Adaptation of a microbial consortium to pelagic Sargassum modifies its taxonomic and functional profile that improves biomethane potential

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However, the complex composition of these macroalgae acts as a barrier to microbial degradation, thereby limiting methane production. Microbial adaptation has emerged as a promising strategy to improve substrate utilization and stress tolerance. This study aimed to investigate the adaptation of a microbial consortium to enhance methane production from the pelagic Sargassum . Microbial adaptation was carried out for 100 days by progressively feeding Sargassum . The evolution of the microbial community was analyzed by high-throughput sequencing of 16S rRNA amplicons. Additionally, 16S rRNA data were used to predict functional profiles using the iVikodak platform. The results showed that, after adaptation, the consortium was dominated by the bacterial phyla Bacteroidota, Firmicutes, and Atribacterota, as well as methanogens of the families Methanotrichaceae and Methanoregulaceae. The abundance of genes related to different metabolism-related functions decreased on day 60 when the Sargassum concentration increased. However, after 100 d, the functions increased again, enhancing methane production. The adapted consortium (AC) exhibited a biomethane potential of 160.03 ± 4.64 N-mL g − 1 VS and a biodegradability index of 39%, representing a 60% improvement. Additionally, the degradation kinetics and methane production of pelagic Sargassum were improved. The study concludes that microbial adaptation enhances the bioconversion of pelagic Sargassum into methane. It is also suggested that a microbial consortium should be generated to achieve greater efficiency in the bioconversion of Sargassum , along with other pretreatments. Biotechnology and Bioengineering macroalgae inoculum anaerobic digestion metabarcoding Mexican Caribbean Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Since 2011, climatological and environmental conditions have facilitated the transport of pelagic Sargassum species out of the Sargasso Sea, causing them to proliferate in the tropical Atlantic between West Africa and South America. This phenomenon has led to the deposition of tons of this macroalgae along the Atlantic and Caribbean coasts (Robledo et al., 2021 ). The accumulated masses of algae on the beaches undergo decomposition, generating leachates and organic particles, thereby giving rise to brown tides that deplete oxygen, reduce light, and deteriorate water quality (van Tussenbroek et al., 2017 ). Furthermore, the unappealing visual impact of the decaying Sargassum , coupled with the unpleasant odor it emits, has adversely affected the tourism industry—the primary economic driver in the region (Chávez et al., 2020 ). Researchers have explored the valorization of these macroalgae for the generation of biofuels, and have focused mainly on anaerobic digestion (AD) for biogas production (Milledge and Harvey, 2016 ; Thompson et al., 2020 ). However, there are currently few studies on the AD of these invasive species, and published results report a biomethane potential (BMP) ranging from 61 to 145 mL CH 4 g − 1 VS (Milledge et al., 2020 ; Tapia-Tussell et al., 2018 ; Thompson et al., 2020 ). In all cases, these values represent less than 50% of the theoretical biomethane potential (TBMP). The complex composition, characterized by low carbohydrate content, high insoluble fiber content, and other compounds that can potentially inhibit anaerobic digestion (DA inhibitors), has been cited as the primary reason for the low methane yield of Sargassum spp. (Milledge et al., 2020 ; Soto et al., 2015 ). This complexity in the composition of pelagic Sargassum represents an obstacle to microbial degradation. Various strategies have been tested to improve methane yields, such as inhibitor removal and different pre-treatments (Chikani-Cabrera et al., 2022 ; Salgado-Hernández et al., 2023a ). However, the sustainable application of these strategies is dependent on biochemical efficiency, economic feasibility, the energy balance of inputs and outputs, resource utilization, and the amount of waste during the process (Barbot et al., 2016 ; Maneein et al., 2018 ). It has been shown that microbial communities can be adapted to tolerate higher concentrations of salt and other inhibitory compounds to ensure adequate substrate degradation (Barbot et al., 2016 ). The adaptation of the inoculum to a substrate results in the induction of metabolic pathways for biodegradation, an increase in the affinity of the microorganisms for the compound, and an increase in the number of specific degraders (Raposo et al., 2012 ). This work was based on the premise that the adaptation of microorganisms to the characteristics of a non-preferred substrate could improve their ability to consume the substrate (Tan et al., 2022 ), which could increase methane generation. The use of an adapted microbial consortium as a source of inoculum could reduce the use of pretreatments, making AD more feasible and sustainable owing to the need for less energy or chemicals. Adaptation or acclimation of the inoculum has been reported to be a successful strategy for the anaerobic digestion of brown algae (Darko et al., 2022 ; Tedesco and Daniels, 2019 ). However, feeding the culture with a new substrate imposes stress on different microorganisms and alters their proportions in the reactor. Consequently, the proportion of some trophic groups may increase, decrease, or even disappear. In such effects, culture can be transformed into a new one (Ahmed et al., 2019 ). Therefore, the objective of this study was to investigate the adaptation of a microbial consortium to pelagic Sargassum to enhance methane production. Changes in the taxonomic profile of the microbial consortium during the adaptation period were analyzed by high-throughput sequencing of the 16S rRNA gene. The obtained data were used to predict the functional profiles of the consortium. Our results show the importance of inoculum source adaptation to enhance the bioconversion of pelagic Sargassum to methane. In addition, it provides a better understanding of microbial dynamics and metabolic functions during the adaptation stage. 2. Materials and methods 2.1 Algae and inoculum collection Representative samples of Sargassum spp. were randomly collected from algae accumulated on the shore of Xcalacoco Beach in Playa del Carmen (20°37'19.99''N, 87°4'9.98''W), Quintana Roo, Mexico. Sand and other natural contaminants such as plastics, shells, feathers, and other algae attached to algae were removed manually. Samples were transported to the laboratory in a cooler and stored in resealable plastic bags at -4°C until further use. The microbial consortium used as the inoculum was obtained from a pilot-scale reactor fed with the liquid fraction of municipal organic solid waste, which is located at the Instituto Tecnológico de Orizaba (Veracruz, Mexico). Subsequently, the inoculum was incubated at 35°C under anaerobic conditions for seven days and fed with shredded fruit and vegetable waste (FVW) as a substrate to increase the microbial population growth and ensure methane production. Table 1 shows the physicochemical characteristics of the inoculum and FVW. Table 1. Compositional analysis of inoculum, pelagic Sargassum, and FVW. Parameter Inoculum Sargassum FVW pH 7.81 NA 4.50 Proximal análisis (n= 3) TS (%) 3.08 ±0.13 96.11 ±0.23 7.34 ±0.16 VS (% TS) 62.35 ±0.96 67.80 ±1.25 93.06 ±0.02 Ash (% TS) 37.64 ±0.96 28.31 ±1.29 6.94 ±0.02 Ultimate analysis (n= 1) C (%) 35.14 33.44 46.86 H (%) 3.96 4.95 5.01 O (%) 19.7 31.84 39.08 N (%) 3.56 1.46 1.35 S (%) 0.4 0.8 0.1 C: N 9.87 22.9 34.71 Structural analysis (n= 1) Cellulose (%) NA 12.28 10.34 Hemicellulose (%) NA 0.4 10.75 Lignin (%) NA 10.22 4.2 TPC (mg EAG/g) NA 14.4 ±0.08 NA Mineral content (n= 2) Ca (mg/g) NA 94.9 ±24.18 NA Na (mg/g) NA 20.7 ±0.71 NA K (mg/g) NA 10.25 ±0.21 NA Mg (mg/g) NA 7.85 ±2.19 NA FVW= Fruit and vegetable waste TPC= Total phenol content NA = Not Analyzed 2.2 Adaptation of the microbial consortium to Sargassum The microbial consortium was adapted in microcosms made of 1 L GL 45 glass bottles with a working volume of 800 mL, with a biogas capture system using the water displacement method. The microcosms were incubated in duplicate at 35°C under static conditions and manually shaken once a day for 60 s. The operation was carried out by repeated fed-batch; once biogas production was exhausted, part of the medium was removed and cyclically replaced with the substrate. The working volume, except during substrate replenishment passages, remained constant. The inoculum was fed with FVW, and once biogas production was observed, feeding proceeded by gradually increasing the Sargassum biomass (Sb) through different passages. Passage I (75% FVW:25% Sb), passage II (50% FVW:50% Sb), passage III (25% FVW:75% Sb), and passage IV (0% FVW:100% Sb). The amount of substrate fed at each passage was 4:1 based on the volatile solids (VS) content. After passage IV, feeding was continued until stable methane production was observed (passage VI). For microbial community analysis, 5 mL of the consortium was collected at the beginning of the experiment and after passage 4 and 6. 2.3 Biochemical methane potential tests Biochemical methane potential (BMP) tests were performed according to the method described by Holliger et al., ( 2021 ). The bioreactors consisted of 120 mL serum bottles with a working volume of 75 mL. Two experiments were conducted to evaluate the effects of microbial consortium adaptation on the biodegradability and BMP of pelagic Sargassum . In the first experiment, the non-adapted (NA) microbial consortium was used as the inoculum. In the second experiment, the adapted consortium (AC) from passage VI was used. Two control groups were used in both the experiments. The first was a negative control (blank) with inoculum only, to measure the contribution of biogas to the inoculum. The second was a positive control with powdered cellulose used to evaluate the specific methanogenic activity of the inoculum. The inoculum-to-substrate ratio was maintained at 2:1 based on volatile solids (VS). The initial pH of the cultures was adjusted to 7.2 ± 0.1. To achieve anaerobic conditions, all bottles were hermetically sealed with butyl rubber stoppers and aluminum caps, and the headspace was purged with nitrogen gas (purity 99.5–100%, Praxair México S. de R.L. de C.V.) for 3 minutes. The bottles were incubated at 35°C until the daily methane production for three consecutive days was less than 1% of the accumulated volume. During this period, the flasks were shaken daily for 60 seconds. The volume of biogas was measured at regular intervals using 5–60 mL glass syringes with a shut-off valve and a Luer-lock system. The biogas produced by the bioreactors was normalized against the biogas produced by the blank. The results were expressed as the volume of gas (mL) under standard conditions (273 K and 1 atm) per unit mass (g) of aggregated VS. The experiments were conducted in triplicate. 2.4 Analytical methods The biogas composition was analyzed using a gas chromatograph (Buck Scientific 310, Norwalk, USA) with a thermal conductivity detector (TCD) equipped with a 6-inch-long, 1/4-inch diameter Alltech CTR-I packed column. Doses of 2 mL were injected directly into the gas chromatograph to detect CH 4 , CO 2 , O 2 , and N 2 . Helium was used as the carrier gas at 70 psi, column temperature was 36°C, and detector temperature was 121°C. The TS, VS, and ash contents of Sargassum , FVW, and inoculum samples were measured according to Standard Methods (APHA, 2005 ). pH was measured using a Thermo Scientific Orion Versa Star pH meter (Waltham, USA). The total phenol content (TPC) of the macroalgae was analyzed by the Folin-Ciocalteu method using a UV-Vis spectrophotometer (Shimadzu, UV 1280, Kyoto, Japan). The carbon, hydrogen, and nitrogen contents of the inoculum and the Sargassum and FVW samples were analyzed using an elemental analyzer (PerkinElmer Series II CHNS/O Analyzer 2400, Waltham, USA). Total sulfur (S) was quantified by turbidimetry with gum arabic as a stabilizer using a UV/Vis spectrophotometer (Spectronic 200, Thermo Scientific, Waltham, USA). Oxygen content was estimated by difference. All the above analyses were performed in duplicate. The cellulose, hemicellulose, and lignin contents of the macroalgae were determined using the Van Soest method on an ANKOM 200 fiber analyzer. (ANKOM Technology, Fairport, NY, USA). The content of mineral salts in the macroalgae was determined using a Sherwood Scientific Ltd. flame photometer (Corning 410, Cambridge, UK) for Na and K and an Agilent Technologies, Inc. atomic absorption spectrometer (Varian 240FS, Santa Clara, USA) for Ca and Mg. The above analyses were performed in duplicates. 2.5 Anaerobic biodegradability and kinetics study \({C}_{n}{H}_{a}{O}_{b}{N}_{c}{S}_{d}+\left(n-\frac{a}{4}-\frac{b}{2}+\frac{3c}{4}+\frac{d}{2}\right){H}_{2}O\underrightarrow{yields}\left(\frac{n}{2}+\frac{a}{8}-\frac{b}{4}-\frac{3c}{8}+\frac{d}{4}\right){CH}_{4}+\left(\frac{n}{2}-\frac{a}{8}+\frac{b}{4}+\frac{3c}{8}+\frac{d}{4}\right){CO}_{2}+{cNH}_{3}+d{H}_{2}\) Eq. 1 To calculate the biodegradability of pelagic Sargassum , its theoretical biomethane potential (TBMP) and empirical formula (C n H a O b N c ) were initially determined using the Buswell equation (Eq. 1) and the Boyle equation (Eq. 2) (Achinas and Euverink, 2016 ). \(TBMP \left(L {CH}_{4 }kg {VS}^{-1}\right)=\frac{22.4\times \left(\frac{n}{2}+\frac{a}{8}-\frac{b}{4}-\frac{3c}{8}-\frac{d}{4}\right)}{12n+a+16b+14c+32d}\) Eq. 2 where 22.4 is the volume (L) of 1 mole of gas at standard temperature and pressure. The biodegradability index (BI) was calculated as the ratio of BMP to TBMP, expressed as the percentage of TBMP achieved by the feedstock at the end of the digestion period (Eq. 3). \(BI \left(\%\right)=\frac{BMP}{TBMP}\times 100\) Eq. 3 The kinetic behavior of the BMP test results was described using first-order and transfer function models. First-order kinetics (Eq. 4) for particulate organic matter were employed to determine the decay constant ( k , day − 1 ), or hydrolysis constant, representing the rate of substrate degradation (Maneein et al., 2021 ; Membere and Sallis, 2018 ). This model considers substrate availability as the limiting factor and hydrolysis is assumed to govern the overall process. Therefore, the lag phase is not considered. The transfer function model (Eq. 5), was used to predict the maximum biogas yield ( B 0 , mL CH 4 g − 1 VS), the maximum methane production rate ( R max , mL CH 4 g − 1 VS day − 1 ), and the duration of the lag phase (λ, days), representing the number of days before significant CH 4 production began (de la Lama et al., 2021 ). This model fits a first-order curve to relate methane production to microbial activity (Veluchamy and Kalamdhad, 2017 ). \(B \left(t\right)={B}_{0}\times \left(1-{exp}^{-k\times t}\right)\) Eq. 4 \(B \left(t\right)={B}_{0}\times \left\{1-exp\left[-\frac{{R}_{max}\times \left(\lambda -t\right)}{{B}_{0}}\right]\right\}\) Eq. 5 IBM SPSS Statistics version 27 was used to perform a nonlinear Least Squares regression analysis to determine B 0 , R max , and λ . The value of k in Eq. 4 was estimated by plotting \(\text{ln}\left(1-B/{B}_{0}\right)\) versus time. The statistical indicators R 2 (correlation coefficient) and root mean square error (RMSE) were calculated to assess the goodness of fit and accuracy of the results of both models. 2.6 Analysis of the microbial community evolution 2.6.1 DNA extraction Samples (3 mL) of the non-adapted and adapted microbial consortia were collected after days 60 (passage IV) and 100 (passage VI). The samples were centrifuged at 12 000 x g for 3 min to remove the liquid. DNA was obtained using the DNeasy PowerSoil kit from QIAGEN (Hilden, Germany) according to the manufacturer's instructions. DNA concentration, quality, and purity were estimated using agarose gel electrophoresis (1%) and a UV-Vis NanodropTM 2000 spectrophotometer (Thermo Scientific, Waltham, USA). 