1 reveals genes associated with aromatic compound and lignin metabolism, reflecting its high metabolic adaptability for biotransformation (Nilza et al. 2024 ). Trametes trogii 301 likewise possesses a rich set of oxidizing enzymes, including laccases and peroxidases, facilitating efficient lignin depolymerization (Liu et al. 2019 ). Soft rot fungi, A. niger , have been reported in the literature for lignin degradation, but their whole genome analysis in this context remains unexplored. In this study, a newly isolated A. niger AZ2 strain was used in a lab-scale bioreactor, achieving significant reductions in lignin, phenols, COD, and color, demonstrating efficient bioremediation. The whole genome sequencing analysis explained this performance by revealing a wide range of ligninolytic enzymes, including laccases, multi-copper oxidases, and peroxidases, along with a CAZymes profile dominated by glycoside hydrolases and auxiliary activity enzymes. This enzyme diversity indicates a strong and synergistic ability to degrade lignocellulosic biomass and aromatic compounds. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis further highlighted genes involved in oxidative degradation and carbon metabolism. This integrated reactor genomics approach presents the first combined reactor and whole genome analysis of A. niger AZ2 for black liquor treatment, highlighting its promising application in black liquor bioremediation. Materials and methods Sample collection Black liquor samples were collected as grab samples from the effluent treatment plant of Century Paper and Board Mills, Kasur, Punjab, Pakistan, during routine mill operation. Samples were collected in sterile polypropylene containers, transported to the laboratory under cooled conditions, and stored at 4°C. All physicochemical analyses were performed within 24 h of collection to minimize compositional changes in the effluent. Physicochemical Analysis of Black Liquor Sample before treatment Using established protocols from the American Public Health Association (Bridgewater et al. 2017 ), a preliminary examination of the black liquor sample was conducted using calibrated analytical instruments to measure its pH, nitrates (NO 3 ), total dissolved solids (TDS), total suspended solids (TSS), and sulfates (SO 4 ). The manufacturer's instructions were followed in order to quantify the chemical oxygen demand (COD) in the effluent using the COD test kits. The lignin concentration and color were evaluated using the procedures outlined by Ojha and Tiwari (Ojha and Tiwari 2016 ) and the Pulp and Paper Mills Association of Canada (CPPA and Association 1974). Isolation and characterization of lignin-degrading fungi The fungal strain A. niger AZ2 was isolated from black liquor collected at the same effluent treatment plant. The strain was screened for ligninolytic potential and identified using a combination of morphological characteristics and ITS rDNA sequencing. Phylogenetic analysis confirmed its taxonomic placement. A detailed description of the isolation, screening, and molecular identification procedures has been reported previously (Khan et al. 2025 ). Design and construction of Fungal Batch Reactor (FBR) for black liquor treatment The bioreactor was constructed with two plexiglass chambers (dimensions: length = 18″, width = 10″, and height = 12″) arranged on a tabletop at different heights to maintain water flow under the force of gravity. One chamber served as the fungal chamber (FC), while the other served as a sand-bed (SB) filtration unit. Sterile wood chips (2–3 cm 3 ), autoclaved at 121°C for 20 min, were used as packing material in the fungal chamber to support fungal attachment and biofilm formation. The wood chips were supplemented with defined nutrients (10 g/L glucose and 5 g/L yeast extract) to facilitate initial biomass development. Fungal inoculation was performed by aseptically transferring 6–8 mm diameter mycelial plugs from actively growing cultures onto multiple locations on the wood chips. The reactor was incubated statically at 30°C for 10–14 days to allow the formation of dense and uniform fungal biomass. A non-inoculated reactor containing sterile wood chips served as an abiotic control. Operational Setup of FBR for Black Liquor Treatment Following biomass establishment, diluted black liquor (5% v/v) was introduced into the fungal chamber. This dilution was selected based on preliminary tolerance screening to avoid acute toxicity and allow gradual microbial adaptation to the complex effluent matrix. The effective working volume of the fungal chamber was approximately 12 L. The reactor was operated in batch mode at 30–32°C, maintained using an aquarium heater. Continuous aeration was supplied through aquarium pumps connected to porous air diffusers, providing fine bubbles to enhance oxygen transfer and maintain aerobic conditions. Aeration was maintained at a constant flow rate sufficient to prevent oxygen limitation. The treatment was conducted for 10 days, during which samples were collected periodically for analysis. After treatment, the effluent was passed through the sand bed filtration unit to remove suspended solids, residual biomass, and particulate organic matter before further evaluation. All experiments were performed in triplicate to ensure reproducibility. A schematic representation of the FBR is shown in Fig. 1 . Analysis of treated black liquor Measurement of Color Reduction The color was measured according to the standard method of the Canadian Pulp and Paper Association (CPPA and Association 1974) using a UV–Vis spectrophotometer. Measurement of Lignin Content Residual lignin content was quantified following the method described by Ojha and Tiwari (Ojha and Tiwari 2016 ). Chemical oxygen demand (COD) COD was measured using commercially available digestion vials containing potassium dichromate as the oxidizing agent. Effluent samples (3 mL) were digested at 148°C for 120 min in a preheated thermo-reactor, and absorbance was measured spectrophotometrically according to the manufacturer’s guidelines. Phenol content detection The Folin-Ciocalteu assay was used to quantify the total phenols according to Singleton and Rossi (Singleton and Rossi 1965 ) with slight modifications. Gallic acid was used as the calibration standard, and results were expressed as mg gallic acid equivalents (GAE). Phytotoxicity assessment Phytotoxicity of untreated and treated effluent samples was evaluated using a seed germination assay following (Saadi et al. 2007 ). Seeds of Triticum aestivum were placed in 90 mm Petri dishes lined with filter paper and saturated with 4 mL of filtered effluent. Tap water was used as the control. The plates were incubated in the dark at room temperature for 5 days. Germinated seeds were counted, and phytotoxicity was expressed as germination percentage (GP). All treatments were performed in triplicate. The germination percentage was calculated using the following formula: Bioinformatics analysis DNA isolation, Genome sequencing, and assembly The genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions (protocol for fungal culture). The isolated DNA was finally eluted with 50 µL of buffer AE and stored at -20°C. The purity and integrity of the genomic DNA were evaluated using 1% agarose gel electrophoresis and densitometry in comparably sized standards. The yield and purity of the DNA were determined using a NanoDrop TM 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and a Qubit® 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). The high-quality genomic DNA of the A. niger AZ2 strain was then subjected to whole genome sequencing via Illumina NovaSeq (300 bp inserts library with 150 bp paired-end sequencing). The sequence quality was assessed using FastQC version 0.11.6, and adapter and low-quality sequences were trimmed by Trimmomatic version 0.36. The SPAdes 3.13.0 genome assembler ( http://cab.spbu.ru/software/spades ) was then used to perform de novo genomic assembly. The quality and completeness of the assembly were evaluated using the Quality Assessment Tool for Genome Assemblies (QUAST, version 5.2.0) and the Benchmarking Universal Single Copy Orthologs (BUSCO, version 3.0.2). Functional annotations Genome annotation of A. niger AZ2 was performed using AUGUSTUS, which integrates extrinsic evidence from protein homology data to improve gene prediction accuracy. Functional annotation of the predicted genes was carried out using the eggNOG online server, providing classification into orthologous groups and functional categories. Pathway analysis was conducted with the KEGG Mapper, enabling the reconstruction of metabolic pathways. To specifically identify lignin degradation genes, a targeted approach was applied. First, protein sequences associated with lignin degradation were retrieved from the NCBI database using the keyword “Lignin degradation genes,” and a custom database of fungal ligninolytic proteins was constructed. The annotated proteome of A. niger AZ2 was then subjected to BLASTp against this custom database, with an e-value cutoff of 1e − 4 to ensure significant matches. Sequences showing homology to known lignin-degrading proteins were extracted, thereby enabling the identification of candidate genes potentially involved in lignin breakdown. The genes encoding Carbohydrate-Active Enzymes (CAZymes) (Lombard et al. 2014 ) were annotated using dbCAN2/ meta server (Zhang et al. 2018 ), integrating HMMER, DIAMOND, and Hotpep tools. Only CAZymes supported by at least two methods were retained and classified into GHs, GTs, CEs, PLs, AAs, and CBMs. The distribution of CAZyme families was visualized using bar charts. Statistical Analysis The data were analyzed using SPSS Statistics 29.0.2.0. The groups were compared using ANOVA followed by Dunnett’s post hoc test, with three independent experiments conducted for each investigation. The results are presented as mean ± standard error (SE) (n = 3). A one-way ANOVA was employed for statistical evaluation, with a P-value of less than 0.05 deemed statistically significant. Results and Discussion Pretreatment Analysis of Black Liquor Physicochemical characterization of the black liquor revealed a dark brown and alkaline effluent with a pH of 8.0, high pollutants loads including; chemical oxygen demand (21,020 mg/L), sulfates (1783 mg/L), nitrates (1,264 mg/L), lignin (7,416 mg/L), phenol content (748 mg/L), total suspended solids (TSS) 800 mg/L, and total dissolved solids (TDS), 722 mg/L (Table 1 ). These characteristics are consistent with those reported for similar pulp and paper mill black liquors, including (Zheng et al. 2014 ), supporting the comparability of the present analysis. The industrial site used an aerated lagoon system to treat effluent, resulting in a reduction in pollutant concentration. However, the pollutant concentration was still above the limits established by the Environmental Protection Agency (EPA) in 2002 (Table 1 ). Lignin, a major constituent present in lignocellulosic biomass, is recalcitrant and also responsible for high concentrations of COD and the dark brown color of effluent from the paper industry (Bridgewater et al. 2017 ). Table 1 Physicochemical characterization of black liquor Sample and permissible limit pH COD (mg/l) Lignin (mg/l) TDS (mg/l) Sulphate (mg/l) Nitrates (mg/l) TSS (mg/l) Phenol content (mg/l) Black liquor 8.0 21020 7416 722 1783 1264 800 748 Permissible limit (USEPA, 2002) 5–9 120 0.05 252 10 *(–): not specified. Performance evaluation of Fungal Batch Reactor FBR Color estimation Pulp and paper wastewaters usually contain high amounts of color because of their high lignin and dissolved organic matter (DOM) content (Kamali and Khodaparast 2015 ). Pulping, bleaching, and chemical recovery sections are the major sources of color in the pulp and paper industry. The wastewater originating from the alkaline extraction of the bleaching unit is highly colored, around 4000 − 6000 Pt − Co, and accounts for 80% of the color from the total contaminant load in the mill’s wastewater (Choudhary et al. 2015 ). In our study, the treatment of black liquor using A. niger AZ2 in a lab-scale fungal batch reactor demonstrated a significant color removal efficiency of 62% (Fig. 2 A. and Table 2 ). Screening of 10 white-rot fungal isolates revealed Trametes elegans as the most efficient strain, achieving 63.6% color removal of pulp and paper mill effluent within 10 days (Ridtibud et al. 2023 ). Similarly, a Nigrospora–Curvularia consortium demonstrated superior performance, achieving 82.3% color removal and 76.1% lignin degradation. This enhanced efficiency was attributed to significantly elevated activities of ligninolytic enzymes, which facilitated the breakdown of chromophoric structures and complex lignin polymers in a repeated-batch bioreactor system (Rajwar et al. 2017 ). A comparative study tested a basidiomycete ( Phanerochaete chrysosporium ) and an ascomycete ( Aspergillus uvarum ) in batch reactor trials treating kraft black liquor (10 days, 25°C, shaking). The study achieved significant reductions in COD, BOD, color, and phenolics, showing that both fungal phyla can remediate black liquor, with performance largely influenced by differences in enzyme systems and pH tolerance (Díaz et al. 2022 ). These comparisons indicate that filamentous fungi, including non-white-rot Ascomycetes , can effectively degrade lignin, with lower efficiencies likely due to black liquor recalcitrance, enzyme profiles, or reactor design. Table 2 Physicochemical characteristics of wastewater before and after treatment in FBR Physicochemical Parameters Lignin (mg/L) Color (CU) COD (mg/L) Phenol (mg/L) Before treatment 3267 ± 0.9 2751 ± 0. 