Estrogen and estrogen receptor (ER) play a fundamental role in breast cancer. To support the rapid proliferation of ER+ breast cancer cells, estrogen increases glucose uptake and reprograms glucose ...metabolism. Meanwhile, estrogen/ER activates the anticipatory unfolded protein response (UPR) preparing cancer cells for the increased protein production required for subsequent cell proliferation. Here, we report that thioredoxin-interacting protein (TXNIP) is an important regulator of glucose metabolism in ER+ breast cancer cells, and estrogen/ER increases glucose uptake and reprograms glucose metabolism via activating anticipatory UPR and subsequently repressing TXNIP expression. In 2 widely used ER+ breast cancer cell lines, MCF7 and T47D, we showed that MCF7 cells express high TXNIP levels and exhibit mitochondrial oxidative phosphorylation (OXPHOS) phenotype, while T47D cells express low TXNIP levels and display aerobic glycolysis (Warburg effect) phenotype. Knockdown of TXNIP promoted glucose uptake and Warburg effect, while forced overexpression of TXNIP inhibited glucose uptake and Warburg effect. We further showed that estrogen represses TXNIP expression and activates UPR sensor inositol-requiring enzyme 1 (IRE1) via ER in the breast cancer cells, and IRE1 activity is required for estrogen suppression of TXNIP expression and estrogen-induced cell proliferation. Our study suggests that TXNIP is involved in estrogen-induced glucose uptake and metabolic reprogramming in ER+ breast cancer cells and links anticipatory UPR to estrogen reprogramming glucose metabolism.
In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect ...of traditional Chinese medicine during Corona Virus Disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
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•The sources of multi-source data of medicinal plants and the strategies for processing multi-source data are summarized.•This paper summarizes several machine learning algorithms commonly used to analyze multi-source data of medicinal plants.•This paper summarizes the application of machine learning combined with multi-source data in medicinal plants in recent years, and prospects the development of this field in the future.
Multiple elements are required to be allocated to different organs to meet the demands for plant growth, reproduction, and maintenance. However, our knowledge remains limited on the stoichiometry in ...all plant organs in response to heterogeneous environments. Here, we present the systematic investigation of multielemental stoichiometry in organs of the alpine plant
across different environmental conditions. The slopes of N-P stoichiometric relationships among organs in
did not differ significantly between environments even in flowers, the most active organ with the highest N and P level. C:P ratios had strong positive relationships with N:P ratios within and between organs. Zn had strong positive correlations with Fe, S, or Cu in each organ, indicating the potential interactions among the homeostases of these elements. The contents of macroelements, such as C, N, P, Ca, Mg, and S, were higher in plant organs than those in soil and exhibited a relatively narrow range in plant organs. However,
reduced Fe uptake from soil and showed the strictest homeostasis in its root, implying its resistance to excess Fe. Furthermore, precipitation and temperature associated with geography, followed by soil P, were the main divers for the multielemental stoichiometry in this species. Plant stoichiometry responded differently to abiotic environmental factors, depending on organ type and element. N:P ratio, no matter in which organ, showed little flexibility to climate factors. The results have implications for understanding the regulation of multielemental stoichiometry in plant individuals to environmental changes. Further studies are needed on the interactions of multielement homeostasis in plants.
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•Aromatase inhibitor (AI) resistant breast cancer cells are addicted to glutamine.•Glutamine consumption is independent of estrogen but still requires ER in AI resistant breast cancer ...cells.•c-MYC expression is up-regulated through the cross-talk between ER and HER2 in AI resistant breast cancer cells.•Targeting c-MYC-mediated glutamine metabolism inhibits the proliferation of AI resistant breast cancer cells.
Resistance to endocrine therapies in hormone receptor (HR)-positive breast cancer is a significant clinical problem for a considerable number of patients. The oncogenic transcription factor c-MYC (hereafter referred to as MYC), which regulates glutamine metabolism in cancer cells, has been linked to endocrine resistance. We were interested in whether MYC-mediated glutamine metabolism is also associated with aromatase inhibitor (AI) resistant breast cancer. We studied the expression and regulation of MYC and the effects of inhibition of MYC expression in both AI sensitive and resistant breast cancer cells. Considering the role of MYC in glutamine metabolism, we evaluated the contribution of glutamine to the proliferation of AI sensitive and resistant cells, and performed RNA-sequencing to investigate mechanisms of MYC-mediated glutamine utilization in AI resistance. We found that glutamine metabolism was independent of estrogen but still required estrogen receptor (ER) in AI resistant breast cancer cells. The expression of MYC oncogene was up-regulated through the cross-talk between ER and human epidermal growth factor receptor 2 (HER2) in AI resistant breast cancer cells. Moreover, the glutamine transporter solute carrier family (SLC) 1A5 was significantly up-regulated in AI resistant breast cancer cells. ER down-regulator fulvestrant inhibited MYC, SLC1A5, glutaminase (GLS) and glutamine consumption in AI resistant breast cancer cells. Inhibition of MYC, SLC1A5 and GLS decreased AI resistant breast cancer cell proliferation. Our study has uncovered that MYC expression is up-regulated by the cross-talk between ER and HER2 in AI resistant breast cancer cells. MYC-mediated glutamine metabolism is associated with AI resistance of breast cancer.