2.6.2 Metabarcoding of 16S rRNA gene and bioinformatics analysis Once the quality of the DNA samples was ensured, PCR amplification of the V4 hypervariable regions for Bacteria and V4-V5 for Archaea of the 16S rRNA gene was conducted. Primer sets 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGGGTWTCTAAT-3′) were used for Bacteria. For Archaea, Arch519F (5'-CAGCCGCCGCCGCGGGGTAA-3') and Arch915R (5'-GTGCTCCCCCCCGCCAATTCCT-3') were utilized. Library construction and sequencing services on the Illumina NovaSeq PE250 platform were provided by Novogene Co (Beijing, China). The raw high throughput next-generation sequencing data was submitted to the National Center for Biotechnology Information (NCBI) database under BioProject number PRJNA1047747 (accession number SUB14012728). Following sequencing, sequences of forward and reverse reads with barcoding and primers removed were analyzed using QIIME2 software (v2021.11). The demultiplexed reads were denoised and assigned to amplicon sequence variants (ASVs) using the DADA2 algorithm. Taxonomic assignment was based on classifiers trained on the hypervariable region V4-V5 for Archaea and V4 for Bacteria extracted from the Greengenes 2022.10 sequence database. From the taxonomic abundance profiles, the potential functions of the microbial communities in the consortium were inferred using the IVikodak platform (Nagpal et al., 2019 ). A single metadata file was constructed using taxonomic data obtained from bacterial and archaeal sequences. The Global Mapper module was used to identify the most important functions based on the KEGG database (Kyoto Encyclopedia of Genes and Genomes). 2.7 Statistical analysis A t-student test was conducted to assess the difference between the adapted and non-adapted consortium in terms of BMP at a 95% confidence interval. Statistical analyses were carried out using R (version 4.0.2). 3. Results and discussion 3.1 Compositional analysis of pelagic Sargassum The composition of pelagic Sargassum collected in Playa del Carmen is detailed in Table 1 . The proximate analysis indicated that Sargassum had a moisture content of 3.89% after drying at 105°C for 24 h. A volatile solids (VS) content of 67.80% and an ash content of 28.31% were observed. These results align with previous reports on pelagic Sargassum from the Mexican Caribbean (Chikani-Cabrera et al., 2022 ; Salgado-Hernández et al., 2023b ). These characteristics can be attributed to the preceding washing process applied to the samples before analysis, as they are known to have a high ash content (~ 50%) (Salgado-Hernández et al., 2023a ). The ultimate analysis revealed C (33.4%) and N (1.46%) contents similar to those previously reported in other studies on pelagic Sargassum from the Mexican Caribbean (Chikani-Cabrera et al., 2022 ; Salgado-Hernández et al., 2023b ). These contents resulted in a C: N ratio of 22.9, which falls within the optimal range for AD (Montingelli et al., 2015 ). The C: N ratio in pelagic Sargassum does not appear to be a hindrance for AD, as recent studies have reported C: N ratios > 20 (Chikani-Cabrera et al., 2022 ; Thompson et al., 2020 ). From the ultimate analysis, the theoretical biomethane potential was calculated using Equations 1 and 2. First, the empirical formula of Sargassum (C 2.8 H 5 N 0.1 O 2 S 0.02 ) was determined, and subsequently, the TBMP was obtained (407.29 mL CH 4 g − 1 VS), a value similar to that reported by Chikani-Cabrera et al., ( 2022 ). However, the biomethane potential of pelagic Sargassum reported in other studies ranges from 335 to 503 mL CH 4 g − 1 VS (Salgado-Hernández et al., 2023a ). Structural analysis of Sargassum indicated that its cell walls consist mainly of cellulose, lignin, and hemicellulose. Brown algae are characterized by a high content of insoluble fibers compared with other types of algae. This characteristic can be limiting because these compounds, which provide structural support to Sargassum , act as a barrier and impede access for microorganisms during degradation (Maneein et al., 2018 ). However, the lignin, cellulose, and hemicellulose contents found in pelagic Sargassum are lower than those of other lignocellulosic biomasses (Hernández-Beltrán et al., 2019 ). In addition, pelagic Sargassum showed a high content of phenols, which also play an important role in cell wall rigidity (Maneein et al., 2018 ). Phenols have been described as being responsible for the low methane yield in AD because of their inhibitory effect on microorganisms (Milledge et al., 2019 ). Mineral salt analysis revealed that Sargassum has a high calcium (Ca) content, followed by sodium (Na), potassium (K), and magnesium (Mg). Salts can be released during the decomposition of organic matter, and elevated salt levels induce bacterial cell dehydration due to osmotic pressure (Zhang et al., 2020 ). Sodium primarily determines the toxicity of the salt, but other light metal ions, such as potassium, can also be toxic to methanogens at high levels (Maneein et al., 2018 ). 3.2 Adaptation of microbial consortium The adaptation process of the microbial consortium was conducted for 100 days. FVW was chosen as the starting substrate due to its high biodegradability and because the microbial consortium originated from a bioreactor that treated FVW. FVW was progressively replaced by Sargassum to mitigate the potential inhibition of microorganisms. Sargassum concentrations gradually increased from Passages I to IV. Figure 1 a illustrates the variations in cumulative biogas and methane production across the six passages. Passages I and II exhibited cumulative biogas production of 1218.67 and 1294.54 (N-mL), respectively. However, in Passage III, the accumulated biogas production decreased by 28% compared with the value achieved in Passage II. In Passage I, biogas production concluded after 7 days, whereas in Passages II and III, the time for biogas production increased to 12 and 14 days, respectively. Passage IV, representing 100% Sargassum , achieved biogas production similar to Passage III but over a longer duration (29 days). After Passage V, the time for biogas production was shorter (17 days), but biogas production decreased by 16% compared with Passage IV. Surprisingly, after Passage VI, a higher cumulative biogas production (1415.31 N-mL) was observed in 22 days. Figure 1 b illustrates the methane production rates (N-mL day − 1 ) concerning the amount of added Sargassum for each of the six passages in the adaptation stage. In the initial three passages, it was observed that the consortium promptly initiated degradation of the provided substrate at a relatively high rate. It can be assumed that the elevated rates and short durations of methane production observed from Passages I to III are attributed to the presence of FVW. FVW was characterized as highly biodegradable because of its high moisture content, volatile solids (VS), and high C: N ratio. In addition, FVW was characterized by a low lignocellulosic matter content (Table 1 ). However, from Passage IV onward, the methane production rate drastically decreased as the FVW was removed and the amount of Sargassum increased. This indicates the limited ability of microorganisms to degrade algal biomass, given that Sargassum had a significant insoluble fiber content (Table 1 ). The presence of lignin in Sargassum cell walls acts as a barrier to microorganisms (Rosellon et al., 2022 ). In addition, Sargassum is characterized by the presence of salts and polyphenols, which at high concentrations act as inhibitors of microbial activity (Milledge et al., 2020 ; Salgado-Hernández et al., 2023a ). Several studies have indicated that as salinity increases, microorganisms need more time to adapt to stress conditions, resulting in a decline in their metabolic activity. This decrease is reflected in reduced biogas production and an extended production time (Lefebvre et al., 2007 ; Zhang et al., 2020 ). Finally, after Passages V and VI, higher methane production was observed at a greater rate and in a shorter time, suggesting an adaptation to Sargassum by the microorganisms. Adaptation may result in acclimatization to a stressful environment by increasing the product yield or growth rate of the microorganism (Tan et al., 2022 ). Miura et al., ( 2015 ) found that after several rounds of feeding, the microbial communities acclimatize to the substrate, thereby reducing the period for methane production and increasing their yield. Additionally, Ahmed et al., ( 2019 ) observed that after 35 days of adaptation, methane production rates from lignocellulosic substrates increased. 3.3 Taxonomic profile of the microbial consortium The bacterial and archaeal community of the microbial consortium that adapted to pelagic Sargassum for 100 days was analyzed through high-throughput sequencing of 16S rRNA gene amplicons. Samples from the initial (non-adapted) microbial consortium and days 60 and 100 of the adaptation period were analyzed. 3.3.1 Bacterial diversity During the adaptation period of the microbial consortium, shifts in bacterial phyla dominance were observed. The evolution of the phyla at days 60 and 100 of adaptation is detailed below and illustrated in Fig. 2 a. In its initial stage, the microbial consortium was dominated by the phylum Bacteroidota, accounting for a relative abundance of 40.49%. Other phyla were also abundant but in smaller proportions, such as Desulfobacterota_G_459544 (11.63%), Cloacimonadota (15.87%), and Firmicutes_A (6.88%). After 60 days of adaptation (Passage IV), the phylum Bacteroidota remained the most dominant, although its relative abundance decreased to 25.17%. The phylum Atribacterota showed a significant increase in relative abundance, rising from 0.73–22.33% at day 60. Other phyla, such as Firmicutes_A (13.16%), Firmicutes_D (9.0%), Synergistota (6.28%), and Chloroflexota (5.67%), were also present, but in lower proportions. Finally, after 100 days of adaptation (Passage VI), the phylum Bacteroidota remained the most dominant, with a relative abundance of 26.16%. The phylum Atribacterota experienced a decrease in relative abundance but remained significant at 14.65%. Other phyla such as Firmicutes_G (9.75%), Proteobacteria (7.26%), Chloroflexota (6.38%), and Desulfobacterota_G_459544 (5.22%) were also present at the end of Passage VI. Figure 2 b illustrates the relative abundances of the bacterial community at the family level. In the non-adapted microbial consortium, the dominant family was VadinHA17_877549, with a relative abundance of 21.96%. Other families were in smaller proportions, including Cloacimonadaceae (15.85%), Smithellaceae (8.6%), and UBA932 (5.92%). At the 60-day adapted stage, a shift in the dominance of bacterial families was observed. Family 34–128 became the most dominant, with a relative abundance of 19.68%. Conversely, the family VadinHA17_877549 experienced a considerable decrease in relative abundance, dropping to 0.59%. Other families, such as Marinilabiliaceae (9.3%), UBA7960 (8.22%), Cellulosilyticaceae (5.61%), and Thermovirgaceae (5.09%), increased their dominance. UBA932 (2.93%), Smithellaceae (0.28%), and Cloacimonadaceae (0.12%) were also present but in lower abundance. At 100 days of adaptation, the microbial consortium was dominated by the 34–128 family with a relative abundance of 13.57%, although its abundance was lower than that found at 60 days. On the other hand, families such as UBA932 (6.96%) and UBA8346 (8.97%) increased their abundance. UBA7960 (6.76%) and Marinilabiliaceae (4.46%) also showed a significant presence but were slightly lower than that found at day 60. These families belong to diverse bacterial phyla and could play an important role in the microbial community adapted to pelagic Sargassum . The non-adapted microbial consortium was primarily dominated by families of the phyla Bacteroidota, Cloacimonadota, and Desulfobacterota. The phylum Bacteroidota produces several lytic enzymes, including hydrolases and lipases, which are involved in the degradation of complex organic compounds (Alalawy et al., 2021 ). Cloacimonadota are anaerobic, acetogenic, and fermentative bacteria (Johnson and Hug, 2021 ). Cloacimonadota plays a role in amino acid fermentation, the syntrophic oxidation of propionate, and the establishment of syntrophic interactions with hydrogen scavengers (Alalawy et al., 2021 ). Smithellaceae, belonging to the phylum Desulfobacterota, is characterized as a syntrophic propionate oxidizing bacterium by a C6 dismutation pathway to acetate and butyrate (Dyksma and Gallert, 2022 ). Systems operating on easily digestible substrates have been reported to be dominated by members of the phylum Bacteroidota, especially the order Bacteroidales (Theuerl et al., 2019 ). On the other hand, the adapted consortium was dominated by families of the phyla Bacteroidota, Firmicutes, and Atribacterota. Specifically, the families UBA932, UBA7960, and Marinilabiliaceae of the order Bacteroidales, belonging to the phylum Bacteroidota, perform hydrolytic functions that convert complex organic substrates, such as carbohydrates, into simple monomers like glucose. Meanwhile, Firmicutes participate in both hydrolytic and fermentative roles and can sometimes even act as syntrophic fatty oxidizers (Ohemeng-Ntiamoah and Datta, 2021 ). Firmicutes can produce VFA, such as acetic acid, the main precursor of acetoclastic methanogenesis (Alalawy et al., 2021 ). The predominance of Firmicutes and Bacteroidota has been reported as an indicator of stability in AD (Alalawy et al., 2021 ). Members of Atribacterota have fermentative potential using various substrates and a possible syntrophic association with hydrogenotrophic methanogens through acetate oxidation (Lee et al., 2018 ). 