6 1470 ± 1.2 44.75 ± 0.8 After treatment 1491 ± 1.3 1034 ± 0.7 493 ± 0.2 18.68 ± 0.5 Estimation of Lignin Lignin in pulp and paper industry effluents is highly recalcitrant due to its complex, cross-linked structure and hydrophobic nature, which impedes biodegradation. Furthermore, the alkaline conditions of kraft pulping generate condensed lignin fragments and low-molecular-weight phenolics that are toxic to many microorganisms, further limiting natural attenuation processes. In the current study, the fungal batch bioreactor (FBR) inoculated with A. niger AZ2 demonstrated a 55% reduction in lignin content after 8 days of incubation (Fig. 2 B and Table 2 ), followed by a slight decline over the next two days, likely due to nutrient depletion and the accumulation of inhibitory metabolites. Non-white-rot ascomycetes, such as Aspergillus ochraceus DY1, have demonstrated lignin-degrading capabilities comparable to those of white-rot fungi. Under optimized conditions (pH 7 and temperature 25°C), A. ochraceus DY1 achieved 63.6% alkali lignin degradation over a 40-day incubation period, as confirmed by GC-MS and NMR analyses. Although this strain required an extended incubation time, its performance indicates that non-white-rot fungi can achieve efficient lignin depolymerization under prolonged cultivation (Nilza et al. 2024 ). A mixed culture of Lenzites betulina and Trametes versicolor showed synergistic effects, achieving about 50% lignin degradation in batch cultivation, outperforming single-species systems (Cui et al. 2021 ). In another study conducted by Srivastava et al. ( 2024 ), among the white-rot fungi tested, Trametes pubescens demonstrated the highest treatment performance, achieving removal efficiencies of 82% for color, 78% for COD, 83% for lignin, and 77% for AOX within just 4 days of treatment. In comparison, the present study demonstrates that the non-white-rot ascomycete A. niger AZ2 can achieve substantial lignin degradation under alkaline black liquor conditions. Differences from previously reported outcomes likely reflect variations in wastewater composition, operating conditions, and reactor design. Chemical Oxygen Demand (COD) Reduction Chemical oxygen demand (COD) was determined using standard COD vials covering a concentration range of 15–1500 mg/L. As shown in Fig. 2 C and Table 2 , treatment of black liquor with A. niger AZ2 in the fungal batch reactor (FBR) resulted in a 66% reduction in COD, indicating substantial removal of organic load. Comparable reductions have been reported for A. niger in earlier studies; for example, Dhanushree and Kousar ( 2017 ) found a 52% decrease in COD following A. niger treatment. The variations in the wastewater composition, adaptation of the fungus strain, or the working conditions may have added to the enhanced COD removal. These results show the effectiveness of the FBR system in the treatment of pulp and paper wastewater. Rasool et al. ( 2024 ) also reported considerable efficiencies of COD removal with a Gravity Driven Bioreactor (GDB). The GDB showed its effectiveness in removing organic pollutants in wastewater through an average of 76% reduction in COD after treatment. In another study by Sari ( 2016 ) Ceriporiopsis sp achieved up to 70% COD reduction in treated black liquor. Collectively, these findings indicate that efficient fungal COD removal depends on a combination of strain-specific enzymatic capabilities, tolerance to harsh effluent conditions, and reactor configuration, supporting the effectiveness of the FBR system employed in this study. Detection of Phenol Content Phenol and its derivatives are common environmental pollutants, usually found in effluent from many industrial plants, such as oil refineries, petrochemical plants, pulp and paper industries, textile manufacturing industries, chemical and rubber industries, ceramic and steel industries, electronic industries, and pesticide industries. Phenols and other harmful substances in wastewater need special treatment before being released to water bodies (Biglari et al. 2017 ). The application of fungi in the treatment of industrial effluents showed high potential for phenol removal. In the current study, the phenol content was reduced by 57% when treated with A. niger in the FBR (Fig. 2 D and Table 2 ). These results are in line with an earlier study in which A. niger immobilized on alginate beads was effective in degrading phenol in industrial effluents and synthetic effluents. Phenol was reduced in industrial effluent by immobilized and free cells, respectively, by 268 mg/L (59%) and 119 mg/L (56%). This shows the better performance of immobilized A. niger in removing phenol in wastewater (Sharma and Gupta 2012 ). Khalil et al. ( 2021 ) evaluated six fungal strains for phenol removal and reported removal efficiencies of 71.82% for Cochliobolus australiensis , 42.34% for Aspergillus japonicus 4r2, and 37.4% for Fusarium poae 11r7 after 5 days. Extended incubation resulted in complete phenol depletion across all strains, confirming effective fungal-mediated biodegradation. A phenol-degrading mixed microbial culture has been observed to comprise Candida tropicalis , Aspergillus fumigatus , Candida albicans , Candida haemulonis , and Streptomyces alboflavus , in which degradation was greatest at an initial concentration of 1000 mg/L, a temperature of 35°C, and a pH of 7.0 (Sivasubramanian and Namasivayam 2015 ). The findings of these studies support the prospect of fungal-based bioreactors as an effective and sustainable solution for phenolic wastewater treatment. Assessment of Phytotoxicity To determine the toxicity of untreated and treated black liquor (BL), a seed germination test was performed. BL was diluted to 5% (v/v) and subjected to the treatment of A. niger AZ2 over 5 days. Treatment with A. niger AZ2 rescued seed germination in black liquor, resulting in a 90% success rate compared to 0% in the untreated pure BL. Growth was evaluated by measuring the root and shoot length with a scale (Table 3 and Fig. 3 .). Evaluating the toxicity of the pulp mill in a study by Ren et al. ( 2022 ), revealed a significant reduction in the rate of germination of Vigna unguiculata seeds in untreated effluent (92.3% (distilled water) to 66.67%), and the length of the radicle (1.57 to 0.68 cm). A study conducted by An and Barapatre (2016) on effluent treated with Aspergillus flavus strain F10 using phytotoxicity tests showed that seed germination improved significantly. Having a high index of germination in the treated samples reflects that the fungal treatment was able to render the effluent sufficiently harmless to be discharged into the environment. In another study conducted by Dhanushree and Kousar ( 2017 ), the results showed that the Vigna radiata seed germinated in effluent treated with A. niger had a high germination rate, longer root and shoot length, and a germination index (GI) of 83.07%, which indicates less toxicity. The untreated effluent, however, reduced germination and growth of the seeds. This work has demonstrated that effluent treatment with A. niger has the potential to substantially reduce the toxicity of effluent to enable increased plant growth. Table 3 Phytotoxicity results of A. niger AZ2 with tap water, treated, diluted, and pure black liquor Growth Tap Water (positive control) Treated sample Diluted BL (5%) Pure BL Root length (mm) 13.5 ± 0.3 2.5 ± 0.2 2.0 ± 0.5 0.0 ± 0.0 Shoot Length (mm) 12.1 ± 0.7 11.0 ± 0.3 1.8 ± 0.1 0.0 ± 0.0 Germinated Seeds 10/10 ± 0.5 09/10 ± 0.1 04/10 ± 0.3 0/10 ± 0.0 Germination Rate % 100 ± 0.2 90 ± 0.9 40 ± 0.6 0 ± 0.0 Genomic features of A. niger AZ2 The whole-genome assembly of A. niger AZ2 provided a detailed overview of its genomic characteristics, as shown in Table 4 , and confirmed its close relatedness to reference strains. The final assembly comprised 1,502 contigs, with a total length of 34 Mb derived from contigs exceeding 25,000 bases in length. The assembly metrics indicated good quality, as reflected by an N50 value of 282,956 and an L50 of 39, suggesting a relatively contiguous and reliable assembly. The assembly was also found to be accurate, with the genome's GC content measuring 50.02%, consistent with the previously reported A. niger genomes. A BUSCO completeness assessment yielded a score of 99.01%, indicating that the assembly is nearly complete and captures the majority of expected single-copy orthologs. The comparison of the genomes using the Average Nucleotide Identity (ANI) indicated that the isolate shared 98.73% of similarity with A. niger strain CBS, indicating that the isolate is closely related at the species level. The overall results of the study show that the resulting genome of A. niger AZ2 is of high quality and can serve as a powerful basis for further analyses. Table 4 Genome statistics obtained after genome assembly Property Aspergillus niger AZ2 Genome size (in bp) 35,252,226 Completeness 99.01 N50 statistic 282956 GC % 50.02 Non-coding RNAs microRNAs (miRNAs) 33 small RNAs (sRNAs) 22 nuclear RNAs (snRNAs) 5 nucleolar RNAs (snoRNA) 87 long noncoding RNAs (lncRNA) 11 ribosomal RNAs (rRNA) 94 transfer RNAs (tRNA) 253 Eggnog-mapper functional annotation Genes with KO assigned 4519 / 10814 (41%) Genes with COG assigned 9436 / 10814 (87%) Functional Genomics Insights KEGG Pathway Analysis Genome annotation of A. niger AZ2 identified 11,554 predicted coding sequences (CDSs), of which approximately 41% were assigned to Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology and 87% to clusters of orthologous groups (COGs), indicating extensive functional coverage. COG-based classification revealed gene families predominantly associated with carbohydrate metabolism, degradation of aromatic compounds, and cellular regulation, reflecting the metabolic versatility and adaptive capacity of the strain. Similar genome-wide analyses across Aspergillus species have demonstrated that enrichment of these functional categories underpins efficient utilization of complex polysaccharides and aromatic substrates, particularly in lignocellulose- and lignin-rich environments (de Vries et al. 2017 ; Lubbers 2025 ). Pathway reconstruction further revealed well-represented networks involved in central carbon metabolism, secondary metabolism, and xenobiotic biodegradation, highlighting the strain’s potential for the transformation of recalcitrant organic compounds. Within the annotated subsystems, 