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2024 Volume 3
Article Contents
Abstract
Introduction
Materials and methods
Soil seed bank density data
Climate data
Soil nutrient data
Forest stand factors
Plant functional traits
Forest net primary productivity data (NPP)
Analysis
Results
Discussion
Author contributions
Data availability
Acknowledgments
Conflict of interest
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References
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ARTICLE   Open Access    

The effect of static magnetic field on inducing the binding of bovine serum albumin and cyanidin-3-O-glucoside

  • 1.

    College of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China

  • 2.

    Liaoning Academy of Agricultural Sciences, Liaoning Grain Science Research Institute, Shenyang 110032, China

  • 3.

    Department of Pharmacognosy, Faculty of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-kharj 11942, Saudi Arabia

  • 4.

    Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt

More Information
  • Serum albumin can bind with a diverse range of small molecules. It could therefore serve a protective or carrier function, and effectively address the issue of anthocyanins' susceptibility to decomposition. The anisotropic effect of the magnetic field (MF) can influence their interaction, thereby playing a distinct role in molecular bonding. In this study, bovine serum albumin (BSA) and cyanidin-3-O-glucoside (C3G) were used as raw materials. The mechanism underlying the formation of BSA-C3G complexes induced by static magnetic field (SMF) was investigated through analyses of secondary structure, functional groups, dipole moment, crystal cell dimensions, and microstructural characteristics. BSA and C3G were treated with 50, 100, 150, and 200 mT, respectively. As the magnetic intensity increased, the secondary structure of the complex changed, the α-spiral content, β-corner content, and irregular curl content decreased, while, the β-folding content increased. The average grain size of the BSA-C3G composite was observed to decrease. Furthermore, alterations in the crystal cell dimensions of the BSA-C3G complex were noted, accompanied by a tendency for the microstructure to become more flattened. This study offers valuable insights into the influence of SMF on the assembly behavior and structural characteristics of proteins and anthocyanins.

  • Introduction

    Soil seed banks are a crucial component of forest ecosystems, directly influencing ecosystem structure and function, as well as the assembly and succession of forest communities[1]. It remains unclear whether there are significant linear differences in forest soil seed bank abundance along geographical scales[2]. Additionally, forest community assembly patterns differ between different forest origins (planted forests vs natural forests), and it is uncertain whether these origin differences affect soil seed bank density (SSBD)[3]. Therefore, investigating the distribution patterns and key factors influencing soil seed density between planted and natural forest ecosystems at a macroscale is of great significance for sustainable forest management.

    Natural forests are characterized by a series of successional stages of plant communities that develop on primary or secondary bare land[4]. Dominated by native species, these forests can regenerate naturally, boasting complex ecosystems and high biodiversity[5]. In contrast, planted forests are predominantly created through artificial sowing, cultivation, and management, exhibiting uniform age and simplified structure due to human intervention[6]. Compared to natural forests, planted forests generally exhibit lower biodiversity and diminished ecosystem functions[5]. Within both natural and planted forest ecosystems, soil seed banks play a crucial role in maintaining population size and diversity through temporal storage effects[7]. Soil seed banks have the ability to restore degraded ecosystems and accelerate forest succession, so the renewal of natural and planted forests is largely dependent on soil seed banks[8]. Consequently, understanding the dynamics of soil seed banks is of paramount importance in forestry, as it provides valuable insights into the natural regeneration of forests, guiding future forest management practices.

    Numerous studies have highlighted the pivotal role of climatic factors, notably temperature and precipitation, in regulating the growth of both planted and natural forests, as well as in resource allocation[3,9]. Likewise, a substantial body of research indicates that temperature and precipitation primarily drive the variances in soil seed banks at the macro scale[10−12]. Hence, climatic factors may exert significant impacts on the soil seed bank of both planted and natural forests. As temperatures rise and rainfall increases, soil seed density significantly decreases[13]. This is mainly because, with higher temperatures and increased precipitation, trees adopt a strategy of rapid investment-reward in resource utilization, leading to faster growth, enhanced inter/intra-species resource competition, increased investment in resource competition, and reduced investment in reproduction. This ultimately results in lower levels of soil seed density[14]. Daylight duration also significantly affects soil seed density, as different daylight durations can alter plant growth cycles, influencing flowering and fruiting times, thus impacting seed production and density[15]. Furthermore, changes in light conditions can affect competitive relationships among plant species, with some plants being more adapted to longer periods of sunlight, while others may have a competitive advantage under shorter light conditions[16]. Such differences can influence species survival and reproduction, subsequently affecting the density of seeds in the soil.

    Soil, as the direct living environment for trees, plays a crucial role in their growth, development, and reproduction[17,18]. Research has also found that the resource allocation strategies of both planted and natural forests are significantly constrained by soil nutrients[3]. Therefore, soil nutrients may represent another type of abiotic factor that limits the soil seed bank of forests. Under conditions of ample nutrients, plants may produce more seeds, increasing seed density[19]. Additionally, soil nutrients influence seed viability and germination capacity[20]. The nutritional status of the soil can also affect soil seed density by influencing competitive relationships among plant species[21]. In a nutrient-rich environment, competitively dominant species may prevail, whereas, in a nutrient-poor environment, species with strong adaptability may have a better chance of survival. These differing competitive pressures can impact the density and abundance of species in the seed bank[22]. Soil nutrients also influence the activity of soil microorganisms, which, in turn, affect the physical and chemical properties of the soil, subsequently impacting seed survival and germination[23].

    In different developmental stages of forests, trees exhibit various reproductive strategies, which may consequently have an impact on the soil seed bank. Many studies found that forest stand characteristics, such as stand age, mean diameter at breast height, key leaf traits, and forest productivity can influence SSBD[24−26]. With increasing stand age, the intensity of interspecific competition can change, and the microenvironment within the forest, including factors like light, humidity, and soil structure, can undergo alterations, subsequently affecting the soil seed bank[27]. In recent years, numerous studies have highlighted the critical role of key leaf traits in explaining various ecological phenomena. Species with higher specific leaf area (SLA) and lower leaf dry matter content (LDMC) tend to adopt a fast investment-reward resource utilization strategy, allocating more resources to interspecific competition and reducing investment in reproduction[18]. This leads to lower levels of soil seed bank density. Similarly, some research has found that forests with higher productivity typically have greater biomass, resulting in more seed production and increased seed bank density[28]. However, it should be noted that forests with higher productivity may also experience more intense interspecific competition, which can lead to lower soil seed density[27].

    In this study, based on SSBD data collected from 537 natural forests and 383 planted forests within China through field surveys and literature sources, the aim is to investigate the differences in SSBD between plantation and natural forests at the macroscale and the key factors driving these differences. To address these questions, the following hypotheses are made: (1) SSBD in planted forests will significantly exceed that in natural forests; (2) Climatic factors will be the primary drivers of the macro-scale differences in SSBD between planted and natural forests; (3) Climatic factors will influence SSBD in planted and natural forests by adjusting soil nutrients and stand characteristics.