3.3.2 Archaeal diversity Figure 3 a shows the distribution of Archaea phyla at the non-adapted microbial consortium and after 60 and 100 days of adaptation. The non-adapted community was mainly composed of Halobacteriota with a relative abundance of 74.68%. Other phyla such as Methanobacteriota (11.3%), Thermosplasmatota (5.85%), and Thermoproteota (5.5%) were present in minor proportions. After 60 days of adaptation, the Halobacteriota phylum maintained its dominance with a relative abundance of 93.07%. A reduction in the relative abundance of other phyla, including Methanobacteriota (1.43%), Thermosplasmatota (1.21%), and Thermoproteota (4%) was observed. After 100 days, Halobacteriota remained the dominant phylum, but its relative abundance declined to 79.77%. Over the same period, relative abundance of Methanobacteriota increased from 1.43–4.61%. Other phyla, including Thermosplasmatota (3.33%) and Thermoproteota (6.71%), were also present, albeit in smaller amounts. Figure 3 b displays the Archaean community at the family level. Within the non-adapted consortium, Methanosarcinaceae was the dominant family with a relative abundance of 46.76%. Additional families were detected in smaller proportions, including Methanotrichaceae (11.67%), Methanoregulaceae (13.92%), and Methanophastidiosaceae (11.3%). After 60 days of adaptation, a shift in the dominance of archaeal families was observed. The family Methanotrichaceae achieved the highest dominance level with a relative abundance of 58.98%. Likewise, Methanoregulaceae (25.13%) also experienced an increase in relative abundance. Conversely, the families Methanosarcinaceae and Methanophastidiosaceae exhibited a significant decrease in relative abundance, down to 7.57% and 1.43%, respectively. In the 100-day adapted state, the Methanotrichaceae family remained the most dominant, although its relative abundance decreased to 33.41%. On the other hand, the Methanoregulaceae family showed an increase in relative abundance from 25.13% at 60 days to 29.3% at 100 days. In addition to these families, Methanosarcinaceae (6.02%), UBA233 (6.67%), Methanoculleaceae (6.9%), and Methanophastidiosaceae (4.61%) were detected. The non-adapted microbial consortium was largely dominated by Methanosarcinaceae, a fast-growing and versatile substrate methanogen that can utilize acetate, H 2 + CO 2 , methanol, and methylamines for methanogenesis (Wojcieszak et al., 2017 ). According to previous research, Methanosarcinaceae exhibits greater dominance than Methanotrichaceae at high concentrations of VFA and TAN (Wang et al., 2018 ). This indicates that the non-adapted microbial consortium originated from an environment with a high VFA concentration because it was fed with FVW, which is characterized as highly fermentable. The adapted consortium was dominated by Methanotrichaceae (formerly Methanosaetaceae), an acetoclastic obligate methanogen, and Methanoregulaceae, a hydrogenotrophic methanogen (Cui et al., 2023 ). Methanotrichaceae exhibit greater acetate affinity than Methanosarcinaceae. Elevated levels of acetate in the culture medium promote the proliferation of Methanosarcinacea, whereas decreased levels encourage the growth of Methanotrichaceae (Wang et al., 2018 ). These results indicate that the concentration of acetate during the adaptation period was low because of the slow bioconversion of pelagic Sargassum . As a result, the microbial consortium adapted more toward H 2 + CO 2 use. This is evidenced by the increased abundance of hydrogenotrophic methanogens such as Methanoregulaceae and Methanoculleaceae. In addition, the increase in family 34–128 is another indication of methane production via the hydrogenotrophic pathway. It has been reported that members of the Atribacterota phylum may form a syntrophic association with hydrogenotrophic methanogens by oxidizing acetate (Lee et al., 2018 ). These findings contribute to a better understanding of the microbial ecology and degradation processes of pelagic Sargassum . In summary, the results obtained demonstrate that the adaptation of the microbial consortium is reflected in a significant remodeling of the microbial community. These changes reveal the plasticity and adaptability of microbial communities to specific substrates, such as pelagic Sargassum . Furthermore, bacterial phyla like Bacteroidota, Firmicutes, Atribacterota, and methanogens such as Methanotrichaceae and Methanoregulaceae exhibit a significant affinity for pelagic Sargassum compounds. 3.4 Potential functional characteristics of the microbial consortium Functional profile analysis of the non-adapted and adapted microbial consortium (MC) was conducted at 60 and 100 days using the KEGG database. The results in Fig. 4 the top five functions classified as level 2 according to the KEGG database. The majority of these functions are related to metabolism, while a few correspond to genetic information processing functions such as replication and repair. Carbohydrate metabolism had the highest gene abundance, followed by amino acid metabolism and energy metabolism. After 60 days of adaptation, a decrease was observed in the abundance of most of these functions. It has been reported that feeding a complex substrate reduces carbohydrate metabolism (Basak et al., 2022 ). Thus, the reduction of certain metabolic pathways can be attributed to limited substrate availability stemming from the intricate chemical composition of Sargassum spp. Nonetheless, the abundance of these functions displayed a subsequent increase at the 100-day mark of adaptation, albeit slightly less than that observed in the non-adapted consortium. Figure 6 displays variations in the crucial functions of the microbial consortium, which are categorized as level 3 based on the KEGG database. Noteworthy KEGG modules related to biomethanation include methane metabolism, sulfur, carbohydrates, fatty acid oxidation, and quorum sensing (Basak et al., 2022 ). This study uncovered a responsive and ever-changing functional profile of the microbial consortium through adaptation. Notably, certain functions manifested distinct fold changes at 60 and 100 days of adaptation. The heat map (see Fig. 6 ) illustrates the decline of various metabolic pathways and processes, particularly during the 60-day period when the consortium was exclusively fed 100% Sargassum . Changes in abiotic environmental factors can trigger stress responses in microorganisms that focus more on survival than growth (Wani et al., 2022 ). Fold-change values demonstrate a decrease in ABC transporter expression after 60 days of adaptation. These transporters play pivotal roles in the uptake and transport of nutrients. This decline suggests a lower demand for specific nutrients or alterations in nutrient use. Consequently, certain functions may be reduced or impeded as the consortium tackles the obstacles accompanying acclimation to a new substrate and environment. Microorganisms may undergo modifications through acetylation or methylation that slowly affect gene expression until normal growth is restored (Tan et al., 2022 ). Some metabolic processes or pathways in the microbial community experienced a decline at 60 days but recovered after 100 days of adaptation. Notably, some functions exceeded their initial stage, including the metabolism pathways of glycerolipids and glycerophospholipids, as well as the biosynthesis of folate, terpenes, valine, leucine, and isoleucine. Methane and sulfur metabolism related to energy pathways exhibited reduced abundance at 60 days of adaptation but recovered by day 100. Notably, the methane metabolism pathway remained relatively comparable to the initial stage after 100 days. This indicates that the consortium’s capacity to produce methane remained relatively stable after the adaptation stage. Changes in the metabolic pathways responsible for carbohydrate metabolism were observed in addition to energy metabolism functions. These changes include glycolysis/glycogenesis, pyruvate, butanoate, and starch and sucrose metabolism. Glycolysis and glycogenesis are the primary pathways for glucose synthesis and metabolism. Many anaerobic microorganisms rely exclusively on these pathways for survival. Additionally, glycolysis, pyruvate metabolism, and fatty acid degradation play crucial roles in the production of volatile fatty acids (Li et al., 2022 ). A reduction in these pathways was noted after 60 and 100 days of the adaptation period with Sargassum . This reduction can be traced back to the decrease in glucose concentration in the medium, which is caused by the gradual shift of substrate from fruits and vegetables to pelagic Sargassum . Davis et al., ( 2021 ) reported the monosaccharide composition of Sargassum spp., noting a low level of glucose. Moreover, the pathway for starch and sucrose metabolism decreased during the adaptation stage. This finding suggests a transition away from starch and sucrose as carbon sources, which could be attributed to the presence of other compounds in pelagic Sargassum . Also, the metabolism of butanoate or butyrate, a VFA produced during the acidogenic fermentation of carbohydrates by Bacteroides spp. (Vemuri et al., 2018 ), decreased after 60 days but increased after 100 days. This result suggests that VFA production decreased, possibly because of a reduction in carbohydrate hydrolysis. In general, the observed changes in functional traits during adaptation suggest that there was an up- or down-regulating of gene abundance to better utilize pelagic Sargassum . When microorganisms are initially exposed to a stressful environment, they may enter a self-protective state and exhibit slow or stagnant growth (Tan et al., 2022 ). Therefore, the stress response induced by Sargassum could be responsible for the down-regulating of gene abundance for certain functions after 60 days of adaptation (Fig. 6 ). During the adaptation period, microorganisms can regulate their growth through epigenetic modifications or alter their metabolic state by regulating gene expression (Tan et al., 2022 ). The decrease in these functions could affect methane production, as shown in Fig. 1 , <where low and slow methane production was observed when feeding only with Sargassum . However, after 100 days, an up regulating of most of the functions was observed. This increase suggests a more advanced stage of adaptation, where the microorganisms in the consortium have adapted and fine-tuned their metabolic pathways in response to the Sargassum environment. There may have been an improvement in pathways more favorable to their growth and methane production, as shown in Fig. 1 . The microbial adaptation to Sargassum is largely attributed to its genetic composition and regulation. However, more sophisticated techniques such as metagenomics for functional analysis and metatranscriptomics to understand the activity of gene profiles during the adaptation stage are needed. 3.5 Biochemical methane potential of pelagic Sargassum Biomethane potential tests were conducted using the non-adapted microbial consortium (NA) and the consortium adapted for 100 days (AC) as the inoculum source. After 32 days, the methane yield from cellulose achieved by NA and AC was 339.7 ± 2.28 and 329.5 ± 6.16 (N-mL CH 4 g − 1 VS), respectively (Fig. 5 ). The amount of methane obtained from cellulose validated the suitability of the inoculum for methane production, as established by Holliger et al., ( 2021 ). There were no significant differences between the two yields obtained using NA and AC ( p > 0.05 ). These results indicate that the microbial consortium does not decrease its methanogenic activity after the adaptation phase. However, the methane yield curves show a lag phase when AC is used, unlike NA, where methane is immediately produced. This could be due to the high affinity developed by the microorganisms for Sargassum after the adaptation stage, which delays the consumption of cellulose. The methane yields from Sargassum obtained by AC and NA were 160.03 ± 4.64 and 98.26 ± 3.65 (N-mL CH 4 g − 1 VS), respectively. The inoculum effect was confirmed by the statistical analysis of the methane yields, which showed a significant difference ( p < 0.05 ) between the final methane produced by AC and NA. Observation of the methane production curves (Fig. 5 ) showed that Sargassum was inhibitory to NA microorganisms in the initial digestion phase. This caused the methane production to be lower than the blank, resulting in a negative methane yield (Filer et al., 2019 ). The inhibitory effect was also confirmed by the diauxic behavior of the curve throughout the digestion period (Koch et al., 2019 ). However, with AC, Sargassum is easily biodegraded. Methane is produced immediately, and the methane production curve becomes more stable during the digestion period. This suggests that after the adaptation phase, there is an increase in the affinity of microorganisms for Sargassum and a possible increase in the number of specific degraders (Raposo et al., 2012 ). The use of AC showed a 63% improvement in the methane yield. Based on the experimental results, it was possible to calculate the biodegradability index (BI), which was calculated using Eq. 2 from the molecular formula (C 2.8 H 5 N 0.1 O 2 S 0.02 ) of pelagic Sargassum . AC achieved a BI of 39%, whereas NA achieved a value of 24%, as shown in Fig. 6 . Previous studies have reported that methane yields from pelagic Sargassum range from 41 to 116 mL g − 1 VS (Salgado-Hernández et al., 2023a ; Tapia-Tussell et al., 2018 ; Thompson et al., 2020 ), although these yields vary because of the use of different pretreatments. For example, a yield of 159.7 mL CH 4 g − 1 VS with a BI of 39% was reported using the liquid fraction obtained from the solid-liquid separation of Sargassum (Salgado-Hernández et al., 2023b ). Similar values have been reported when Sargassum species are separated and individually subjected to anaerobic digestion (Milledge et al., 2020 ). Although yields have been improved by various pretreatments, they are still well below the theoretical potential. Recently, Chikani-Cabrera et al., ( 2022 ) reported a methane yield of 387 mL g − 1 VS with a BI of 95%, using a combination of physical, chemical, and enzymatic pretreatments. However, the use of combined pretreatments further complicates their large-scale application because of the high cost of energy, chemical reagents, and enzymes. Adapting the inoculum source may be an economical solution in comparison to costly pretreatments, leading to a notable rise in methane yields from Sargassum . A precultivation stage of the inoculum by progressively feeding Sargassum can decrease the requirement for aggressive pretreatments and ensure the success of large-scale anaerobic digestion. Our observations demonstrate that even with inoculum adaptation, Sargassum biodegradability remains below 50%. Therefore, the application of a pretreatment specifically focused on hydrolyzing recalcitrant Sargassum compounds is necessary in addition to adapting the inoculum source. 3.5 Modeling of methane production kinetics Figure 5 displays the cumulative production of CH4 for NA and AC, along with the corresponding fitting curves obtained using the first-order kinetic and transfer function models. Table 2 presents the kinetic parameters for each model, which were calculated using IBM SPSS Statistics 27. Both models achieved a good fit to the experimental results, as indicated by the R 2 values ( R 2 ≥ 0.90). Additionally, the RMSE values provide a statistical measure of the model error. It has been confirmed that the first-order kinetic model is most appropriate for cellulose with NA, whereas the transfer function model is preferable when AC is used. Since these models exhibited the lowest RMSE values (11.34 and 9.55) with cellulose. The transfer function model best fit the data for pelagic Sargassum when NA was used. Conversely, when AC was employed, the first-order model provided the best fit. This is attributed to the first-order model having a better fit in the absence of a lag phase (Filer et al., 2019 ). The calculated value of k suggests that the degradation rates of pelagic Sargassum were slightly higher with NA, as shown in Table 2 . The k values discovered in this study were lower than those previously reported for other brown algae (Membere and Sallis, 2018 ). Table 2 Results from the kinetic study using the first-order and transfer function models. First-order kinetics model Transference funtion model B 0 a (mL CH 4 g − 1 VS) k a (day − 1 ) R 2 RMSE B 0 a (mL CH 4 g − 1 VS) R max a (mL CH 4 g − 1 VS day − 1 ) λ a (day) R 2 RMSE Non-adapted Sargassum 90.53 0.160 0.899 10.87 82.67 13.29 2.74 0.902 9.95 Cellulose 310.97 0.273 0.982 11.34 302.93 54.46 1.76 0.965 16.47 Adapted Sargassum 151.91 0.148 0.962 9.32 150.23 44.45 0 0.947 11.03 Cellulose 349.03 0.102 0.970 21.68 316.73 79.45 5.04 0.994 9.55 a Calculated at a confidence interval of 95% The adapted consortium yielded the highest values for both the maximum predictable methane potential ( B 0 ) and the maximum methane production rate ( R max ) when tested with cellulose and Sargassum . With AC, Sargassum showed a maximum methane production rate of 44.45 mL g − 1 VS day − 1 , which was 3.2 times greater than that observed with NA (13.29 mL g − 1 VS day − 1 ). Additionally, AC demonstrated immediate methane production ( λ = 0), whereas a delay phase ( λ ) of 2.74 days was observed with NA. The R max value for cellulose was higher with AC (79.45 mL g − 1 VS day − 1 ) compared to the NA (54.46 mL g − 1 VS day − 1 ). However, the degradation of cellulose was slower when AC was used, resulting in an increase in the lag phase from 1.76 to 5.04 days. Results from the kinetic study suggest that Sargassum is more susceptible to degradation by the adapted microbial consortium, highlighting the better survival of the microorganisms under adapted conditions. Consequently, biogas production began on the first day. In addition, the use of AC resulted in a higher R max value than that previously reported for pelagic Sargassum (Salgado-Hernández et al., 2023b ). 