88 genes were associated with carbon metabolism, supporting a strong capacity for carbon assimilation, while 11 genes linked to aromatic compound degradation suggest an inherent potential for processing lignin-derived intermediates (Fig. 4 .). Recent studies have emphasized the importance of such aromatic degradation pathways in A. niger for bioremediation and lignin valorization applications (Lubbers 2025 ). In addition, nine genes related to quorum-sensing and regulatory functions were identified, indicating coordinated metabolic regulation and environmental responsiveness. Collectively, the prediction of nearly 800 metabolic pathways underscores the extensive enzymatic and regulatory repertoire of A. niger AZ2, supporting its suitability for lignin-rich wastewater treatment and biotechnological applications (de Vries et al. 2017 ). Blast for Lignin Degradation Genes To identify lignin degradation–associated genes, BLASTP analysis was performed against a curated lignin-degrading protein database, with results summarized in Table 5 . The genome of A. niger AZ2 encodes a suite of oxidative enzymes central to lignin breakdown, including laccases and laccase-like multicopper oxidases (LMCOs). Laccases are extracellular multicopper oxidases that catalyze the oxidation of phenolic subunits in lignin and related aromatics using molecular oxygen as the electron acceptor, contributing to radical-mediated depolymerization (Khan et al. 2023 ). LMCOs, a broader class including laccase variants, also act on diverse lignin-related substrates and have been identified from fungal genomes as potential ligninolytic catalysts under alkaline conditions (Sharan et al. 2024 ). Table 5 Enzymes involved in lignin degradation by Aspergillus niger AZ2 Enzyme Accession Protein Length Function Role in Lignin Degradation Laccase-like multicopper oxidase CAK30041.1 342 Extracellular oxidation Catalyzes lignin polymer oxidation Laccase-1 Q70KY3 623 Oxidizes phenolic compounds Lignin degradation & detoxification Pyranose 2-oxidase P79076 623 Generates H₂O₂ Supports lignin & manganese peroxidase activity Oxalate oxidase CAD91553.1 461 Produces H₂O₂ Provides an oxidant for lignin degradation 3-O-methyltransferase 2 P0CT90 447 Methylates phenolic compounds Inactivates inhibitors of lignin peroxidases Peroxidase WLV76000.1 364 Oxidation of lignin derivatives Breaks lignin into smaller molecules Peroxidases complement laccase activity by catalyzing H₂O₂-dependent oxidative cleavage of both phenolic and non-phenolic lignin moieties (Civzele and Mezule 2024 ). The presence of auxiliary oxidase enzymes, such as pyranose-2-oxidase and other H₂O₂-generating oxidases, supports the production of hydrogen peroxide necessary for peroxidase function, enabling sustained oxidative capacity in ligninolytic systems (Asemoloye et al. 2021 ). Oxalate peroxidases (OxPs) support lignin degradation by using oxalate to generate H₂O₂, which fuels the oxidative reactions of primary ligninolytic peroxidases. The co-occurrence of ligninolytic and auxiliary enzymes in the A. niger AZ2 genome suggests a coordinated oxidative network capable of initiating and sustaining lignin depolymerization, reinforcing the functional potential of this strain for lignin modification and bioremediation. Genome-Wide CAZymes Annotation Carbohydrate active enzymes (CAZymes) annotation using the dbCAN pipeline identified a diverse set of CAZymes in the genome. About 500 Glycoside Hydrolase (GH) genes were identified, the greatest CAZymes group, which implies a high degree of polysaccharide degradation ability (Fig. 5 .). The most well-known role of GHs in cellulose, hemicellulose, and other plant polysaccharide hydrolysis is the initial catalyst of glycosidic bond separation in complex carbohydrates, which plays a key role in the lignocellulosic biomass degradation process (Wardman and Withers 2024 ). CAZymes profiles have been reported across diverse microbial genomes and metagenomes, where GHs underpin efficient polysaccharide breakdown and contribute substantially to biomass conversion potential (Wongfaed et al. 2023 ). The second most abundant group was Auxiliary Activity (AA) enzymes (≈ approximately 220 genes), with some of the families being linked to the oxidation of lignin. AA enzymes, such as lytic polysaccharide monooxygenases and other oxidoreductases, are increasingly recognized for their roles in disrupting recalcitrant structures and enhancing access of hydrolytic enzymes to polysaccharide substrates (Levasseur et al. 2013 ). Notably, the gene distribution in our strain (≈ 500 GHs vs ≈ 220 AAs) results in a GH/AA ratio (~ 2.3:1) that is much lower than that typically reported for Aspergillus niger genomes, where GHs are disproportionately dominant over AAs, reflecting polysaccharide degradation potential (e.g., A. niger CSR3: ~213 GHs vs ~ 65 AAs) (Lubna et al. 2022 ). The relatively high abundance of auxiliary activity enzymes reflects increased oxidative capability and supports the lignin-modifying potential observed in this non-white-rot Ascomycete. Glycosyltransferases (GTs) were also well represented (≈ 195 genes), reflecting their roles in carbohydrate biosynthesis and cell wall modification (Wardman and Withers 2024 ). In contrast, Carbohydrate Esterases (CEs), Polysaccharide Lyases (PLs), and Carbohydrate-Binding Modules (CBMs) were present in smaller numbers. Carbohydrate esterases (CEs) remove ester-linked substitutions and lignin–carbohydrate linkages, increasing polysaccharide accessibility, while polysaccharide lyases (PLs) cleave uronic acid-containing polysaccharides via β-elimination, supporting hemicellulose and pectin degradation (Østby and Várnai 2023 ). Carbohydrate-binding modules (CBMs), though non-catalytic, significantly enhance enzymatic hydrolysis by targeting and anchoring catalytic domains to insoluble lignocellulosic substrates, improving substrate recognition and overall degradation efficiency (Shi et al. 2023 ). Overall, the CAZymes distribution demonstrates a broad enzymatic repertoire, with GHs and AAs dominating the profile, supporting the organism’s potential for lignocellulosic substrate transformation. Conclusions This study demonstrates the effectiveness of a simple, cost-effective lab-scale fungal batch reactor (FBR) for black liquor treatment using A. niger AZ2 . Operating under mesophilic temperature and alkaline pH, the FBR achieved 66% COD removal, 54% lignin degradation, 57% phenol reduction, and 62% color removal. Phytotoxicity tests showed a substantial improvement in seed germination (90%) compared to the untreated control (40%), confirming reduced effluent toxicity. Genome sequencing and pathway analysis of A. niger AZ2 highlighted the presence of multiple genes and metabolic routes associated with aromatic compounds breakdown and lignin-derived intermediate transformation. CAZymes profiling showed dominance of glycoside hydrolases and auxiliary activity enzymes, indicating strong lignocellulosic and lignin-degrading potential. The elevated AA content relative to GHs suggests a lignin-oriented oxidative metabolism, distinguishing this strain from typical A. niger genomes. The integration of reactor-based degradation performance with genomic insights establishes A. niger AZ2 as a promising candidate for sustainable bioremediation and detoxification of lignin-rich industrial effluents. Declarations Acknowledgments We are thankful to the Higher Education Commission of Pakistan for providing funds to conduct this project at Quaid-i-Azam University, Islamabad, Pakistan. Funding The Higher Education Commission of Pakistan funded this research work under the ‘Pak Turk Researchers’ Mobility Grant Program 2017. Authors Contributions AAS, ZZ: Conceptualization, Methodology, Project administration, Funding acquisition; AZK, SUD, MS: Preparation of the overall research plan, methodology and write up of original draft; SIK, SK, MB, ZZ, AAS: Facilitated the interpretation of various analyses and lignin degradation experiments in the current research project; SK, MB, ZZ, AAS: Guided the design of a lab-scale bioreactor to perform black liquor treatments and results analysis for phenol, chemical oxygen demand COD) reduction. MYA, AZK, MANK; facilitated the whole genome analysis. AZK, SUD, MS, MANK, AAS: Write up of the manuscript; SUD, SK, MB, ZZ, AAS: Proofreading of the overall manuscript for English comprehension and typing mistakes. Ethics approval Not applicable Consent to participate All authors have read the final manuscript and agreed to submit it. Consent for publication All authors have carefully read the final manuscript and agreed to publish it in “Environmental Science and Pollution Research. Competing interests The authors declare no competing interests Data Availability Statement All supporting data are included in the paper. References An B (2016) Decolourization and Biological Treatment of Pulp and Paper Mill Effluent by Lignin-Degrading Fungus Aspergillus flavus Strain F10. IntJCurrMicrobiolAppSci 5(5):19–32. ttps://doi.org/10.20546/ijcmas.2016.505.003 Asemoloye MD, Marchisio MA, Gupta VK, Pecoraro L (2021) Genome-based engineering of ligninolytic enzymes in fungi. Microb Cell Fact 20(1):20. ttps://doi.org/10.1186/s12934-021-01510-9 Azubuike CC, Chikere CB, Okpokwasili GC (2016) Bioremediation techniques–classification based on site of application: principles, advantages, limitations and prospects. 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J Basic Microbiol 54(2):152–161. ttps://doi.org/10.1002/jobm.201200340 Supplementary Files GA.png GRAPHICAL ABSTRACT Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Samiullah\",\"middleName\":\"\",\"lastName\":\"Khan\",\"suffix\":\"\"},{\"id\":612511018,\"identity\":\"59a2f68a-4087-49bd-905e-99cbb1541c64\",\"order_by\":9,\"name\":\"Aamer Ali Shah\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACdsYGZgaGA3IQHhsQ8xDSwgzRYkyKFjA6kNhAtBb+ZubmzwU1d9L7jp8xYPhQdpjBnOcAfi0ShxnbpGcce5Y780yOAeOMc4cZLHsbCFgD1MLMw3Y4d8OBHANm3rbDDAbnCeiQP8zY/Jnn3+F0g/NvDJj/EqPF4DBjgzTQ8ASDG0BbGEFazhJwmCHIL7x9hw1n3nhWcLDnXDqPZc8B/Frkjrc//szz7bA83/nkjQ9+lFnLmfMkEHAZHBwAIwYeA2I1QNSDAAlaRsEoGAWjYIQAAGmlR7mgF4yeAAAAAElFTkSuQmCC\",\"orcid\":\"https://orcid.org/0000-0001-7454-4105\",\"institution\":\"Quaid-i-Azam University Faculty of Biological Sciences\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Aamer\",\"middleName\":\"Ali\",\"lastName\":\"Shah\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-07 05:49:44\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9055675/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9055675/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":105558368,\"identity\":\"319a9925-b42f-4571-abd3-cbc86d66f9b8\",\"added_by\":\"auto\",\"created_at\":\"2026-03-27 11:29:51\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":189785,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic diagram of the overall treatment units of the lab-scale fungal batch reactor (FBR)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9055675/v1/8f01bb000004ec219433d551.png\"},{\"id\":105558366,\"identity\":\"e3d4e2ec-0bb9-4d66-b24e-afe23522a852\",\"added_by\":\"auto\",\"created_at\":\"2026-03-27 11:29:51\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":228259,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBlack liquor treated sample during 10 days of operation in the fungal batch reactor (FBR (A) lignin content, (B) change in color, (C) concentration of chemical oxygen demand (COD), and (D) phenol concentration\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9055675/v1/6ca5fe76c896b9d0f8953d5f.png\"},{\"id\":105566385,\"identity\":\"9a8dc902-c652-4fc6-8387-6f2da8c90dc8\",\"added_by\":\"auto\",\"created_at\":\"2026-03-27 12:56:19\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":548484,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGermination of seeds (a) before incubation, (b) after 5 days of incubation at 28 °C\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9055675/v1/dcb3f8948a293600b10d05e9.png\"},{\"id\":105558370,\"identity\":\"61a3478a-58dd-42d5-be91-58f3f8ea1616\",\"added_by\":\"auto\",\"created_at\":\"2026-03-27 11:29:51\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":155449,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOf the 27 subsystem categories, 88 genetic elements were associated with carbon metabolism, 11 with aromatic carbon degradation, and 09 with quorum sensing. A total of 800 metabolic pathways is present in the genome\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9055675/v1/38065336f684d7fbb16130e9.png\"},{\"id\":105558371,\"identity\":\"b6205fe1-9411-4190-a5cb-94f75845d104\",\"added_by\":\"auto\",\"created_at\":\"2026-03-27 11:29:51\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":26127,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnnotation of \\u003cem\\u003eA. niger \\u003c/em\\u003eAZ2 carbohydrate-active enzymes\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9055675/v1/e17338b33384663ed0d77103.png\"},{\"id\":108699693,\"identity\":\"d06c957c-f716-43a3-951c-33108b165ec7\",\"added_by\":\"auto\",\"created_at\":\"2026-05-07 12:32:15\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1838838,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9055675/v1/4a5dedc1-21bb-4440-8919-0ed110e65395.pdf\"},{\"id\":105567169,\"identity\":\"99a0bc3a-12a4-4cea-ad2f-6976f13ca139\",\"added_by\":\"auto\",\"created_at\":\"2026-03-27 12:58:32\",\"extension\":\"png\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1348881,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGRAPHICAL ABSTRACT\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"GA.