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  • Published: 01 April 2022

Phytochemical analysis of some selected traditional medicinal plants in Ethiopia

  • Misganaw Gedlu Agidew 1  

Bulletin of the National Research Centre volume  46 , Article number:  87 ( 2022 ) Cite this article

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This review of relevant medicinal plants is based on the fundamental knowledge accumulated by indigenous people of Ethiopia and to identify which types of selected medicinal plants for phytochemical analysis were analyzed and which one is not analyzed at Ethiopian levels. In this review, the most traditional medicinal plant species found and used in Ethiopia are chosen.

The qualitative phytochemical analysis, some of which are the most important phytochemicals such as phenolic, tannins, alkaloids, saponins, cardiac glycosides, steroids, terpenoids, flavonoids, phlobatannins, anthraquinones, and reducing sugars are studied by the researcher. Most studies have revealed that some phytochemicals are present in some medicinal plants while some are absent. The phytochemical properties of some species were studied like Artemisia afra (Ariti), Aloe Vera (Erret), Yzygium guineense (Dokuma), Ruta chalepensis (Tenadam), Ocimum grattissimum (Damakese), Nigella sativa (Tikur Azmud), Lepidium sativum (Feto), Hagenia abyssinica (Kosso), Croton macrostachyus (Bisana), and Rhamnus prinoides (Gesho).

Conclusions

This review has shown that traditional medicinal plants whose phytochemical properties are not studied have various medicinal purposes like treating mastitis, preventing boils, hemorrhoids, congestion, headache, hepatitis, liver, vertigo, stomatitis, kidneys, liver, and vision for treating anemia, hemorrhoid coughs, fluxes, and stomatitis in most animals and human beings. So that identifying the plants based on the investigation and analysis of phytochemical properties of such plant species are more important than Ethiopian levels.

Medicinal plants still play important roles in the daily lives of people living in developing countries of Asia and Africa, including Ethiopia. Medicinal plants not only serve as complements or substitutes for modern medical treatments, which are often inadequately available but also enhance the health and security of local people. Thus, these plants play indispensable roles in daily life and are deeply connected to diverse social, cultural, and economic events associated with life, aging, illness, and death (JAFICOAF 2008). Medicinal plants are used to treat and diagnose diseases and infections. From ancient times, plants have been rich sources of effective and safe medicines (Russell-Smith et al. 2006 ).

The world health organization (WHO) defined traditional medicine as the total combination of knowledge and practices that can be formally explained or used in the prevention and elimination of physical, mental, or social imbalance and relying exclusively on practical experience and observation handed down from generation to generation, whether verbally or in writing. About 75–90% of the rural population in the world (excluding western countries) relies on traditional medicines as their only health care system. This is not only because of poverty where people cannot afford to buy expensive modern drugs, but traditional systems are also more culturally acceptable and meet the psychological needs in a way modern medicine does not (Fassil Kibebe 2001 ).

Ethnomedicinal practices are believed to be one of the potential bases for the development of safe and effective treatments. Ethiopia has a long history of a traditional health care system, but studies on traditional medicinal plants (TMP) have been limited in comparison to the country’s multiethnic, cultural, and flora diversity (Fentahun et al. 2017 ), Also, the use of medicinal plants to treat infections is an old practice in large parts of Ethiopia to solve health problems for livestock and humans (Redda et al. 2014 ; Giday et al. 2009 ; Regassa 2013 ; Abera 2014 ; Tamene 2020 ; Mulatu 2020 ).

Increasing traditional medicines and natural plant products

The main phytochemical components, present in medicinal plants are tannins, alkaloids, saponins, cardiac glycosides, steroids, terpenoids, flavonoids, phlobatannins, anthraquinones, and reducing sugars. As proposed by WHO, the primary health care of most population of developing countries depend on traditional medicines and mostly natural plant products (Vines 2004 ). Like the worldwide countries, populations of Ethiopia use traditional medicines in both rural and urban areas. Traditional practice and activities have a long history in many areas in the Ethiopia and it will continue to give useful and applicable tools for treating disease (Helen et al. 2019 ).

Different traditional medicinal plant species are studied by different researchers in the world and in the Ethiopian. Ethiopia comprises people with many languages, cultures, and beliefs. This makes for a rich and diverse knowledge and practice of traditional medicine, including herbal remedies (Helen et al. 2019 ). There are different literature reviews that investigated and studied the Ethnobotanical and Ethnopharmacological evidence of some Ethiopian medicinal plants traditionally used for the Treatment of Cancer, skin problems, leprosy, and external parasites, Evil eye, and wound treatment in the Ethiopia. However, there is no report that could show phytochemical composition and its expanded pharmacological application in the folk medicine of some traditional medicinal plants in the country of Ethiopia. Moreover, this knowledge of identifications of studied and unstudied phytochemical composition of medicinal plants in Ethiopia can serve as the baseline data for researchers and analyzers for the further study of traditional medicinal plants in Ethiopia (Helen et al. 2019 ).