4. Conclusions The adaptation of a microbial consortium to pelagic Sargassum , has been a successful strategy for enhancing biodegradability and methane production. Observations during adaptation indicate a substrate-specific response with significant changes in the composition and function of the microbial community. The presence of Atribacterota, Firmicutes, Proteobacteria, Methanotrichaceae, and Methanoregulaceae, at the end of the adaptation period indicates a growing preference for Sargassum . The functional profile prediction indicated an initial decline in metabolic functions, followed by an upswing after 100 days, indicating the adaptation and optimization of metabolic capability. The enhancement of physiological capacities led to faster biogas production, resulting in a 60% increase in biomethane potential and an improvement in the biodegradability index. Kinetic models provide evidence supporting the efficacy of acclimation during the anaerobic digestion process of pelagic Sargassum , thereby surmounting obstacles linked to low methane production. Although these achievements are notable, it is necessary to implement complementary strategies, such as extra pretreatments, to overcome persistent limitations in biodegradability and improve organic matter availability. Such a comprehensive approach yields valuable insights for developing sustainable waste treatment technologies and effectively managing pelagic Sargassum in coastal regions, highlighting the crucial importance of microbial adaptation in this context. Declarations Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors thank the federal maritime-terrestrial zone (ZOFEMAT) of the municipality of Solidaridad (Quintana Roo, México) for obtaining the macroalgae. Enrique Salgado Hernández thanks CONACYT for a doctoral scholarship (817679). References Achinas, S., Euverink, G.J.W., 2016. 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Frontiers in Microbiology 13, 888746. https://doi.org/10.3389/fmicb.2022.888746 Tapia-Tussell, R., Avila-Arias, J., Domínguez Maldonado, J., Valero, D., Olguin-Maciel, E., Pérez-Brito, D., Alzate-Gaviria, L., 2018. Biological Pretreatment of Mexican Caribbean Macroalgae Consortiums Using Bm-2 Strain (Trametes hirsuta) and Its Enzymatic Broth to Improve Biomethane Potential. Energies 11, 494. https://doi.org/10.3390/en11030494 Tedesco, S., Daniels, S., 2019. Evaluation of inoculum acclimatation and biochemical seasonal variation for the production of renewable gaseous fuel from biorefined Laminaria sp. waste streams. Renewable Energy 139, 1–8. https://doi.org/10.1016/j.renene.2019.02.057 Theuerl, S., Klang, J., Prochnow, A., 2019. Process Disturbances in Agricultural Biogas Production—Causes, Mechanisms and Effects on the Biogas Microbiome: A Review. Energies 12, 365. https://doi.org/10.3390/en12030365 Thompson, T.M., Young, B.R., Baroutian, S., 2020. Pelagic Sargassum for energy and fertiliser production in the Caribbean: A case study on Barbados. Renewable and Sustainable Energy Reviews 118, 109564. https://doi.org/10.1016/j.rser.2019.109564 Thompson, Terrell M., Young, B.R., Baroutian, S., 2020. Efficiency of hydrothermal pretreatment on the anaerobic digestion of pelagic Sargassum for biogas and fertiliser recovery. Fuel 279, 118527. https://doi.org/10.1016/j.fuel.2020.118527 van Tussenbroek, B.I., Hernández Arana, H.A., Rodríguez-Martínez, R.E., Espinoza-Avalos, J., Canizales-Flores, H.M., González-Godoy, C.E., Barba-Santos, M.G., Vega-Zepeda, A., Collado-Vides, L., 2017. Severe impacts of brown tides caused by Sargassum spp. on near-shore Caribbean seagrass communities. Marine Pollution Bulletin 122, 272–281. https://doi.org/10.1016/j.marpolbul.2017.06.057 Veluchamy, C., Kalamdhad, A.S., 2017. Enhanced methane production and its kinetics model of thermally pretreated lignocellulose waste material. Bioresource Technology 241, 1–9. https://doi.org/10.1016/j.biortech.2017.05.068 Vemuri, R., Shinde, T., Gundamaraju, R., Gondalia, S., Karpe, A., Beale, D., Martoni, C., Eri, R., 2018. Lactobacillus acidophilus DDS-1 Modulates the Gut Microbiota and Improves Metabolic Profiles in Aging Mice. Nutrients 10, 1255. https://doi.org/10.3390/nu10091255 Wang, P., Wang, H., Qiu, Y., Ren, L., Jiang, B., 2018. Microbial characteristics in anaerobic digestion process of food waste for methane production–A review. Bioresource Technology 248, 29–36. https://doi.org/10.1016/j.biortech.2017.06.152 Wani, A.K., Akhtar, N., Sher, F., Navarrete, A.A., Américo-Pinheiro, J.H.P., 2022. Microbial adaptation to different environmental conditions: molecular perspective of evolved genetic and cellular systems. Arch Microbiol 204, 144. https://doi.org/10.1007/s00203-022-02757-5 Wojcieszak, M., Pyzik, A., Poszytek, K., Krawczyk, P.S., Sobczak, A., Lipinski, L., Roubinek, O., Palige, J., Sklodowska, A., Drewniak, L., 2017. Adaptation of Methanogenic Inocula to Anaerobic Digestion of Maize Silage. Front. Microbiol. 8, 1881. https://doi.org/10.3389/fmicb.2017.01881 Zhang, J., Zhang, R., He, Q., Ji, B., Wang, H., Yang, K., 2020. Adaptation to salinity: Response of biogas production and microbial communities in anaerobic digestion of kitchen waste to salinity stress. Journal of Bioscience and Bioengineering 130, 173–178. https://doi.org/10.1016/j.jbiosc.2019.11.011 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3819248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264197754,"identity":"66bd1ee1-c1bf-4d33-b7f0-15ca3496dbd4","order_by":0,"name":"Enrique Salgado-Hernández","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACAwh1QI6fvQHEtSBei7FkzwEQV4J4LYkbbiSAGERoMWfvPfaZ588dxoabz69u+FEgwcDf3p2AV4tlz7nk2bxtz5gZZ+eU3ewBOkzizNkN+B12I8eYmbfhMBuzdE7aDR6gFgOJXAJa7r8xZub5c5iHTfJM2s0/RGm5wQPUwnZYgkeC/dht4mw5k2PMOLftsIEETw7bbRkgRdgvx88YM7z5c7h+//Hjz26++WMjx9/ei18LCDDxgCkecBzxEFQOAow/wBT7A6JUj4JRMApGwcgDALPnSXkAMn8CAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3578-9913","institution":"Universidad Veracruzana","correspondingAuthor":true,"prefix":"","firstName":"Enrique","middleName":"","lastName":"Salgado-Hernández","suffix":""},{"id":264199170,"identity":"0dd421ab-a52c-4bd7-8755-039819802cc6","order_by":1,"name":"Ángel Isauro Ortiz-Ceballos","email":"","orcid":"https://orcid.org/0000-0001-8700-4503","institution":"Universidad Veracruzana","correspondingAuthor":false,"prefix":"","firstName":"Ángel","middleName":"Isauro","lastName":"Ortiz-Ceballos","suffix":""},{"id":264199171,"identity":"7209a7b8-4032-4f0d-8ddf-342c2747d44c","order_by":2,"name":"Alejandro Alvarado-Lassman","email":"","orcid":"https://orcid.org/0000-0001-9818-4300","institution":"Instituto Tecnológico de Orizaba","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Alvarado-Lassman","suffix":""},{"id":264199172,"identity":"9a62622c-7b47-4dfd-8a1f-cfa2390ae9b9","order_by":3,"name":"Sergio Martínez-Hernández","email":"","orcid":"","institution":"Universidad Veracruzana","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Martínez-Hernández","suffix":""},{"id":264199173,"identity":"c101a549-a973-4634-91ed-66659180b198","order_by":4,"name":"Ana Elena Dorantes-Acosta","email":"","orcid":"","institution":"Universidad Veracruzana","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Elena","lastName":"Dorantes-Acosta","suffix":""},{"id":264199174,"identity":"679187c0-b286-4154-883d-08d6371fe0c3","order_by":5,"name":"Erik Samuel Rosas-Mendoza","email":"","orcid":"https://orcid.org/0000-0002-2353-9143","institution":"CONACYT","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"Samuel","lastName":"Rosas-Mendoza","suffix":""}],"badges":[],"createdAt":"2023-12-29 03:12:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-3819248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3819248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49289885,"identity":"b90675d0-cfb3-4738-8eb9-08b3f779b9ed","added_by":"auto","created_at":"2024-01-08 04:18:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1899800,"visible":true,"origin":"","legend":"\u003cp\u003eBiogas and methane production (a) and the methane production rate in relation to the amount of \u003cem\u003eSargassum\u003c/em\u003e added (b) at each passage of the adaptation process. The graph includes numerical labels (I-VI) for each passage. Error bars indicate the standard deviation of the mean (n = 2).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/357e106c990e0c6f5ac6bfd2.png"},{"id":49289889,"identity":"35861601-69fe-4f2e-9830-6ae8ac65edc3","added_by":"auto","created_at":"2024-01-08 04:18:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":714144,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of Bacteria at the phylum level (a) and family level (b) of the non-adapted and adapted microbial consortium after 60 and 100 days of incubation. Bacterial groups with abundances \u0026lt;1% were grouped as \"others\".\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/b28928a9bf5ff80a5c95245f.png"},{"id":49289890,"identity":"222bbc62-350b-4086-b59b-83b96f9537d4","added_by":"auto","created_at":"2024-01-08 04:18:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":486189,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of Archaea at the phylum level (a) and family level (b) of the non-adapted and adapted microbial consortium after 60 and 100 days of incubation. Archaeal groups with abundances \u0026lt;1% were grouped as \"others\".\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/ee06f09473c9768f1f8f18b2.png"},{"id":49289886,"identity":"2944a77c-3790-4cde-9c4c-d97712c91b3c","added_by":"auto","created_at":"2024-01-08 04:18:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":270524,"visible":true,"origin":"","legend":"\u003cp\u003eGene abundances of the five top-classified level 2 functions identified in the non-adapted and adapted microbial consortium after 60 and 100 days of incubation.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/a5215e95f1897246abe3cf43.png"},{"id":49290466,"identity":"c7838863-5263-4472-88c9-9ffa2a28575b","added_by":"auto","created_at":"2024-01-08 04:34:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":641699,"visible":true,"origin":"","legend":"\u003cp\u003eFold change in gene abundances classified as level 3 metabolic functions in the non-adapted and adapted microbial consortium after 60 and 100 days of incubation.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/42d5798d82b18157e0239170.png"},{"id":49289985,"identity":"2c5b421e-6b28-495d-b25f-eb6a16bded96","added_by":"auto","created_at":"2024-01-08 04:26:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":740154,"visible":true,"origin":"","legend":"\u003cp\u003eBiomethane potential expressed in N-mL g\u003csup\u003e-1\u003c/sup\u003e VS of pelagic \u003cem\u003eSargassum\u003c/em\u003e and cellulose using the non-adapted (a) and adapted (b) microbial consortium. Experimental and modeled data. Error bars represent the standard deviation of the mean (n=3).\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/4ba3390c24882b4802370b63.png"},{"id":49289983,"identity":"9d2b82e2-f802-4a15-8224-5c283aa5fcdd","added_by":"auto","created_at":"2024-01-08 04:26:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":231395,"visible":true,"origin":"","legend":"\u003cp\u003eBiomethane potential, experimental compared to theoretical, and biodegradability indices of pelagic \u003cem\u003eSargassum\u003c/em\u003e using the adapted and non-adapted consortium.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/e759a70c3cafc1f7f15bb1fd.png"},{"id":49290686,"identity":"64bc95fd-1679-4ba1-a399-5f6b8fc90657","added_by":"auto","created_at":"2024-01-08 04:42:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1638048,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/452dfaeb-bb70-48d9-8dc4-ff95c5e90087.pdf"},{"id":49289892,"identity":"b35e059c-dde2-4903-b7d5-477603035bb3","added_by":"auto","created_at":"2024-01-08 04:18:44","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9117976,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"Graphicalabstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-3819248/v1/cdb4ad0b75625b1c3b125ca4.tif"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAdaptation of a microbial consortium to pelagic \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSargassum \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emodifies its taxonomic and functional profile that improves biomethane potential\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince 2011, climatological and environmental conditions have facilitated the transport of pelagic \u003cem\u003eSargassum\u003c/em\u003e species out of the Sargasso Sea, causing them to proliferate in the tropical Atlantic between West Africa and South America. This phenomenon has led to the deposition of tons of this macroalgae along the Atlantic and Caribbean coasts (Robledo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The accumulated masses of algae on the beaches undergo decomposition, generating leachates and organic particles, thereby giving rise to brown tides that deplete oxygen, reduce light, and deteriorate water quality (van Tussenbroek et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, the unappealing visual impact of the decaying \u003cem\u003eSargassum\u003c/em\u003e, coupled with the unpleasant odor it emits, has adversely affected the tourism industry\u0026mdash;the primary economic driver in the region (Ch\u0026aacute;vez et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers have explored the valorization of these macroalgae for the generation of biofuels, and have focused mainly on anaerobic digestion (AD) for biogas production (Milledge and Harvey, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, there are currently few studies on the AD of these invasive species, and published results report a biomethane potential (BMP) ranging from 61 to 145 mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS (Milledge et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tapia-Tussell et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In all cases, these values represent less than 50% of the theoretical biomethane potential (TBMP). The complex composition, characterized by low carbohydrate content, high insoluble fiber content, and other compounds that can potentially inhibit anaerobic digestion (DA inhibitors), has been cited as the primary reason for the low methane yield of \u003cem\u003eSargassum\u003c/em\u003e spp. (Milledge et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Soto et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This complexity in the composition of pelagic \u003cem\u003eSargassum\u003c/em\u003e represents an obstacle to microbial degradation.