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9055675/v1/59fda8d4a3731babf2a9bbf3.png\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Performance Evaluation of Lab-Scale Fungal Batch Reactor and Genomic Insights of a Newly Isolated Aspergillus niger AZ2 for Black Liquor Treatment\",\"fulltext\":[{\"header\":\"Research Highlights\",\"content\":\"\\u003cp\\u003e\\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eA lab-scale batch reactor was designed for black liquor treatment using \\u003cem\\u003eAspergillus niger\\u003c/em\\u003e AZ2.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eTreatment achieved 66% COD, 54% lignin, 57% phenol, and 62% color reduction, increasing seed germination from 40% to 90%.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eGenome analysis identified ligninolytic enzymes and aromatic compound degradation pathways.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eCAZymes profiling indicated strong lignocellulolytic potential dominated by GHs and AAs.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e\\u003c/p\\u003e\"},{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe pulp and paper industries play an important role in modern society, providing paper, packaging, and hygiene products, creating jobs, and contributing to socio-economic growth. However, with the passage of time, rapid industrialization has increased environmental damage, as the paper and pulp industries are major contributors to water pollution (Gupta and Gupta \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The paper and pulp industries are among the most water-intensive, consuming approximately 250 to 300 m\\u003csup\\u003e3\\u003c/sup\\u003e of water per ton of paper produced across stages such as pulping, bleaching, and papermaking (Chaudhry and Paliwal \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). This results in a large volume of wastewater containing over 250 chemical compounds that are discharged into the environment (Kumar et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The addition of various chemicals generates highly polluted and toxic effluent known as black liquor (Kujur \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Younas et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). This liquor contains a variety of xenobiotic compounds, including high lignin, chlorinated phenols, metal ions, and organic acids (Chaudhry and Paliwal \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). This dark-colored effluent poses a risk to the aquatic environment by blocking sunlight, altering pH, and reducing dissolved oxygen. It can also induce clastogenic, mutagenic, carcinogenic, and endocrine disruption in aquatic flora and fauna (Bai and Acharya \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Tripathy et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These untreated wastewater discharge causes loss of water quality, ecological imbalance, and increase soil salinity, ultimately contributing to economic and environmental instability (Hossain and Ismail \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Patel and Dudhagara \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo address the severe environmental impacts caused by the discharge of highly polluted pulp and paper mill effluents, various treatment methods are used conventionally for the treatment of pulp and paper mill wastewater, including coagulation, flocculation, adsorption, membrane separation, advanced oxidation process, electro-oxidation, and bio-reactor-based systems (Patel et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, many of these conventional treatment approaches are energy-intensive, costly, and the chemicals used can cause secondary pollution. Consequently, bioremediation has gained attention due to its low cost and eco-friendly approach for the treatment of paper and pulp mill effluent (Mir-Tutusaus et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). This approach uses the metabolic abilities of microbes such as bacteria, fungi, and algae to break down the complex organic pollutants with the help of different processes like depolymerization and transformation, making it suitable for treating pulp and paper mill effluent that is rich in lignin and other organic pollutants (Kameshwar and Qin \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Baghel et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFilamentous fungi, particularly ligninolytic species, are well recognized for their ability to degrade lignocellulosic biomass through extracellular oxidative enzymes. White rot fungi are considered the most effective due to their superior enzyme production for black liquor treatment and their tolerance to extreme conditions (Civzele and Mezule \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Several species, including \\u003cem\\u003eSchizophyllum commune\\u003c/em\\u003e, \\u003cem\\u003eTrametes versicolor\\u003c/em\\u003e, and \\u003cem\\u003ePhanerochaete chrysosporium\\u003c/em\\u003e, have been reported in the literature for lignin degradation and effluent decolorization due to their strong ligninolytic system (Baghel et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). These white rot fungi have been used in different bioreactors to assess their utilization, including fluidized bed bioreactors, airlift reactors, and trickling filters (Bajpai \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). A bioreactor can be designed to operate under anaerobic or aerobic, microaerobic, and environments with configurations such as membrane systems, airlift columns, and agitation, providing efficient oxygen and stable growth conditions (Chaudhry and Paliwal \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Brink et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Their modular and flexible design allows easy optimization for the remediation process, making them suitable for sustainable wastewater treatment (Azubuike et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRecent genome analyses of ligninolytic and biomass-degrading fungi have demonstrated that sequencing and annotation can reveal extensive enzyme repertoires essential for aromatic compound degradation (Ma et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Novel wood-degrading enzymes of fungal genomes studies demonstrate ecological significance and provide adaptive advantages in natural habitats (Janusz et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Comparative genomics of \\u003cem\\u003ePenicillium funiculosum\\u003c/em\\u003e and \\u003cem\\u003eAspergillus terreus\\u003c/em\\u003e have revealed abundant CAZymes and auxiliary activity (AA) enzymes crucial for lignin degradation (Pasari et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). \\u003cem\\u003eAspergillus sydowii\\u003c/em\\u003e C6d encodes over 140 carbohydrate-active enzymes linked to lignocellulosic biomass degradation, indicating strong biotechnological potential (Tulsani et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). \\u003cem\\u003eAspergillus ochraceus\\u003c/em\\u003e DY1 reveals genes associated with aromatic compound and lignin metabolism, reflecting its high metabolic adaptability for biotransformation (Nilza et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). \\u003cem\\u003eTrametes trogii\\u003c/em\\u003e 301 likewise possesses a rich set of oxidizing enzymes, including laccases and peroxidases, facilitating efficient lignin depolymerization (Liu et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Soft rot fungi, \\u003cem\\u003eA. niger\\u003c/em\\u003e, have been reported in the literature for lignin degradation, but their whole genome analysis in this context remains unexplored.\\u003c/p\\u003e \\u003cp\\u003eIn this study, a newly isolated \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 strain was used in a lab-scale bioreactor, achieving significant reductions in lignin, phenols, COD, and color, demonstrating efficient bioremediation. The whole genome sequencing analysis explained this performance by revealing a wide range of ligninolytic enzymes, including laccases, multi-copper oxidases, and peroxidases, along with a CAZymes profile dominated by glycoside hydrolases and auxiliary activity enzymes. This enzyme diversity indicates a strong and synergistic ability to degrade lignocellulosic biomass and aromatic compounds. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis further highlighted genes involved in oxidative degradation and carbon metabolism. This integrated reactor genomics approach presents the first combined reactor and whole genome analysis of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 for black liquor treatment, highlighting its promising application in black liquor bioremediation.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSample collection\\u003c/h2\\u003e \\u003cp\\u003eBlack liquor samples were collected as grab samples from the effluent treatment plant of Century Paper and Board Mills, Kasur, Punjab, Pakistan, during routine mill operation. Samples were collected in sterile polypropylene containers, transported to the laboratory under cooled conditions, and stored at 4\\u0026deg;C. All physicochemical analyses were performed within 24 h of collection to minimize compositional changes in the effluent.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003ePhysicochemical Analysis of Black Liquor Sample before treatment\\u003c/h3\\u003e\\n\\u003cp\\u003eUsing established protocols from the American Public Health Association (Bridgewater et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), a preliminary examination of the black liquor sample was conducted using calibrated analytical instruments to measure its pH, nitrates (NO\\u003csub\\u003e3\\u003c/sub\\u003e), total dissolved solids (TDS), total suspended solids (TSS), and sulfates (SO\\u003csub\\u003e4\\u003c/sub\\u003e). The manufacturer's instructions were followed in order to quantify the chemical oxygen demand (COD) in the effluent using the COD test kits. The lignin concentration and color were evaluated using the procedures outlined by Ojha and Tiwari (Ojha and Tiwari \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) and the Pulp and Paper Mills Association of Canada (CPPA and Association 1974).