The medicinal power of traditional plants species lies in phytochemical components that cause definite pharmacological action on the human body (Naseem 2014 ). Based on their metabolism activity in the plant, phytochemicals components are generally can be mainly divided into two groups, which are primary which has mainly sugars, amino acids, chlorophyll and proteins, and secondary constituents while secondary constituents consist of alkaloids, flavonoids, saponins, tannins, phenolic compounds and many more (Krishnaiah 2007 ).

The most important components of the medicinal plant were isolated by the extraction methods by using the right solvent. Each researcher in the published articles in this review, different methods of extraction such as ethanol, methanol, chloroform, acetone, hexane, petroleum ether, ethyl acetate, and aqueous (water) were used to the phytochemical composition of plant species. The objective of this review was to collect and summarize the information about the medicinal plant and to classify the plants based on the studies of their phytochemical composition as well as this provides information for the research community to conduct further scientific investigations in Ethiopia’s medicinal plants.

Materials and methods

In this review, the data and information on the traditional medicinal plants in Ethiopia were collected from the published papers, which are available online in different forms such as books, published articles, and research reports. Different online sources such as Google Scholar and gray literature were the source of published articles by browsing the different words or terms like medicinal plants and Ethiopian traditional plants. For this review, scientific name, family name, local name, and important, obtained from the published articles that were obtained online, and the data are shown in Table 23 .

There are various traditional medicinal plants used to treat different illnesses and diseases in Ethiopia which did not describe plant species by scientific names; and review articles, are excluded. For this review paper, a total of 53 plant species that are recognized and grown in Ethiopia are documented. From those plant species, the phytochemical composition of some plant species is studied by a researcher and some are not studied. The most important components of the medicinal plant were isolated by the extraction methods by using different solvents. In all reported literature, different solvent such as ethanol, methanol, chloroform, acetone, hexane, petroleum ether, ethyl acetate, and water was used as solvent.

The main aim of this review is to collect and summarize the information about the medicinal plant and to classify the plants based on the studies of their phytochemical composition as well as to provide information for the research community to conduct further scientific investigations on the Ethiopia medicinal plants.

Results and discussions

Phytochemical analysis.

Traditional medicine plays a significant role in the healthcare of the people in developing countries, including Ethiopia, and medicinal plants provide a valuable contribution to this practice (Tesfahuneygn and Gebreegziabher 2019 ). In this review, around 33 medicinal plants species were identified from published articles. The different parts of the plant such as root, leaves, and fruit, in which these different parts have many traditional values, pharmacological uses, and phytochemical constituents were mentioned. From few medication values of plant parts, to treat rheumatism, madness, snakebite, chest pain, jaundice chest pain, malaria, headache, cough, etc. All the medicinal plants are shown in the table form with the scientific name, families, local name, and importance. Most plants were reported and investigated in Ethiopia. As reported by many authors, some medicinal plants with their scientific name, family, local name and their importance are shown in Table 23 , and these plant species listed in this review were often used by the people in Ethiopia.

Phytochemicals

Analysis of the phytochemical properties of the medicinal plants used to show and isolate the drug, lead compounds and components from the parts of the plant. The unique biological activity of the plants can be identified by their phytochemicals properties. Most parts of the plants used for the analysis of the phytochemical properties were leaves, roots, stem barks, and fruits. In this review, medicinal plants were investigated for phytochemical constituents of ethanol, methanol, chloroform, acetone, hexane, petroleum ether, ethyl acetate, and aqueous (water) extraction of different phytochemicals.

In this review, the most published articles recognized the presence of phytochemical components in the plants was indicated by the positive sign (+) and the absence of phytochemical components in the plants, by the negative sign (−) as shown in table.

Alkaloids are one of the main and largest components produced by plants, and they are metabolic byproducts that are derived from the amino acids (Naseem 2014 ). Based on the published articles in these reviews, alkaloids were extracted from the different parts of the plants using different solvents such as ethanol, methanol, chloroform, acetone, hexane, petroleum ether, ethyl acetate, and aqueous (water). These types of solvents extract phytochemical components from medicinal plants like leaves, roots, stem bark, and fruits.

Flavonoids consist of a large group of polyphenol compounds having a benzoyl-γ-pyrone structure and are ubiquitously present in plants. They are synthesized by the phenylpropanoid pathway. Available reports tend to show that secondary metabolites of a phenolic nature including flavonoids, are responsible for the variety of pharmacological activities (Mahomoodally et al. 2005 ; Pandey 2007 ). Flavonoids are hydroxylated phenolic substances and are known to be synthesized by plants in response to microbial infection (Dixon et al. 1983 ). In this review, flavonoids were detected in most plant species but in some medicinal plants were not present the same plant but different solvents like eucalyptus and Agenda Abyssinia leaves.