\u003c/p\u003e \u003cp\u003eVarious strategies have been tested to improve methane yields, such as inhibitor removal and different pre-treatments (Chikani-Cabrera et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). However, the sustainable application of these strategies is dependent on biochemical efficiency, economic feasibility, the energy balance of inputs and outputs, resource utilization, and the amount of waste during the process (Barbot et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Maneein et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It has been shown that microbial communities can be adapted to tolerate higher concentrations of salt and other inhibitory compounds to ensure adequate substrate degradation (Barbot et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The adaptation of the inoculum to a substrate results in the induction of metabolic pathways for biodegradation, an increase in the affinity of the microorganisms for the compound, and an increase in the number of specific degraders (Raposo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis work was based on the premise that the adaptation of microorganisms to the characteristics of a non-preferred substrate could improve their ability to consume the substrate (Tan et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which could increase methane generation. The use of an adapted microbial consortium as a source of inoculum could reduce the use of pretreatments, making AD more feasible and sustainable owing to the need for less energy or chemicals. Adaptation or acclimation of the inoculum has been reported to be a successful strategy for the anaerobic digestion of brown algae (Darko et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tedesco and Daniels, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, feeding the culture with a new substrate imposes stress on different microorganisms and alters their proportions in the reactor. Consequently, the proportion of some trophic groups may increase, decrease, or even disappear. In such effects, culture can be transformed into a new one (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, the objective of this study was to investigate the adaptation of a microbial consortium to pelagic \u003cem\u003eSargassum\u003c/em\u003e to enhance methane production. Changes in the taxonomic profile of the microbial consortium during the adaptation period were analyzed by high-throughput sequencing of the 16S rRNA gene. The obtained data were used to predict the functional profiles of the consortium. Our results show the importance of inoculum source adaptation to enhance the bioconversion of pelagic \u003cem\u003eSargassum\u003c/em\u003e to methane. In addition, it provides a better understanding of microbial dynamics and metabolic functions during the adaptation stage.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Algae and inoculum collection\u003c/h2\u003e \u003cp\u003eRepresentative samples of \u003cem\u003eSargassum\u003c/em\u003e spp. were randomly collected from algae accumulated on the shore of Xcalacoco Beach in Playa del Carmen (20\u0026deg;37'19.99''N, 87\u0026deg;4'9.98''W), Quintana Roo, Mexico. Sand and other natural contaminants such as plastics, shells, feathers, and other algae attached to algae were removed manually. Samples were transported to the laboratory in a cooler and stored in resealable plastic bags at -4\u0026deg;C until further use.\u003c/p\u003e \u003cp\u003eThe microbial consortium used as the inoculum was obtained from a pilot-scale reactor fed with the liquid fraction of municipal organic solid waste, which is located at the Instituto Tecnol\u0026oacute;gico de Orizaba (Veracruz, Mexico). Subsequently, the inoculum was incubated at 35\u0026deg;C under anaerobic conditions for seven days and fed with shredded fruit and vegetable waste (FVW) as a substrate to increase the microbial population growth and ensure methane production. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the physicochemical characteristics of the inoculum and FVW.\u003c/p\u003e\n\u003cp\u003eTable 1. Compositional analysis of inoculum, pelagic\u0026nbsp;Sargassum, and FVW.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.537562604340568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.706176961602672%\" valign=\"top\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.530884808013354%\" valign=\"top\"\u003e\n \u003cp\u003eInoculum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.697829716193656%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSargassum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52754590984975%\" valign=\"top\"\u003e\n \u003cp\u003eFVW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.537562604340568%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.706176961602672%\" valign=\"top\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.530884808013354%\" valign=\"top\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.697829716193656%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52754590984975%\" valign=\"top\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.537562604340568%\" rowspan=\"3\"\u003e\n \u003cp\u003eProximal an\u0026aacute;lisis (n= 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.706176961602672%\" valign=\"top\"\u003e\n \u003cp\u003eTS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.530884808013354%\" valign=\"top\"\u003e\n \u003cp\u003e3.08 \u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.697829716193656%\" valign=\"top\"\u003e\n \u003cp\u003e96.11 \u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52754590984975%\" valign=\"top\"\u003e\n \u003cp\u003e7.34 \u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eVS (% TS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003e62.35 \u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e67.80 \u0026plusmn;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e93.06 \u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eAsh (% TS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003e37.64 \u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e28.31 \u0026plusmn;1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e6.94 \u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.537562604340568%\" rowspan=\"6\"\u003e\n \u003cp\u003eUltimate analysis (n= 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.706176961602672%\" valign=\"top\"\u003e\n \u003cp\u003eC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.530884808013354%\" valign=\"top\"\u003e\n \u003cp\u003e35.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.697829716193656%\" valign=\"top\"\u003e\n \u003cp\u003e33.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52754590984975%\" valign=\"top\"\u003e\n \u003cp\u003e46.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eH (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eO (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e31.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e39.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eC: N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003e9.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e34.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.537562604340568%\" rowspan=\"4\"\u003e\n \u003cp\u003eStructural analysis (n= 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.706176961602672%\" valign=\"top\"\u003e\n \u003cp\u003eCellulose (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.530884808013354%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.697829716193656%\" valign=\"top\"\u003e\n \u003cp\u003e12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52754590984975%\" valign=\"top\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eHemicellulose (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eLignin (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eTPC (mg EAG/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e14.4 \u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.537562604340568%\" rowspan=\"4\"\u003e\n \u003cp\u003eMineral content (n= 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.706176961602672%\" valign=\"top\"\u003e\n \u003cp\u003eCa (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.530884808013354%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.697829716193656%\" valign=\"top\"\u003e\n \u003cp\u003e94.9 \u0026plusmn;24.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.52754590984975%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eNa (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e20.7 \u0026plusmn;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eK (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e10.25 \u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.603448275862068%\" valign=\"top\"\u003e\n \u003cp\u003eMg (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.92241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.137931034482758%\" valign=\"top\"\u003e\n \u003cp\u003e7.85 \u0026plusmn;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.336206896551722%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFVW= Fruit and vegetable waste\u003c/p\u003e\n\u003cp\u003eTPC= Total phenol content\u003c/p\u003e\n\u003cp\u003eNA = Not Analyzed\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Adaptation of the microbial consortium to \u003cem\u003eSargassum\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe microbial consortium was adapted in microcosms made of 1 L GL 45 glass bottles with a working volume of 800 mL, with a biogas capture system using the water displacement method. The microcosms were incubated in duplicate at 35\u0026deg;C under static conditions and manually shaken once a day for 60 s. The operation was carried out by repeated fed-batch; once biogas production was exhausted, part of the medium was removed and cyclically replaced with the substrate. The working volume, except during substrate replenishment passages, remained constant. The inoculum was fed with FVW, and once biogas production was observed, feeding proceeded by gradually increasing the \u003cem\u003eSargassum\u003c/em\u003e biomass (Sb) through different passages. Passage I (75% FVW:25% Sb), passage II (50% FVW:50% Sb), passage III (25% FVW:75% Sb), and passage IV (0% FVW:100% Sb). The amount of substrate fed at each passage was 4:1 based on the volatile solids (VS) content. After passage IV, feeding was continued until stable methane production was observed (passage VI). For microbial community analysis, 5 mL of the consortium was collected at the beginning of the experiment and after passage 4 and 6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Biochemical methane potential tests\u003c/h2\u003e \u003cp\u003eBiochemical methane potential (BMP) tests were performed according to the method described by Holliger et al., (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The bioreactors consisted of 120 mL serum bottles with a working volume of 75 mL. Two experiments were conducted to evaluate the effects of microbial consortium adaptation on the biodegradability and BMP of pelagic \u003cem\u003eSargassum\u003c/em\u003e. In the first experiment, the non-adapted (NA) microbial consortium was used as the inoculum. In the second experiment, the adapted consortium (AC) from passage VI was used. Two control groups were used in both the experiments. The first was a negative control (blank) with inoculum only, to measure the contribution of biogas to the inoculum. The second was a positive control with powdered cellulose used to evaluate the specific methanogenic activity of the inoculum. The inoculum-to-substrate ratio was maintained at 2:1 based on volatile solids (VS). The initial pH of the cultures was adjusted to 7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1. To achieve anaerobic conditions, all bottles were hermetically sealed with butyl rubber stoppers and aluminum caps, and the headspace was purged with nitrogen gas (purity 99.5\u0026ndash;100%, Praxair M\u0026eacute;xico S. de R.L. de C.V.) for 3 minutes. The bottles were incubated at 35\u0026deg;C until the daily methane production for three consecutive days was less than 1% of the accumulated volume. During this period, the flasks were shaken daily for 60 seconds. The volume of biogas was measured at regular intervals using 5\u0026ndash;60 mL glass syringes with a shut-off valve and a Luer-lock system. The biogas produced by the bioreactors was normalized against the biogas produced by the blank. The results were expressed as the volume of gas (mL) under standard conditions (273 K and 1 atm) per unit mass (g) of aggregated VS. The experiments were conducted in triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analytical methods\u003c/h2\u003e \u003cp\u003eThe biogas composition was analyzed using a gas chromatograph (Buck Scientific 310, Norwalk, USA) with a thermal conductivity detector (TCD) equipped with a 6-inch-long, 1/4-inch diameter Alltech CTR-I packed column. Doses of 2 mL were injected directly into the gas chromatograph to detect CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e, and N\u003csub\u003e2\u003c/sub\u003e. Helium was used as the carrier gas at 70 psi, column temperature was 36\u0026deg;C, and detector temperature was 121\u0026deg;C. The TS, VS, and ash contents of \u003cem\u003eSargassum\u003c/em\u003e, FVW, and inoculum samples were measured according to Standard Methods (APHA, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). pH was measured using a Thermo Scientific Orion Versa Star pH meter (Waltham, USA). The total phenol content (TPC) of the macroalgae was analyzed by the Folin-Ciocalteu method using a UV-Vis spectrophotometer (Shimadzu, UV 1280, Kyoto, Japan). The carbon, hydrogen, and nitrogen contents of the inoculum and the \u003cem\u003eSargassum\u003c/em\u003e and FVW samples were analyzed using an elemental analyzer (PerkinElmer Series II CHNS/O Analyzer 2400, Waltham, USA). Total sulfur (S) was quantified by turbidimetry with gum arabic as a stabilizer using a UV/Vis spectrophotometer (Spectronic 200, Thermo Scientific, Waltham, USA). Oxygen content was estimated by difference. All the above analyses were performed in duplicate. The cellulose, hemicellulose, and lignin contents of the macroalgae were determined using the Van Soest method on an ANKOM 200 fiber analyzer. (ANKOM Technology, Fairport, NY, USA). The content of mineral salts in the macroalgae was determined using a Sherwood Scientific Ltd. flame photometer (Corning 410, Cambridge, UK) for Na and K and an Agilent Technologies, Inc. atomic absorption spectrometer (Varian 240FS, Santa Clara, USA) for Ca and Mg. The above analyses were performed in duplicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Anaerobic biodegradability and kinetics study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{n}{H}_{a}{O}_{b}{N}_{c}{S}_{d}+\\left(n-\\frac{a}{4}-\\frac{b}{2}+\\frac{3c}{4}+\\frac{d}{2}\\right){H}_{2}O\\underrightarrow{yields}\\left(\\frac{n}{2}+\\frac{a}{8}-\\frac{b}{4}-\\frac{3c}{8}+\\frac{d}{4}\\right){CH}_{4}+\\left(\\frac{n}{2}-\\frac{a}{8}+\\frac{b}{4}+\\frac{3c}{8}+\\frac{d}{4}\\right){CO}_{2}+{cNH}_{3}+d{H}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;1\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\u003eTo calculate the biodegradability of pelagic \u003cem\u003eSargassum\u003c/em\u003e, its theoretical biomethane potential (TBMP) and empirical formula (C\u003csub\u003en\u003c/sub\u003eH\u003csub\u003ea\u003c/sub\u003eO\u003csub\u003eb\u003c/sub\u003eN\u003csub\u003ec\u003c/sub\u003e) were initially determined using the Buswell equation (Eq.\u0026nbsp;1) and the Boyle equation (Eq.\u0026nbsp;2) (Achinas and Euverink, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(TBMP \\left(L {CH}_{4 }kg {VS}^{-1}\\right)=\\frac{22.4\\times \\left(\\frac{n}{2}+\\frac{a}{8}-\\frac{b}{4}-\\frac{3c}{8}-\\frac{d}{4}\\right)}{12n+a+16b+14c+32d}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;2\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\u003ewhere 22.4 is the volume (L) of 1 mole of gas at standard temperature and pressure.\u003c/p\u003e \u003cp\u003eThe biodegradability index (BI) was calculated as the ratio of BMP to TBMP, expressed as the percentage of TBMP achieved by the feedstock at the end of the digestion period (Eq.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(BI \\left(\\%\\right)=\\frac{BMP}{TBMP}\\times 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;3\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\u003eThe kinetic behavior of the BMP test results was described using first-order and transfer function models. First-order kinetics (Eq.\u0026nbsp;4) for particulate organic matter were employed to determine the decay constant (\u003cem\u003ek\u003c/em\u003e, day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), or hydrolysis constant, representing the rate of substrate degradation (Maneein et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Membere and Sallis, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This model considers substrate availability as the limiting factor and hydrolysis is assumed to govern the overall process. Therefore, the lag phase is not considered. The transfer function model (Eq.