\\u003c/p\\u003e\\n\\u003ch3\\u003eIsolation and characterization of lignin-degrading fungi\\u003c/h3\\u003e\\n\\u003cp\\u003eThe fungal strain \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 was isolated from black liquor collected at the same effluent treatment plant. The strain was screened for ligninolytic potential and identified using a combination of morphological characteristics and ITS rDNA sequencing. Phylogenetic analysis confirmed its taxonomic placement. A detailed description of the isolation, screening, and molecular identification procedures has been reported previously (Khan et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003eDesign and construction of Fungal Batch Reactor (FBR) for black liquor treatment\\u003c/h3\\u003e\\n\\u003cp\\u003eThe bioreactor was constructed with two plexiglass chambers (dimensions: length\\u0026thinsp;=\\u0026thinsp;18\\u0026Prime;, width\\u0026thinsp;=\\u0026thinsp;10\\u0026Prime;, and height\\u0026thinsp;=\\u0026thinsp;12\\u0026Prime;) arranged on a tabletop at different heights to maintain water flow under the force of gravity. One chamber served as the fungal chamber (FC), while the other served as a sand-bed (SB) filtration unit. Sterile wood chips (2\\u0026ndash;3 cm\\u003csup\\u003e3\\u003c/sup\\u003e), autoclaved at 121\\u0026deg;C for 20 min, were used as packing material in the fungal chamber to support fungal attachment and biofilm formation. The wood chips were supplemented with defined nutrients (10 g/L glucose and 5 g/L yeast extract) to facilitate initial biomass development. Fungal inoculation was performed by aseptically transferring 6\\u0026ndash;8 mm diameter mycelial plugs from actively growing cultures onto multiple locations on the wood chips. The reactor was incubated statically at 30\\u0026deg;C for 10\\u0026ndash;14 days to allow the formation of dense and uniform fungal biomass. A non-inoculated reactor containing sterile wood chips served as an abiotic control.\\u003c/p\\u003e\\n\\u003ch3\\u003eOperational Setup of FBR for Black Liquor Treatment\\u003c/h3\\u003e\\n\\u003cp\\u003eFollowing biomass establishment, diluted black liquor (5% v/v) was introduced into the fungal chamber. This dilution was selected based on preliminary tolerance screening to avoid acute toxicity and allow gradual microbial adaptation to the complex effluent matrix. The effective working volume of the fungal chamber was approximately 12 L. The reactor was operated in batch mode at 30\\u0026ndash;32\\u0026deg;C, maintained using an aquarium heater. Continuous aeration was supplied through aquarium pumps connected to porous air diffusers, providing fine bubbles to enhance oxygen transfer and maintain aerobic conditions. Aeration was maintained at a constant flow rate sufficient to prevent oxygen limitation. The treatment was conducted for 10 days, during which samples were collected periodically for analysis. After treatment, the effluent was passed through the sand bed filtration unit to remove suspended solids, residual biomass, and particulate organic matter before further evaluation. All experiments were performed in triplicate to ensure reproducibility. A schematic representation of the FBR is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAnalysis of treated black liquor\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eMeasurement of Color Reduction\\u003c/h2\\u003e \\u003cp\\u003eThe color was measured according to the standard method of the Canadian Pulp and Paper Association (CPPA and Association 1974) using a UV\\u0026ndash;Vis spectrophotometer.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eMeasurement of Lignin Content\\u003c/h3\\u003e\\n\\u003cp\\u003eResidual lignin content was quantified following the method described by Ojha and Tiwari (Ojha and Tiwari \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eChemical oxygen demand (COD)\\u003c/h2\\u003e \\u003cp\\u003eCOD was measured using commercially available digestion vials containing potassium dichromate as the oxidizing agent. Effluent samples (3 mL) were digested at 148\\u0026deg;C for 120 min in a preheated thermo-reactor, and absorbance was measured spectrophotometrically according to the manufacturer\\u0026rsquo;s guidelines.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePhenol content detection\\u003c/h2\\u003e \\u003cp\\u003eThe Folin-Ciocalteu assay was used to quantify the total phenols according to Singleton and Rossi (Singleton and Rossi \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e1965\\u003c/span\\u003e) with slight modifications. Gallic acid was used as the calibration standard, and results were expressed as mg gallic acid equivalents (GAE).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePhytotoxicity assessment\\u003c/h2\\u003e \\u003cp\\u003ePhytotoxicity of untreated and treated effluent samples was evaluated using a seed germination assay following (Saadi et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). Seeds of \\u003cem\\u003eTriticum aestivum\\u003c/em\\u003e were placed in 90 mm Petri dishes lined with filter paper and saturated with 4 mL of filtered effluent. Tap water was used as the control. The plates were incubated in the dark at room temperature for 5 days. Germinated seeds were counted, and phytotoxicity was expressed as germination percentage (GP). All treatments were performed in triplicate.\\u003c/p\\u003e \\u003cp\\u003eThe germination percentage was calculated using the following formula:\\u003c/p\\u003e \\u003c/div\\u003e\\u003cp\\u003e\\u003cimg 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\\\" width=\\\"490\\\" height=\\\"61\\\"\\u003e\\u003c/p\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBioinformatics analysis\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eDNA isolation, Genome sequencing, and assembly\\u003c/h2\\u003e \\u003cp\\u003eThe genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) following the manufacturer\\u0026rsquo;s instructions (protocol for fungal culture). The isolated DNA was finally eluted with 50 \\u0026micro;L of buffer AE and stored at -20\\u0026deg;C. The purity and integrity of the genomic DNA were evaluated using 1% agarose gel electrophoresis and densitometry in comparably sized standards. The yield and purity of the DNA were determined using a NanoDrop TM 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and a Qubit\\u0026reg; 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). The high-quality genomic DNA of the \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 strain was then subjected to whole genome sequencing via Illumina NovaSeq (300 bp inserts library with 150 bp paired-end sequencing). The sequence quality was assessed using FastQC version 0.11.6, and adapter and low-quality sequences were trimmed by Trimmomatic version 0.36. The SPAdes 3.13.0 genome assembler (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://cab.spbu.ru/software/spades\\u003c/span\\u003e\\u003cspan address=\\\"http://cab.spbu.ru/software/spades\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) was then used to perform de novo genomic assembly. The quality and completeness of the assembly were evaluated using the Quality Assessment Tool for Genome Assemblies (QUAST, version 5.2.0) and the Benchmarking Universal Single Copy Orthologs (BUSCO, version 3.0.2).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFunctional annotations\\u003c/h2\\u003e \\u003cp\\u003eGenome annotation of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 was performed using AUGUSTUS, which integrates extrinsic evidence from protein homology data to improve gene prediction accuracy. Functional annotation of the predicted genes was carried out using the eggNOG online server, providing classification into orthologous groups and functional categories. Pathway analysis was conducted with the KEGG Mapper, enabling the reconstruction of metabolic pathways. To specifically identify lignin degradation genes, a targeted approach was applied. First, protein sequences associated with lignin degradation were retrieved from the NCBI database using the keyword \\u0026ldquo;Lignin degradation genes,\\u0026rdquo; and a custom database of fungal ligninolytic proteins was constructed. The annotated proteome of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 was then subjected to BLASTp against this custom database, with an e-value cutoff of 1e\\u003csup\\u003e\\u0026minus;\\u0026thinsp;4\\u003c/sup\\u003e to ensure significant matches. Sequences showing homology to known lignin-degrading proteins were extracted, thereby enabling the identification of candidate genes potentially involved in lignin breakdown.\\u003c/p\\u003e \\u003cp\\u003eThe genes encoding Carbohydrate-Active Enzymes (CAZymes) (Lombard et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) were annotated using dbCAN2/ meta server (Zhang et al. \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), integrating HMMER, DIAMOND, and Hotpep tools. Only CAZymes supported by at least two methods were retained and classified into GHs, GTs, CEs, PLs, AAs, and CBMs. The distribution of CAZyme families was visualized using bar charts.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe data were analyzed using SPSS Statistics 29.0.2.0. The groups were compared using ANOVA followed by Dunnett\\u0026rsquo;s post hoc test, with three independent experiments conducted for each investigation. The results are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard error (SE) (n\\u0026thinsp;=\\u0026thinsp;3). A one-way ANOVA was employed for statistical evaluation, with a P-value of less than 0.05 deemed statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results and Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePretreatment Analysis of Black Liquor\\u003c/h2\\u003e \\u003cp\\u003ePhysicochemical characterization of the black liquor revealed a dark brown and alkaline effluent with a pH of 8.0, high pollutants loads including; chemical oxygen demand (21,020 mg/L), sulfates (1783 mg/L), nitrates (1,264 mg/L), lignin (7,416 mg/L), phenol content (748 mg/L), total suspended solids (TSS) 800 mg/L, and total dissolved solids (TDS), 722 mg/L (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). These characteristics are consistent with those reported for similar pulp and paper mill black liquors, including (Zheng et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), supporting the comparability of the present analysis. The industrial site used an aerated lagoon system to treat effluent, resulting in a reduction in pollutant concentration. However, the pollutant concentration was still above the limits established by the Environmental Protection Agency (EPA) in 2002 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Lignin, a major constituent present in lignocellulosic biomass, is recalcitrant and also responsible for high concentrations of COD and the dark brown color of effluent from the paper industry (Bridgewater et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePhysicochemical characterization of black liquor\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSample and permissible limit\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003epH\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCOD (mg/l)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLignin (mg/l)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTDS\\u003c/p\\u003e \\u003cp\\u003e(mg/l)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eSulphate\\u003c/p\\u003e \\u003cp\\u003e(mg/l)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNitrates\\u003c/p\\u003e \\u003cp\\u003e(mg/l)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eTSS\\u003c/p\\u003e \\u003cp\\u003e(mg/l)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003ePhenol content (mg/l)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBlack liquor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21020\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7416\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e722\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1783\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1264\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e800\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e748\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePermissible limit (USEPA, 2002)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u0026ndash;9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e120\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e252\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003e*(\\u0026ndash;): not specified.