    Materials and methods

    Soil seed bank density data

    The density data of the soil seed bank were collected partly from literature searches and partly from field measurements. The specific data are listed in Supplementary Table S1. Relevant peer-reviewed journal articles published between 2005 and 2020 were searched in Web of Science, Google Scholar, and CNKI. The keyword combinations used in the search were 'forest' and 'soil seed bank'. A total of 108 relevant papers containing 623 data points were retrieved. The data in the literature was then screened using the following criteria: (1) The latitude and longitude of the plots should be provided by the study, and the plots should be categorized as either natural or planted forests; (2) The study should provide or allow the calculation of the mean, standard deviation, or standard error of soil seed bank density data in the sample plot; (3) The study should present the results of field studies rather than retrospective or simulation studies; and (4) The sampling period should be outside of peak germination seasons to minimize seasonal effects on soil seed bank density estimates. For the articles meeting the criteria, the index of soil seed density in the 0–10 cm soil surface layer was extracted. If a study has multiple sampling depths from 0 to 10 cm at the same site, these observations were treated as independent samples. In these articles, as much information as possible was collected on tree species, stand age, stand density, tree DBH, and other stand characteristics of each sample plot.

    Twenty seven sites in the field were collected and data measured from 297 forest plots. The latitude, longitude, elevation, and slope of each site were recorded for comprehensive analysis, and the site location, tree species, stand age, tree DBH, and stand density documented in real time. At each site, at least four 20 m × 20 m forest plots with typical zonal vegetation were selected. For sampling, the same method was used as described in the literature to measure soil seed bank density: after removing litter from the surface of each sample plot, five soil samples, each with dimensions of 10 cm × 10 cm × 10 cm, were randomly collected. The litter layer was removed to focus on the persistent soil seed bank in the 0–10 cm soil layer, minimizing the effects of short-term seed input and ensuring consistency across sites. The samples were thoroughly mixed and then placed in soil bags, which were sieved to remove debris upon return to the laboratory. The samples were stored in a dry, dark environment until germination experiments began in May of the following year. In May, the labeled soil was evenly spread in germination trays to a depth of about 5 cm. Iron arches were set up over the trays and covered with film to prevent external seeds from entering. The temperature inside the enclosure was maintained at 25–30 °C, with natural light and a humidity level of around 70%. Water was applied every 3–5 d to keep the soil moist. The germination and growth of the seeds were observed and recorded. From the onset of sprouting, daily records were kept of the number of seedlings and their morphological characteristics. Finally, the remaining seeds in the germination trays were checked for germination. The remaining ungerminated seeds were tested for viability using the tetrazolium chloride (TTC) staining method, with seeds soaked in a 1% TTC solution at 30 °C for 24 h. Seeds that displayed a reddish color in their embryos were considered viable[29]. For seeds that did not show a clear TTC staining result, manual examination was performed by cutting to check for intact embryos. The seed bank germination experiment lasted from May to November of the following year. The number of seedlings for all species recorded during the experiment was used to calculate the seed bank density, expressed as the number of seedlings per unit area, for further analysis.

    Climate data

    Climate data, including Mean Annual Temperature (MAT), Mean Annual Precipitation (MAP), Annual Sunlight Duration (ASD), and Mean Annual Evaporation (MAE), were obtained from WorldClim (https://worldclim.org) at a 1 km spatial resolution.

    Soil nutrient data

    Data for total nitrogen content and total phosphorus content of 0−20 cm soil at a 1 km resolution was extracted from the Harmonized World Soils Database version 2.0 (https://gaez.fao.org/pages/hwsd).

    Forest stand factors

    Forest stand factors include forest age, forest density, and forest mean diameter at breast height (DBH). Forest age is mainly obtained from the literature reviewed. For literature without forest age information, local forestry bureau and ecological station websites were referred to, as well as consultation with specific personnel in charge. The forest DBH represents the average DBH of all trees (DBH > 5 cm) within the plot. For species identification, local flora references were relied on, and for species that were difficult to classify, the WFO Plant List (https://wfoplantlist.org) was used to confirm taxonomic status. For each sample plot, five dominant trees were randomly selected based on their relative dominance (e.g., height and canopy spread) to represent the primary structural characteristics of the stand. The selected trees could either be from the same species or different species, depending on the composition of the plot. To minimize sampling bias, trees with abnormal growth patterns were excluded. Stand-level measurements, such as forest density, were calculated as the number of individual trees per unit area, and species diversity was determined based on the identified species in each plot. Altitude, slope, aspect, and other stand factors of the actual survey plots were measured using handheld GPS devices. Due to the limited number of actual field survey plots, stand factors such as altitude were not considered in the subsequent calculations.

    Plant functional traits

    In this study, five plant functional traits were selected to represent diverse strategies of plant resource utilization: leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen content (LN), and leaf phosphorus content (LP). The data on plant functional traits of regional tree species collected in the literature were obtained from the TRY database[30]. During field measurements, five dominant trees were randomly selected from each sample plot, ensuring they were situated away from the plot edges. Leaves were collected from various directions at the same height in the middle of the canopy of each selected tree. Twenty leaves of similar maturity, free from diseases and pests, were gathered and stored in ziplock bags for transport to the laboratory. Upon arrival at the laboratory, the leaf area (LA) was measured using a portable laser planimeter (CI-202, Walz, Camma, USA)[31]. Subsequently, the leaves were submerged in water and placed in a dark environment at a constant temperature of 4 °C for 12 h. Once the surface water was absorbed, the saturated fresh weight of the leaves was measured using an electronic balance. The leaves were then placed in an oven at 120 °C for 30 min, followed by drying at 80 °C for 24 h, and the dry weight of the leaves was recorded. Leaf nitrogen content was determined using the Kjeldahl method, while leaf phosphorus content was measured using the Mo-Sb colorimetry method[32]. Specific leaf area (SLA) and leaf dry matter content (LDMC) were calculated using the following formulas: Specific leaf area (SLA) = Leaf area/Leaf dry weight; Leaf dry matter content (LDMC) = Leaf dry weight/Leaf saturated fresh weight. While acknowledging potential differences in plant functional traits among species, this study focused on exploring these traits at the community scale. Therefore, the community-weighted mean value (CWM) was utilized to represent the average trait value of each plot.

    CWM=∑Si=1Di×Traiti (1)

    where, CWM denotes community-weighted functional trait values, Di is the abundance of dominant species, and Traiti is the specific functional trait[33].

    Forest net primary productivity data (NPP)

    China's MOD17A3H vegetation net primary productivity (NPP) data was obtained from the NASA website (https://search.earthdata.nasa.gov/search), with a spatial scale of 500 m and a time scale of years. The NPP estimates were generated using the Carnegie-Ames-Stanford Approach (CASA) model, employing the following calculation method:

    NPP(x,t)=APAR(x,t)×ε(x,t) (2)

    where, APAR(x,t) represents the photosynthetically active radiation (PAR) absorbed at the x pixel in the t-th month, with units in MJ/m². ε(x,t) represents the actual light use efficiency at the x pixel in the t-th month, measured in g·C/MJ[34].

    Analysis

    Initially, the soil seed bank density data was transformed logarithmically to normalize it, and all subsequent analyses were performed using these log-transformed data. Before analysis, all variables were standardized for a comparable scale in interpreting parameter estimates.

