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Prediction of active ingredients in Salvia miltiorrhiza Bunge. based on soil elements and artificial neural network

The roots of Salvia miltiorrhiza Bunge. are commonly used in the treatment of cardiovascular diseases, and tanshinones and salvianolic acids are its main active ingredients. However, the composition and content of active ingredients of S. miltiorrhiza planted in different regions of the soil environment are also quite different, which adds new difficulties to the large-scale and standardization of artificial cultivation. Therefore, in this study, we measured the active ingredients in the roots of S. miltiorrhiza and the contents of rhizosphere soil elements from 25 production areas in eight provinces in China, and used the data to develop a prediction model based on BP (back propagation) neural network. The results showed that the active ingredients had different degrees of correlation with soil macronutrients and trace elements, the prediction model had the best performance (MSE = 0.0203, 0.0164; R2 = 0.93, 0.94). The artificial neural network model was shown to be a method that can be used to screen the suitable cultivation sites and proper fertilization. It can also be used to optimize the fertilizer application at specific sites. It also suggested that soil testing formula fertilization should be carried out for medicinal plants like S. miltiorrhiza, which is grown in multiple origins, rather than promoting the use of “special fertilizer” on a large scale. Therefore, the model is helpful for efficient, rational, and scientific guidance of fertilization management in the cultivation of S. miltiorrhiza.

INTRODUCTION
Salvia miltiorrhiza Bunge. is a persistent herb of the genus Salvia of the mint family, Lamiaceae (Chinese Flora Commission of the Chinese Academy of Sciences, 1974). In China, Japan, and the United States (Su et al., 2015), its dried roots are widely used as one of the most commonly used traditional medicinal herbs to treat cardiovascular diseases, especially angina pectoris and myocardial infarction (Wang et al., 2017). Researches have shown that diterpenoid quinones and hydrophilic phenolic acids are its principal bioactive components (Mei et al., 2019). As cardiovascular disease is a common and frequently-occurring disease (Jagannathan et al., 2019), the wild resources are unable to meet the ever-increasing market demand. Currently, 19 provinces in China, including Shandong, Henan, Sichuan, Hebei, Hubei, Jiangsu, Shanxi, and Shaanxi (Lu, 2019), have been introduced and cultivated S. miltiorrhiza, but there are significant differences in the content of active ingredients in the same species planted in different regions (Huang et al., 2019;Yang et al., 2011b). Soil is the material basis for the survival of plants, and the abundance and deficiency of major and trace elements in soil affect plant growth and development and physiological and biochemical metabolism, which also affect the yield and the composition and content of active ingredients in medicinal plants (Zhang et al., 2018). Fertilization is the primary way often used in agricultural production to improve soil nutrition, and proper fertilization is an important measure to ensure the yield and quality of medicinal plants. Therefore, analyzing the relationship between soil elements and active components of medicinal plants and establishing related models can provide data support and technical guidance for selecting suitable sites and fertilization management in the cultivation of S. miltiorrhiza.
Although the use of models based on the relationship between soil environment and crop quality or the quality of medicinal plants to predict suitable land, crop yield, and quality has received widespread attention, most studies have used regression analysis models to predict the relationship between the two (Li et al., 2020). Regression analysis models are built on the assumption of some idealized linear relationship between predictor and response variables. However, environmental factors show very high intra-and interindividual variability, which means that the biological responses of plants in response to the environment are uncertain and nonlinear in nature (Gago et al., 2010). Therefore, many biological interactions cannot be explained by simple stepwise algorithms or exact formulations, especially in complex or noisy data. Artificial neural network methods have proven effective in solving such problems in different disciplines (e.g., in vitro culture (Hesami & Jones, 2020), remote sensing studies (Wang et al., 2019)). The artificial neural network methods can learn and create nonlinear and complex relationships and can flexibly solve the complex problems of multiple interacting variables (Bayat et al., 2019). It is one of the best techniques for extracting information from inaccurate and nonlinear data (Caselli et al., 2009). Among them, BP neural network model is the most widely used neural network model in the fields of economics, engineering, and botany (Armaghani et al., 2018;Chen et al., 2019). The BP neural network has been used to monitor crop growth and crop yield prediction (Akbar et al., 2018;Wang et al., 2019). However, few reports on the application of the BP neural network predict active ingredients of medicinal plants based on soil elements. Therefore, in this study, we took S. miltiorrhiza as the research object, measured the active ingredients in the roots of S. miltiorrhiza. and the contents of rhizosphere soil elements from 25 production areas in eight provinces in China, analyzed the relationship between them, and established a BP neural network model for the prediction of active ingredients using soil elements as input values, which expands the application of artificial neural network methods in the field of medicinal botany and also provides references for its application in other directions in the field of medicinal plant cultivation, and verifies the feasibility of using artificial neural network model to effectively improve the accuracy of the prediction of the content of active ingredients of plants.

Sample collection and processing
Roots and rhizosphere soil of S. miltiorrhiza (cultivation or wild) were collected from 25 producing areas in eight provinces of China from mid-November to early December 2007 ( Fig. 1, Table S1). The samples were collected by the ''S'' parallel sampling and multi-point mixing method, each sample was collected at 20 to 25 points, and the rhizosphere soil was collected by the root shaking method, and finally, the samples for analysis were obtained by the quadratic method, of which 2 kg/sample of rhizosphere soil and 5 kg/sample of medicinal parts were retained. After the samples were collected and quickly transported back to the laboratory, the herbs were processed routinely, and the soil samples were air-dried and prepared.

BP (back propagation) neural network
Bootstrap is a statistical inference method based on resampling and data simulation. Due to the nonlinear nature of small sample sizes and the difficulty of characterizing the overall distribution, the Bootstrap method can be applied to improve the estimation accuracy of the model (Wang et al., 2018). Therefore, the Bootstrap method has the potential to be widely applied to modeling estimations of small sample sizes. The steps are described as follows: (i) perform resampling to select a certain number (given) of samples and to allow repeat sampling; (ii) calculate the given statistics T based on the given samples; and (iii) repeat the above steps N times to gain N number of statistics T (Wang et al., 2018). The back propagation neural network proposed by Rumelhart in 1986 is a multi-layer feedforward network trained according to the error back propagation algorithm and is one of the most widely used neural network models (Rumelhart, Hinton & Williams, 1986). The BP neural network model algorithm consists of two aspects: the forward propagation of the signal and the back propagation of the error. In other words, the actual output is calculated according to the direction from input to output. However, when the actual output contradicts the expected output, the back propagation of error is performed according to the direction from output to input, and the output error of each layer neuron is calculated layer by layer. Then, the weights and thresholds of each layer are adjusted according to the error gradient descent method to achieve the final output value that can be close to the expected value. The model-building process was described in detail as follows.
In this study, a BP neural network model was constructed using the neural fitting tool (nftool) in MATLAB 2019b mathematical software and trained with soil element parameters as input and the content of plant active ingredients as output. The input variables were as follows: Mn, Cu, Zn, N, P, and K; output variables: water-soluble components (danshensu, protocatechuic aldehyde, caffeic acid, rosmarinic acid, salvianolic acid B, salvianolic acid A), and lipid-soluble components (dihydrotanshinone I, cryptotanshinone, tanshinone I, and tanshinone IIA). In the solution process, 70% of the samples (n = 18) were used to obtain samples using the bootstrap method and build a BP neural network model. The remaining 30% of the samples (n = 8) were used to verify the model. First, to prevent the negative impact of different ranges of input variables on the model's efficiency, the input variables in the model were therefore normalized for the specified range (0-1) (Eq. (1)): In the equation, F j , X j , X min and X max are the standardized value, original value, minimum value and maximum value of the input variable respectively.
Second, the number of hidden layers and the number of neurons in each hidden layer in a BP neural network impact the overall neural network structure. Currently, many empirical formulas are applied to determine the number of neurons in the hidden layer, and one of these formulas is as follows (Eq. (2)): In the equation, m (m =6) is the number of nodes in the input layer, n (n =2) is the number of nodes in the output layer, and a (1 ≤a ≤10) is a constant.
According to Eq.
(2), the number of nodes in the hidden layer was set as an integer between 4 and 12. Eventually, the number of neurons in the hidden layer was determined to be 8 by iterative trials. A neural network model with 6-8-2 structure was finally established (Fig. 2), in which the input layer consisted of 6 neurons corresponding to the 6 input variables, and the output layer had 2 neurons representing the content of active components in the model.
Third, another problem in establishing neural network models is the choice of network learning or training algorithms. Since the Levenberg-Marquardt algorithm minimizes the sum of the error function of the form (Eq. (3)), thus the Levenberg-Marquardt algorithm has the best performance (R-value) compared to other training algorithms (i.e., Bayesian regularization (BR) and scaled conjugate gradient (SCG)) (Mahmoudi & Mahmoudi, 2014). Therefore, the Levenberg-Marquardt algorithm will be used to train the network. The training epochs, learning rate, and minimum performance gradient were set as 1000, 0.01, and 1e −7 .
where e k is the error in the k th exemplar or pattern and e is a vector with element e k . During the BP training process, a sigmoid function (Eq. (4)) is used to describe the nonlinear relationship between the input and output of each neuron in the hidden layer as follows (Yi et al. 2007).
The output y j of the hidden layer neuron j is calculated by Eq. (5): In the equation, ø(x) represents the activation function of the hidden layer, n is the number of neurons in the input layer, X i is the input of the input layer neuron i, W ij is the weight from the input layer neuron i to the hidden layer neuron j, and θ j is the threshold value of the hidden layer neuron j.
The output z k of the output layer neuron k are calculated by Eq. (6): In the equation, f (x) represents the activation function of the output layer, and m is the number of neurons in the hidden layer, V jk is the weight of the hidden layer neuron j to the output layer neuron k, and α k is the threshold value of the output layer neuron k.
The network weights adjustment is defined as follows (Eq. (7)) (Huang et al., 1996): where α and β are assumed constants, called the learning rate and momentum factor, respectively, E is the error function (in which MSE was used), ω is the weight vector, and t is the iteration number (epoch in MATLAB). Finally, the steps of training can be summarized as follows: (i) applying the input vectors, (ii) calculating the output of the network and comparing it with the corresponding target vectors, (iii) feeding the difference (error) through the network, and (iv) changing the weights according to the algorithm, which tends to minimize the error. The vectors of the training set are applied sequentially. This process is repeated several times using the entire training set until the error is within acceptable criteria or until the output does not change significantly.