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePerformance evaluation of Fungal Batch Reactor FBR\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eColor estimation\\u003c/h2\\u003e \\u003cp\\u003ePulp and paper wastewaters usually contain high amounts of color because of their high lignin and dissolved organic matter (DOM) content (Kamali and Khodaparast \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Pulping, bleaching, and chemical recovery sections are the major sources of color in the pulp and paper industry. The wastewater originating from the alkaline extraction of the bleaching unit is highly colored, around 4000\\u0026thinsp;\\u0026minus;\\u0026thinsp;6000 Pt\\u0026thinsp;\\u0026minus;\\u0026thinsp;Co, and accounts for 80% of the color from the total contaminant load in the mill\\u0026rsquo;s wastewater (Choudhary et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). In our study, the treatment of black liquor using \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 in a lab-scale fungal batch reactor demonstrated a significant color removal efficiency of 62% (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA. and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Screening of 10 white-rot fungal isolates revealed \\u003cem\\u003eTrametes elegans\\u003c/em\\u003e as the most efficient strain, achieving 63.6% color removal of pulp and paper mill effluent within 10 days (Ridtibud et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Similarly, a \\u003cem\\u003eNigrospora\\u0026ndash;Curvularia\\u003c/em\\u003e consortium demonstrated superior performance, achieving 82.3% color removal and 76.1% lignin degradation. This enhanced efficiency was attributed to significantly elevated activities of ligninolytic enzymes, which facilitated the breakdown of chromophoric structures and complex lignin polymers in a repeated-batch bioreactor system (Rajwar et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). A comparative study tested a basidiomycete (\\u003cem\\u003ePhanerochaete chrysosporium\\u003c/em\\u003e) and an ascomycete (\\u003cem\\u003eAspergillus uvarum\\u003c/em\\u003e) in batch reactor trials treating kraft black liquor (10 days, 25\\u0026deg;C, shaking). The study achieved significant reductions in COD, BOD, color, and phenolics, showing that both fungal phyla can remediate black liquor, with performance largely influenced by differences in enzyme systems and pH tolerance (D\\u0026iacute;az et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These comparisons indicate that filamentous fungi, including non-white-rot \\u003cem\\u003eAscomycetes\\u003c/em\\u003e, can effectively degrade lignin, with lower efficiencies likely due to black liquor recalcitrance, enzyme profiles, or reactor design.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePhysicochemical characteristics of wastewater before and after treatment in FBR\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePhysicochemical Parameters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLignin (mg/L)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eColor (CU)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCOD (mg/L)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePhenol (mg/L)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBefore treatment\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3267\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2751\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0. 6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1470\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e44.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAfter treatment\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1491\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1034\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e493\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e18.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEstimation of Lignin\\u003c/h2\\u003e \\u003cp\\u003eLignin in pulp and paper industry effluents is highly recalcitrant due to its complex, cross-linked structure and hydrophobic nature, which impedes biodegradation. Furthermore, the alkaline conditions of kraft pulping generate condensed lignin fragments and low-molecular-weight phenolics that are toxic to many microorganisms, further limiting natural attenuation processes. In the current study, the fungal batch bioreactor (FBR) inoculated with \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 demonstrated a 55% reduction in lignin content after 8 days of incubation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), followed by a slight decline over the next two days, likely due to nutrient depletion and the accumulation of inhibitory metabolites. Non-white-rot ascomycetes, such as \\u003cem\\u003eAspergillus ochraceus\\u003c/em\\u003e DY1, have demonstrated lignin-degrading capabilities comparable to those of white-rot fungi. Under optimized conditions (pH 7 and temperature 25\\u0026deg;C), \\u003cem\\u003eA. ochraceus\\u003c/em\\u003e DY1 achieved 63.6% alkali lignin degradation over a 40-day incubation period, as confirmed by GC-MS and NMR analyses. Although this strain required an extended incubation time, its performance indicates that non-white-rot fungi can achieve efficient lignin depolymerization under prolonged cultivation (Nilza et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eA mixed culture of \\u003cem\\u003eLenzites betulina\\u003c/em\\u003e and \\u003cem\\u003eTrametes versicolor\\u003c/em\\u003e showed synergistic effects, achieving about 50% lignin degradation in batch cultivation, outperforming single-species systems (Cui et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In another study conducted by Srivastava et al. (\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), among the white-rot fungi tested, \\u003cem\\u003eTrametes pubescens\\u003c/em\\u003e demonstrated the highest treatment performance, achieving removal efficiencies of 82% for color, 78% for COD, 83% for lignin, and 77% for AOX within just 4 days of treatment. In comparison, the present study demonstrates that the non-white-rot ascomycete \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 can achieve substantial lignin degradation under alkaline black liquor conditions. Differences from previously reported outcomes likely reflect variations in wastewater composition, operating conditions, and reactor design.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eChemical Oxygen Demand (COD) Reduction\\u003c/h2\\u003e \\u003cp\\u003eChemical oxygen demand (COD) was determined using standard COD vials covering a concentration range of 15\\u0026ndash;1500 mg/L. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, treatment of black liquor with \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 in the fungal batch reactor (FBR) resulted in a 66% reduction in COD, indicating substantial removal of organic load. Comparable reductions have been reported for \\u003cem\\u003eA. niger\\u003c/em\\u003e in earlier studies; for example, Dhanushree and Kousar (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) found a 52% decrease in COD following \\u003cem\\u003eA. niger\\u003c/em\\u003e treatment. The variations in the wastewater composition, adaptation of the fungus strain, or the working conditions may have added to the enhanced COD removal. These results show the effectiveness of the FBR system in the treatment of pulp and paper wastewater. Rasool et al. (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) also reported considerable efficiencies of COD removal with a Gravity Driven Bioreactor (GDB). The GDB showed its effectiveness in removing organic pollutants in wastewater through an average of 76% reduction in COD after treatment. In another study by Sari (\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) \\u003cem\\u003eCeriporiopsis sp\\u003c/em\\u003e achieved up to 70% COD reduction in treated black liquor. Collectively, these findings indicate that efficient fungal COD removal depends on a combination of strain-specific enzymatic capabilities, tolerance to harsh effluent conditions, and reactor configuration, supporting the effectiveness of the FBR system employed in this study.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDetection of Phenol Content\\u003c/h2\\u003e \\u003cp\\u003ePhenol and its derivatives are common environmental pollutants, usually found in effluent from many industrial plants, such as oil refineries, petrochemical plants, pulp and paper industries, textile manufacturing industries, chemical and rubber industries, ceramic and steel industries, electronic industries, and pesticide industries. Phenols and other harmful substances in wastewater need special treatment before being released to water bodies (Biglari et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The application of fungi in the treatment of industrial effluents showed high potential for phenol removal. In the current study, the phenol content was reduced by 57% when treated with \\u003cem\\u003eA. niger\\u003c/em\\u003e in the FBR (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). These results are in line with an earlier study in which \\u003cem\\u003eA. niger\\u003c/em\\u003e immobilized on alginate beads was effective in degrading phenol in industrial effluents and synthetic effluents. Phenol was reduced in industrial effluent by immobilized and free cells, respectively, by 268 mg/L (59%) and 119 mg/L (56%). This shows the better performance of immobilized \\u003cem\\u003eA. niger\\u003c/em\\u003e in removing phenol in wastewater (Sharma and Gupta \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Khalil et al. (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) evaluated six fungal strains for phenol removal and reported removal efficiencies of 71.82% for \\u003cem\\u003eCochliobolus australiensis\\u003c/em\\u003e, 42.34% for \\u003cem\\u003eAspergillus japonicus\\u003c/em\\u003e 4r2, and 37.4% for \\u003cem\\u003eFusarium poae\\u003c/em\\u003e 11r7 after 5 days. Extended incubation resulted in complete phenol depletion across all strains, confirming effective fungal-mediated biodegradation. A phenol-degrading mixed microbial culture has been observed to comprise \\u003cem\\u003eCandida tropicalis\\u003c/em\\u003e, \\u003cem\\u003eAspergillus fumigatus\\u003c/em\\u003e, \\u003cem\\u003eCandida albicans\\u003c/em\\u003e, \\u003cem\\u003eCandida haemulonis\\u003c/em\\u003e, and \\u003cem\\u003eStreptomyces alboflavus\\u003c/em\\u003e, in which degradation was greatest at an initial concentration of 1000 mg/L, a temperature of 35\\u0026deg;C, and a pH of 7.0 (Sivasubramanian and Namasivayam \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). The findings of these studies support the prospect of fungal-based bioreactors as an effective and sustainable solution for phenolic wastewater treatment.