The term tannin is widely applied to a complex large biomolecule of polyphenol nature having sufficient hydroxyls and other suitable groups such as carboxyl to form strong complexes with various macromolecules (Navarrete 2013 ). In this present review, tannins were detected in most plant species like peel and juice of Citrus medica, mango ( Mangifera indica L .) leaves, Avocado fruit ( Persea Americana ), Dioscorea alata leaf , of Leucas aspera L . leaf and root, Ocimum gratissimum Linn leaf, Rhamnus prinoides root, extract of Rhizomes , Zingiber officinale and Curcuma longa and also for different solvent give different response for the same plant species like Bersama abyssinica leaf, F lax seeds , Nigella sativa , Ruta chalepensis leaves, and Syzygium guineense and not totally detected in part of plants like Lepidium sativum seeds and love Gilbetii root. Tannins are generally used in the tanning process and used as healing agents in inflammation, burn, piles, and gonorrhea (Boroushaki et al. 2016 ).

Saponins are an important group of plant secondary metabolites that are widespread throughout the plant kingdom. Saponins are basically phytochemicals that are found in most vegetables, beans, and herbs (Francis et al. 2002 ; Haralampidis et al. 2002 ). In this review, saponins were detected in most medicinal plants like citrus fruit juice , of Mango ( Mangifera indica L .) leaves, Avocado fruit ( Persea americana ), Leucas aspera L . leaf, and root, Rhamnus prinoides root, Bitter ( Vernonia amygdalina ) leaf and Stem bark of Vernonia amygdalina in common plant species and some plants were shown different results, that depends on solvent and also not totally detected in part of the plant such as Bersama abyssinica leaf, Dioscorea alata leaf, love Gilbertii root, and Flax seeds .

The word steroid is derived from sterol, which is a natural or synthetic chemically active hormone-like element. A steroid is one of a large group of chemical substances classified by a specific carbon structure. Steroids include drugs used to relieve swelling and inflammation, such as prednisone and cortisone; vitamin D; and some sex hormones, such as testosterone and estradiol (Hill et al. 2007 ). For this review, Steroids were detected in most plant species like citrus fruit juice , peel and juice of citrus Medica , Flaxseeds , Nigella sativa , Ocimum gratissimum Linn leaf, Syzygium guineans root, and Root and Stem bark of Vernonia amygdalina in common plant species while in some plant species were shown variable result that depends on the given solvents and not totally detected in the part of the plant like Rhamnus prinoides root.

Terpenoids are small molecular products synthesized by plants and are probably the most widespread group of natural products. Terpenoids show significant pharmacological activities, such as antiviral, antibacterial, antimalarial, anti-inflammatory, inhibition of cholesterol synthesis, and anti-cancer activities (Boroushaki et al. 2016 ). As mentioned earlier, Terpenoids were detected in most analysis plant species such as citrus fruit juice , Hagenia abyssinica leaves, Leucas aspera L . leaf and root, Flax seeds , Ocimum gratissimum linn leaf, Ruta chalepensis leaves, and Syzygium guineans root while in some plants its result depends on the types of solvents.

Phenolic compounds are secondary metabolites, which are produced in the shikimic acid of plants and pentose phosphate through phenylpropanoid metabolization (Derong Lin et al. 2016 ). In this review, phenolic was detected in most the medicinal plants like citrus fruit juice, peel and juice of citrus medica, mango ( Mangifera indica L .) leaves and Avocado fruit ( Persea Americana ), eucalyptus leaves, Flax seeds , Rhamnus prinoides root, of Rhizomes , Zingiber officinale, and Curcuma longa but some medicinal plant is given different response and depend on the solvents.

Even though there are so many medicinal plants in Ethiopia, this review of the phytochemical analysis shows that some medicinal plants were studied by the investigator in different areas of Ethiopia, while some traditional plants are not studied. According to the data of published articles, the extraction techniques of the medicinal plants were mainly digestion and aqueous-alcohol extraction. From Tables show that phytochemical investigation results are available in the Ethiopia area levels.

Above the Table 1 , phytochemical screening of alkaloids, tannins, saponins, flavonoids, phenols and phytosterols were the secondary metabolites found in the crude extract of Echinops amplexicaulis , Ruta chalepensis , and Salix subserrata . The methanol extracts of Echinops amplexicaulis and Salix subserrata contain most of the secondary metabolites.

In terms of the qualitative phytochemical investigation of the medicinal plants, the medicinal plants extract had different phytochemicals constituents such as saponins, tannins, alkaloids, terpenoids, anthraquinones, phenolic compounds, cardiac glycosides, and flavonoids (Table 2 ).

Phytochemical investigations from these medicinal plants have shown a large number of organic complex and biologically active compounds.