Predictive performance evaluation
To evaluate the predictive power of the model, the MSE (mean square error) and the R 2 (coefficient of determination) were used to evaluate the overall performance of the model (Eqs. (8)-(9)): In the equation, Y i is the experimental value of the evaluation model,Ŷ i is the corresponding predicted data,Ȳ i is the average of the experimental data and n is the number of experimental data.

Correlation analysis of active ingredients and rhizosphere soil elements
In this study, the active ingredients and rhizosphere soil elements of S. miltiorrhiza from 25 producing areas in eight provinces of China were determined, and the contents of 6 elements (Mn, Cu, Zn, N, P, K) (Fig. S1) and 10 the kinds of active ingredients (danshensu (D1), protocatechuic aldehyde (D2), caffeic acid (D3), rosmarinic acid (D4), salvianolic acid B (D5), salvianolic acid A (D6), dihydrotanshinone I (D7)), cryptotanshinone (D8), tanshinone I (D9), tanshinone IIA (D10)) (Fig. S2). From the perspective of linear relationship, Fig. 3 shows the correlation between the content of different active ingredients and soil elements. Most active ingredients are related to major and trace elements (r>0). In addition, the relationships between soil elements (Fig. 3) showed that Mn had a significant positive correlation with Zn (p = 0.004<0.01); Cu showed a weak correlation with Zn, K (r < 0.4); N showed a weak correlation with P (r < 0.4); Zn showed a weak correlation with K (r < 0.4). It is suggested that there were synergistic properties in the utilization of elements by S. miltiorrhiza. Then, the study used the kappa value to evaluate the multicollinearity of typical indicators. A k value below 100 was interpreted as low multicollinearity, and a k value exceeding 1000 indicates high multicollinearity (Ma et al., 2021). The kappa value (k = 480.153) was exceeded 100. These results indicated multicollinearity among fundamental soil indicators, and linear regression cannot be performed directly. From the perspective of a nonlinear relationship, Figs. 4 and 5 showed the scatter and regression plots between the active ingredients of S. miltiorrhiza and the soil elements. The results showed that there was not a simple increase or decrease between the active ingredients of S. miltiorrhiza and soil elements, but an inevitable fluctuation would follow, which means that as the content of soil elements increases, the content of active ingredients of S. miltiorrhiza will increase and decrease. These results indicated that the active ingredients of S. miltiorrhiza had a nonlinear relationship with multiple soil elements, and a particular element index cannot be used to express the effective ingredients of S. miltiorrhiza. The artificial neural network methods can learn and create nonlinear and complex relationships and can flexibly solve the complex problems of multiple interacting variables (Bayat et al., 2019). It is one of the best techniques for extracting information from inaccurate and nonlinear data (Caselli et al., 2009). Therefore, in this study, a BP neural network model with soil elements as input values was established to explore the relationship between soil elements and the active ingredients for predicting the active ingredients.

Predictive performance of BP neural network model
In this study, a BP neural network model was constructed using the neural fitting tool (nftool) in MATLAB 2019b mathematical software and trained with soil element parameters as input and the content of plant active ingredients as output. The input variables were as follows: Mn, Cu, Zn, N, P, and K; output variables: water-soluble components (danshensu, protocatechuic aldehyde, caffeic acid, rosmarinic acid, salvianolic acid B, salvianolic acid A), and lipid-soluble components (dihydrotanshinone I, cryptotanshinone, tanshinone I, and tanshinone IIA). Through repeated training, a neural network model with 6-8-2 structure was finally established (Fig. 2). In order to evaluate the predictive ability of the established BP neural network model, the MSE (mean square error) and the coefficient of determination (R 2 ) were used to evaluate the overall performance of the model. Ideally. The closer the MSE value is to zero, the closer the R 2 value is to 1, which indicates that the average training and testing performance is appropriate. The BP model showed fast training and high simulation accuracy in model testing (Fig. 6A). The relationship between the predicted and measured active ingredients content was favorable, thereby indicating that this model has a basic consistency and a high degree of simulation. A scatter diagram was constructed between the predicted and real values of the inversion model. The coefficient of determination R2 between the predicted and real values of the inversion model was 0.93, the linear regression line between the measured and predicted values were close to 1 (i.e., linear), and the MSE (MSE =0.0203) was low. Therefore, the predictive ability of the model was relatively high and showed strong nonlinear fitting ability, indicating that the soil elemental content could be used for accurate inversion of the active ingredient content. From the results obtained, the empirical equation based on the Levenberg-Marquardt algorithm for predicting active ingredient content in normalized form is Eq. (10): The transfer function ''purelin'' correlated the linear relationship between the input and output variables, while ''logsig '' calculated the layer's output from the network input. The variables j 1 , j 2 , j 3 , j 4 , j 5 , and j 6 were input weights from the input layer to the hidden layer. Also, Lw 1 , Lw 2 were hidden layer weights from the hidden layer to the output layer. The variables b i and b k were biases connected to the hidden layer neurons and output layer neurons, respectively. The values for these weights and biases for the model were in Table 1.
To further evaluate the model's generalizability, the developed neural network model was tested using 41 new datasets. The new datasets were derived from the results of Zhang's study (Zhang et al., 2018b). The correlation coefficient, mean square error, was used to evaluate the generalization ability of the model. A scatter diagram (Fig. 6B) was constructed between the predicted and real values of the inversion model. The coefficient of determination R 2 between the predicted and real values of the inversion model was 0.94, the linear regression line between the measured and predicted values were close to 1:1 (i.e., linear), and the MSE (MSE =0.0164) was low. Therefore, the predictive ability of the model was relatively high. Our results indicate that the BP neural network model based  Table 1 Weights and biases of the model. i

DISCUSSION
Soil is the material basis for the survival of plants. The large and trace elements in the soil can provide nutrients for plants, provide good growth and metabolic status, and enhance their resistance to adversity (Alam & Naik, 2009;Zhang et al., 2018). These are the basis for the production of secondary metabolites of the plant. Isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP) are the precursors of all terpenoids (Ma et al., 2015). The synthesis of both requires P. The phenylpropanoid pathway and the tyrosinederived pathway are the main pathways for the biosynthesis of salvianolic acids (Ma et al., 2015). The synthesis of phenylalanine, the starting point of the phenylpropane pathway, requires the participation of N. K not only regulates plant water metabolism but also acts as an activator of enzymes involved in respiration and photosynthesis (e.g., glutathione synthase) (Liu, 2009). Trace elements (Mn, Cu, Zn) are involved in the biosynthesis of plant active ingredients as cofactors, constituent elements, and activators of some enzymes (Guo et al., 2005). Therefore, the biosynthesis of the active ingredients of S. miltiorrhiza cannot be achieved without the participation of soil elements. In this study, the results of correlation analysis between soil elements and active ingredients based on the harvesting period of the herb showed that the active ingredients of S. miltiorrhiza interacted with several soil elements and showed a nonlinear relationship, which may be different from the results of fertilization experiments, for example, Han & Liang (2005) reported that phosphorus fertilization showed a positive effect on the accumulation of danshensu and tanshinone IIA, but this effect was not observed for the effective phosphorus content in this study.
Combined with the Mao's research results (Mao et al., 2009), it is believed that it is a matter of elemental availability or antagonism in the utilization between elements. Moreover, there were different levels of correlation between the affective states of the elements, suggesting a synergistic or antagonistic effect on elemental uptake by S. miltiorrhiza. The results of our study further suggest that soil testing formula fertilization should be implemented for medicinal plants like S. miltiorrhiza, which is grown in multiple origins, rather than promoting the use of ''special fertilizer'' on a large scale. The relationship between fertilizer requirements of medicinal plants and soil supply should be coordinated. The required nutrients should be supplemented in a targeted manner to achieve a balanced supply of nutrients and guide the fertilization management of medicinal plants in an efficient, rational, and rational scientific manner. Therefore, it is imperative to establish a method that can be used for screening suitable cultivation sites and rational fertilization of medicinal plants.
Here, we presented and validated the application of artificial neural networks in predicting the active ingredients of medicinal plants. Artificial neural networks have received more attention in various applications due to their sensitivity, accuracy, nondestructive nature, and rapidity (Wu, Liu & Lu, 2012). Many researchers have used artificial neural networks to monitor crop growth and crop yield prediction (Akbar et al., 2018;Wang et al., 2019;Yang et al., 2018), but there are few reports on the use of artificial neural networks to predict active ingredients of medicinal plants based on soil elements. Our results showed that artificial neural network models provided more appropriate predictions of the content of S. miltiorrhiza using the site's soil elemental characteristics conditions. We recorded minimal differences between the predicted and observed data (MSE =0.0203, 0.0164; R 2 = 0.93,0.94), and the excellent agreement between the results support the high efficiency of artificial neural networks while demonstrating that the use of mathematical models (e.g., artificial neural networks) to predict the active ingredient content in medicinal plants can reduce the time and cost required for analytical methods.
Artificial neural network models offer significant advantages over traditional mathematical models by using nonlinear network connections and allowing analysis that explores the efficacy of all input variables simultaneously, thereby improving the accuracy of results (Alam & Naik, 2009;Tušek et al., 2018a;Tušek et al., 2018b). However, the capability of artificial neural network models is usually also limited by drawbacks such as slow learning speed, overfitting, and local minima, which suggests that building some hybrid neural network models to reduce their drawbacks and improve their performance is an essential direction for future research.
Many medicinal plants have greater medicinal productivity in their original habitat than in cultivated areas. Soil nutrient characteristics similar to the original habitat must be best suited for the production of active metabolites (Alam & Naik, 2009). Based on the results of this study, an artificial neural network model can be used to screen suitable cultivation sites for medicinal plants and simulate soil conditions similar to the original soil conditions through soil management, thus increasing the yield of active ingredients. The specific measures are as follows: firstly, measuring the key nutrient factors of the soil, such as massive elements and trace elements; and finally balancing fertilization, such as targeted supplementation of required nutrients during plant growth and harvest, to achieve a balanced supply of nutrients and to efficiently, rationally and scientifically guide the fertilization management of medicinal plants. However, the soil matrix is a complex organic ecosystem, while the plants' secondary metabolites themselves are a complex physiological process, so the effect of soil on plant active ingredients is far more complex than what is involved in the current study. So future work could also extend this modeling process by using a more inclusive range of biotic and abiotic variables to obtain more accurate estimates.