\u0026nbsp;5), was used to predict the maximum biogas yield (\u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e, mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS), the maximum methane production rate (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and the duration of the lag phase (λ, days), representing the number of days before significant CH\u003csub\u003e4\u003c/sub\u003e production began (de la Lama et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This model fits a first-order curve to relate methane production to microbial activity (Veluchamy and Kalamdhad, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\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\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(B \\left(t\\right)={B}_{0}\\times \\left(1-{exp}^{-k\\times t}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(B \\left(t\\right)={B}_{0}\\times \\left\\{1-exp\\left[-\\frac{{R}_{max}\\times \\left(\\lambda -t\\right)}{{B}_{0}}\\right]\\right\\}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;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 \u003cp\u003eIBM SPSS Statistics version 27 was used to perform a nonlinear Least Squares regression analysis to determine \u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eλ\u003c/em\u003e. The value of k in Eq.\u0026nbsp;4 was estimated by plotting \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{ln}\\left(1-B/{B}_{0}\\right)\\)\u003c/span\u003e\u003c/span\u003e versus time. The statistical indicators R\u003csup\u003e2\u003c/sup\u003e (correlation coefficient) and root mean square error (RMSE) were calculated to assess the goodness of fit and accuracy of the results of both models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Analysis of the microbial community evolution\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 DNA extraction\u003c/h2\u003e \u003cp\u003eSamples (3 mL) of the non-adapted and adapted microbial consortia were collected after days 60 (passage IV) and 100 (passage VI). The samples were centrifuged at 12 000 x g for 3 min to remove the liquid. DNA was obtained using the DNeasy PowerSoil kit from QIAGEN (Hilden, Germany) according to the manufacturer's instructions. DNA concentration, quality, and purity were estimated using agarose gel electrophoresis (1%) and a UV-Vis NanodropTM 2000 spectrophotometer (Thermo Scientific, Waltham, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Metabarcoding of 16S rRNA gene and bioinformatics analysis\u003c/h2\u003e \u003cp\u003eOnce the quality of the DNA samples was ensured, PCR amplification of the V4 hypervariable regions for Bacteria and V4-V5 for Archaea of the 16S rRNA gene was conducted. Primer sets 515F (5\u0026prime;-GTGCCAGCMGCCGCGGTAA-3\u0026prime;) and 806R (5\u0026prime;-GGACTACHVGGGGGTWTCTAAT-3\u0026prime;) were used for Bacteria. For Archaea, Arch519F (5'-CAGCCGCCGCCGCGGGGTAA-3') and Arch915R (5'-GTGCTCCCCCCCGCCAATTCCT-3') were utilized. Library construction and sequencing services on the Illumina NovaSeq PE250 platform were provided by Novogene Co (Beijing, China). The raw high throughput next-generation sequencing data was submitted to the National Center for Biotechnology Information (NCBI) database under BioProject number PRJNA1047747 (accession number SUB14012728). Following sequencing, sequences of forward and reverse reads with barcoding and primers removed were analyzed using QIIME2 software (v2021.11). The demultiplexed reads were denoised and assigned to amplicon sequence variants (ASVs) using the DADA2 algorithm. Taxonomic assignment was based on classifiers trained on the hypervariable region V4-V5 for Archaea and V4 for Bacteria extracted from the Greengenes 2022.10 sequence database. From the taxonomic abundance profiles, the potential functions of the microbial communities in the consortium were inferred using the IVikodak platform (Nagpal et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A single metadata file was constructed using taxonomic data obtained from bacterial and archaeal sequences. The Global Mapper module was used to identify the most important functions based on the KEGG database (Kyoto Encyclopedia of Genes and Genomes).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eA t-student test was conducted to assess the difference between the adapted and non-adapted consortium in terms of BMP at a 95% confidence interval. Statistical analyses were carried out using R (version 4.0.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Compositional analysis of pelagic \u003cem\u003eSargassum\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe composition of pelagic \u003cem\u003eSargassum\u003c/em\u003e collected in Playa del Carmen is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The proximate analysis indicated that \u003cem\u003eSargassum\u003c/em\u003e had a moisture content of 3.89% after drying at 105\u0026deg;C for 24 h. A volatile solids (VS) content of 67.80% and an ash content of 28.31% were observed. These results align with previous reports on pelagic \u003cem\u003eSargassum\u003c/em\u003e from the Mexican Caribbean (Chikani-Cabrera et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). These characteristics can be attributed to the preceding washing process applied to the samples before analysis, as they are known to have a high ash content (~\u0026thinsp;50%) (Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). The ultimate analysis revealed C (33.4%) and N (1.46%) contents similar to those previously reported in other studies on pelagic \u003cem\u003eSargassum\u003c/em\u003e from the Mexican Caribbean (Chikani-Cabrera et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). These contents resulted in a C: N ratio of 22.9, which falls within the optimal range for AD (Montingelli et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The C: N ratio in pelagic \u003cem\u003eSargassum\u003c/em\u003e does not appear to be a hindrance for AD, as recent studies have reported C: N ratios\u0026thinsp;\u0026gt;\u0026thinsp;20 (Chikani-Cabrera et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From the ultimate analysis, the theoretical biomethane potential was calculated using Equations 1 and 2. First, the empirical formula of \u003cem\u003eSargassum\u003c/em\u003e (C\u003csub\u003e2.8\u003c/sub\u003e H\u003csub\u003e5\u003c/sub\u003e N\u003csub\u003e0.1\u003c/sub\u003e O\u003csub\u003e2\u003c/sub\u003e S\u003csub\u003e0.02\u003c/sub\u003e) was determined, and subsequently, the TBMP was obtained (407.29 mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS), a value similar to that reported by Chikani-Cabrera et al., (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the biomethane potential of pelagic \u003cem\u003eSargassum\u003c/em\u003e reported in other studies ranges from 335 to 503 mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS (Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Structural analysis of \u003cem\u003eSargassum\u003c/em\u003e indicated that its cell walls consist mainly of cellulose, lignin, and hemicellulose. Brown algae are characterized by a high content of insoluble fibers compared with other types of algae. This characteristic can be limiting because these compounds, which provide structural support to \u003cem\u003eSargassum\u003c/em\u003e, act as a barrier and impede access for microorganisms during degradation (Maneein et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the lignin, cellulose, and hemicellulose contents found in pelagic \u003cem\u003eSargassum\u003c/em\u003e are lower than those of other lignocellulosic biomasses (Hern\u0026aacute;ndez-Beltr\u0026aacute;n et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, pelagic \u003cem\u003eSargassum\u003c/em\u003e showed a high content of phenols, which also play an important role in cell wall rigidity (Maneein et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Phenols have been described as being responsible for the low methane yield in AD because of their inhibitory effect on microorganisms (Milledge et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Mineral salt analysis revealed that \u003cem\u003eSargassum\u003c/em\u003e has a high calcium (Ca) content, followed by sodium (Na), potassium (K), and magnesium (Mg). Salts can be released during the decomposition of organic matter, and elevated salt levels induce bacterial cell dehydration due to osmotic pressure (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sodium primarily determines the toxicity of the salt, but other light metal ions, such as potassium, can also be toxic to methanogens at high levels (Maneein et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Adaptation of microbial consortium\u003c/h2\u003e \u003cp\u003eThe adaptation process of the microbial consortium was conducted for 100 days. FVW was chosen as the starting substrate due to its high biodegradability and because the microbial consortium originated from a bioreactor that treated FVW. FVW was progressively replaced by \u003cem\u003eSargassum\u003c/em\u003e to mitigate the potential inhibition of microorganisms. \u003cem\u003eSargassum\u003c/em\u003e concentrations gradually increased from Passages I to IV. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea illustrates the variations in cumulative biogas and methane production across the six passages. Passages I and II exhibited cumulative biogas production of 1218.67 and 1294.54 (N-mL), respectively. However, in Passage III, the accumulated biogas production decreased by 28% compared with the value achieved in Passage II. In Passage I, biogas production concluded after 7 days, whereas in Passages II and III, the time for biogas production increased to 12 and 14 days, respectively. Passage IV, representing 100% \u003cem\u003eSargassum\u003c/em\u003e, achieved biogas production similar to Passage III but over a longer duration (29 days). After Passage V, the time for biogas production was shorter (17 days), but biogas production decreased by 16% compared with Passage IV. Surprisingly, after Passage VI, a higher cumulative biogas production (1415.31 N-mL) was observed in 22 days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb illustrates the methane production rates (N-mL day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) concerning the amount of added \u003cem\u003eSargassum\u003c/em\u003e for each of the six passages in the adaptation stage. In the initial three passages, it was observed that the consortium promptly initiated degradation of the provided substrate at a relatively high rate. It can be assumed that the elevated rates and short durations of methane production observed from Passages I to III are attributed to the presence of FVW. FVW was characterized as highly biodegradable because of its high moisture content, volatile solids (VS), and high C: N ratio. In addition, FVW was characterized by a low lignocellulosic matter content (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, from Passage IV onward, the methane production rate drastically decreased as the FVW was removed and the amount of \u003cem\u003eSargassum\u003c/em\u003e increased. This indicates the limited ability of microorganisms to degrade algal biomass, given that \u003cem\u003eSargassum\u003c/em\u003e had a significant insoluble fiber content (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The presence of lignin in \u003cem\u003eSargassum\u003c/em\u003e cell walls acts as a barrier to microorganisms (Rosellon et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, \u003cem\u003eSargassum\u003c/em\u003e is characterized by the presence of salts and polyphenols, which at high concentrations act as inhibitors of microbial activity (Milledge et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Several studies have indicated that as salinity increases, microorganisms need more time to adapt to stress conditions, resulting in a decline in their metabolic activity. This decrease is reflected in reduced biogas production and an extended production time (Lefebvre et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, after Passages V and VI, higher methane production was observed at a greater rate and in a shorter time, suggesting an adaptation to \u003cem\u003eSargassum\u003c/em\u003e by the microorganisms. Adaptation may result in acclimatization to a stressful environment by increasing the product yield or growth rate of the microorganism (Tan et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Miura et al., (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that after several rounds of feeding, the microbial communities acclimatize to the substrate, thereby reducing the period for methane production and increasing their yield. Additionally, Ahmed et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observed that after 35 days of adaptation, methane production rates from lignocellulosic substrates increased.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Taxonomic profile of the microbial consortium\u003c/h2\u003e \u003cp\u003eThe bacterial and archaeal community of the microbial consortium that adapted to pelagic \u003cem\u003eSargassum\u003c/em\u003e for 100 days was analyzed through high-throughput sequencing of 16S rRNA gene amplicons. Samples from the initial (non-adapted) microbial consortium and days 60 and 100 of the adaptation period were analyzed.