The results of the qualitative phytochemicals analysis showed that the leaf extracts of Lippia adonis var. koseret also indicated the presence of tannins, flavonoids, polyphenols, alkaloids and saponins, while in the case of ethyl acetate alkaloids were not detected and tannins were absent in petroleum ether extract (Table 3 ). Amino acids and carbohydrates were absent in all three extracts.

In this review, phytochemical screening of Bersama abyssinica leaf in Table 4 shown that the most published articles recognized the presences of specific phytochemical components in the plants was indicated by the positive sign (+) and the absence of phytochemical components in the plants, by the negative sign (−). These phytochemical constituents in Bersama abyssinica leaf were shown variable results that depend on the given solvents and are not totally detected in Bersama abyssinica leaf.

The results in Table 5 show that there are phytochemical components in Citrus fruit juice concentrates. These phytochemical constituents all are found in citrus fruit juice concentrates except cardiac glycosides were not detected in lemon and they indicated highly medicinal values. It can be suggested that the presence of phenols, alkaloids, flavonoids, saponins, steroids, and reducing sugar in Citrus fruit juice indicates are highly medicinal value.

From Table 6 , flavonoids, phenols, tannins, steroids, coumarin and cardioactive glycosides: have shown positive tests of ethyl acetate, and methanol extracts of peel and juice of citrus medica, while some phytochemical positive test and totally not detected like (anthraquinones, alkaloids, and terpenoids). These secondary metabolites are known to be biologically active and play significant roles in the bioactivity of medicinal plants because the medicinal values of the medicinal plant lie in these phytochemical compounds which produce a definite and specific action on the human body.

Based on the given data from Table 7 , phytochemical screening of ethanol extract of mango ( Mangifera indica L .) leaves and Avocado ( Persea americana ) fruits almost all are were detected but terpenoids were not detected in Mango ( Mangifera indica L .). The phytochemical are naturally occurring chemicals in plants which serve as medicinal for the protection of human disease; the phytochemicals are nonnutritive plants chemical that have protection or disease preventive properties.

In this review, the phytochemical analysis revealed the presence of flavonoids, phenols, and tannins while the terpenoids positive test of methanol extract and the remaining phytochemical components are were not detected. These results show that phytochemical depend on solvents (Table 8 ).

Table 9 , the presence of flavonoid, tannin, and phenol in methanol extract. The acetone extract obtained from the eucalyptus leaves was screened for phytochemicals. Qualitative phytochemical screening of acetone extract of eucalyptus leaves demonstrated the presence of saponins, carbohydrate, tannin, and phenol, while quinone, fat, protein, and flavonoid were absent.

In this review, the methanol, ethanol, n-hexane, and petroleum ether extract obtained from the Hagenia abyssinica leaves were screened for various phytochemicals from Table 10 . Qualitative phytochemical screening of methanol extract of Hagenia abyssinica leaves demonstrated the presence of saponins, flavonoids, phenols, terpenoids, steroids, and glycosides, while tannins, anthraquinones, and alkaloids were absent. Phytochemical analysis of ethanol extract of Hagenia abyssinica leaves demonstrated the presence of saponins, tannins, phenols, terpenoids, and alkaloids, while steroids, glycosides and phlobatannins were absent. A similarity that phytochemical screening of n -hexane extract of Hagenia abyssinica leaves demonstrated the presence of flavonoids, anthraquinones and terpenoids but saponins, tannins, alkaloids, steroids, glycosides, and phlobatannins are not detected and Hagenia abyssinica leaves extracted by petroleum ether were obtained presence of phytochemical only saponins and terpenoids, while other phytochemicals are not detected.

Phytochemicals screening in the plant extracts revealed the presence of flavonoid, stereol and polyterpenes, and saponified present in both methanol and ethyl acetate extract of Lepidium sativum s eeds and also flavonoids were present in petroleum ether extract of Lepidium sativum seeds while other phytochemical components were not detected (Table 11 ).

In this review, phytochemical screening of the aqueous, methanol, and hexane extracts of Leucas aspera L . leaf and root revealed the presence of various medically active constituents from Table 12 . Almost all phytochemical compounds present in the aqueous, methanol, and hexane extracts of Leucas aspera L . leaf and root were identified except cholesterol and steroids in the parts of leaf and root by aqueous. These plants indicate highly medicinal values.

Phytochemical screening of the love Gilbertii root suggests the presence of major phytochemicals in the root extracts (Table 13 ). Dichloromethane: methanol of roots showed the presence of alkaloids, anthraquinones, and flavonoids whereas; tannins, saponins, and terpenoids were not presented.