    All data analyses were conducted using R (version 4.2.2, www.R-project.org). The 'ggsignif' package was used to test the difference in soil seed bank density between natural forests and planted forests at the 0.05 significance level. To reduce collinearity among multiple plant functional traits, the 'pcaMethods' package was employed for PCA analysis of plant functional traits and the first two principal components, PC1 and PC2 were extracted[35]. The general linear regression model in the 'lme4' package was utilized to analyze the effects of climate, soil, and plant factors on soil seed bank density in plantations and natural forests, with R² used to evaluate model fitting[36]. To visualize the relationship between various factors and soil seed bank density, a correlation heat map was created using the 'linkET' package[37].

    A multiple linear regression model was constructed, based on modified Akaike information criteria (AICc; ΔAICc < 2) selection procedure to select the best predictors of soil seed bank density. The 'dredge' function in the MuMIn package was used to create all possible subset models, ranking them based on their AICc values (AIC value corrected for sample size), and selecting the model with the lowest AIC value as the optimal model[38]. The contributions of various factors in the optimal model to SSBD were recorded. Variance decomposition was then performed using the rdacca.hp function, assessing the variance contributions of climatic, soil, and plant factors to the optimal model, expressed as percentages[39].

    Structural equation models (SEM) can be used to evaluate complex causality between variables by translating hypothetical causality into the expected statistical relationship pattern in the data[40]. To study the direct and indirect effects of each factor on soil seed bank density, a structural equation model was constructed using the 'piecewiseSEM' software package. The SEM model was fitted using the psem function in the 'piecewiseSEM' package, based on generalized least squares, with the optimal model having the smallest AIC score and a Chi-Square p-value greater than 0.05[41].

    Results

    The soil seed bank density (SSBD) of planted forests and natural forests exhibited significant geographical differences. The average SSBD of natural forests was 2.876 m−2, ranging from 1.395 to 4.049 m−2. In contrast, the average SSBD for planted forests was 3.137 m−2, ranging from 1.536 to 3.858 m−2. The difference of SSBD between natural forest and planted forest is very significant, and the SSBD value of planted forest is generally higher than that of natural forest (Fig. 1b).

    Figure 1.  Spatial distribution of soil seed banks and plot locations in planted and natural forests. (a) Comparison of SSBD between natural forests and planted forests. (b) Significance of the differences was assessed using a t-test, with significance at the 0.001 level. *** p < 0.001.
    DownLoad: Full-Size Img PowerPoint

    Both planted and natural forests showed similar trends in SSBD in response to changes in climatic factors. SSBD decreased significantly with increasing temperature and precipitation (p < 0.001), while it increased with longer sunlight exposure and higher evaporation rates. Overall, natural forests exhibited greater climatic plasticity in SSBD (Fig. 2).

    Figure 2.  The relationships between climatic factors and SSBD in natural forests and planted forests. R2 represents the goodness of fit, and P-values indicate significance. Climatic factors include: (a) Mean Annual Temperature (MAT); (b) Mean Annual Precipitation (MAP); (c) Annual Sunlight Duration (ASD); (d) Mean Annual Evaporation (MAE).
    DownLoad: Full-Size Img PowerPoint

    Compared to planted forests, SSBD in natural forests was more sensitive to changes in soil nutrients (higher R2). The SSBD of both forest types increased significantly with the increase of soil nitrogen content (Fig. 3a, b). With the increase of soil phosphorus content and soil pH, planted forest SSBD showed a significant decline trend (Fig. 3b, c).

    Figure 3.  The relationships between soil factors and SSBD in natural forests and planted forests. R2 represents the goodness of fit, and P-values indicate significance. Soil factors include: (a) Soil total nitrogen content (Soil N); (b) soil total phosphorus content (Soil P); (c) Soil pH.
    DownLoad: Full-Size Img PowerPoint

    SSBD in both forest types showed similar trends in response to changes in forest stand factors. SSBD in planted and natural forests positively correlated with forest age and forest DBH, but negatively correlated with stand density and leaf functional traits (Fig. 4). The impact of forest productivity on natural forest SSBD (R2 = 0.17) was greater than on planted forests (R2 < 0.01) (Fig. 4f). There was a general collinear correlation between potential influencing factors of SSBD in planted and natural forests (Fig. 5).

    Figure 4.  The relationships between forest stand factors and SSBD in natural forest and planted forests. R2 represents the goodness of fit, and p-values indicate significance. Forest stand factors include: (a) Forest age; (b) Forest diameter at breast height (average DBH); (c) Forest density; (d) Leaf functional traits PC1; (e) Leaf functional traits PC2; (f) Net primary productivity (NPP).
    DownLoad: Full-Size Img PowerPoint
    Figure 5.  Multivariate correlation analysis of potential influencing factors on SSBD in (a) natural forests, and (b) planted forests. MAP: Mean annual precipitation; MAE: Mean annual evaporation; MACT: Mean annual coldest month temperature; ASD: Annual sunlight duration; Soil N: Soil total nitrogen content; Soil P: Soil total phosphorus content; Soil pH: Soil pH; Traits PC1: Leaf functional traits PC1; Traits PC2: Leaf functional traits PC2.
    DownLoad: Full-Size Img PowerPoint

    All potential influencing factors explained 75.7% of the variance in SSBD for natural forests and 66.1% for planted forests (Fig. 6). Soil nutrient factors (R2 = 0.361; R2 = 0.377) had a stronger explanatory power for the spatial variability of SSBD in both forest types than climatic factors (R2 = 0.301; R2 = 0.073) and forest stand factors (R2 = 0.094; R2 = 0.211) (Fig. 6). Soil pH made the largest independent contribution to the spatial variability of SSBD in natural forests (Fig. 6a), while soil nitrogen content contributed most significantly to the spatial variability of SSBD in planted forests (Fig. 6b).

    Figure 6.  Impact of potential factors on SSBD in (a) natural forests, and (b) planted forests. The figure presents the average parameter estimates (standardized regression coefficients), related 95% confidence intervals, and the relative importance of each factor, expressed as the percentage of explained variance. The adjusted R2 for the average model and the p-values for each predictive factor are denoted as follows: * p < 0.05; ** p < 0.01; *** p < 0.001.
    DownLoad: Full-Size Img PowerPoint

    Soil pH had the greatest direct impact on SSBD in natural forests. MAT and MAP not only directly affected SSBD in natural forests but also indirectly through effects on soil pH, stand density, and forest NPP, with the direct impacts being greater than the indirect ones (Fig. 7a). For planted forests, soil nitrogen content had the greatest direct impact on SSBD. MAE influenced SSBD in planted forests indirectly through its impact on soil nitrogen content, with its indirect effect being greater than the direct effect (Fig. 7b).