is an important medicinal plant in China, but there are some limitations in the ecological suitability study, such as incomplete investigation of species distribution, single regionalization ...modeling, and lack of collaborative evaluation of ecological suitability, and quality suitability. In this study, the maximum entropy model was used to analyze the ecological suitability of
under current and future climates. The multi-source chemical information of samples was collected to evaluate the uniformity between quality and ecology. The results showed that the current suitable habitat was mainly in southwest China. In the future climate scenarios, the high suitable habitat will be severely degraded. Modeling based on different regionalization could predict larger suitable habitat areas. The samples in the high suitable habitat had both quality suitability and ecological suitability, and the accumulation of chemical components had different responses to different environmental factors. Two-dimensional correlation spectroscopy combined with deep learning could achieve rapid identification of samples from different suitable habitats. In conclusion, global warming is not conducive to the distribution and spread of
. The high suitable habitat was conducive to the cultivation of high-quality medicinal materials. Actual regionalization modeling had more guiding significance for the selection of suitable habitats in a small area. The multi-regionalization modeling theory proposed in this study could provide a new perspective for the ecological suitability study of similar medicinal plants. The results provided a reference for the introduction and cultivation, and lay the foundation for the scientific and standardized production of high-quality
.
As a fungus with both medicinal and edible value,
Wolfiporia cocos
(F. A. Wolf) Ryvarden & Gilb. has drawn more public attention. Chemical components’ content fluctuates in wild and cultivated
W. ...cocos
, whereas the accumulation ability of chemical components in different parts is different. In order to perform a quality assessment of
W. cocos
, we proposed a comprehensive method which was mainly realized by Fourier transform near-infrared (FT-NIR) spectroscopy and ultra-fast liquid chromatography (UFLC). A qualitative analysis means was built a residual convolutional neural network (ResNet) to recognize synchronous two-dimensional correlation spectroscopy (2DCOS) images. It can rapidly identify samples from wild and cultivated
W. cocos
in different parts. As a quantitative analysis method, UFLC was used to determine the contents of three triterpene acids in 547 samples. The results showed that a simultaneous qualitative and quantitative strategy could accurately evaluate the quality of
W. cocos
. The accuracy of ResNet models combined synchronous FT-NIR 2DCOS in identifying wild and cultivated
W. cocos
in different parts was as high as 100%. The contents of three triterpene acids in Poriae Cutis were higher than that in Poria, and the one with wild Poriae Cutis was the highest. In addition, the suitable habitat plays a crucial role in the quality of
W. cocos
. The maximum entropy (MaxEnt) model is a common method to predict the suitable habitat area for
W. cocos
under the current climate. Through the results, we found that suitable habitats were mostly situated in Yunnan Province of China, which accounted for approximately 49% of the total suitable habitat area of China. The research results not only pave the way for the rational planting in Yunnan Province of China and resource utilization of
W. cocos
, but also provide a basis for quality assessment of medicinal fungi.
Dendrobium officinale is a medicinal plant with high commercial value. The Dendrobium officinale market in Yunnan is affected by the standardization of medicinal material quality control and the ...increase in market demand, mainly due to the inappropriate harvest time, which puts it under increasing resource pressure. In this study, considering the high polysaccharide content of Dendrobium leaves and its contribution to today's medical industry, (Fourier Transform Infrared Spectrometer) FTIR combined with chemometrics was used to combine the yields of both stem and leaf parts of Dendrobium officinale to identify the different harvesting periods and to predict the dry matter content for the selection of the optimal harvesting period.
The Three-dimensional correlation spectroscopy (3DCOS) images of Dendrobium stems to build a (Split-Attention Networks) ResNet model can identify different harvesting periods 100%, which is 90% faster than (Support Vector Machine) SVM, and provides a scientific basis for modeling a large number of samples. The (Partial Least Squares Regression) PLSR model based on MSC preprocessing can predict the dry matter content of Dendrobium stems with Factor = 7, RMSE = 0.47, R
= 0.99, RPD = 8.79; the PLSR model based on SG preprocessing can predict the dry matter content of Dendrobium leaves with Factor = 9, RMSE = 0.2, R
= 0.99, RPD = 9.55.