As result in Table 14 , screening for phytochemicals in the plant extracts almost all presents in both acetone and methanol extracts of Flax seeds, while some phytochemical is not detected like tannins, saponins in acetone extract of Flax seeds and also saponins were presented by methanol extract of flaxseeds. In addition to this phytochemicals screening of ethanol and water extract of flaxseeds almost phytochemical components presents and some phytochemicals not totally detected. These secondary metabolites are known to be biologically active and play significant roles in the bioactivity of medicinal plants because the medicinal values of the medicinal plant lie in these phytochemical compounds which produce a definite and specific action on the human body.

This review was shown in the (Table 15 ) phytochemical analysis of petroleum ether and ethyl acetate seed extract of Nigella sativa contains tannins, steroids, terpenoids and alkaloids, flavonoids, phenol, glycosides and steroids were found in the extract and are potent methanol soluble while some phytochemicals were not presented since it depends on the solvents.

In the present review, phytochemical screening of methanol and aqueous extracts of Ocimum gratissimum Linn leaf showed that the presence of tannins, phlorotannins, steroids, terpenoids, flavonoids and cardiac glycosides with steroidal ring whereas, saponins and sugar were not present in methanol solvent and also alkaloids were not absent in Table 16 . These detected phytochemical compounds are known to have beneficial importance in medicinal as well as physiological activities. In this manner, isolating and identifying these bioactive compounds, new drugs can be formulated to treat various diseases and disorders.

Table 17 shows the phytochemicals detected in Rhamnus prinoides root extract. Tests for triterpenes, saponins, tannins, phenols, glycosides, cardiac glycosides, and resins were positive in both aqueous and methanol/water extracts. Alkaloids were detected only in the methanol/water extract while steroids, flavonoids, flavones, and anthraquinones were not detected in both aqueous and methanol/water extracts. These phytochemicals may be responsible for the medicinal value of Rhamnus prinoides .

Phytochemical screening of ethanol/water (1:1) extract of Rhizomes, Zingiber officinale, and Curcuma longa showed the presence of phenolic, flavonoids, glycosides, and tannins whereas alkaloids were not present (Table 18 ).

The phytochemical analysis of Ruta chalepensis leaves extract in methanol showed that phytochemical components include; alkaloids, flavonoids, terpenoids, cardiac glycosides, phenols, saponins, tannins and anthraquinones and steroids were not present. Steroids, terpenoids and saponins were additionally present in both ethyl acetate and acetone extract, and also flavonoids, terpenoids, and anthraquinones were detected in the n-hexane extract, while others were not totally found in Table 19 .

In Table 20 , the presence of steroids, terpenoids, saponins, flavonoids, flavonoids, tannins, alkaloids, phenol, and glycosides were present in both dichloromethane/methanol and methanol extracts and steroids and terpenoids also were present in n-hexane extract whereas other phytochemicals components were not detected.

From Table 21 , it can be seen that the sample extracts showed positive tests for the presence of alkaloids, saponin, tannins, phlorotannin, glycosides, and flavonoids except for anthraquinones. Therefore, Bitter ( Vernonia amygdalina ) is the most frequently used for medicinal purposes.

In this review, the results revealed the presence of alkaloids, steroids, glycosides, saponin, and phlorotannin methanol extracts from the root and stem bark of Vernonia amygdalina whereas only tannins and phenols were not detected (Table 22 ). Therefore, the phytochemical screening results reveals that the presence of these phytochemical constituents supports the use of the Vernonia amygdalina plant in folklore medications and it is probable that these phytochemicals are responsible for the healing properties.

A total of 53 traditional medicinal plants were identified in this review. All of the reviewed plants have direct traditional uses for treating either ailment with cancer-like symptoms (determined by the traditional practitioner) or for laboratory-confirmed cancer cases. Medicinal plants have continued to be the most affordable and easily accessible source for the treatment of several human and livestock ailments in Ethiopia. Besides treating cancer, the plants selected in this review are also cited for their various traditional uses, including for the treatment of eczema, leprosy, rheumatism, gout, ringworm, diabetes, respiratory complaints, warts, hemorrhoids, syphilis, and skin diseases (Table 23 ). The output calls for the need for further phytochemical and pharmacological investigation giving priority to those plants which have been cited most for their use to treat cancer.

In Ethiopia, there are increasing demands for many most popular, more available, and effective plant species by the people. As stated by the different authors in the above Tables, different phytochemicals were investigated in different plant species with different solvent concentrations. Even though different phytochemicals were analyzed for different plant species, their concentration varied from one plant species to another plant species for different parts of the plant. Based on the above information from the Table, one type of phytochemical cannot be detected in all plant species and the concentration of one phytochemical content varies from one part of the plant to another part which mean the concentration of one phytochemical content in leaves can vary from the concentrations of phytochemical contents in root and fruits. Generally, even though there are various medicinal plants in Ethiopia, there are no studies that show enough information about qualitative and quantitative phytochemical contents for most plant species in the country. This may be due to the lack of enough laboratory facilities and modern technology available in the country for improving the synthesis and extraction of phytochemical components for developing the new drug product and drug leading compounds from the different parts of the medicinal plants by the government and private company.