CONCLUSIONS
In this study, an artificial neural network model for predicting the active ingredients of S. miltiorrhiza using rhizosphere soil elements as input values were developed to localize the effects of these factors on the active ingredients. This expands the application of artificial neural network methods in medicinal botany and provides a reference for other directions in the field of medicinal plant cultivation. The results show that, in combination with soil data, we can use artificial neural network models to successfully predict the content of S. miltiorrhiza. This further validates the feasibility that artificial neural network models can effectively improve the accuracy of the prediction of the active ingredient content of medicinal plants. In addition, the model can provide broader applicability for ranch managers, manufacturers, and producers of medicinal plants to screen suitable sites for medicinal plant cultivation in a robust and reproducible manner. It can also optimize the fertilizer application at specific sites and guide the fertilization management of medicinal plants in an efficient, rational, and rational scientific manner.


provenance: AgriculturalAndFoodSciences,2022-2022-Agricultural And Food Sciences-2022.jsonl.gz.gz

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of … Characteristics of Chitooligosaccharides Hydrolysate from Crab Shell ( Portunus Pelagicus ) Waste by Chitosanase Hydrolysis

Enzymatic hydrolysis of chitooligosaccharides can be carried out using the chitosanase enzyme. Chitosanase enzyme is a glycosyl hydrolase enzyme that catalyzes the hydrolysis of β- 1,4 glycosidic bonds of chitosan to produce low molecular weight chitooligosaccharides. The crab shell (Portunus pelagicus) has the potential to be used as chitooligosaccharides because the crab shell contains 20-30% chitin. This study aimed to determine the effect of chitosanase enzyme concentration and duration of hydrolysis on the characteristics of chitooligosaccharides (COS) from crab shells. This research was conducted using a factorial completely randomized design (CRD) method with two factors and two replications. The factor I was enzyme concentration (0.5%, 1%, and 1.5%), factor II was hydrolysis time (3 hours, 4 hours and 5 hours). The data were obtained using ANOVA if there was a significant difference, followed by Duncan's Test (DMRT). The best results were obtained in the A2B2 treatment, enzyme concentration (1.0%) and hydrolysis time (4 hours) with a chemical composition of 84.96% yield, 4.83 KDa molecular weight, 86.87% degree of deacetylation and FTIR test results indicated the availability of a functional groups, the OH group was obtained at a wavelength of 3624.25 cm -1 and the NH group is at a wavelength of 3404.36 cm -1 .

INTRODUCTION
Chitin is a polymer that is insoluble in water and weak acids, while chitosan is chitin in the form of partially deacetylated (more than 50%) or 100% free of acetyl, which has better properties than chitin in solubility and functional properties. Chitosan is soluble in weak acids and has a wider application than chitin because it has more active groups. However, because it is only soluble in weak acids, the application of chitosan also has limitations in industry. Chemical modification of chitosan with the addition of a hydroxyl group (hydrophilic) chemically so that it becomes carboxyl methyl chitosan which is water-soluble, is a strategy in utilizing chitosan (Yusro Nuri Fawzya et al., 2009).
According to several researchers studying on chitooligosaccharides (COS) that compounds derived from chitosan resulted in the chitin deacetylation process, which is a complex compound of the glycoprotein group that has β-1,4 glucosamine bonds (Agnes Sri Harti, 2011); and the chitosan (1, 4) glycosidic bonds chain cutting several techniques such as chemical degradation, enzymatic, and mechanical treatmen (Singh, 2016). Enzymatic hydrolysis is specific, is controlled, is produced chitosan oligomers with higher polymer degrees and less glucosamine which are environmentally friendly (Sarni et al., 2016). In the opinion of other researchers the chitosanase enzyme is a glycosyl hydrolase that catalyzes the hydrolysis of β-1, 4 glycosidic bonds of chitosan to produce glucosamine and low molecular weight COS (Heggset, 2012) and (de Assis et al., 2010).
Chitooligosaccharides (COS) are compounds derived from chitosan resulted from the deacetylation of chitin with complex compounds belonging to the glycoprotein group that has 1, 4 glucosamine bonds (Listiyana, 2018). In addition, COS has chains of 20 or less and is water-

Demineralization Process
A total of 100 g of crab shell powder was put into 2 L glass beaker, added 1.5 L of 1.5 M HCl solution in a ratio of 1:15 (w/v). Those ingredients were heated on a hotplate at 70°C for about 4 hours and stirred it with a magnetic stirrer. Then, It was filtered. To obtaine insoluble precipitate then washed with distilled water until the supernatant was neutral. It was dried in the oven for 24 hours at 70°C.

Deproteination Process
A total of 23 g of demineralized powder was then added with 230 ml of 3.5% NaOH solution in a ratio of 1:10 (w/v). The mixture was heated at 70°C for 4 hours while stirred with a magnetic stirrer then filtered and the precipitate was washed with distilled water until the supernatant was neutral. Dry in the oven for 24 hours at 70°C, and then carry out the process of taking chitosan

Deacetylation Process
As many as 17 g of deproteinized powder was added with 170 ml 50% NaOH solution in a ratio of 1:10 (w/v). The mixture was heated at 100°C for 4 hours while stirred with magnetic stirrer. It was filtered and the precipitate was washed until the filtrate was neutral. Dried in the oven for 24 hours at 70°C. The resulted chitosan was then characterized by FT-IR.

Chitooligosaccharide Manufacturing Process
As many as 5 g of chitosan was dissolved in 500 ml of 1% acetic acid and stirred for 10 minutes and the pH was adjusted at 7 ± 0.5. Chitosanase enzyme concentrations of 0.5%, 1%, and 1.5% were added to the chitosan solution and hydrolyzed at 60 o C for 3, 4, and 5 hours. The enzyme was inactivated at 100 o C for 5 minutes. The results were centrifuged for 15 minutes at 9000 rpm to obtain a supernatant containing chitooligosaccharides. The results of the hydrolysis were stored in a refrigerator then identified and other tests were carried out.

Chitooligosaccharide Analysis
Yield is one of the important parameters in determination of the final weight of a material after the production process. The percentage (%) by weight of dissolved was calculated by the formula: The degree of deacetylation was analyzed using an IR spectrophotometer. The sample was made into pellets with 1% KBr, then scanned in the frequency range between 4000 cm -1 -400 cm -1 . The degree of deacetylation of chitosan can be determined by the "Base Line" method. The calculation of the degree of deacetylation (DD) was by comparing the absorbance value at a wavwavenumbereof 1655 cm -1 (amide absorption band) with a wavenumber of 2450 cm -1 (hydroxyl band absorption). The ratio of absorbance at 1655 cm -1 with absorbance at 3450 cm -1 was doubled one by one with the standard N-diacetyl chitosan (1,33). The measurement of the degree of deacetylation can be calculated using the formula: The molecular weight of COS was determined from the calculation of the intrinsic viscosity value, then calculated using the Mark-Khun Houwing equation (Wang et al., 2010). The FOODSCITECH, Vol. 4 No.2 January 2022ISSN 2622-1985  Calculation of the molecular weight of chitosan is presented in the following equation:

Determination of the Best Treatment
The best treatment was determined based on the effective index method (DeGarmo et al., 2003). This method was based on the following procedure: variables were sorted by priority and contribute to the results. It should be given a burden value to each variable (BV) according to its contribution with a relative number of 0-1. These weights vary depend on the importance of each variable which results were obtained as a result of the treatment. Normal weight (BN) was determined from each variable by dividing the variable weight (BV) by the sum of all weighted values. Deveded the analyzed variables into two groups, namely: 1. Group A, consisted of variables in which the larger the average the better the value (desirable for processed products). 2. Group B consisted of variables that were getting worse (unwanted). The effectiveness value (NE) of the variable was determined, using the formula: treatment value -the worst value and the best valuethe worst value, for the variable with a larger average is better, that the lowest value is the worst value and the highest value is the best. Besides that, for variables with smaller values, the better, the highest value being the worst value and the lowest value being the best. Calculate the yield value (NH) of each variable obtained by multiplying the normal weight (BN) with the effectiveness value (NE). So that the lowest value is the worst value and the highest value is the best. On the other hand, for variables with smaller values, the better, the highest value being the worst value and the lowest value being the best. Calculate the yield value (NH) of each variable obtained by multiplying the normal weight (BN) with the effectiveness value (NE). So that the lowest value is the worst value and the highest value is the best. On the other hand, for variables with smaller values, the better, the highest value being the worst value and the lowest value being the best. Calculate the yield value (NH) of each variable obtained by multiplying the normal weight (BN) with the effectiveness value (NE). The amount of the result values of all variables then be calculated. The best combination was selected from the treatment combination that has the highest yield value (NH) :(Alain Gohi et al., 2016) The results of testing the activity of the chitosanase enzyme can be seen in Table 1. According to Uhlig (1998), the increasing of enzyme activities was proportional to the increasing of temperature until reaching expected optimum temperature. This is because the kinetic energy of enzyme molecules increases before reaching the optimum. In addition, microbial chitosanase produced relatively higher COS yields, compared to enzymes from other sources (Kim et al., 2007). The higher decrease in chitosan viscosity (up to 99.5%) at 16 hours of hydrolysis use a similar enzyme (activity 36.212 U/g) on a larger scale (10-15 times capacity) (Alain Gohi et al., 2016). Hydrolysis of chitosan (DD 74.6%) using pepsin reduced the viscosity by 86% after 1 hours (Yusro Nuri Fawzya et al., 2019). Based on Table 2, the yield of chitosan obtained in this study was 5.17%. This result was different from the result of the research by (Renuka et al., 2019) by 4.4%. This was probably due to differences in temperature and heating time during the demineralization process, differences in temperature and duration of the deacetylation process, as well as the length of the drying process of chitosan. In the deproteination process, were dissolved in 1.5 M hydrochloric acid solution. After that, were heated at different temperatures, namely to crab shell. in this study at a temperature of 70℃ for 4 hours, while in the study of (Renuka et al., 2019) using shrimp shell material at a demineralization temperature of 36 0 C for 30 minutes. The difference in temperature and duration of the demineralization process can affect the difference in yield results. Maryati et al., (2016) stated that the yield decrease with temperature treatment and the length of the demineralization process increases. During the demineralization process, calcium compounds react with hydrochloric acid which is soluble in water. Crustaceae shells contain minerals calcium carbonate (CaCO3) and calcium phosphate (Ca3(PO4)2). These minerals can be removed from the matrix using HCl solution (Aisyah, 2012).

Chemical Properties of Chitosan
Based on the data shown in Table 2, deacetylation degree of crab shell chitosan was 75.6 ± 0.07%, while the degree of deacetylation of shrimp shell in (Renuka et al., 2019) was 76.43 ± 0.91% This indicates that the amine content contained in the crab shell lower than shrimp shells. The deacetylation process in this study used a temperature of 100 0 C with a processing time of 4 hours, while in the study of (Renuka et al., 2019) used a temperature of 900 O C with a processing time of 3 hours. In other opinion of researcher stated that the higher of heating temperature and the longer oh the reaction time, causing the higher the degree of deacetylation (Omali et al., 2015).
This difference could be caused by the initial difference in the value of the degree of deacetylation of chitin before the formation of chitosan. In this study, the degree of deacetylation of chitin in crab shells was 62.71 ± 0.31%, while in the study of Renuka et al. (2019) the degree of deacetylation of chitin was 69.21 ± 0.72%.
The next parameter, namely the molecular weight of chitosan obtained in this study was shown in Table 4. The molecular weight of crab chitosan was 178.41 ± 0.35 kDa which is different from the research by (Renuka et al., 2019), the molecular weight of shrimp shell chitosan was 110.64 ± 1.65 kDa. This result was not in accordance with the research of (Sularsih, 2013) that the value of the molecular weight increases in line with the increase in temperature and deacetylation time. The deacetylation process that uses high temperatures can cause a polymer to depolymerization and in turning also cause the breakdown of polymer molecular chains, FOODSCITECH, Vol. 4 No.2 January 2022ISSN 2622-1985 Sularsih ( 2013) the molecular weight of chitosan can be grouped into three types, namely low, medium and high molecular weight. Chitosan with low molecular weight has a molecular weight of 150 kDa, medium molecular weight chitosan is 600 kDa and has a molecular weight of 1250 KDa. Based on the Table 3, the yield content of the chito oligosaccharide ranged from 79.85% to 87.05%. Based on the analysis of variance, it can be seen that there was a significant interaction (p≥0.05). The higher the enzyme concentration and the longer the hydrolysis time, the higher the yield of chitooligosaccharides.

Chemical Properties of Chitooligosaccharide
Chitosanase enzyme with a certain concentration can break the acetyl group chain, causing the yield value to increase. According to (Siagian, 2002) the yield increase with temperature treatment, and the length of the process of neutralization increases. During the demineralization process, calcium compounds react with hydrochloric acid which is soluble in the water. Crustacea shells contain minerals calcium carbonate (CaCO3) and calcium phosphate (Ca3(PO4)2) These minerals can be removed from the matrix using HCl solution The longer the hydrolysis time, the higher the yield, which indicates the hydrolysis process was more effective. According to Rokhati et al. (2017) which states that the efficiency and effectiveness of the raw material extraction process in the manufacture of chitosan can be seen from the yield value produced. The greater the yield produced, the more efficient the treatment applied.
Based on the analysis of variance (Table 3), there was a significant interaction (p≥0.05). The higher concentration of the chitosanase enzyme and the duration of hydrolysis, the lower the molecular weight. This was because of the breakdown of chitosan and the duration of hydrolysis. Then, the 1,4 beta glycosidic bonds were cut more and more this causes the viscosity to decrease. So that the molecular weight also decreases. This is in the statement of (Heggset, 2012) and (de Assis et al., 2010), the chitosanase enzyme is a glycosyl hydrolase that catalyzes the hydrolysis of 1,4 glycosidic bonds of chitosan to produce glucosamine and produce low molecular weight COS.
Based on Table 3, the degree of deacetylation ranged from 86.24% to 88.64%. Based on the analysis of variance, it can be seen that there was a significant interaction (p≥0.05). The higher the concentration of chitosanase enzyme and the duration of hydrolysis, the degree of deacetylation increases. This was due to the high amine content in crab shell chitosan, which increases the degree of deacetylation. From the results obtained, the higher the enzyme concentration, the higher the degree of deacetylation in chitooligosaccharides, this was because the percentage of the acetyl group in chitosan is eliminated by the chitosanase enzyme, resulting in chitooligosaccharides containing amines, as well as the duration of hydrolysis. The longer the hydrolysis time, the process of forming chitooligosaccharides will occur in the process of breaking the double bond. In this process, the OH group enters the NHCOCH3 group, and then the elimination of the CH3COO group results in an amine, namely chito oligosaccharides. According to (Sartika et al., 1996) statement in (Agusnar, 2007), in determining the degree of deacetylation, the amide and hydroxyl absorption bands play an importaht role. The higher the amine content contained in chitosan, the higher the degree of deacetylation produced. Based on the analysis of chitosan, it was known that the degree of deacetylation of gold snail shell chitosan was 77.38%, this result is higher than the research of (Panggalo et al., 2016), namely the deacetylation degree of chitosan of mangrove snail shells 64%.

Determination of the Best Treatment
Determination of the best treatment that can be seen in Table 4 was carried out by scoring method. The data needed for decision analysis was the aspect of quantity : yield, molecular weight, and degree of deacetylation. Determination was done by giving a score on each average of the results of the response analysis and giving a rank for each sample that has the lowest to the highest score. The sample with the highest score was the sample with the highest rating and was automatically the product of choice. The selected sample was analyzed by FTIR. Based on the Table 4, the results of the analysis carried out, COS with enzyme concentration treatment (1,0%) and hydrolysis time (4 hours) was the product with the best treatment with chemical composition (A2B2), namely yield of 84.96%, molecular weight 4.83 kDa, degree of deacetylation 86.87%. This alternative then be continued with functional group of COS analysis FOODSCITECH, Vol. 4 No.2 January 2022ISSN 2622-1985

The Functional Group of COS Analysis
Based on the determination of the selected product through the scoring test, it was known that the A2B2 sample, namely the treatment of enzyme concentration (1.0%) and hydrolysis time (4 hours) becomes the selected product to be tested for the functional group.
FTIR test results show the spectrum of chitooligosaccharides at a wavelength of 400-3700 cm -1 can be seen in Figure 1.  Figure 1, chitooligosaccharides have a typical FTIR spectrum absorption band on the OH-and NH functional groups of amines. The results of the OH-functional group from the FTIR absorption spectrum in this study showed a peak wavelength of 3624.25 cm -1 . There is not much difference from the results of S, namely the OH-group has a wavenumber of 3423.41 cm -1 . According to Matheis (Zulaikah et al., 2014) that the absorption of OH a wavelength of 3650-3100 cm -1 . For a wavelength of 1633.71 cm -1 in this study, it showed the presence of amide I and amide II groups. This amide group was also found in Sarni et al. (2016) on chitooligosaccharides from tiger prawns at wavelengths of 1658 cm -1 , 1625 cm -1 , and 1554 cm -1 . The NH stretching functional group in this study shows the wavenumber at the peak of 1271.09 cm -1 ; 1157.29 cm -1 and 1078.21 cm -1 . Ridho et al. (2017) states that the wavelength of NH stretching is in the range of 1180-1360 cm -1 . The stretching vibration of the -COCgroup appears at the absorption wavelength of 1091.75 which indicates the presence of β-1,4 glycosidic bonds that have not been hydrolyzed. According to Thadathil & Velappan (2014), the enzymechitosanase cuts β-(1,4) glycosidic bonds from the interior of the chitosan structure and produce chitosan oligomers with chain lengths of 2-10. These results were not much different from the results of Rokhati et al.( 2017) that wavelength -COCis 1072.3 cm -1 .