    Figure 7.  Relationships between SSBD and climatic factors, soil nutrients, and forest stand factors in (a) natural forests, and (b) planted forests. The path diagrams represent the standardized results of the final Structural Equation Models (SEMs) testing relationships between variables. Numbers alongside the paths indicate the standardized SEM coefficients, and asterisks denote significance (*** p < 0.001; ** p < 0.01; * p < 0.05). R2 indicates the goodness of fit for the generalized additive models. The best SEMs were selected based on the lowest Akaike information criterion.
    DownLoad: Full-Size Img PowerPoint

    Discussion

    The results of this study show that the SSBD of planted forests is significantly higher than that of natural forests, confirming the first hypothesis. Planted forests, characterized by shorter planting periods and younger ages, tend to have higher SSBD compared to older, mature natural forests growing in their natural state[8,42]. In planted forests, the density and distribution of trees are often carefully planned to maximize land use efficiency and productivity[43]. Intensive planting will increase the coverage of vegetation, and after the soil surface is covered by vegetation, soil erosion and seed loss caused by erosion can be reduced, which is conducive to the accumulation and maintenance of seeds in the soil[44]. Moreover, tree species in planted forests are often selected for high yield or rapid growth, which may produce higher seed outputs, thereby increasing the density of the soil seed bank. Planted forests undergo regular cycles of harvesting and replanting. This periodic human intervention might lead to a regular renewal of seeds in the seed bank, thereby maintaining or increasing its density[45]. Compared to natural forests, planted forests generally harbor (or yield) a large number of light-demanding tree species with broad ecological niches[46]. These tree species often produce abundant seeds, and these seeds can persist in the soil for extended periods.

    Numerous studies have shown that climatic factors significantly influence the SSBD in forests[13,47,48]. The experimental results indicate that the SSBD in both planted and natural forests decreases with rising Mean Annual Temperature (MAT). Research suggests that temperature is a key climatic factor affecting seed dormancy and stimulating germination[49]. Cold conditions slow down the metabolic rate of seed embryos and germination rates. Seeds that grow in colder regions tend to have higher longevity and survival rates compared to those in warmer regions[50]. As MAT increases, seed germination rates rise, while seed vitality and persistence decrease. Studies also show a positive correlation between temperature and the frequency of predator activities; higher MAT can increase the predation rate of germinated seeds in the soil[19]. Our results demonstrate that SSBD in planted forests is more sensitive to temperature changes than in natural forests (Fig. 2). This could be due to the forest climate formed in natural forests over time[51]. Natural forests have more developed ecosystems and a stronger resistance to environmental changes, making their SSA less sensitive to increases in MAT compared to planted forests[9]. Therefore, the response of natural forests to MAT rise in SSBD is less sensitive than that of plantation forests. Research shows that SSBD significantly decreases with increased precipitation, consistent with our findings[48]. Increased rainfall can break seed dormancy and stimulate germination. However, early germination is not conducive to seed growth; changes in rainfall affect the longevity of the seed bank, and increased Mean Annual Precipitation (MAP) directly impacts the risk dispersal mechanisms of seeds, potentially causing a decrease in SSBD[13,48]. The results show that SSBD in natural forests is more sensitive to MAP compared to planted forests, possibly because planted forests, due to artificial irrigation, have less water demand. In contrast, natural forests are often in a state of drought and water scarcity, making their soil seed banks more responsive to rainfall compared to those in planted forests[52]. In the present study, other climatic factors also affect the SSBD of planted and natural forests, but according to the results of the comprehensive structural equation model, MAT and MAP are the key climatic factors affecting SSBD in both planted and natural forests.

    The experimental results of this study show that there is a close relationship between the SSBD in planted and natural forests and soil nitrogen content, phosphorus content, as well as soil pH. Being in wild and impoverished soils, natural forests are limited by soil nitrogen nutrients, while planted forests, under artificial cultivation, still require timely nitrogen fertilization to ensure normal tree growth[53]. Both planted and natural forests are limited by nitrogen in their soil environments. The development of forests in China is primarily limited by nitrogen elements[54]. Therefore, an increase in soil nitrogen content is conducive to the growth and development of germinating seeds in the soil[55]. Our results show that SSBD in both planted and natural forests is positively correlated with soil nitrogen content. Compared to the limitation of soil nitrogen content on planted and natural forests, the limitation of soil phosphorus content is not very strong. Chen et al. have shown that seed vigor in the soil seed bank is positively correlated with soil available P content, which also explains the experimental results of this study[56]. In planted and natural forests, seed vigor is positively correlated with soil total phosphorus content. Higher seed vigor in soil seeds changes their bet-hedging ability and risk dispersal strategies, increasing their risk of extinction. Therefore, SSBD tends to be lower in environments with higher soil phosphorus content[57]. Seed germination in acidic soils is limited, and as soil pH gradually changes from acidic to neutral, plant efficiency in utilizing soil nutrients increases[58]. In natural forests, an increase in soil pH improved the nutrient uptake efficiency of seeds in the soil and significantly increased SSBD. In planted forests, however, there is no significant linear relationship between SSBD and soil pH, possibly because the soil pH in planted forests, due to artificial afforestation, is mostly neutral[59].

    In both planted and natural forests, older forests with larger average diameters at breast height (DBH) typically have longer successional periods[9]. As forests age, the ecosystem gradually evolves towards a more mature state, during which the number of seeds usually increases[27]. In the later stages of succession, interspecific competition among forest trees diminishes, resources shift towards reproduction, and more seeds are produced[60]. Additionally, due to prolonged seed deposition, the soil seed bank gradually accumulates more seeds. Therefore, SSBD shows a positive correlation with forest age and average DBH[27]. Experimental results indicate that forest stand density is significantly negatively correlated with SSBD. Forests with higher stand density have higher canopy closure, resulting in less light reaching the understory vegetation and soil seed germination, making nutrient uptake more difficult and lowering SSBD[61]. Studies have shown that leaf functional traits such as LA, SLA, LDMC, LN, LP, etc. can affect soil structure and nutrient cycling under the influence of leaf litter, thus disturbing soil seed bank density changes. The effect of leaf functional traits on soil seed bank density in natural and planted forest communities was driven by multi-dimensional traits rather than single traits. SSBD in both planted and natural forests decreases with an increase in leaf functional traits PC1 and PC2, indicating a consistent response of SSBD in planted and natural forests to changes in leaf functional traits. Numerous studies have shown that key leaf traits can effectively predict the productivity of forest communities[3,35,62]. In communities with higher productivity, trees allocate more resources to growth and development and engage in greater interspecific competition. Consequently, trees that reduce their reproduction result in fewer seeds produced by trees, resulting in lower SSBD[61,63,64].

    Variance decomposition results indicate that, compared to climatic and forest stand factors, soil factors are the primary drivers affecting the SSBD in both planted and natural forests. This finding contradicts Hypothesis 2. Nutrients in the soil directly influence the germination and growth of soil seeds, having a more direct and intense impact than climatic factors, consistent with predictions by Yang et al. regarding global soil seed bank density influencers[2]. This study also found that among the biotic and abiotic factors affecting SSBD, soil pH is the most significant factor for natural forests, while soil nitrogen content is the most significant for planted forests. Similar results were found in Ma et al.'s study of the herb layer seed bank on the Tibetan plateau[13]. Increased soil pH enhances seed persistence, and soil pH might be indirectly influenced by precipitation, affecting SSBD in natural forests. Nitrogen, one of the most limiting factors for plant growth in terrestrial ecosystems plays a key role in influencing seed germination and growth. In planted forest ecosystems, which are generally low in nitrogen, growth is limited by nitrogen availability[53]. Acidic soils may affect seed size, lifespan, and vigor, and increased nitrogen content benefits plant carbon storage and promotes the accumulation of soil organic matter[65]. Therefore, the nitrogen content in planted forests impacts soil nutrients, and increasing nitrogen availability can alter community structure and composition. Increasing the availability of nitrogen can increase the richness of vegetation in the above-ground herbaceous layer, accelerate the growth and propagation of trees, and increase SSBD[66].