In conclusion, this study showed the wide use of medicinal plants in Ethiopia. Even though there is a wealth of indigenous knowledge transfer is declining from generation to generation as a result of oral transmission. Human beings around the world have spent their lives for a long time to discovering a new drug to diagnose, prevent and treat various diseases. To save their lives from dangerous diseases, a new and powerful drug must be discovered and developed from the different parts of the plant. In order to future promote for development of new drug synthesis and extraction of bioactive components from the parts of the plant, availability, and value of information is very important. From tables, phytochemicals analysis of different medicinal plants revealed the presence of various bioactive compounds such as polyphenols, flavonoids, phenolic compounds alkaloids, saponins, tannins, phlobatannins, glycosides, anthraquinones, steroids, terpenoids, and triterpene. Based on the above data available in the review, most phytochemical components of traditional medicinal plants in Ethiopia are not analyzed. This leads to more traditional plants in Ethiopia are not being recognized by the international scientific organization, not how to use medicinal plants for disease treatment and they do not have scientific names. This review recommended finding further most common medicinal plants to investigate in scientific research and to governing them in the scientific naming system and as well as further studies should focus on green synthesis of heavy metals on different types of medicinal plants in Ethiopia. Based on this review, the studied phytochemical characteristics of medicinal plants in Ethiopia are few, so further study could be needed for examining, and characterizing the properties of unrecognized plant species in Ethiopia.

Availability of data and materials

The datasets used during the current study are available online in different forms such as books, various published journals and google scholar.

Abbreviations

Cirsium Englerianum

Cucumis Pustulatus

Discopodium Penninervium

Euphorbia Depauperata

Lippia Adoensis

Polysphaeria Aethiopica

Rumex Abyssinica

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  • Medicinal plants
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phytochemical analysis research paper pdf

ORIGINAL RESEARCH article

Qualitative and quantitative phytochemical analysis of ononis hairy root cultures.

\r\nNra Gampe

  • 1 Department of Pharmacognosy, Semmelweis University, Budapest, Hungary
  • 2 Spectroscopic Research Department, Gedeon Richter Plc., Budapest, Hungary
  • 3 Natural Bioactive Compounds Group, Institutional Excellence Program, Department of Plant Anatomy, Eötvös Loránd University, Budapest, Hungary
  • 4 Timiryazev Institute of Plant Physiology, Russian Academy of Sciences, Moscow, Russia

Hairy root cultures are genetically and biochemically stable, and they regularly possess the same or better biosynthetic capabilities for specialized (secondary) metabolite production compared to the intact plant. Ononis species are well-known herbal remedies in ethnopharmacology and rich sources of isoflavonoids. Besides isoflavones, less prevalent isoflavones and pterocarpans with valuable biological effects can be found in Ononis species as well. As these plants are only collected but not cultivated, biotechnological methods could play a role in the larger-scale extraction of Ononis isoflavonoids. Regarding this information, we aimed to establish Ononis spinosa and Ononis arvensis hairy root cultures (HRCs) and analyze the isoflavonoid profile of hairy root cultures qualitatively and quantitatively, in order to define their capacity to produce biologically valuable isoflavonoids. During the qualitative description, beside isoflavonoids, two new phenolic lactones, namely, bulatlactone 2″- O -β -D- glucoside and ononilactone, were isolated, and their structures were characterized for the first time. Altogether, 29 compounds were identified by the means of UPLC-Orbitrap-MS/MS. Based on UHPLC-UV-DAD measurements, the isoflavonoid spectrum of the Ononis HRCs differed markedly from wild-grown samples, as they produce a limited range of the scaffolds. The most abundant compounds in the HRCs were medicarpin glucoside and sativanone glucoside. The overall isoflavonoid production of the cultures was comparable to wild-grown O. arvensis and approximately twice as high as in wild-grown O. spinosa samples. As the overall content of wild-grown samples include more isoflavonoid derivatives, the HRCs contain structurally less divergent isoflavonoids but in higher quantity.

Introduction

Bioactive natural products are molecules perfected by evolution, which, based on their physico-chemical properties, are much more likely to become potential drug candidates than synthetic compounds produced by combinatorial chemical methods ( Larsson et al., 2005 ). Their sources might be herbs that are easy to cultivate, e.g., lavender or chamomile, etc., but in many cases, it is only possible to isolate it from wild-growing populations, e.g., taxol. This poses a major threat to ecological diversity, habitat undisturbedness, or even the survival of the species. An additional difficulty could be if the intact plant contains the active compound only as a minor component, and its extraction is not economical ( Atanasov et al., 2015 ).