CONCLUSION
The results of statistical analysis of chitooligosaccharides from crab shells showed that, there was an interaction between enzyme concentration which was significant (p≤0,05) in the treatment and duration of hydrolysis on yield parameters, molecular weight, and degree of deacetylation. Based on the results of decision analysis, the best treatment of chitooligosaccharides from crab shells that can be accepted by consumers was the treatment of enzyme concentration (1%) and hydrolysis time of 4 hours with a yield of 84.96%, molecular weight 4.83 kDa, degree of deacetylation 86.87%.


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Metabolomics Analysis Provides New Insights Into the Molecular Mechanisms of Parasitic Plant Dodder Elongation in vitro

Dodder (Cuscuta spp.) species are obligate parasitic flowering plants that totally depend on host plants for growth and reproduction and severely suppress hosts’ growth. As a rootless and leafless plant, excised dodder shoots exhibit rapid growth and elongation for several days to hunt for new host stems, and parasitization could be reestablished. This is one unique ability of the dodder to facilitate its success in nature. Clearly, excised dodder stems have to recycle stored nutrients to elongate as much as possible. However, the mechanism of stored nutrient recycling in the in vitro dodder shoots is still poorly understood. Here, we found that dodder is a carbohydrate-rich holoparasitic plant. During the in vitro dodder shoot development, starch was dramatically and thoroughly degraded in the dodder shoots. Sucrose derived from starch degradation in the basal stems was transported to the shoot tips, in which EMP and TCA pathways were activated to compensate for carbon demand for the following elongation according to the variations of sugar content related to the crucial gene expression, and the metabolomics analysis. Additionally, antioxidants were significantly accumulated in the shoot tips in contrast to those in the basal stems. The variations of phytohormones (jasmonic acid, indole-3-acetic acid, and abscisic acid) indicated that they played essential roles in this process. All these data suggested that starch and sucrose degradation, EMP and TCA activation, antioxidants, and phytohormones were crucial and associated with the in vitro dodder shoot elongation.

INTRODUCTION
Cuscuta species (dodders) are holoparasitic plants that can grow on a wide range of dicot plants, such as alfalfa, tomato, and soybean (Dawson, 1989;Lanini and Kogan, 2005). As a rootless and leafless plant, dodder is characterized by the vining growth around host stems to gain access to water and nutrients from hosts via a specialized organ termed haustorium (Heide-Jørgensen, 2008;Kim and Westwood, 2015). The parasitic plant, therefore, represents a strong sink for the parasitized host plants. Dodders exhibit severely compromised or no photosynthetic activity, which is likely to contribute less than 1% of the total carbohydrates needed by dodders (van der Kooij et al., 2000;Revill et al., 2005), so they mainly obtain carbohydrates from their plant hosts to maintain growth. On the contrary, dodder shoots may naturally break off from hosts due to physical forces (Singh et al., 1968;Kelly et al., 2001). These detached shoots do not wither away quickly; instead, they are capable of searching for the host to resume the parasitic lifestyle before exhausting the limited resources (Shimizu and Aoki, 2019). This process is very much similar to dodder seedlings germinated from seeds to hunt for host plants. For either detached dodder shoots or new dodder seedlings, the dodder must grow as long as possible to find a host exclusively depending on stored energy. Thus, efficient resource translocation to support growth is a unique ability for dodder to survive in nature. Nevertheless, the metabolic mechanism of detached dodder shoots is still not well characterized.
In plants, carbohydrates are essential as respiratory substrates for the generation of energy and key components for the biosynthesis of storage and structural polysaccharides, such as starch and cellulose. Carbohydrate synthesis, transport, and utilization are dynamic processes strongly dependent on cell physiology, plant organs, developmental stages, and environmental conditions (Ramon et al., 2008;Morkunas et al., 2012). Moreover, carbohydrates have hormone-like functions as physiological signals, which directly regulate the expression of genes and interact with other signaling pathways, which in turn modulate metabolic responses (Ho et al., 2001;Tran et al., 2007). Plants have developed effective mechanisms of perception and transduction of sugar signals, which involve invertases (INVs), sucrose transporters (and other specific sugar receptors), and hexokinase (HXK) (Hanson and Smeekens, 2009;Smeekens et al., 2010). Undernutrition deficiency, before oxidative phosphorylation, Embden Meyerhof Parnas (EMP), Tricarboxylic Acid (TCA), and Pentose Phosphate Pathway (PPP) are the main energy respiration pathways. EMP starts from glucose, which is an end product of starch degradation. In addition, maltose, sucrose, and fructose are also involve in the EMP pathway after conversion to glucose. Since lack of an energy source from host plants, reusing stored carbohydrates is essential for the in vitro dodder shoots' growth and re-parasitizing. However, little systematic information is available regarding the involvement of carbohydrates and other metabolites in modulating the in vitro development of dodder shoots.
Due to the regulatory and signaling function, carbohydrates could significantly affect the metabolism and development by controlling the essential metabolic processes required to adapt to various stresses, such as drought, chilling, and nutrition starvation (Rolland et al., 2002;Borek et al., 2011Borek et al., , 2012Lin et al., 2014). Sugar starvation in plant cells, depending on its duration, could trigger physiological-biochemical changes of varying severity, aiming to maintain respiration and other basic metabolic processes (Azad et al., 2008;Borek et al., 2012). Thus, the priority is the acquisition of energy for cell survival, particularly at the later stage of sugar starvation, even at the expense of organelles except for the nucleus and the mitochondria supplying energy (Morkunas et al., 2012). Meanwhile, under nutrition starvation, the accumulation of free radicals is also intensified, potentially leading to oxidative damage. Correspondingly, specific defense strategies are initiated, such as the activation of the enzymatic antioxidant system and phytoferritin accumulation to alleviate oxidative stress (Morkunas and Bednarski, 2008). Additionally, the plant's ability to respond to nutritional stress generally integrates with internal developmental programs controlled directly by hormones (Rolland et al., 2006;Hammond and White, 2008;Agulló-Antón et al., 2010). Although basic physiological activities of various phytohormones in higher plants are well described, the information on their contents and the possible role in the in vitro dodder growth are very limited.
In addition, metabolomics, as an important tool to study the metabolic pathways of the biological system, may provide a new perspective for the understanding of dodder shoot development in vitro. Here, the temporal and spatial distribution characteristics of carbohydrates were determined in the in vitro dodder shoots, and expression profiles of carbohydrate metabolism-related genes were also detected. Meanwhile, metabolic changes of in vitro dodder shoot development were analyzed through the metabolomics data, suggesting that differential metabolites are involved in various metabolic pathways, such as carbon metabolism, secondary metabolite synthesis, oxidative stress, and hormone metabolism. Furthermore, the contents of plant hormones, including abscisic acid (ABA), jasmonic acid (JA), and indole-3-acetic acid (IAA), were first quantified during the in vitro dodder shoot growth in this study. These results would provide valuable information for preventing the in vitro dodder attack and source-sink partitioning in plants.

Plant Material
Seeds of dodder (Cuscuta suaveolens, a gift from Dr. P. Reisen, Forage Genetics International, Indiana) were treated with sulfuric acid for 30 min to break seed dormancy, and then sulfuric acid was removed by rinsing with water. Seeds were kept at 23 • C for several days on wet filter paper until the seedlings were approximately 3 cm long. Dodder seedlings were placed at the base of 1-week-old alfalfa seedlings. To facilitate parasitization, dodder radicles were slightly submerged in the soil to anchor the host seedlings. A total of 10 dodder seedlings in each pot succeeded in twisting around the stems of alfalfa seedlings and developing haustoria. The day/night length was 16 h/8 h, and the temperature was maintained at 23 • C (day) and 18 • C (night) in the greenhouse of China Agricultural University, Beijing. After parasitization for 3 weeks, healthy dodder shoot tips (about 7 cm long) were collected and used in this study. Detached shoots were grown in a box containing a layer of wet gauze without any nutrition supplement and incubated in a growth chamber in dark at 23 • C. Later on, the detached dodder shoots were harvested on different days (0, 1, 2, and 4 days) after incubation. The morphological basal stem (5 cm) of detached dodder shoot was divided into two segments on average, and the shoot tip (apical bud) was defined as the third segment ( Figure 1A), which FIGURE 1 | Physiological characteristics of the in vitro dodder shoots growth. Morphology (A), length (B), shoot dry weight kinetics (C), and relative water content (D) of in vitro dodder shoots on days 0, 1, 2, and 4 after excised from host (B, basal stems; M, middle stems; T, shoot tips). Vertical bars are standard deviation of the mean (n = 3). Bars marked with the same letter represent no significant difference between each other protected with Fisher's least significant difference (LSD) at P < 0.05. was used to study the metabolism characteristics of in vitro dodder shoot growth.

Sample Collections
Dodder shoots were harvested on 0, 1, 2, and 4 days after being cultured as described above. The length and fresh weight of harvested dodder shoots were measured. To determine the dry weight and relative water content, samples were oven-dried at 80 • C to a constant weight. The fresh samples for gene expression and metabolomic assays were frozen in liquid nitrogen rapidly and stored at -80 • C until determination.