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eAssessment of Phytotoxicity\\u003c/h2\\u003e \\u003cp\\u003eTo determine the toxicity of untreated and treated black liquor (BL), a seed germination test was performed. BL was diluted to 5% (v/v) and subjected to the treatment of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 over 5 days. Treatment with \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 rescued seed germination in black liquor, resulting in a 90% success rate compared to 0% in the untreated pure BL. Growth was evaluated by measuring the root and shoot length with a scale (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.). Evaluating the toxicity of the pulp mill in a study by Ren et al. (\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), revealed a significant reduction in the rate of germination of \\u003cem\\u003eVigna unguiculata\\u003c/em\\u003e seeds in untreated effluent (92.3% (distilled water) to 66.67%), and the length of the radicle (1.57 to 0.68 cm). A study conducted by An and Barapatre (2016) on effluent treated with \\u003cem\\u003eAspergillus flavus\\u003c/em\\u003e strain F10 using phytotoxicity tests showed that seed germination improved significantly. Having a high index of germination in the treated samples reflects that the fungal treatment was able to render the effluent sufficiently harmless to be discharged into the environment. In another study conducted by Dhanushree and Kousar (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), the results showed that the \\u003cem\\u003eVigna radiata\\u003c/em\\u003e seed germinated in effluent treated with \\u003cem\\u003eA. niger\\u003c/em\\u003e had a high germination rate, longer root and shoot length, and a germination index (GI) of 83.07%, which indicates less toxicity. The untreated effluent, however, reduced germination and growth of the seeds. This work has demonstrated that effluent treatment with \\u003cem\\u003eA. niger\\u003c/em\\u003e has the potential to substantially reduce the toxicity of effluent to enable increased plant growth.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePhytotoxicity results of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 with tap water, treated, diluted, and pure black liquor\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGrowth\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTap Water\\u003c/p\\u003e \\u003cp\\u003e(positive control)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTreated sample\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDiluted BL (5%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePure BL\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRoot length (mm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eShoot Length (mm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGerminated Seeds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10/10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e09/10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e04/10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0/10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGermination Rate %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eGenomic features of\\u003c/b\\u003e \\u003cb\\u003eA. niger\\u003c/b\\u003e \\u003cb\\u003eAZ2\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe whole-genome assembly of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 provided a detailed overview of its genomic characteristics, as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, and confirmed its close relatedness to reference strains. The final assembly comprised 1,502 contigs, with a total length of 34 Mb derived from contigs exceeding 25,000 bases in length. The assembly metrics indicated good quality, as reflected by an N50 value of 282,956 and an L50 of 39, suggesting a relatively contiguous and reliable assembly. The assembly was also found to be accurate, with the genome's GC content measuring 50.02%, consistent with the previously reported \\u003cem\\u003eA. niger\\u003c/em\\u003e genomes. A BUSCO completeness assessment yielded a score of 99.01%, indicating that the assembly is nearly complete and captures the majority of expected single-copy orthologs. The comparison of the genomes using the Average Nucleotide Identity (ANI) indicated that the isolate shared 98.73% of similarity with \\u003cem\\u003eA. niger\\u003c/em\\u003e strain CBS, indicating that the isolate is closely related at the species level. The overall results of the study show that the resulting genome of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 is of high quality and can serve as a powerful basis for further analyses.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eGenome statistics obtained after genome assembly\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProperty\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAspergillus niger\\u003c/em\\u003e AZ2\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenome size (in bp)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35,252,226\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCompleteness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e99.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN50 statistic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e282956\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGC %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-coding RNAs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emicroRNAs (miRNAs)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esmall RNAs (sRNAs)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003enuclear RNAs (snRNAs)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003enucleolar RNAs (snoRNA)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e87\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003elong noncoding RNAs (lncRNA)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eribosomal RNAs (rRNA)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e94\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003etransfer RNAs (tRNA)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e253\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEggnog-mapper functional annotation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenes with KO assigned\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4519 / 10814 (41%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenes with COG assigned\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9436 / 10814 (87%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec26\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eFunctional Genomics Insights\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec27\\\" class=\\\"Section4\\\"\\u003e \\u003ch2\\u003eKEGG Pathway Analysis\\u003c/h2\\u003e \\u003cp\\u003eGenome annotation of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 identified 11,554 predicted coding sequences (CDSs), of which approximately 41% were assigned to Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology and 87% to clusters of orthologous groups (COGs), indicating extensive functional coverage. COG-based classification revealed gene families predominantly associated with carbohydrate metabolism, degradation of aromatic compounds, and cellular regulation, reflecting the metabolic versatility and adaptive capacity of the strain. Similar genome-wide analyses across \\u003cem\\u003eAspergillus\\u003c/em\\u003e species have demonstrated that enrichment of these functional categories underpins efficient utilization of complex polysaccharides and aromatic substrates, particularly in lignocellulose- and lignin-rich environments (de Vries et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Lubbers \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Pathway reconstruction further revealed well-represented networks involved in central carbon metabolism, secondary metabolism, and xenobiotic biodegradation, highlighting the strain\\u0026rsquo;s potential for the transformation of recalcitrant organic compounds. Within the annotated subsystems, 88 genes were associated with carbon metabolism, supporting a strong capacity for carbon assimilation, while 11 genes linked to aromatic compound degradation suggest an inherent potential for processing lignin-derived intermediates (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e.). Recent studies have emphasized the importance of such aromatic degradation pathways in \\u003cem\\u003eA. niger\\u003c/em\\u003e for bioremediation and lignin valorization applications (Lubbers \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). In addition, nine genes related to quorum-sensing and regulatory functions were identified, indicating coordinated metabolic regulation and environmental responsiveness. Collectively, the prediction of nearly 800 metabolic pathways underscores the extensive enzymatic and regulatory repertoire of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2, supporting its suitability for lignin-rich wastewater treatment and biotechnological applications (de Vries et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec28\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBlast for Lignin Degradation Genes\\u003c/h2\\u003e \\u003cp\\u003eTo identify lignin degradation\\u0026ndash;associated genes, BLASTP analysis was performed against a curated lignin-degrading protein database, with results summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e. The genome of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 encodes a suite of oxidative enzymes central to lignin breakdown, including laccases and laccase-like multicopper oxidases (LMCOs). Laccases are extracellular multicopper oxidases that catalyze the oxidation of phenolic subunits in lignin and related aromatics using molecular oxygen as the electron acceptor, contributing to radical-mediated depolymerization (Khan et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). LMCOs, a broader class including laccase variants, also act on diverse lignin-related substrates and have been identified from fungal genomes as potential ligninolytic catalysts under alkaline conditions (Sharan et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eEnzymes involved in lignin degradation by \\u003cem\\u003eAspergillus niger\\u003c/em\\u003e AZ2\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEnzyme\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAccession\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eProtein Length\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eFunction\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eRole in Lignin Degradation\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLaccase-like multicopper oxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCAK30041.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e342\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eExtracellular oxidation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eCatalyzes lignin polymer oxidation\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLaccase-1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ70KY3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e623\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOxidizes phenolic compounds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eLignin degradation \\u0026amp; detoxification\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePyranose 2-oxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP79076\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e623\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGenerates H₂O₂\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSupports lignin \\u0026amp; manganese peroxidase activity\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOxalate oxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCAD91553.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e461\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eProduces H₂O₂\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eProvides an oxidant for lignin degradation\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3-O-methyltransferase 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP0CT90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e447\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMethylates phenolic compounds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eInactivates inhibitors of lignin peroxidases\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePeroxidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWLV76000.