In vitro cultivation of medicinal plants can provide a solution to this problem. Micro-propagation, organ, and cell cultures present an opportunity to produce the desired species under laboratory conditions and to bioreplicate individuals and/or organs with the highest active substance content. Although plant cell cultures seemed to be very promising tools in the production of specialized metabolites, unfortunately, they often did not live up to their expectations. The reasons can lie in the difficulty of industrial upscaling. Another problem is that the lack of differentiation and compartmentalization, that can result in different metabolic profiles compared to the intact plant ( Atanasov et al., 2015 ). Hairy root cultures, as transgenic tissue cultures, show a higher degree of differentiation. These are created by the bacterial infection of Agrobacterium rhizogenes , during which bacterial plasmid is incorporated into the plant DNA causing prolific growth of neoplastic roots. Hairy root systems can be maintained without the use of phytohormones. As opposed to cell cultures, they are genetically and biochemically stable, and they regularly possess the same or better biosynthetic capabilities for specialized metabolite production as the intact plant ( Georgiev et al., 2012 ).

Isoflavonoids are products of the phenylpropanoid biosynthesis route, and their main groups are isoflavones, isoflavanones, and pterocarpans ( Davies and Schwinn, 2006 ). Their best-known representatives are compounds belonging to the group of isoflavones, which are found in food and agricultural crops such as soy, alfalfa, or red clover. These compounds are used mainly because of their phytoestrogenic effect, and since the wild-grown plants are cultivated in large areas, they are easy to obtain ( Clifford and Brown, 2006 ). In addition to isoflavones, the group of isoflavanones and pterocarpans also include compounds with valuable biological effects; however, the plants that contain them mostly live only in the wild. For example, Ononis species are well-known herbal remedies in the Mediterranean region. As a member of the Fabaceae ( Leguminosae ) family, they are rich in isoflavonoids, and beside isoflavones, they produce less-prevalent isoflavanones and pterocarpans ( Gampe et al., 2016 , 2018b ). Despite the medicinal benefits ( Addotey et al., 2018 ; Deipenbrock et al., 2020 ; Spiegler et al., 2020 ; Stojkovic et al., 2020 ; Stojković et al., 2020 ), the plant is not cultivated. The part of the herb mostly used in phytomedicine is the extremely hardy root, making the collection cumbersome. Regarding these aspects, biotechnological methods could play a role in the larger-scale yield of Ononis isoflavonoids. Tumova et al. investigated the flavonoid content of callus cultures and cell suspensions of Ononis arvensis , but their experiments covered only the measurement of total flavonoid content in elicited cultures ( Tůmová and Rusková, 1998 ; Tůmová et al., 2003 , 2011 ; Tůmová and Polívková, 2006 ). Regarding this information, we aimed to analyze the isoflavonoid profile of hairy root cultures qualitatively and quantitatively, in order to define their capacity to produce biologically valuable isoflavonoids.

Materials and Methods

General materials.

Standard compound naringenin were purchased from Sigma-Aldrich (St. Louis, MO, United States), and formononetin, pseudobaptigenin, onogenin, sativanone, medicarpin, and maackiain were purified from hydrolyzed extracts of Ononis spinosa root in our laboratory. The isoflavone glucoside standards (formononetin-, pseudobaptigenin-, onogenin-, sativanone-, maackiain-, and medicarpin glucoside) were isolated in our laboratory, too ( Gampe et al., 2020 ). High-performance liquid chromatography (HPLC) and Mass spectrometry (MS)-grade methanol and acetonitrile were purchased from Fischer Scientific Co. (Fair Lawn, NJ, United States); LiChropur formic- and acetic acid were obtained from Merck (Darmstadt, Germany). Purified water was prepared using a Millipore Direct-Q system (Millipore Corp., Bedford, MA, United States).

Plant Material

Transformed root cultures of Ononis spinosa L. and Ononis arvensis L. were obtained by the inoculation of sterile 6-week-old plants with Agrobacterium rhizogenes (strain R-1601). The segments of hypocotyls with fast-growing adventitious roots were transferred to Petri dishes containing MS medium ( Murashige and Skoog, 1962 ), and cefotaxime was added to the medium (500 mg/l) for several subcultures until the total disappearance of Agrobacterium ( Kuzovkina et al., 1996 ). After elimination of bacteria, the hairy roots were cultured in liquid Gamborg B5 ( Gamborg et al., 1968 ) media in Erlenmeyer flasks, in a CERTOMAT BS-4 programmable incubation shaking cabinet (Braun Biotech International, Melsungen, Germany) at 100 rpm at 23 ± 2°C in the dark, and were subcultured every 21 days. Genomic DNA was extracted from hairy roots using the protocol and reagents of the Maxwell 16 LEV Plant DNA Kit (Madison, WI, United States). Polymerase chain reaction was executed for the confirmation of the presence of rolB rooting locus. The primers used to detect rolB were forward 5′-GAAGGTGCAAGCTACCTCTC-3′ and reverse 5′-GCTCTTGCAGTGCTAGATTT-3′ designed by Furner et al. (1986) (Bio Basic Canada Inc.). A PCR program described by Bertóti et al. (2019) was applied in a Bio-Rad iCycler machine (Hercules, CA, United States). Amplified PCR products were separated using electrophoresis on a 2% w / v agarose gel (Bio-Rad, CA, United States). From the two species, three parallel samples were inoculated (both 21-day-old, O. spinosa average mass 0.74 g and O. arvensis average mass 0.58 g) in 100-ml Erlenmeyer flasks with fresh liquid B5 medium (40 ml) and cultivated in the same shaking cabinet as before mentioned (100 rpm at 23 ± 2°C in the dark). Sampling of plant material was carried out at the time of inoculation (0 h) and at 7, 13, 21, and 28 days after inoculation.