    Gong et al. found that the interaction between climatic and soil factors significantly affect the ecosystem functions of planted and natural forests[3]. An et al. also discovered in their study of the soil seed bank on the Qinghai-Tibet Plateau that climatic changes affect SSBD by influencing above-ground community structure and soil nutrient availability[48]. This study also found that climatic, soil, and forest stand factors not only have a direct impact on SSBD but also that climatic factors indirectly affect SSBD in planted and natural forests by influencing forest community succession and soil nutrient availability, confirming Hypothesis 3. Rising temperatures accelerate microbial activity in the soil, increasing the decomposition rate of organic substances such as nitrogen and phosphorus. This makes more nutrients available for seeds in the soil[67]. Higher temperatures also increase community productivity, promote tree growth and development, increase forest canopy closure, reduce the light available to understory vegetation, and decrease the richness and density of the soil seed bank[68]. Increased precipitation, on the one hand, raises soil moisture and water content, increasing pathogens around soil seeds, reducing seed vigor and density[13]. On the other hand, increased precipitation limits nutrient transport in plant roots and restricts nitrogen mineralization in soil, reducing nutrients available for seed absorption[67]. Studies have found that precipitation and tree layer productivity are positively correlated; increased precipitation promotes forest tree growth. Trees adopt growth strategies over reproductive strategies with increased rainfall, reducing seed production. Additionally, tree growth increases forest canopy closure, reducing the light required for seed germination[46], thereby affecting SSBD.

  • The authors confirm contribution to the paper as follows: study conception and design: Gao J, Guo X; data analysis: Wang J, Wang R, Zhang X, Xu J, Zhang X; draft manuscript preparation: Wang J, Guo X, Gao J; manuscript revision: Wang J, Gao J. All authors contributed to the discussion of results, manuscript preparation, and approved the final version.

  • All data generated or analyzed during this study are included in this published article and its supplementary information files.

    • Acknowledgments

    This work was supported by the Xinjiang Normal University Young Top Talent Project (Grant No. XJNUQB2023-14), Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2022D01A213), Fundamental Research Funds for Universities in Xinjiang (Grant No. XJEDU2023P071), National Natural Science Foundation of China (Grant No. 32201543), Innovation and Entrepreneurship Training Program for College Students in 2023 (Grant No. S202310762004), Xinjiang Normal University Landmark Achievements Cultivation Project (Grant No. XJNUBS2301), and the Xinjiang Graduate Innovation and Entrepreneurship Project and Tianchi Talent Program.

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Zhang Z, Shen Y, Xin G, Deng W, Tan H, et al. 2024. The effect of static magnetic field on inducing the binding of bovine serum albumin and cyanidin-3-O-glucoside. Food Innovation and Advances 3(4): 449−456 doi: 10.48130/fia-0024-0042
    Zhang Z, Shen Y, Xin G, Deng W, Tan H, et al. 2024. The effect of static magnetic field on inducing the binding of bovine serum albumin and cyanidin-3-O-glucoside. Food Innovation and Advances 3(4): 449−456 doi: 10.48130/fia-0024-0042
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The effect of static magnetic field on inducing the binding of bovine serum albumin and cyanidin-3-O-glucoside

  • 1.

    College of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China

  • 2.

    Liaoning Academy of Agricultural Sciences, Liaoning Grain Science Research Institute, Shenyang 110032, China

  • 3.

    Department of Pharmacognosy, Faculty of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-kharj 11942, Saudi Arabia

  • 4.

    Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt

  • Received: 28 June 2024
  • Revised: 11 December 2024
  • Accepted: 11 December 2024
  • Published online: 24 December 2024
Food Innovation and Advances  3,  2024, 3(4): 449−456  |  Cite this article

Abstract: Serum albumin can bind with a diverse range of small molecules. It could therefore serve a protective or carrier function, and effectively address the issue of anthocyanins' susceptibility to decomposition. The anisotropic effect of the magnetic field (MF) can influence their interaction, thereby playing a distinct role in molecular bonding. In this study, bovine serum albumin (BSA) and cyanidin-3-O-glucoside (C3G) were used as raw materials. The mechanism underlying the formation of BSA-C3G complexes induced by static magnetic field (SMF) was investigated through analyses of secondary structure, functional groups, dipole moment, crystal cell dimensions, and microstructural characteristics. BSA and C3G were treated with 50, 100, 150, and 200 mT, respectively. As the magnetic intensity increased, the secondary structure of the complex changed, the α-spiral content, β-corner content, and irregular curl content decreased, while, the β-folding content increased. The average grain size of the BSA-C3G composite was observed to decrease. Furthermore, alterations in the crystal cell dimensions of the BSA-C3G complex were noted, accompanied by a tendency for the microstructure to become more flattened. This study offers valuable insights into the influence of SMF on the assembly behavior and structural characteristics of proteins and anthocyanins.

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    Introduction

    • Black rice is a rare variety of rice with a broad geographical distribution[1]. The content of trace elements manganese and zinc is 1−3 times higher than that of ordinary rice. Moreover, it encompasses distinctive components such as vitamin C, chlorophyll, and anthocyanins. As a result, black rice possesses a higher nutritional value compared to regular rice. Recent research has demonstrated that black rice possesses antioxidant, anti-inflammatory, and anti-tumor properties, as well as the ability to improve type 2 diabetes[2,3]. Additionally, it has been shown to prevent the occurrence of cardiovascular and cerebrovascular diseases, along with exhibiting other distinct biological activities[4]. The physiological functions of these compounds are likely attributable to their anthocyanin content[5]. However, their stability is relatively low and they are susceptible to degradation under external conditions. Anthocyanins in black rice are primarily comprised of centaurin-3-O-glucoside (C3G), which accounts for approximately 88% of the total anthocyanin content[6]. To improve the stability of anthocyanins, the structural modification and manipulation of environmental conditions have been used in recent studies[7−9]. The specific structural methods encompass copolymerization, acylation, and biosynthesis. Environmental approaches involve liposomes, microencapsulation, and encapsulation of nanoparticles[10]. However, these techniques are prone to certain limitations. For instance, acylation may introduce potentially detrimental residues, while, encapsulation has the potential to decrease water solubility.

      During the food production process, anthocyanins can interact with a variety of proteins. It is also essential for anthocyanins to form complexes with carrier proteins to facilitate normal metabolism, transportation, and absorption in vivo[11]. The interaction can be either non-covalent or covalent, depending on the chemical structure of the reactants and the reaction conditions[12], which further influences the conformation of carrier proteins[13]. Consequently, the investigation of exploration between anthocyanins and proteins is indispensable for understanding the in vivo transport and metabolism of anthocyanins at a molecular level. Currently, protein binding has been demonstrated to be an effective approach for the stabilization of anthocyanins[14]. The enhancement of stability is related to the molecular structure of the complex. At present, the stability of anthocyanins can be improved by changing the structure of the complex, but the mechanism of action is not yet clear. Bovine serum albumin (BSA), as one of the predominant transport proteins in plasma, plays a crucial role in maintaining the stability of plasma colloid osmotic pressure, facilitating material exchange with interstitial fluid[15]. It also possesses a distinct hydrophobic cavity that serves as a binding site for anthocyanins, facilitating their interaction[16]. Consequently, it is of great significance to investigate the binding mechanism between BSA and anthocyanins.