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Bacterial diversity\u003c/h2\u003e \u003cp\u003eDuring the adaptation period of the microbial consortium, shifts in bacterial phyla dominance were observed. The evolution of the phyla at days 60 and 100 of adaptation is detailed below and illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. In its initial stage, the microbial consortium was dominated by the phylum Bacteroidota, accounting for a relative abundance of 40.49%. Other phyla were also abundant but in smaller proportions, such as Desulfobacterota_G_459544 (11.63%), Cloacimonadota (15.87%), and Firmicutes_A (6.88%). After 60 days of adaptation (Passage IV), the phylum Bacteroidota remained the most dominant, although its relative abundance decreased to 25.17%. The phylum Atribacterota showed a significant increase in relative abundance, rising from 0.73\u0026ndash;22.33% at day 60. Other phyla, such as Firmicutes_A (13.16%), Firmicutes_D (9.0%), Synergistota (6.28%), and Chloroflexota (5.67%), were also present, but in lower proportions. Finally, after 100 days of adaptation (Passage VI), the phylum Bacteroidota remained the most dominant, with a relative abundance of 26.16%. The phylum Atribacterota experienced a decrease in relative abundance but remained significant at 14.65%. Other phyla such as Firmicutes_G (9.75%), Proteobacteria (7.26%), Chloroflexota (6.38%), and Desulfobacterota_G_459544 (5.22%) were also present at the end of Passage VI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb illustrates the relative abundances of the bacterial community at the family level. In the non-adapted microbial consortium, the dominant family was VadinHA17_877549, with a relative abundance of 21.96%. Other families were in smaller proportions, including Cloacimonadaceae (15.85%), Smithellaceae (8.6%), and UBA932 (5.92%). At the 60-day adapted stage, a shift in the dominance of bacterial families was observed. Family 34\u0026ndash;128 became the most dominant, with a relative abundance of 19.68%. Conversely, the family VadinHA17_877549 experienced a considerable decrease in relative abundance, dropping to 0.59%. Other families, such as Marinilabiliaceae (9.3%), UBA7960 (8.22%), Cellulosilyticaceae (5.61%), and Thermovirgaceae (5.09%), increased their dominance. UBA932 (2.93%), Smithellaceae (0.28%), and Cloacimonadaceae (0.12%) were also present but in lower abundance. At 100 days of adaptation, the microbial consortium was dominated by the 34\u0026ndash;128 family with a relative abundance of 13.57%, although its abundance was lower than that found at 60 days. On the other hand, families such as UBA932 (6.96%) and UBA8346 (8.97%) increased their abundance. UBA7960 (6.76%) and Marinilabiliaceae (4.46%) also showed a significant presence but were slightly lower than that found at day 60. These families belong to diverse bacterial phyla and could play an important role in the microbial community adapted to pelagic \u003cem\u003eSargassum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe non-adapted microbial consortium was primarily dominated by families of the phyla Bacteroidota, Cloacimonadota, and Desulfobacterota. The phylum Bacteroidota produces several lytic enzymes, including hydrolases and lipases, which are involved in the degradation of complex organic compounds (Alalawy et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cloacimonadota are anaerobic, acetogenic, and fermentative bacteria (Johnson and Hug, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cloacimonadota plays a role in amino acid fermentation, the syntrophic oxidation of propionate, and the establishment of syntrophic interactions with hydrogen scavengers (Alalawy et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Smithellaceae, belonging to the phylum Desulfobacterota, is characterized as a syntrophic propionate oxidizing bacterium by a C6 dismutation pathway to acetate and butyrate (Dyksma and Gallert, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Systems operating on easily digestible substrates have been reported to be dominated by members of the phylum Bacteroidota, especially the order Bacteroidales (Theuerl et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the adapted consortium was dominated by families of the phyla Bacteroidota, Firmicutes, and Atribacterota. Specifically, the families UBA932, UBA7960, and Marinilabiliaceae of the order Bacteroidales, belonging to the phylum Bacteroidota, perform hydrolytic functions that convert complex organic substrates, such as carbohydrates, into simple monomers like glucose. Meanwhile, Firmicutes participate in both hydrolytic and fermentative roles and can sometimes even act as syntrophic fatty oxidizers (Ohemeng-Ntiamoah and Datta, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Firmicutes can produce VFA, such as acetic acid, the main precursor of acetoclastic methanogenesis (Alalawy et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The predominance of Firmicutes and Bacteroidota has been reported as an indicator of stability in AD (Alalawy et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Members of Atribacterota have fermentative potential using various substrates and a possible syntrophic association with hydrogenotrophic methanogens through acetate oxidation (Lee et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Archaeal diversity\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea shows the distribution of Archaea phyla at the non-adapted microbial consortium and after 60 and 100 days of adaptation. The non-adapted community was mainly composed of Halobacteriota with a relative abundance of 74.68%. Other phyla such as Methanobacteriota (11.3%), Thermosplasmatota (5.85%), and Thermoproteota (5.5%) were present in minor proportions. After 60 days of adaptation, the Halobacteriota phylum maintained its dominance with a relative abundance of 93.07%. A reduction in the relative abundance of other phyla, including Methanobacteriota (1.43%), Thermosplasmatota (1.21%), and Thermoproteota (4%) was observed. After 100 days, Halobacteriota remained the dominant phylum, but its relative abundance declined to 79.77%. Over the same period, relative abundance of Methanobacteriota increased from 1.43\u0026ndash;4.61%. Other phyla, including Thermosplasmatota (3.33%) and Thermoproteota (6.71%), were also present, albeit in smaller amounts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb displays the Archaean community at the family level. Within the non-adapted consortium, Methanosarcinaceae was the dominant family with a relative abundance of 46.76%. Additional families were detected in smaller proportions, including Methanotrichaceae (11.67%), Methanoregulaceae (13.92%), and Methanophastidiosaceae (11.3%). After 60 days of adaptation, a shift in the dominance of archaeal families was observed. The family Methanotrichaceae achieved the highest dominance level with a relative abundance of 58.98%. Likewise, Methanoregulaceae (25.13%) also experienced an increase in relative abundance. Conversely, the families Methanosarcinaceae and Methanophastidiosaceae exhibited a significant decrease in relative abundance, down to 7.57% and 1.43%, respectively. In the 100-day adapted state, the Methanotrichaceae family remained the most dominant, although its relative abundance decreased to 33.41%. On the other hand, the Methanoregulaceae family showed an increase in relative abundance from 25.13% at 60 days to 29.3% at 100 days. In addition to these families, Methanosarcinaceae (6.02%), UBA233 (6.67%), Methanoculleaceae (6.9%), and Methanophastidiosaceae (4.61%) were detected.\u003c/p\u003e \u003cp\u003eThe non-adapted microbial consortium was largely dominated by Methanosarcinaceae, a fast-growing and versatile substrate methanogen that can utilize acetate, H\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CO\u003csub\u003e2\u003c/sub\u003e, methanol, and methylamines for methanogenesis (Wojcieszak et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to previous research, Methanosarcinaceae exhibits greater dominance than Methanotrichaceae at high concentrations of VFA and TAN (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This indicates that the non-adapted microbial consortium originated from an environment with a high VFA concentration because it was fed with FVW, which is characterized as highly fermentable. The adapted consortium was dominated by Methanotrichaceae (formerly Methanosaetaceae), an acetoclastic obligate methanogen, and Methanoregulaceae, a hydrogenotrophic methanogen (Cui et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Methanotrichaceae exhibit greater acetate affinity than Methanosarcinaceae. Elevated levels of acetate in the culture medium promote the proliferation of Methanosarcinacea, whereas decreased levels encourage the growth of Methanotrichaceae (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These results indicate that the concentration of acetate during the adaptation period was low because of the slow bioconversion of pelagic \u003cem\u003eSargassum\u003c/em\u003e. As a result, the microbial consortium adapted more toward H\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CO\u003csub\u003e2\u003c/sub\u003e use. This is evidenced by the increased abundance of hydrogenotrophic methanogens such as Methanoregulaceae and Methanoculleaceae. In addition, the increase in family 34\u0026ndash;128 is another indication of methane production via the hydrogenotrophic pathway. It has been reported that members of the Atribacterota phylum may form a syntrophic association with hydrogenotrophic methanogens by oxidizing acetate (Lee et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings contribute to a better understanding of the microbial ecology and degradation processes of pelagic \u003cem\u003eSargassum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn summary, the results obtained demonstrate that the adaptation of the microbial consortium is reflected in a significant remodeling of the microbial community. These changes reveal the plasticity and adaptability of microbial communities to specific substrates, such as pelagic \u003cem\u003eSargassum\u003c/em\u003e. Furthermore, bacterial phyla like Bacteroidota, Firmicutes, Atribacterota, and methanogens such as Methanotrichaceae and Methanoregulaceae exhibit a significant affinity for pelagic \u003cem\u003eSargassum\u003c/em\u003e compounds.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Potential functional characteristics of the microbial consortium\u003c/h2\u003e \u003cp\u003eFunctional profile analysis of the non-adapted and adapted microbial consortium (MC) was conducted at 60 and 100 days using the KEGG database. The results in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e the top five functions classified as level 2 according to the KEGG database. The majority of these functions are related to metabolism, while a few correspond to genetic information processing functions such as replication and repair. Carbohydrate metabolism had the highest gene abundance, followed by amino acid metabolism and energy metabolism. After 60 days of adaptation, a decrease was observed in the abundance of most of these functions. It has been reported that feeding a complex substrate reduces carbohydrate metabolism (Basak et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the reduction of certain metabolic pathways can be attributed to limited substrate availability stemming from the intricate chemical composition of \u003cem\u003eSargassum\u003c/em\u003e spp. Nonetheless, the abundance of these functions displayed a subsequent increase at the 100-day mark of adaptation, albeit slightly less than that observed in the non-adapted consortium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays variations in the crucial functions of the microbial consortium, which are categorized as level 3 based on the KEGG database. Noteworthy KEGG modules related to biomethanation include methane metabolism, sulfur, carbohydrates, fatty acid oxidation, and quorum sensing (Basak et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study uncovered a responsive and ever-changing functional profile of the microbial consortium through adaptation. Notably, certain functions manifested distinct fold changes at 60 and 100 days of adaptation. The heat map (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) illustrates the decline of various metabolic pathways and processes, particularly during the 60-day period when the consortium was exclusively fed 100% \u003cem\u003eSargassum\u003c/em\u003e. Changes in abiotic environmental factors can trigger stress responses in microorganisms that focus more on survival than growth (Wani et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Fold-change values demonstrate a decrease in ABC transporter expression after 60 days of adaptation. These transporters play pivotal roles in the uptake and transport of nutrients. This decline suggests a lower demand for specific nutrients or alterations in nutrient use. Consequently, certain functions may be reduced or impeded as the consortium tackles the obstacles accompanying acclimation to a new substrate and environment. Microorganisms may undergo modifications through acetylation or methylation that slowly affect gene expression until normal growth is restored (Tan et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Some metabolic processes or pathways in the microbial community experienced a decline at 60 days but recovered after 100 days of adaptation. Notably, some functions exceeded their initial stage, including the metabolism pathways of glycerolipids and glycerophospholipids, as well as the biosynthesis of folate, terpenes, valine, leucine, and isoleucine.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMethane and sulfur metabolism related to energy pathways exhibited reduced abundance at 60 days of adaptation but recovered by day 100. Notably, the methane metabolism pathway remained relatively comparable to the initial stage after 100 days. This indicates that the consortium\u0026rsquo;s capacity to produce methane remained relatively stable after the adaptation stage. Changes in the metabolic pathways responsible for carbohydrate metabolism were observed in addition to energy metabolism functions. These changes include glycolysis/glycogenesis, pyruvate, butanoate, and starch and sucrose metabolism. Glycolysis and glycogenesis are the primary pathways for glucose synthesis and metabolism. Many anaerobic microorganisms rely exclusively on these pathways for survival. Additionally, glycolysis, pyruvate metabolism, and fatty acid degradation play crucial roles in the production of volatile fatty acids (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A reduction in these pathways was noted after 60 and 100 days of the adaptation period with \u003cem\u003eSargassum\u003c/em\u003e. This reduction can be traced back to the decrease in glucose concentration in the medium, which is caused by the gradual shift of substrate from fruits and vegetables to pelagic \u003cem\u003eSargassum\u003c/em\u003e. Davis et al., (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported the monosaccharide composition of \u003cem\u003eSargassum\u003c/em\u003e spp., noting a low level of glucose. Moreover, the pathway for starch and sucrose metabolism decreased during the adaptation stage. This finding suggests a transition away from starch and sucrose as carbon sources, which could be attributed to the presence of other compounds in pelagic \u003cem\u003eSargassum\u003c/em\u003e. Also, the metabolism of butanoate or butyrate, a VFA produced during the acidogenic fermentation of carbohydrates by Bacteroides spp. (Vemuri et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), decreased after 60 days but increased after 100 days. This result suggests that VFA production decreased, possibly because of a reduction in carbohydrate hydrolysis.\u003c/p\u003e \u003cp\u003eIn general, the observed changes in functional traits during adaptation suggest that there was an up- or down-regulating of gene abundance to better utilize pelagic \u003cem\u003eSargassum\u003c/em\u003e. When microorganisms are initially exposed to a stressful environment, they may enter a self-protective state and exhibit slow or stagnant growth (Tan et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the stress response induced by \u003cem\u003eSargassum\u003c/em\u003e could be responsible for the down-regulating of gene abundance for certain functions after 60 days of adaptation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). During the adaptation period, microorganisms can regulate their growth through epigenetic modifications or alter their metabolic state by regulating gene expression (Tan et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The decrease in these functions could affect methane production, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u0026lt;where low and slow methane production was observed when feeding only with \u003cem\u003eSargassum\u003c/em\u003e. However, after 100 days, an up regulating of most of the functions was observed. This increase suggests a more advanced stage of adaptation, where the microorganisms in the consortium have adapted and fine-tuned their metabolic pathways in response to the \u003cem\u003eSargassum\u003c/em\u003e environment. There may have been an improvement in pathways more favorable to their growth and methane production, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The microbial adaptation to \u003cem\u003eSargassum\u003c/em\u003e is largely attributed to its genetic composition and regulation. However, more sophisticated techniques such as metagenomics for functional analysis and metatranscriptomics to understand the activity of gene profiles during the adaptation stage are needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Biochemical methane potential of pelagic \u003cem\u003eSargassum\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eBiomethane potential tests were conducted using the non-adapted microbial consortium (NA) and the consortium adapted for 100 days (AC) as the inoculum source. After 32 days, the methane yield from cellulose achieved by NA and AC was 339.