Quantification of Sugar Content
The in vitro dodder shoot segments were collected on different days (0, 1, 2, and 4 days) after incubation. The content of starch was determined according to the previous method with some modifications (Hansen and Møller, 1975). Briefly, for measurements of soluble sugar contents including glucose, fructose, and sucrose, fresh samples (approximately 100 mg freeze-dried powder) of in vitro dodder shoot segments were prepared, samples were extracted by distilled water for 30 min and centrifuged at 4 • C for 10 min, and the supernatant was filtered through a 0.1-µm microfuge spin filter and analyzed by high-performance liquid chromatography (HPLC). Concentrations of glucose, fructose, and sucrose were determined using individual standards (Klann et al., 1993).

Total RNA Extraction and qRT-PCR
Total RNA was extracted from dodder shoots using the RNA Simple Total RNA Kit (TIANGEN, China) following the instructions of the manufacturer. RNA concentration was determined by using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Inc.). Reverse transcription was performed using a PrimeScriptTM RT reagent kit (TaKaRa, Japan). Then, all qRT-PCRs were performed in triplicate using TB Green R Premix Ex Taq TM II (TaKaRa, Japan) in an ABI-7500 fast Real-Time PCR Detection System. A 2 − Ct -based process was used to quantify the relative gene expression level. The EF-1α gene was chosen as an internal control to normalize the data in this study (Zhang et al., 2014). Three biological replicates were assayed for each sample. The primers used for real-time qPCR amplification were ordered from Beijing Genomics Institute. The sequences of genes used in this study were produced by the transcriptome database of C. suaveolens (Jiang et al., 2013). In particular, the neutral invertase NINVs had only one contig in the database. The SPS had two contigs; the long sequence (3,522 bp) was used for the expression analysis of SPS. The other one was a truncated sequence. Due to lack of gene member annotation, we searched the sequence on the NCBI website, finding that it was homologous with SPS1 of other plant species, and we defined it as SPS1 in this study. Similarly, SUS2 and SUT2 were also defined by their corresponding homologs of other plant species, respectively. The gene names currently given are based on the best aligned homologous genes using the standard databases (nr, etc.). They may change when better genome sequence information of dodder becomes available. The primers used for real-time qPCR amplification are listed in Supplementary Table 1.

Metabolite Extraction
Sample extraction was performed according to the previous study with some modifications (Dunn et al., 2011). First, approximately 200 mg of freeze-dried sample was extracted with 1 ml of methanol/acetonitrile/water (2:2:1, v/v/v) containing 2 µl of L-2-chlorophenylalanine (1 mg/ml) as an internal standard. After a 30 s vortex, samples were homogenized at 35 Hz for 4 min and sonicated for 5 min (incubated in ice water), which was repeated three times. Samples were incubated at -20 • C for 1 h and then centrifuged at 15,000 × g for 15 min at 4 • C. The supernatant was transferred into a new tube, and the extracts were vacuumdried. Second, the dried extracts were reconstituted with 500 µl of acetonitrile/water (1:1, v/v) by sonication on ice for 10 min. The extracts were then centrifuged at 15,000 × g at 4 • C. Finally, the supernatant (about 70 µl) was filtered and transferred to a fresh 2-ml glass vial for LC/MS analysis. Meanwhile, 10 µl of every sample was prepared as a QC sample for UHPLC-QTOF-MS detection.

LC-MS/MS Analysis
The freeze-dried extracts were used for metabolomic analyses. LC-MS/MS analyses were performed using an UHPLC equipment (Infinity 1290, Agilent Tech., Santa Clara, CA, United States) with a UPLC BEH Amide column (2.1 × 100 mm, 1.7 µm, Waters Corporation, Milford, MA, United States). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water (pH = 9.75) (A) and acetonitrile (B). The gradient elution procedure was carried out as follows: 0 min, 95% B; 0.5 min, 95% B; 7 min, 65% B; 8 min, 40% B; 9 min, 40% B; 9.1 min, 95% B; and 12 min, 95% B. The injection volume was 2 µl (positive or negative), respectively. The triple TOF-MS (AB Sciex) was used to acquire MS/MS spectra based on an information-dependent acquisition (IDA). In this mode, the full scan survey MS data was continuously evaluated by the acquisition software (Analyst TF 1.7, AB Sciex), which triggered the acquisition of MS/MS spectra depending on the preset parameters. In each cycle, 12 precursor ions with an intensity above 100 were chosen for MS/MS at 30 V collision energy. The condition of the electrospray ionization (ESI) source was set as follows: ion source gas 1 as 60 psi., gas 2 as 60 psi., curtain gas as 35 psi., source temperature as 600 • C, and ion spray voltage floating (ISVF) as 5,000 or 4,000 V in positive or negative modes, respectively.

Data Processing and Differential Metabolite Analysis
Ions with a relative standard deviation (RSD) < 20% in the QC sample analyses were used for further univariate and multivariate statistics (Smith et al., 2006;Saccenti et al., 2014). We performed a series of multivariate pattern recognition analyses, and principal component analysis (PCA) was first used to initially visualize the differences between the groups. To filter out the unrelated orthogonal variables and obtain the more reliable metabolite information between groups, the orthogonal projection to latent structures-discriminate analysis (OPLS-DA) was used to analyze all comparison groups. In this mode, the differential metabolites were screened by the Student's t-test (P-value < 0.05) and the variable importance in the projection (VIP > 1) of the first principal component of the OPLS-DA model. In addition, the KEGG database was used for annotation of the pathways of differential metabolites (DMs). The DMs were used for further analysis and screening of the key pathways based on enrichment analysis and hierarchical clustering analysis.

Measurements of Hormone Contents
Dodder stems were harvested after growth at different times in vitro. LC-MS/MS method with minor modification was used to quantify the phytohormones (Zhang et al., 2020). Briefly, approximately 500 mg of fresh dodder tissue per repeat was ground in liquid nitrogen. First, 200 µl working solution of internal standards (the internal standards of IAA, JA, and ABA were indole-3-acetic-2,2-d2 acid, jasmonic acid-d5, and abscisic acid-d6, respectively) was added to each tube, then 2 ml extraction solvent was added and shook at 4 • C for 30 min. Subsequently, 2 ml dichloromethane was added and centrifuged at 15,000 × g for 5 min at 4 • C, transferring the solvent from the lower phase into a screw-cap vial followed by concentrating through a nitrogen evaporator. The dried extracts were redissolved in 200 µl methanol and then centrifuged at 15,000 × g for 10 min at 4 • C. The supernatants were transferred to 1.5-ml liquid chromatography (LC) vials and then injected into the LC/MS system.

Statistical Analysis
Statistical analysis of all data was performed using the SAS software (V8, SAS Institute Inc., United States). Significant differences between treatments were determined by one-way ANOVA followed by Fisher's least significant difference (LSD) test with a P-value < 0.05.

Morphological and Physiological Characteristics of the in vitro Dodder Shoots Growth
For detached dodder shoots, the basal stems grew slowly and appeared to be senescence; however, the shoot tips continued to grow rapidly, extending about 5 cm long on day 4 compared with that on day 0. The growth rate was highest from 0 to 1 days and then gradually declined from 1 to 4 days (Figures 1A,B). To understand the tissue-specific physiology changes in this process, the shoot was divided into three segments, as shown in Figure 1A. Among the different segments, the dry mass of the basal (B) and middle (M) segments of dodder shoots was reduced by approximately 50% on day 4 after culture in vitro. In contrast, the dry weight presented an upward tendency from 0 to 2 days and then decreased from 2 to 4 days in shoot tips (T) (Figure 1C). Meanwhile, the relative water content was significantly decreased in different parts of in vitro dodder shoots, especially in the basal stems. In contrast, the shoot tips had higher relative water content compared to other parts ( Figure 1D).

The Variation of Carbohydrates During Dodder Shoots Growth in vitro
To examine how the in vitro dodder shoots recycled stored carbon to maintain the shoot tip development, the contents of starch and soluble sugar were analyzed in this study. During in vitro dodder shoot growth, starch content was dramatically decreased on day 1 and then reduced continuously on days 1-4, representing 28.6% and 31.1% of those on day 0 in the basal and middle stems, respectively. A similar pattern was observed in the shoot tips, however, starch content remained at a high level on day 4 in the shoot tips, more than 50% of that on day 0 (Figure 2A). However, sucrose, glucose, and fructose showed the different metabolic patterns between the shoot tips and the basalmiddle stems. Sucrose content presented a significant uptrend in the basal and middle stems over the first 4 days in vitro. By comparison, sucrose content had a slight increase, peaking on day 2 and then decreasing on day 4 in the shoot tips, which was sustained at a much lower level than that in the basal and middle stems ( Figure 2B). Conversely, glucose and fructose contents continuously decreased over time in the basal and middle stems, whereas they were dramatically increased on day 1 and then remained at a higher level in the shoot tips as time progressed in vitro (Figures 2C,D).
To further investigate the roles of carbohydrates, the expression pattern of amylase, known as the digestive enzyme of starch, was determined. The levels of α-amylase and βamylase were significantly upregulated on day 1 after culture in vitro and sustained at the high levels with time in the basal and middle stems, whereas the expression of α-amylase and βamylase was maintained at a low level in the shoot tips over time (Figures 3A,B). These results explain the dramatic and continuous decrease in starch content in the basal stems, which might be related to the expression of amylases after being isolated from the host. In addition, sucrose, as the primary translocated carbon, needs to be degraded into hexoses for energy supply by either sucrose synthase (SUS) or invertase (INVs) after unloading in sinks (Ruan, 2012). The expression of SUS2 was significantly induced on the first 2 days in the basal and middle stems, and the shoot tips showed much lower SUS2 expression levels than other segments on days 0-2 and had a dramatic increase on day 4 ( Figure 3C). The NINVs (alkaline/neutral invertases genes) irreversibly catalyze the hydrolysis of sucrose into fructose and glucose. The expression of NINVs was markedly induced in different segments and remained at a high level on day 4 after culture in vitro ( Figure 3D). The expression of SPS1 was upregulated on day 1, and then decreased on days 2-4 in the basal and middle stems. Meanwhile, the expression of SPS1 fluctuated with a peak on day 2 and was higher in the shoot tips than those in the basal and middle stems on day 4 ( Figure 3E). In contrast, the expression of SUT2 was dramatically upregulated in dodder shoots after culture in vitro, especially in the basal and middle stems, and the level in the shoot tips was much lower than that in the other segments during this process ( Figure 3F). Taken together, these genes were responsible for the regulation of sucrose metabolism to facilitate the source-to-sink translocation of in vitro dodder shoots under nutritional stress.