      External static magnetic fields (SMF) have been approved to affect chemical or biological molecular interaction via regulating molecular binding[17]. From a microscopic perspective, all the molecules are composed of atoms. When the number of protons or neutrons is odd, the nucleus becomes a magnetic nucleus and the spin of the magnetic nucleus forms a current loop. It results in the generation of a magnetization vector with specific magnitude and direction, as depicted in Fig. 1. In solution systems, the SMF induces molecular binding among proteins, small molecules, and solvents[18]. The effects of SMF on proteins include the changes of secondary bonds, peptide bonds, and distribution of electrons and molecules[19]. For example, the spatial structure of proteins is primarily maintained by secondary bonds. SMF can lead to the exposure of a distinct number of tryptophan groups, internal tyrosine groups, and aliphatic groups on the protein surface by affecting secondary bonds, such as the disruption of certain disulfide bonds[20]. Secondly, the diamagnetic anisotropy of proteins is contributed by peptide bonds, such as the α-helix structure. Thirdly, the SMF induces alterations in the distribution of electrons and molecules, resulting in the polarization and displacement of atoms and molecules. This subsequently leads to modifications in electron transition probability, dipole moment transition, and molecular vibration state, while maintaining the atomic and molecular composition unchanged[19]. These effects may result in the formation of protein clusters in SMF, commonly denominated as magnetic domains, which significantly enhance the efficiency of protein binding to small molecules. In addition, the effects of the SMF on small molecules mainly encompass molecular distortions, increased interactions, and changes in bond angles. The physico-chemical properties of the reaction solution can be affected by the SMF. Firstly, the viscosity and surface tension of the solution could be affected by the changes in molecular interactions under the inducement of SMF. Secondly, SMF causes the changes in the hydrated ion layer and the hydrated water structure which further influences the water conductivity[20]. For polar compounds with high diamagnetism, the SMF have distinct advantages for inducing intermolecular binding. It can transfer energy to the microstructure of matter without direct contact, which is attributed to the influence of SMF on the mutual transformation of electron triplets and singlets of free radicals. Due to the reason of energy, free radicals in triplets are less prone to Gemini recombination[21]. Moreover, SMF have the capability to alter bond arrangement and orientation. Thus, it could provide superior control over microstructure control through adjusting the direction and intensity of the magnetic field[22].

      Figure 1. 

      The spin of a magnetic nucleus produces a magnetization vector (MF: magnetic field, N: magnetic north, S: magnetic south).

      In previous experiments conducted by the research group, the SMF had an impact on stability. In this paper, the formation and structural changes of composites during magnetic processing were further explored, and delve into the mechanism of the influence of SMF on the structure of composites. Currently, while the interactions between proteins and small molecules have been documented, the impact of SMF induction on the binding mode between BAS and C3G remains inadequately explored. Therefore, the related mechanism would be revealed by the analysis of secondary structure, functional groups, dipole moment, unit cell size, and microstructure of the complex.

    Materials and methods

      Materials

    • BSA ( purity ≥ 97%, GENVIEW), C3G (purity ≥ 98%, Vicky Biotechnology Co., Ltd), potassium bromide (spectrally pure, Tianjin Damao), trimethylol aminomethane (Tris, purity ≥ 99%, Amresco), HCl (Tianjin Damao), anhydrous ethanol (Tianjin Damao), NaCl (Tianjin Damao). All other reagents were domestic analytical pure. The water used in the experiment was tertiary ultra-clean water.

      Reagent preparation

    • A Tris-HCl buffer (0.05 mol/L, pH 7.4) of 0.10 mol/L NaCl was prepared to maintain the ionic strength and pH of the solution. A BSA solution (1 × 10−6 mol/L) was prepared with the Tris-HCl buffer and stored at 4 °C for later use. A stock solution of C3G (3 × 10−3 mol/L) was prepared in anhydrous ethanol and stored at 4 °C for later use.

      Molecular docking simulation

    • Schrodinger molecular docking software was used to predict the molecular binding conformation of BSA and C3G[23]. First, protein macromolecular file was prepared. In File-Get PDB, the molecular file of BSA was loaded through the functions provided by maestro. The PDB ID was entered as 4F5S and downloaded. The Protein Preparation Wizard module was selected, and the Fill in missing side chains using the Prime option checked under the Import and Process processing box. The conserved water molecules were retained and charged. The hydrogen bond network of amino acid residues were optimized and the energy was minimized. After running, the prepared protein molecular file was obtained. Second, the file of the ligand small molecule was prepared. The CAS number 7084-24-4 of C3G was searched in Pubchem, the 2D structure file (sdf type) downloaded, and then the downloaded file uploaded into maestro. The small molecule file in the LigPrep module was selected, OPLS3e chosen in the Force field, and 'Generate possible states at target pH' chosen when setting the ionization state: 7.0+/−0.5, the following were checked: Epik, Desalt, Generate tautomers, Retain specified chiralities (vary other chiral centers), the 'Generate at most' was set to 32 per ligand, the format set and then perform the operation. After running, the prepared ligand molecular file was obtained. Third, a SiteMap was run on a protein molecule to look for pockets of activity. The option for the whole macromolecule was set in the SiteMap module. The precision setting requires at least 15 site points per reported site, Report up to 5 sites (site-point groupings), and Crop site maps at 4 Atoms come from nearest site point and run. Fourth, Receptor pocket files were generated under the Receptor Grid Generation module and molecular Docking performed under the Ligand Docking module.

      Magnetic processing complex

    • The SMF required for the experiment was provided by a 100 mm × 100 mm × 20 mm Ndfeb magnet, which was purchased from Shanyang District, Jiaozuo City, Xin Heng strong magnetic hardware store (China). The two Ndfeb magnets were fixed in parallel, and the SMF strength changed by changing the distance between the two magnets. The required magnetic induction intensity (50 mT-200 mT) was determined by the Tesla meter. BSA solution (1 × 10−6 mol/L) and C3G solution (3 × 10−3 mol/L) were mixed with a volume ratio of 1:1. The samples were then treated in a SMF of 50, 100, 150, and 200 mT for 4 h, respectively.

      Circular dichroism chromatography

    • The secondary structure changes of the samples were determined by circular dichroism. The experimental instrument is a circular dichrometer (Chirascan V100, applied photophysics, UK). The response time was 0.5 s, the scanning rate was 100 nm/min, the slit width was 2 nm, and the step size was 1 nm. Then, the circular dichrograms of each sample were collected. The secondary structure of polypeptide was calculated, and the content and proportion of each sample were obtained.

      Fourier infrared spectroscopy

    • The samples were ground to less than 200 mesh and dried in a drying oven for 4 h until no clumping appeared. Appropriate amount of powders (1−2 mg) were ground with 200 mg potassium bromide, mixed, and pressed into a tablet. The samples with the treatments of 0 and 200 mT were tested, and the absorption spectra were determined by a Fourier infrared spectrometer. The experimental instrument used was a Fourier transform infrared spectrometer (IRAffinity-1, Shimadzu, Japan). The parameters included a wall-number range of 4,000 to 400 cm−1, 64 scans with an average resolution of 4 cm−1, and an ambient temperature of 25 °C.