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e364\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOxidation of lignin derivatives\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBreaks lignin into smaller molecules\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003ePeroxidases complement laccase activity by catalyzing H₂O₂-dependent oxidative cleavage of both phenolic and non-phenolic lignin moieties (Civzele and Mezule \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The presence of auxiliary oxidase enzymes, such as pyranose-2-oxidase and other H₂O₂-generating oxidases, supports the production of hydrogen peroxide necessary for peroxidase function, enabling sustained oxidative capacity in ligninolytic systems (Asemoloye et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Oxalate peroxidases (OxPs) support lignin degradation by using oxalate to generate H₂O₂, which fuels the oxidative reactions of primary ligninolytic peroxidases. The co-occurrence of ligninolytic and auxiliary enzymes in the \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 genome suggests a coordinated oxidative network capable of initiating and sustaining lignin depolymerization, reinforcing the functional potential of this strain for lignin modification and bioremediation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec29\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGenome-Wide CAZymes Annotation\\u003c/h2\\u003e \\u003cp\\u003eCarbohydrate active enzymes (CAZymes) annotation using the dbCAN pipeline identified a diverse set of CAZymes in the genome. About 500 Glycoside Hydrolase (GH) genes were identified, the greatest CAZymes group, which implies a high degree of polysaccharide degradation ability (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e.). The most well-known role of GHs in cellulose, hemicellulose, and other plant polysaccharide hydrolysis is the initial catalyst of glycosidic bond separation in complex carbohydrates, which plays a key role in the lignocellulosic biomass degradation process (Wardman and Withers \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). CAZymes profiles have been reported across diverse microbial genomes and metagenomes, where GHs underpin efficient polysaccharide breakdown and contribute substantially to biomass conversion potential (Wongfaed et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The second most abundant group was Auxiliary Activity (AA) enzymes (\\u0026asymp;\\u0026thinsp;approximately 220 genes), with some of the families being linked to the oxidation of lignin. AA enzymes, such as lytic polysaccharide monooxygenases and other oxidoreductases, are increasingly recognized for their roles in disrupting recalcitrant structures and enhancing access of hydrolytic enzymes to polysaccharide substrates (Levasseur et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Notably, the gene distribution in our strain (\\u0026asymp;\\u0026thinsp;500 GHs vs\\u0026thinsp;\\u0026asymp;\\u0026thinsp;220 AAs) results in a GH/AA ratio (~\\u0026thinsp;2.3:1) that is much lower than that typically reported for \\u003cem\\u003eAspergillus niger\\u003c/em\\u003e genomes, where GHs are disproportionately dominant over AAs, reflecting polysaccharide degradation potential (e.g., \\u003cem\\u003eA. niger\\u003c/em\\u003e CSR3: ~213 GHs vs\\u0026thinsp;~\\u0026thinsp;65 AAs) (Lubna et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The relatively high abundance of auxiliary activity enzymes reflects increased oxidative capability and supports the lignin-modifying potential observed in this non-white-rot Ascomycete. Glycosyltransferases (GTs) were also well represented (\\u0026asymp;\\u0026thinsp;195 genes), reflecting their roles in carbohydrate biosynthesis and cell wall modification (Wardman and Withers \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In contrast, Carbohydrate Esterases (CEs), Polysaccharide Lyases (PLs), and Carbohydrate-Binding Modules (CBMs) were present in smaller numbers. Carbohydrate esterases (CEs) remove ester-linked substitutions and lignin\\u0026ndash;carbohydrate linkages, increasing polysaccharide accessibility, while polysaccharide lyases (PLs) cleave uronic acid-containing polysaccharides via β-elimination, supporting hemicellulose and pectin degradation (\\u0026Oslash;stby and V\\u0026aacute;rnai \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Carbohydrate-binding modules (CBMs), though non-catalytic, significantly enhance enzymatic hydrolysis by targeting and anchoring catalytic domains to insoluble lignocellulosic substrates, improving substrate recognition and overall degradation efficiency (Shi et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Overall, the CAZymes distribution demonstrates a broad enzymatic repertoire, with GHs and AAs dominating the profile, supporting the organism\\u0026rsquo;s potential for lignocellulosic substrate transformation.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study demonstrates the effectiveness of a simple, cost-effective lab-scale fungal batch reactor (FBR) for black liquor treatment using \\u003cem\\u003eA. niger AZ2\\u003c/em\\u003e. Operating under mesophilic temperature and alkaline pH, the FBR achieved 66% COD removal, 54% lignin degradation, 57% phenol reduction, and 62% color removal. Phytotoxicity tests showed a substantial improvement in seed germination (90%) compared to the untreated control (40%), confirming reduced effluent toxicity. Genome sequencing and pathway analysis of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 highlighted the presence of multiple genes and metabolic routes associated with aromatic compounds breakdown and lignin-derived intermediate transformation. CAZymes profiling showed dominance of glycoside hydrolases and auxiliary activity enzymes, indicating strong lignocellulosic and lignin-degrading potential. The elevated AA content relative to GHs suggests a lignin-oriented oxidative metabolism, distinguishing this strain from typical \\u003cem\\u003eA. niger\\u003c/em\\u003e genomes. The integration of reactor-based degradation performance with genomic insights establishes \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2 as a promising candidate for sustainable bioremediation and detoxification of lignin-rich industrial effluents.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe are thankful to the Higher Education Commission of Pakistan for providing funds to conduct this project at Quaid-i-Azam University, Islamabad, Pakistan.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Higher Education Commission of Pakistan funded this research work under the \\u0026lsquo;Pak Turk Researchers\\u0026rsquo; Mobility Grant Program 2017.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAAS, ZZ:\\u0026nbsp;Conceptualization, Methodology, Project administration, Funding acquisition; AZK, SUD, MS: Preparation of the overall research plan, methodology and write up of original draft; SIK, SK, MB, ZZ, AAS: Facilitated the interpretation of various analyses and lignin degradation experiments in the current research project; SK, MB, ZZ, AAS: Guided the design of a lab-scale bioreactor to perform black liquor treatments and results analysis for phenol, chemical oxygen demand COD) reduction. MYA, AZK, MANK; facilitated the whole genome analysis. AZK, SUD, MS, MANK, AAS: Write up of the manuscript; SUD, SK, MB, ZZ, AAS: Proofreading of the overall manuscript for English comprehension and typing mistakes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have read the final manuscript and agreed to submit it.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have carefully read the final manuscript and agreed to publish it in \\u0026ldquo;Environmental Science and Pollution Research.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll supporting data are included in the paper.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAn B (2016) Decolourization and Biological Treatment of Pulp and Paper Mill Effluent by Lignin-Degrading Fungus \\u003cem\\u003eAspergillus flavus\\u003c/em\\u003e Strain F10. 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Sci Rep 13(1):2968. ttps://doi.org/10.1038/s41598-023-29895-0\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYounas U, Iqbal S, Saeed Z, Ibrahim S, Khurshid S, Pervaiz M, Saleem A, Zaidi A, Iqbal M, Nazir A (2020) Heavy Metal Enrichment of Soil Irrigatedwith Paper and Board Mill (PBM) Effluents. Pol J Environ Stud 29(6):4463\\u0026ndash;4468. ttps://doi.org/10.15244/pjoes/117653\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, Busk PK, Xu Y, Yin Y (2018) dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46(W1):W95\\u0026ndash;W101. ttps://doi.org/10.1093/nar/gky418\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZheng Y, Chai L, Yang Z, Chen Y, Shi Y, Wang Y (2014) Environmentally safe treatment of black liquor with \\u003cem\\u003eComamonas sp.\\u003c/em\\u003e B-9 under high-alkaline conditions. J Basic Microbiol 54(2):152\\u0026ndash;161. ttps://doi.org/10.1002/jobm.201200340\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Aspergillus niger AZ2, Black liquor, Lignin, Fungal reactor, Phenol, Phytotoxicity, Genome annotation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9055675/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9055675/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe pulp and paper industry generates large volumes of black liquor, a lignin-rich effluent requiring effective treatment before disposal. In this study, a lab-scale fungal batch reactor (FBR) was designed and operated using \\u003cem\\u003eAspergillus niger\\u003c/em\\u003e AZ2 as a lignin-degrading biocatalyst. The reactor achieved 54% lignin reduction, 66% reduction in chemical oxygen demand (COD), 57% phenol removal, and 62% color reduction, demonstrating strong treatment efficiency. Phytotoxicity tests showed a significant improvement in seed germination. These results highlight the potential of the fungal-based reactor as a simple, cost-effective, and eco-friendly strategy for black liquor treatment under alkaline conditions. The genome-based functional analysis of \\u003cem\\u003eA. niger \\u003c/em\\u003erevealed a diverse repertoire of lignin-degrading enzymes, including laccase, peroxidases, oxalate peroxidases, and pyranose oxidases. Pathway mapping further highlighted genes associated with the breakdown of aromatic compounds in auxiliary metabolic processes supporting efficient lignin mineralization. CAZymes profiling revealed a strong lignin-degrading potential, with the genome encoding abundant glycoside hydrolases (GHs) (≈500) and auxiliary activity enzymes (AAs) (≈220), alongside a substantial number of glycosyltransferases (GTs) (≈195). An unusually low GH/AA ratio (~2.3:1) indicates enhanced oxidative capacity and lignin-modifying potential in this non-white-rot \\u003cem\\u003eA. niger\\u003c/em\\u003e strain. Fewer carbohydrate esterase (CEs), polysaccharide lyases (PLs), and carbohydrate-binding modules (CBMs) were identified. This enzyme repertoire highlights the organism's ability to degrade polysaccharides and lignin efficiently. The findings demonstrate the strong ligninolytic potential of \\u003cem\\u003eA. niger\\u003c/em\\u003e AZ2, highlighting its suitability for black liquor treatment and bioremediation.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Performance Evaluation of Lab-Scale Fungal Batch Reactor and Genomic Insights of a Newly Isolated Aspergillus niger AZ2 for Black Liquor Treatment\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-03-27 11:29:46\",\"doi\":\"10.21203/rs.3.rs-9055675/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"1d2b5485-b3d2-4912-985b-0cee2940f33d\",\"owner\":[],\"postedDate\":\"March 27th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Reject\",\"date\":\"2026-05-07T07:16:01+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-07T12:31:51+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-03-27 11:29:46\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9055675\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9055675\",\"identity\":\"rs-9055675\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}