Metabolic Profiles in Response to Carbon Stress in the Basal Stems of in vitro Dodder Shoots
To further investigate the regulatory mechanisms of carbon recycling in the basal stems, which acted as an energy source for in vitro dodder shoot growth, we examined the metabolomics changes based on a non-targeted UHPLC-QTOF/MS approach to gain a better understanding of the biological changes and adaptive strategies. The Principal Component Analysis (PCA) analysis was employed to evaluate the performance of the UPLC-QTOF/MS. As shown, quality control (QC) samples were separated from tested samples, and the experimental samples were clearly separated into the different subgroups by PC1 and PC2 (Supplementary Figure 1). The results indicated that the UPLC-QTOF/MS approach has good reproducibility. The Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA, VIP ≥ 1) and the Student's t-test (P < 0.05) were applied to detect the differential metabolites (DMs) among subsample groups: day 0 vs. day 1, day 0 vs. day 2, and day 0 vs. day 4 (Supplementary Table 2). Totally, 417 DMs were identified in all three comparisons ( Figure 4A) at basal stems. Venn diagrams showed that 121 DMs were shared among these groups (Supplementary Figure 2). Compared with day 0, DMs mainly involved in energy metabolism, secondary metabolism, stress response, and small molecules like amino acids and organic acids. According to the alignment of the KEGG database, the main enrichment pathways of differential metabolites included starch and sucrose metabolism, galactose metabolism, secondary metabolites biosynthesis, and linoleic acid metabolism in the basal stems ( Figure 4A). As the in vitro dodder shoots were grown in the absence of energy sources from host plants, large numbers of carbohydrate metabolism-related DMs appeared at various levels in response to nutrition stress. We checked that the levels of some compounds, such as sucrose, maltopentaose, maltotriose, raffinose, stachyose, and erythritol, were significantly increased in the basal stems after culture in vitro (Figure 4B), suggesting that the massive starch was degraded in the basal stems in this process. Conversely, the TCA cycle, EMP pathway, and pentose phosphate pathway were restrained in this process, which was inferred from the decrease in D-fructose, ADPglucose, UDP-D-glucose, D-fructose-6-phosphate, D-glucose-6phosphate, alpha-ketoglutarate, glycerol 3-phosphate, D-ribulose 5-phosphate, and sedoheptulose compared to those in basal stems on day 0 ( Figure 4B). In addition, some oxidative stress-related DMs were also detected in this process. For example, glutathione disulfide was decreased on day 0 vs. day 1, S-lactoylglutathione was reduced on day 0 vs. day 1 and day 0 vs. day 4, and L-proline was decreased in all three comparison groups, suggesting an increasing oxidative stress in the basal stems of in vitro dodder   Table 2). It is worth noting that ABA, an important phytohormone regulating stress responses, was greatly induced in the first 2 days in vitro and then gradually declined in the basal stems over time (Supplementary Table 2).

Metabolic Analysis of the in vitro Dodder Shoot Tips' Response to Carbon Stress
The in vitro dodder shoot tips acted as a strong sink and required carbon assimilation for growth. To further understand the metabolic regulation mechanism of in vitro shoot tips growth, we checked DMs in dodder shoot tips on 0, 1, 2, and 4 days after culture in vitro. The number of DMs appeared an increased tendency among day 0 vs. day 1, day 0 vs. day 2, and day 0 vs. day 4 (Supplementary Figure 3, Supplementary Table 3). This indicated that some metabolic pathways were activated by prolonged nutritional starvation. To better understand the metabolite changes caused by different starvation durations in the shoot tips, the Venn diagram of DMs was constructed, showing that 148 metabolites were common among three comparisons, i.e., day 0 vs. day 1, day 0 vs. day 2, and day 0 vs. day 4. Especially, 72 metabolites were unique in day 0 vs. day 4, implying that wider changes happened in this group (Supplementary Figure 3). The DMs were mainly distributed in secondary metabolite synthesis, isoquinoline alkaloid biosynthesis, indole alkaloid biosynthesis, phosphate metabolism, phenylalanine metabolism, carbohydrate metabolism, and so on ( Figure 4C).
Additionally, carbohydrate metabolism, such as starch and sucrose metabolism, galactose metabolism, PPP pathway, and TCA cycle, were frequently presented in the enrichment analysis. Hierarchical cluster analyses of these carbohydrate metabolites presented the metabolic characteristics during the in vitro dodder shoot tip growth ( Figure 4D). Among them, the levels of stachyose, erythritol, D-xylose, and D-sorbitol displayed a significant uptrend in the in vitro dodder shoot tips. However, compared with the control (on day 0), the contents of sucrose, D-fructose, and D-mannose were increased on day 1, and then slightly declined on days 2-4. In contrast, the levels of glycerol 3-phosphate, succinate, and D-ribulose 5-phosphate, which are involved in EMP, TCA, and PPP, declined until day 4, indicating that the metabolism slowed down in the in vitro shoot tips due to the limited energy supply.
In terms of secondary metabolism, indole alkaloid biosynthesis and isoquinoline alkaloid biosynthesis, especially the IAA (indole-3-acetic acid) biosynthesis pathway, were activated during the in vitro dodder shoot tips development. Correspondingly, the levels of L-tryptophan, indoleacrylic acid, and indole-3-lactic acid were greatly increased compared with those on day 0, suggesting that these metabolites might play a critical role in the process of the in vitro shoot tips elongating (Supplementary Table 3).

Comparative Analysis of the Metabolites Between the Basal Stems and Shoot Tips as Time Progressed in vitro
To gain a better understanding of the source-to-sink translocation in the dodder shoots, metabolite changes were systematically detected by comparative metabolomics analysis of the basal stems and shoot tips during the in vitro dodder shoot development. Time-specific (days 0, 1, 2, and 4) DMs were analyzed between the basal stems and shoot tips. Venn diagrams analysis revealed that 165 DMs were commonly regulated between the basal stems and the shoot tips on days 0, 1, 2, and 4 ( Figure 5A). Overall, the downregulated DMs showed an increased trend in the in vitro dodder shoots as time progressed (Figure 5B). Pathway analysis of DMs acquired from four groups found that the enriched pathways mainly contained sugar metabolism, secondary metabolism, stress response, and amino acid metabolism (Figure 5C), suggesting that these metabolic pathways might play a critical role during the dodder shoot development in vitro. In detail, compared to the basal stems, large numbers of DMs involved in EMP, TCA cycle, and pentose phosphate pathways were dramatically induced in the shoot tips on day 0, such as D-fructose 6phosphate, succinic acid, α-ketoglutarate, glycerol 3-phosphate, phosphoenolpyruvate, D-ribulose 5-phosphate, citrate, glucose 6-phosphate, and UDP-D-glucose (Supplementary Table 4). Subsequently, we checked the DMs between the basal stems and shoot tips during the dodder shoots development on days 1, 2, and 4 in vitro. Compared with the basal stems, DMs that were significantly increased in the shoot tips were involved in the EMP, TCA cycle and pentose phosphate pathways, such as D-fructose, D-xylose, alpha-ketoglutarate, glycerol 3-phosphate, D-glucose 6-phosphate, D-fructose-6-phosphate, D-glucose 1-phosphate, D-xylulose 5-phosphate, and D-erythrose 4phosphate (Supplementary Table 5-7). The result indicated a good energy preparatory status for the rapid growth of the shoot tips. In contrast, some DMs, such as maltotriose, maltopentaose, and raffinose, were continuously upregulated in the basal stems, reaching higher levels than those in the shoot tips on days 1, 2, and 4 in vitro. Collectively, these results showed that the carbon metabolic pattern in shoot tips totally differed from that in the basal stems.
Plants will suffer energy stress if sugar availability is insufficient, leading to oxidative stress due to the accumulation of ROS (Apel and Hirt, 2004;Tsukagoshi et al., 2010). Indeed, several antioxidants, including S-lactoylglutathione, glutathione disulfide, L-glutamine, dehydroascorbic acid, and alpha-tocopherol (Vitamin E), were dramatically abundant in the shoot tips compared with the basal stems as time prolonged in vitro (Supplementary Table 5-7). Particularly, S-lactoylglutathione, alpha-tocopherol, and glutathione disulfide significantly increased about five folds in the shoot tips compared to those in the basal stems on day 4. These results suggested that robust ROS-scavenging capacity conferred by high antioxidants contents provided crucial support to the rapid elongation of shoot tips.
Plant hormones play vital roles in regulating plant growth and development in response to various environmental conditions (Peleg and Blumwald, 2011). In metabolomics analysis, numerous DMs related to the synthesis and signal transduction of hormones, including indole-3-acetic acid (IAA), jasmonic acid (JA), and abscisic acid (ABA), were revealed to have different metabolic patterns between the basal stems and shoot tips. Compared with the basal stems, DMs that involved in IAA metabolism, such as DL-indole-3-lactic acid, indole, L-tryptophan, indole-3-pyruvic acid, and indoleacrylic acid were dramatically increased in the shoot tips as time progressed ( Figure 6A), indicating that active IAA metabolism, to some extent, contributed to the elongation of in vitro shoot tips. Moreover, the contents of JA and ABA showed an upregulated tendency in the shoot tips compared to those in the basal stems in this process. Subsequently, the contents of JA, IAA, and ABA were also systematically evaluated by LC-MS/MS. JA levels were low from days 0 to 1, and then had a slow increasing tendency and peaked to 10.91 ng/g on day 4 in the shoot tips. In contrast, the contents of JA were dramatically downregulated from days 0 to 1 and reached a lowest level of 7.21 ng/g on day 4 in the basal stems ( Figure 6B). Consistent with the variation of JA contents, IAA level continuously decreased from 27.06 ng/g on day 0 to 6.44 ng/g on day 4 in the basal stems, and from 23.34 ng/g on day 0 to 8.21 ng/g on day 4 in the shoot tips, respectively. Nevertheless, the IAA content in the shoot tips was higher than that in the basal stems from days 1 to 4 ( Figure 6C). The content of ABA was downregulated in the whole process from 14.48 µg/g on day 0 to 4.77 µg/g on day 4 in the shoot tips, in which the content was much higher than that in the basal stems ( Figure 6D). Taken together, the variations of phytohormones indicated they might play essential roles in modulating the in vitro dodder shoot development.
Based on the results described above, a schematic representation of the DMs involved in the in vitro dodder shoot growth was proposed (Figure 7). Soon after detached from host plants, starch was largely degraded into maltopentaose, maltotetraose, maltotriose, and maltose, leading to the increase in sucrose content in basal stems. Sucrose, the primary translocated carbon, was transferred and then degraded into glucose and fructose in the shoot tips, supplying energy for the following development by activation of EMP, TCA, and PPP pathways. All these data suggested that the efficient recycling of stored carbohydrates was the major contributor to the rapid elongation of the in vitro dodder shoots. Meanwhile, phytohormones and antioxidants were also dramatically induced in the shoot tips, indicating their essential roles in this process.