      X-ray wide-angle diffraction

    • The experimental instrument was a X-ray diffractometer (ADVANCE, Brook, Germany). Three grams of the lyophilized sample was ground to a particle size of 40 μm and pressed into tablets. The parameters were as follows: the emission current was 25 mA; the working temperature was 25 °C. The time/step length: 1 s/step length; Interval: 2θ = 4−40°; Scan step size: 0.01. The diffraction peak of the results was smoothed, the back and bottom were subtracted, and the instrument was widened.

      Scanning electron microscope

    • The experimental instrument was a Field emission scanning electron microscope (SIGMA500, Zeiss, Germany). Freeze-dried samples were uniformly fixed on the glue-attached electron microscope injection stage and sprayed gold under vacuum conditions. They were then fixed on the stage to adjust the best field of view and magnification for observation.

      Statistical analysis

    • Each experiment was repeated three times. The experimental data were processed and analyzed by Excel and Origin 2022b, and the correlation analysis was performed by SPSS 26.0, and the significance level was p < 0.05.

    Results and discussion

      Molecular docking technology simulating the formation of BSA-C3G composite

    • Molecular docking is a computer simulation program used to predict the conformation of receptor-ligand complexes[24]. Molecular docking technology can simulate the binding between C3G and BSA, which helps to understand the ligand-receptor interaction better and further verify the experimental conclusions. It has been reported that the degree of hydroxylation on the B-ring of anthocyanins determines the hue and color stability of anthocyanins[25]. The antioxidant capacity of anthocyanins is associated with the number of hydroxyl groups in the B ring. The hydroxyl group at position 4 of the B ring is the most active group[26]. Figure 2 showed that C3G was mainly bound to the II and III domains of BSA, and six amino acid residues docked with C3G molecule. ASP108, LYS114, ARG144, ARG185, and LEU454 interacted with C3G through hydrogen bonding, resulting in the loss of hydrogen donor which further limited its antioxidant properties. Meanwhile, ARG458 was docked to C3G through cation-π interaction. Hence, the hydrogen bond and cation-π interaction are the main force types in the binding process of C3G to BSA. Recent studies have revealed that the predominant binding mechanism between the two entities is non-covalent binding[27,28], which aligns with our initial prediction. The calculated minimum binding energy of the molecular model was determined to be −7.291 kcal/mol (30.52 kJ/mol). These findings suggest that application of a magnetic field may influence the cation-π interaction and subsequently alter the binding conformation of the two entities[29].

      Figure 2. 

      Molecular docking of simulated BSA-C3G conjugates, (a) nine simulation results, (b) BSA-C3G conjugate model, (c) BSA-C3G binding site detail diagram, (d) main force type.

      Effect of SMF on the secondary structure of BSA-C3G composite

    • Circular dichroism (CD) spectroscopy is employed to further investigate the impact of SMF treatment on the binding of C3G-BSA complex (Fig. 3). The complexes subjected to different magnetic fields exhibited two distinct negative absorption peaks at 208 and 221 nm, respectively, indicating the characteristic α-helix structure in the secondary conformation[30,31]. As the intensity of SMF increased, distinct changes were exhibited in the CD of BSA. It suggested that the magnetic field disrupted the protein structure, causing BSA more susceptible to binding with anthocyanins[32]. The experimental results demonstrate that the alterations in absorbance and secondary structure are reverse reactions. Research has found that sometimes the changes in absorbance are small, while the changes in structure are large[33]. In this experiment, this might be attributed to the magnetic field acting on the composite, causing it to form a special structure that influences the absorption of light. As shown in Table 1, an escalation in SMF induction intensity led to a decrease in α-helix content from 32.6% to 23.4%, an increase in β-fold content from 5.5% to 39.6%, a decrease in β-angle content from 22.1% to 8.4%, and a reduction in random coil content from 39.8% to 28.6%. The decrease in α-helix content from 32.6% to 23.4% can be attributed to several factors. For example, the C=O bond of the amide group is capable of forming hydrogen bonds with other functional groups, thereby contributing to the overall secondary structure of the protein complex. The typical α-helix structure is a helical conformation constituted by a hydrogen bond between the C=O of the amino acid at position X and the N-H of the amino acid at position X-4 in the peptide backbone[34]. Magnetic fields can effectively facilitate the transition of hydrogen bonds from disordered to ordered states[35]. In a randomly coiled polypeptide chain, the dipole moment of a single backbone amide group is oriented randomly, resulting in neighboring helices neutralizing each other's dipoles in opposite directions. In α-helices, the hydrogen bond neutralizes their horizontal dipole moments, and the vertical dipole moments point in the same direction[36]. The dipoles of the individual peptides within the helix were combined to form large dipoles. Meanwhile, the amino-terminal pole of the helix becomes positive and the carboxyl-terminal pole becomes negative. Therefore, the charge distribution is asymmetric in the high helical structure of BSA, which reduces the alpha-helical content under the applied magnetic field. The change in the secondary structure of BSA, suggested that C3G bound with amino acids on the main chain of BSA and destroyed the hydrogen bond network.

      Figure 3. 

      Circular dichroism spectra of BSA-C3G conjugates at different magnetic sensing intensities.

      Table 1.  Changes in secondary structure content of BSA-C3G conjugates at different magnetic sensing strengths.

      Magnetic intensity α-helix β-sheet β-turn Random coil
      0 mT 32.6% 5.5% 22.1% 39.8%
      50 mT 29.3% 23.7% 16.9% 30.1%
      100 mT 25.0% 30.9% 12.1% 31.9%
      150 mT 24.3% 35.8% 10.7% 29.2%
      200 mT 23.4% 39.6% 8.4% 28.6%

      Effect of SMF on the intermolecular interaction of BSA-C3G composite

    • To investigate the potential re-dissociation of the complex into individual BSA and C3G molecules in solution with exposure to an SMF, the solution is analyzed using the infrared spectroscopy[37,38]. Based on the result of Fourier infrared spectroscopy, no separation of the complex after binding was observed (Fig. 4). Furthermore, the position and number of absorption peaks in the complexes different magnetic induction treatments remained unchanged. It was indicated that the chemical bond between BSA and C3G was not influenced by the SMF[39]. However, the transmittance underwent a distinct change, which could potentially be ascribed to the dipole moment of the conjugate[40]. With the increasing intensity of the SMF, the infrared spectral transmittance of the sample increased. It may be related to the change of the molecular force of the SMF, resulting in the change of the dipole moment of the sample. The dipole moment changed greatly and the transmittance of the absorption peak increased greatly. The greater the electronegativity difference between the two ends of the bond and the greater the polarity, the greater the transmission is observed. It is concluded that the SMF treatment did not change the chemical bond, but also changed the dipole moment of the bond (the distance between different atoms), then changed the binding effect of the two molecules. Thus, the stronger the magnetic induction intensity of the static field resulted in the greater change of the dipole moment.

      Figure 4. 

      FTIR of BSA-C3G conjugates under different MF conditions.