These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00.
is a precious herbal medicine in China because of its liver-protective and choleretic effects. A method for the qualitative identification and quantitative evaluation of
from Yunnan Province, China, ...has been developed employing Fourier transform infrared (FT-IR) spectroscopy and high performance liquid chromatography (HPLC) with the aid of chemometrics such as partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) regression. Our results indicated that PLS-DA model could efficiently discriminate
from different geographical origins. It was found that the samples which could not be determined accurately were in the margin or outside of the 95% confidence ellipses. Moreover, the result implied that geographical origins variation of root samples were more obvious than that of stems and leaves. The quantitative analysis was based on gentiopicroside content which was the main active constituent in
For the prediction of gentiopicroside, the performances of model based on the parameters selected through grid search algorithm (GS) with seven-fold cross validation were better than those based on genetic algorithm (GA) and particle swarm optimization algorithm (PSO). For the SVM-GS model, the result was satisfactory. FT-IR spectroscopy coupled with PLS-DA and SVM-GS can be an alternative strategy for qualitative identification and quantitative evaluation of
.
Wolfiporia cocos
is a widely used traditional Chinese medicine and dietary supplement. Artificial intelligence algorithms use different types of data based on the different strategies to complete ...multiple tasks such as search and discrimination, which has become a trend to be suitable for solving massive data analysis problems faced in network pharmacology research. In this study, we attempted to screen the potential biomarkers in different parts of
W. cocos
from the perspective of measurability and effectiveness based on fingerprint, machine learning, and network pharmacology. Based on the conclusions drawn from the results, we noted the following: (1) exploratory analysis results showed that differences between different parts were greater than those between different regions, and the partial least squares discriminant analysis and residual network models were excellent to identify
Poria
and
Poriae
cutis based on Fourier transform near-infrared spectroscopy spectra; (2) from the perspective of effectiveness, the results of network pharmacology showed that 11 components such as dehydropachymic acid and 16α-hydroxydehydrotrametenolic acid, and so on had high connectivity in the “component-target-pathway” network and were the main active components. (3) From a measurability perspective, through orthogonal partial least squares discriminant analysis and the variable importance projection > 1, it was confirmed that three components, namely, dehydrotrametenolic acid, poricoic acid A, and pachymic acid, were the main potential biomarkers based on high-performance liquid chromatography. (4) The content of the three components in
Poria
was significantly higher than that in
Poriae
cutis. (5) The integrated analysis showed that dehydrotrametenolic acid, poricoic acid A, and pachymic acid were the potential biomarkers for
Poria
and
Poriae
cutis. Overall, this approach provided a novel strategy to explore potential biomarkers with an explanation for the clinical application and reasonable development and utilization in
Poria
and
Poriae
cutis.
The cultivation and sale of medicinal plants are some of the main ways to meet the increased market demand for plant-based drugs.
is a widely used Chinese medicinal material. The growth and ...accumulation of bioactive constituents mainly depend on a satisfactory growing environment. Additionally, the occurrence of market fraud means that care should be taken when purchasing.
In this study, we report the correlation between saponins and climate factors based on high performance liquid chromatography (HPLC), and evaluate the influence of climate factors on the quality of
. In addition, the synchronous two-dimensional correlation spectroscopy (2D-COS) images of near infrared (NIR) data combined with the deep learning model were applied to traceability of geographic origins of
at two different levels (district and town levels).
The results indicated that the contents of saponins in
are negatively related to the annual mean temperature and the temperature annual range. A lower annual mean temperature and temperature annual range are favorable for the content accumulation of saponins. Additionally, high annual precipitation and high humidity are conducive to the content accumulation of Notoginsenoside R1 (NG-R1), Ginsenosides Rg1 (G-Rg1), and Ginsenosides Rb1 (G-Rb1), while Ginsenosides Rd (G-Rd), this is not the case. Regarding geographic origins, classifications at two different levels could be successfully distinguished through synchronous 2D-COS images combined with the residual convolutional neural network (ResNet) model. The model accuracy of the training set, test set, and external validation is achieved at 100%, and the cross-entropy loss function curves are lower. This demonstrated the potential feasibility of the proposed method for
geographic origin traceability, even if the distance between sampling points is small.
The findings of this study could improve the quality of
, provide a reference for cultivating
in the future and alleviate the occurrence of market fraud.
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