DISCUSSION
Excised dodder shoots continued growth and elongation for several days to hunt for new host plants. Due to the lack of many genes involved in nitrate, potassium, and phosphate uptake and transporters in the dodder genome and the very limited photosynthetic capability (Sun et al., 2018), the in vitro dodder shoots experienced energy stress and mainly relied on their own carbon reserves for development.
In plants, carbohydrate is not only an energetic and structural substrate but also the metabolic intermediate that is used for macromolecule synthesis. Correspondingly, low carbohydrate availability causes a reduction in the rate of growth to retain sufficient carbon for survival (Rolland et al., 2006). Carbonlimiting conditions triggered large numbers of transcriptional responses that led to growth cessation, which included repression of genes involved in metabolism, cell division, and protein repair (Smith and Stitt, 2007). Starch is an integrator of carbon metabolism response; starch turnover and carbon allocation present a central role in the network that coordinates metabolism with growth (Sulpice et al., 2009;Loreti et al., 2018). In this study, starch accounted for about 10% of dry weight, and it was dramatically decreased in the dodder shoots as time progressed, especially in the basal stems (Figure 2A), indicating that starch degradation was highly active and ATP produced by starch metabolism provided sufficient substrate and energy for the following development. The decrease in starch content may be due to the high expressions of α-amylase and β-amylase, which catalyze the starch hydrolysis in the basal stems (Figures 3A,B). Soluble sugar reflects the basic level of available materials and energy supply. Sucrose, as a kind of starch intermediate product, was higher in the basal stems than in the shoot tips after culture in vitro (Figure 2B; Supplementary Table 4-7). In contrast, the contents of glucose and fructose were dramatically increased and sustained at a high level in the shoot tips as time progressed (Figures 2C-D). Sucrose transporter (SUT/SUC) members play vital roles in different biological processes. The role of the SUT2/SUC3 transporter has been implicated in sucrose retrieval along the phloem path (Barth et al., 2003). The strong induction of the sucrose transporter SUT2 in the basal and middle stems compared to that in the shoot tips indicated its role associated with long-distance phloem transport ( Figure 3F). Taken together, we speculated that sucrose, derived from starch degradation in the basal stems, was transported to the shoot tips and degraded into glucose and fructose, which could be rapidly utilized for the in vitro shoot tip growth. Therefore, sucrose might be acted as an energy center to ensure carbon supply in this process. In addition, we found that the sucrose content in the basal stems fluctuated over time according to the physiological experiment and metabolomics data ( Figure 2B; Supplementary  Table 4-7). The detailed regulatory mechanism of sucrose needs to be further studied. Moreover, the metabolomics data also showed that D-maltose, maltotriose, and maltopentaose were upregulated in the basal stems over time, whereas the contents of D-glucose 6-phosphate, alpha-ketoglutarate, D-xylulose 5phosphate, D-erythrose 4-phosphate, UDP-D-glucose, UDP-Dgalactose, and 6-phosphogluconic acid were at higher levels in the shoot tips compared to those in basal stems (Figure 7,  Supplementary Table 4-7). These results further indicated that starch was largely broken down to produce enough substances and energy for the following development. Concurrently, the EMP, TCA and pentose phosphate pathways pathway were activated and directly provided carbon demand for the in vitro dodder shoot tips elongation.
in the basal stems with time, which implied a positive role of IAA during the in vitro dodder shoot growth ( Figure 6C). Jasmonic acid (JA), a growth-regulating substance, was initially identified as a stress-related hormone in plants. Numerous studies have demonstrated that plant growth and development were regulated in a coordinated manner by the JA and IAA signal transduction pathways, respectively . In our results, JA content was slowly increased in the shoot tips, whereas they appeared to be a continuous downtrend in the basal stems ( Figure 6B). Abscisic acid is an important phytohormone for stress signals (Cutler et al., 2010), and additionally, ABA may regulate sugar transport by influencing the expression of sugar transporter genes (Oliver et al., 2007). Here, we found that ABA content decreased in dodder shoots but maintained a much higher level in shoot tips than that in the basal stems all time ( Figure 6C). We deduced that there may be a feedback effect between altered sugars and ABA contents. The functions of these phytohormones need to be further studied in the future.
Plant ROS plays a crucial signaling role in plant response to environmental stress, but the excess ROS could cause oxidative injury to cell components (Morkunas et al., 2003;Gill and Tuteja, 2010). In our previous study, we checked the changes in cell structure, the distribution characteristics of H 2 O 2 , and the activities and expressions of antioxidant enzymes in in vitro dodder shoots, suggesting that ROS presented different patterns in the basal stems and shoot tips (Zhang et al., 2021). The metabolomics data further confirmed that the ROS-scavenging antioxidants played an essential role in the in vitro dodder shoot elongation and development, which was mainly reflected in the levels of several antioxidants, such as FIGURE 8 | Predicted model of metabolites in modulating the in vitro dodder shoot tips elongation. Starch degradation in the basal stems provided the main energy source. Sucrose, derived from starch hydrolysis, was transferred to the shoot tips, in which EMP, TCA and PPP activation directly provide energy and material basis for the following development. Meanwhile, the accumulations of antioxidants and hormone, such as IAA, might play essential roles in this process.
S-lactoylglutathione, glutathione disulfide, dehydroascorbic acid, and alpha-tocopherol (Vitamin E). All of these antioxidants were dramatically upregulated in the shoot tips in contrast to those in the basal stems as time was prolonged in vitro (Supplementary Table 4-7). These results implied that the high levels of antioxidants in the shoot tips were important for the in vitro dodder shoot elongation by maintaining a robust ROSscavenging capacity.

CONCLUSION
In this study, we analyzed the changes in carbohydrate contents, as well as the gene expression patterns during the in vitro dodder shoot growth. Based on these results, we proposed that soluble sugars derived from starch degradation are transported to the shoot tips, supporting the subsequent development of the in vitro dodder shoots. Furthermore, we systematically investigated the metabolic changes during the in vitro dodder shoot growth, and large numbers of DMs were identified in this process. When the shoot tips gradually developed, starch content dramatically decreased, and the TCA, EMP, and PPP pathways related to DMs were upregulated in the shoot tips, providing energy and substances for the following growth. Meanwhile, antioxidants, such as α-naphthylamine and ascorbic acid, remained at a higher level in the shoot tips than those in the basal stems, indicating the robust ROS-scavenging capacity in the shoot tips. Additionally, we also analyzed phytohormone changes in detached dodder shoots, suggesting their possible roles in modulating the in vitro dodder shoot growth. In summary, the large amounts of carbon degradation and transportation, antioxidants in cooperation with phytohormones, contribute to the in vitro dodder shoot growth under nutrition starvation (Figure 8). This study would provide basic and systematic information for the detached dodder shoot development.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries could be directed to the corresponding author.

AUTHOR CONTRIBUTIONS
YXZ and MZ designed the project and oversaw the project management. YXZ and YSZ performed the experiments. YXZ analyzed the data and wrote the draft. YXZ, LJ, ZL, and MZ revised the manuscript. All authors approved the final manuscript.

FUNDING
This study was supported by the National Natural Science Foundation of China (31272055).