      Effect of SMF on the crystal characteristics of BSA-C3G composite

    • The characteristic diffraction pattern included two primary components: the spatial distribution of diffraction, reflected in the peak position within the characteristic diffraction pattern and the intensity of the characteristic diffraction peaks. The distribution of diffraction peaks is predominantly governed by the size, shape, and orientation of the unit cell. Meanwhile, the intensity is mainly determined by both the type of atoms and their positions within the unit cell. As depicted in Fig. 5, all samples exhibited sharp diffraction peaks indicative of a crystalline structure[41]. The peak heights of samples with SMF treatment were observed to be remarkably high, sharp, and narrow at 11° and 22° compared with the control group. Conversely, the height of the above two peaks decreased clearly in SMF treatment and the peak width was slightly wider than that of 0 mT samples. The alteration in average grain size was also attributed to changes in the peaks at 11° and 22°. According to Diaconu et al.[42], these peaks can be identified as randomly oriented helical structures, suggesting that the influence of SMF on these structures was distinct. This can be attributed to the flexibility of the protein, which allows the restructuring of the structure due to exposure to magnetic forces of different strengths and allowed the two helices to unravel. Generally, maintaining the helical structure mainly depends on hydrogen bonds. After the helix unraveled, more hydrogen bond exposure increased the efficiency of small molecules to bind. Table 2 shows the results of peak position, full width at half maxima (FWHM), and crystaline size. Based on Table 2, the grain size of the samples subjected to the SMF generally decreased. This phenomenon may be attributed to the influence of the SMF on the magnetic dipole moment, with a minimum effect observed at 50 mT and a maximum effect at 200 mT. The treatment of 200 mT showed the highest degree of helix unwinding and the highest efficiency of binding. These results were in accordance with the experimental conclusion of circular binary chromatography.

      Figure 5. 

      XRD patterns of BSA-C3G conjugates under different magnetic field conditions.

      Table 2.  Peak position, FWHM, crystaline size, and average size of conjugates.

      Compound Peak position
      (2 Theta)      		 FWHM Crystaline
      size D (nm)      		 Average D
      (nm)
      0 mT 11.00 0.07 115.30 52.44
      15.46 0.20 40.99
      21.84 0.11 71.56
      22.74 0.25 31.90
      23.68 0.19 43.76
      27.62 0.15 55.92
      32.26 0.24 34.82
      32.94 0.20 41.16
      40.78 0.23 36.55
      50 mT 10.89 0.26 31.14 37.36
      21.72 0.27 30.07
      23.64 0.16 51.99
      26.05 0.18 44.46
      31.02 0.20 41.07
      32.22 0.30 26.95
      32.92 0.20 41.55
      40.77 0.27 31.61
      100 mT 10.97 0.18 43.81 39.70
      15.42 0.16 51.03
      21.81 0.23 34.66
      22.73 0.24 34.45
      27.66 0.19 42.07
      31.12 0.19 44.53
      32.28 0.26 31.66
      32.99 0.23 35.41
      150 mT 10.82 0.22 36.57 40.63
      15.39 0.17 46.43
      20.26 0.17 46.86
      21.68 0.23 35.73
      22.61 0.21 38.63
      25.99 0.18 46.48
      30.98 0.18 45.67
      32.17 0.25 33.19
      40.73 0.23 36.14
      200 mT 10.89 0.18 44.21 41.30
      21.74 0.15 52.58
      26.03 0.20 41.67
      32.90 0.24 34.85
      40.74 0.26 33.20

      Effect of SMF on the binding of C3G to BSA composite

    • To provide a more intuitive observation of the microstructure of the BSA-C3G complex, scanning electron microscopy was applied[43]. In Fig. 6, the microstructure was observed under different magnetic field strengths and magnified by different observation factors, which shows a more comprehensive and intuitive trend of the composite under a static magnetic field. In the control sample (0 mT), the microscopic surface of the sample revealed aggregated particles in the plane at a magnification of 1 K. In addition to prominent uplift and depression structures, the sample exhibited a rough layer state with fine fracture structures, primarily located at joints within the laminated structure. However, as the magnetic field increased, there was a gradual flattening of the overall structure and an emergence of cavity structures with spherical formations within them.

      Figure 6. 

      SEM patterns of BSA-C3G conjugates under different magnetic field conditions.

      Scanning electron microscopy revealed the presence of small spherical anthocyanin molecules at magnifications ranging from 1,000 to 10,000 times[44]. After SMF treatment, the previously disordered stacked structure was reorganized into a more regular arrangement. Furthermore, an increase in the magnetic induction intensity of the SMF resulted in a smoother microstructure of the sample.

    Conclusions

    • In the present study, the effect and mechanism of SMF on the interaction between BSA and C3G was investigated. The SMF can affect the secondary structure and unit cell size of the complex, induce the interaction between BSA and C3G molecules, and increase the influence of magnetic induction intensity on the complex. This may be due to the higher magnetic induction intensity, the greater the dipole moment between molecules, and the greater the degree of directional rearrangement of complex molecules. These findings provide insights into the mechanism by which SMF interacts with proteins and anthocyanins, provide a basis for SMF to promote their binding, and point out a new possible pathway for improving anthocyanin stability. In future research, the reaction pathway of this complex in vivo can be further explored to investigate its wide targeted reaction process in living organisms.

      Acknowledgments

      • This research was partially supported by the Natural Science Foundation of Liaoning Province, China (2023-MS-205).

      Author contributions

      • The authors confirm contribution to the paper as follows: conceptualization: Li D, Zhang Z; methodology: Zhang Z, Shen Y; data curation: Zhang Z, Xin G; formal analysis and visualization: Zhang Z, Deng W; writing - original draft: Zhang Z; writing-review & editing: Zhang Z, Deng W, Adel Ashour A, Tan H; funding acquisition and supervision: Li D. All authors reviewed the results and approved the final version of the manuscript.

      Data availability

      • All data generated or analyzed during this study are included in this published article and its supplementary information files.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press on behalf of China Agricultural University, Zhejiang University and Shenyang Agricultural University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (6)  Table (2) References (44)
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    Cite this article
    Zhang Z, Shen Y, Xin G, Deng W, Tan H, et al. 2024. The effect of static magnetic field on inducing the binding of bovine serum albumin and cyanidin-3-O-glucoside. Food Innovation and Advances 3(4): 449−456 doi: 10.48130/fia-0024-0042
    Zhang Z, Shen Y, Xin G, Deng W, Tan H, et al. 2024. The effect of static magnetic field on inducing the binding of bovine serum albumin and cyanidin-3-O-glucoside. Food Innovation and Advances 3(4): 449−456 doi: 10.48130/fia-0024-0042
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  • Introduction
  • Materials and methods

  • Materials

  • Reagent preparation

  • Molecular docking simulation

  • Magnetic processing complex

  • Circular dichroism chromatography

  • Fourier infrared spectroscopy

  • X-ray wide-angle diffraction

  • Scanning electron microscope

  • Statistical analysis

  • Results and discussion

  • Molecular docking technology simulating the formation of BSA-C3G composite

  • Effect of SMF on the secondary structure of BSA-C3G composite

  • Effect of SMF on the intermolecular interaction of BSA-C3G composite

  • Effect of SMF on the crystal characteristics of BSA-C3G composite

  • Effect of SMF on the binding of C3G to BSA composite

  • Conclusions
  • Acknowledgments
  • Author contributions
  • Data availability
  • Conflict of interest
  • Rights and permissions
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