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28 and 329.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16 (N-mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The amount of methane obtained from cellulose validated the suitability of the inoculum for methane production, as established by Holliger et al., (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There were no significant differences between the two yields obtained using NA and AC (\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e). These results indicate that the microbial consortium does not decrease its methanogenic activity after the adaptation phase. However, the methane yield curves show a lag phase when AC is used, unlike NA, where methane is immediately produced. This could be due to the high affinity developed by the microorganisms for \u003cem\u003eSargassum\u003c/em\u003e after the adaptation stage, which delays the consumption of cellulose.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe methane yields from \u003cem\u003eSargassum\u003c/em\u003e obtained by AC and NA were 160.03\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64 and 98.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65 (N-mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS), respectively. The inoculum effect was confirmed by the statistical analysis of the methane yields, which showed a significant difference (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) between the final methane produced by AC and NA. Observation of the methane production curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that \u003cem\u003eSargassum\u003c/em\u003e was inhibitory to NA microorganisms in the initial digestion phase. This caused the methane production to be lower than the blank, resulting in a negative methane yield (Filer et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The inhibitory effect was also confirmed by the diauxic behavior of the curve throughout the digestion period (Koch et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, with AC, \u003cem\u003eSargassum\u003c/em\u003e is easily biodegraded. Methane is produced immediately, and the methane production curve becomes more stable during the digestion period. This suggests that after the adaptation phase, there is an increase in the affinity of microorganisms for \u003cem\u003eSargassum\u003c/em\u003e and a possible increase in the number of specific degraders (Raposo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The use of AC showed a 63% improvement in the methane yield.\u003c/p\u003e \u003cp\u003eBased on the experimental results, it was possible to calculate the biodegradability index (BI), which was calculated using Eq.\u0026nbsp;2 from the molecular formula (C\u003csub\u003e2.8\u003c/sub\u003e H\u003csub\u003e5\u003c/sub\u003e N\u003csub\u003e0.1\u003c/sub\u003e O\u003csub\u003e2\u003c/sub\u003e S\u003csub\u003e0.02\u003c/sub\u003e) of pelagic \u003cem\u003eSargassum\u003c/em\u003e. AC achieved a BI of 39%, whereas NA achieved a value of 24%, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Previous studies have reported that methane yields from pelagic \u003cem\u003eSargassum\u003c/em\u003e range from 41 to 116 mL g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS (Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Tapia-Tussell et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), although these yields vary because of the use of different pretreatments. For example, a yield of 159.7 mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS with a BI of 39% was reported using the liquid fraction obtained from the solid-liquid separation of \u003cem\u003eSargassum\u003c/em\u003e (Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Similar values have been reported when \u003cem\u003eSargassum\u003c/em\u003e species are separated and individually subjected to anaerobic digestion (Milledge et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although yields have been improved by various pretreatments, they are still well below the theoretical potential. Recently, Chikani-Cabrera et al., (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported a methane yield of 387 mL g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS with a BI of 95%, using a combination of physical, chemical, and enzymatic pretreatments. However, the use of combined pretreatments further complicates their large-scale application because of the high cost of energy, chemical reagents, and enzymes. Adapting the inoculum source may be an economical solution in comparison to costly pretreatments, leading to a notable rise in methane yields from \u003cem\u003eSargassum\u003c/em\u003e. A precultivation stage of the inoculum by progressively feeding \u003cem\u003eSargassum\u003c/em\u003e can decrease the requirement for aggressive pretreatments and ensure the success of large-scale anaerobic digestion. Our observations demonstrate that even with inoculum adaptation, \u003cem\u003eSargassum\u003c/em\u003e biodegradability remains below 50%. Therefore, the application of a pretreatment specifically focused on hydrolyzing recalcitrant \u003cem\u003eSargassum\u003c/em\u003e compounds is necessary in addition to adapting the inoculum source.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Modeling of methane production kinetics\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the cumulative production of CH4 for NA and AC, along with the corresponding fitting curves obtained using the first-order kinetic and transfer function models. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the kinetic parameters for each model, which were calculated using IBM SPSS Statistics 27. Both models achieved a good fit to the experimental results, as indicated by the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.90). Additionally, the RMSE values provide a statistical measure of the model error. It has been confirmed that the first-order kinetic model is most appropriate for cellulose with NA, whereas the transfer function model is preferable when AC is used. Since these models exhibited the lowest RMSE values (11.34 and 9.55) with cellulose. The transfer function model best fit the data for pelagic \u003cem\u003eSargassum\u003c/em\u003e when NA was used. Conversely, when AC was employed, the first-order model provided the best fit. This is attributed to the first-order model having a better fit in the absence of a lag phase (Filer et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The calculated value of \u003cem\u003ek\u003c/em\u003e suggests that the degradation rates of pelagic \u003cem\u003eSargassum\u003c/em\u003e were slightly higher with NA, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The \u003cem\u003ek\u003c/em\u003e values discovered in this study were lower than those previously reported for other brown algae (Membere and Sallis, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\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\u003eResults from the kinetic study using the first-order and transfer function models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFirst-order kinetics model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eTransference funtion model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ek\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e (day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(mL CH\u003csub\u003e4\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eλ\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-adapted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSargassum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e302.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdapted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSargassum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e316.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Calculated at a confidence interval of 95%\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe adapted consortium yielded the highest values for both the maximum predictable methane potential (\u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) and the maximum methane production rate (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e) when tested with cellulose and \u003cem\u003eSargassum\u003c/em\u003e. With AC, \u003cem\u003eSargassum\u003c/em\u003e showed a maximum methane production rate of 44.45 mL g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which was 3.2 times greater than that observed with NA (13.29 mL g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Additionally, AC demonstrated immediate methane production (\u003cem\u003eλ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0), whereas a delay phase (\u003cem\u003eλ\u003c/em\u003e) of 2.74 days was observed with NA. The \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e value for cellulose was higher with AC (79.45 mL g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to the NA (54.46 mL g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). However, the degradation of cellulose was slower when AC was used, resulting in an increase in the lag phase from 1.76 to 5.04 days. Results from the kinetic study suggest that \u003cem\u003eSargassum\u003c/em\u003e is more susceptible to degradation by the adapted microbial consortium, highlighting the better survival of the microorganisms under adapted conditions. Consequently, biogas production began on the first day. In addition, the use of AC resulted in a higher \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e value than that previously reported for pelagic \u003cem\u003eSargassum\u003c/em\u003e (Salgado-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe adaptation of a microbial consortium to pelagic \u003cem\u003eSargassum\u003c/em\u003e, has been a successful strategy for enhancing biodegradability and methane production. Observations during adaptation indicate a substrate-specific response with significant changes in the composition and function of the microbial community. The presence of Atribacterota, Firmicutes, Proteobacteria, Methanotrichaceae, and Methanoregulaceae, at the end of the adaptation period indicates a growing preference for \u003cem\u003eSargassum\u003c/em\u003e. The functional profile prediction indicated an initial decline in metabolic functions, followed by an upswing after 100 days, indicating the adaptation and optimization of metabolic capability. The enhancement of physiological capacities led to faster biogas production, resulting in a 60% increase in biomethane potential and an improvement in the biodegradability index. Kinetic models provide evidence supporting the efficacy of acclimation during the anaerobic digestion process of pelagic \u003cem\u003eSargassum\u003c/em\u003e, thereby surmounting obstacles linked to low methane production. Although these achievements are notable, it is necessary to implement complementary strategies, such as extra pretreatments, to overcome persistent limitations in biodegradability and improve organic matter availability. Such a comprehensive approach yields valuable insights for developing sustainable waste treatment technologies and effectively managing pelagic \u003cem\u003eSargassum\u003c/em\u003e in coastal regions, highlighting the crucial importance of microbial adaptation in this context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors thank the federal maritime-terrestrial zone (ZOFEMAT) of the municipality of Solidaridad (Quintana Roo, M\u0026eacute;xico) for obtaining the macroalgae. Enrique Salgado Hern\u0026aacute;ndez thanks CONACYT for a doctoral scholarship (817679).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAchinas, S., Euverink, G.J.W., 2016. Theoretical analysis of biogas potential prediction from agricultural waste. Resource-Efficient Technologies 2, 143\u0026ndash;147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.reffit.2016.08.001\u003c/span\u003e\u003cspan address=\"10.1016/j.reffit.2016.08.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed, A.M.S., Buezo, K.A., Saady, N.M.C., 2019. Adapting anaerobic consortium to pure and complex lignocellulose substrates at low temperature: kinetics evaluation. 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Adaptation to salinity: Response of biogas production and microbial communities in anaerobic digestion of kitchen waste to salinity stress. Journal of Bioscience and Bioengineering 130, 173\u0026ndash;178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbiosc.2019.11.011\u003c/span\u003e\u003cspan address=\"10.1016/j.jbiosc.2019.11.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidad Veracruzana","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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":"macroalgae, inoculum, anaerobic digestion, metabarcoding, Mexican Caribbean","lastPublishedDoi":"10.21203/rs.3.rs-3819248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3819248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, pelagic \u003cem\u003eSargassum\u003c/em\u003e has invaded the Caribbean coasts, and anaerobic digestion has been proposed as a sustainable management option. However, the complex composition of these macroalgae acts as a barrier to microbial degradation, thereby limiting methane production. Microbial adaptation has emerged as a promising strategy to improve substrate utilization and stress tolerance. This study aimed to investigate the adaptation of a microbial consortium to enhance methane production from the pelagic \u003cem\u003eSargassum\u003c/em\u003e. Microbial adaptation was carried out for 100 days by progressively feeding \u003cem\u003eSargassum\u003c/em\u003e. The evolution of the microbial community was analyzed by high-throughput sequencing of 16S rRNA amplicons. Additionally, 16S rRNA data were used to predict functional profiles using the iVikodak platform. The results showed that, after adaptation, the consortium was dominated by the bacterial phyla Bacteroidota, Firmicutes, and Atribacterota, as well as methanogens of the families Methanotrichaceae and Methanoregulaceae. The abundance of genes related to different metabolism-related functions decreased on day 60 when the \u003cem\u003eSargassum\u003c/em\u003e concentration increased. However, after 100 d, the functions increased again, enhancing methane production. The adapted consortium (AC) exhibited a biomethane potential of 160.03\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64 N-mL g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e VS and a biodegradability index of 39%, representing a 60% improvement. Additionally, the degradation kinetics and methane production of pelagic \u003cem\u003eSargassum\u003c/em\u003e were improved. The study concludes that microbial adaptation enhances the bioconversion of pelagic \u003cem\u003eSargassum\u003c/em\u003e into methane. It is also suggested that a microbial consortium should be generated to achieve greater efficiency in the bioconversion of \u003cem\u003eSargassum\u003c/em\u003e, along with other pretreatments.\u003c/p\u003e","manuscriptTitle":"Adaptation of a microbial consortium to pelagic Sargassum modifies its taxonomic and functional profile that improves biomethane potential","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 04:18:39","doi":"10.21203/rs.3.rs-3819248/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"55242948-fb2d-41f5-9e06-6d752c3410a9","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27962138,"name":"Biotechnology and Bioengineering"}],"tags":[],"updatedAt":"2024-01-08T04:18:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 04:18:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3819248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3819248","identity":"rs-3819248","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}