Sample Preparation

For the qualitative study, 0.10 g of freeze-dried (Christ Alpha 1-4 liophilizator, Braun, Melsungen, Germany) and ground hairy root culture was extracted with 5 ml 70% methanol using sonication for 30 min at room temperature. For quantitative analysis, 0.100 g powdered plant material was weighed and 50 μl of the internal standard (2.0 mg/ml naringenin solution) was added first, and then, the samples were extracted with 5 ml 70% methanol by sonication for 30 min. The samples were centrifuged, and the pellet was repeatedly extracted twice more with the same method. The collected supernatants were filled up to 25 ml, out of which 1 ml was filtered through a 0.22-μm PTFE filter (Nantong FilterBio Membrane Co., Ltd., Nantong, Jiangsu, China). From these, 200 μl were taken out and kept at 83°C for 5 h prior to HPLC analysis in order to hydrolyze malonate esters ( Gampe et al., 2020 ). Investigating the liquid media for possible exudation of isoflavonoids, it was filtered and analyzed directly.

UHPLC-ESI-Orbitrap-MS/MS Conditions for the Qualitative Analysis of Hairy Root Samples

For obtaining high-resolution mass spectrometric data of hairy root cultures, a Dionex UltiMate 3000 UHPLC system (3000RS diode array detector, TCC−3000RS column thermostat, HPG−3400RS pump, SRD−3400 solvent rack degasser, and WPS−3000TRS autosampler) was used hyphenated with a Orbitrap Q Exactive Focus Mass Spectrometer equipped with electrospray ionization (Thermo Fisher Scientific, Waltham, MA, United States). The UHPLC separation of the samples was attained on a Waters XSelect CSH Phenyl-Hexyl phase column (100 × 2.1 mm i.d.; 3.5 μm; Waters Corporation, Milford, MA, United States). Mobile phase consisted of 0.1% v / v formic acid (A) and 8:2 acetonitrile:0.1% v / v formic acid (B). The following gradient program was applied: 0 min, 20% B; 15 min, 80% B; 20 min, 80% B; and 22 min, 20% B. Solvent flow rate was 0.3 ml/min, and the column temperature was set to 25°C. The injection volume was 2 μl. The electrospray ionization source was operated in positive ionization mode, and operation parameters were optimized automatically using the built−in software. The working parameters were as follows: spray voltage, 3,500 V; capillary temperature, 256.25°C; sheath gas (N 2 ), 47.5°C; auxiliary gas (N 2 ), 11.25 arbitrary units; and spare gas (N 2 ), 2.25 arbitrary units. The resolution of the full scan was of 70,000, and the scanning range was between 120 and 1,000 m / z units. The most intense ions detected in full scan spectrum were selected for MS/MS scan at a resolving power of 35,000, in the range of 50–1,000 m / z units. Parent ions were fragmented with normalized collision energy of 10%, 30%, and 45%.

Isolation of Bulatlactone 2″- O -β -D-G lucoside and Ononilactone

Using an ultrasonic bath, 4.5 g of lyophilized, powdered sample of 4-week-old hairy root cultures of O. spinosa were extracted with 200 ml 50% methanol for 30 min. The extract was filtered and dried under reduced pressure at 60°C. The residue was redissolved in 5 ml 30% methanol and purified using the same flash chromatographic method mentioned at the isolation of standard compounds. The fractions eluting between 2 and 3 min were unified and further separated on a preparative HPLC system using eluents of 0.3% v / v acetic acid (A) and methanol (B). Gradient elution was used with the following program: 0 min, 30% B; 10 min, 30% B; and 20 min, 100% B with a 10-ml/min flow rate. Bulatlactone eluted at 7.5 min and the yield was 12.1 mg. From flash chromatography fractions eluted between 13 and 14 min, ononilactone was isolated using the same preparative HPLC system with the following eluents: 0.3% v / v acetic acid (A) and acetonitrile (B). The used gradient was as follows: 0 min 40% B up to 43% in 20 min. The peak eluted at 11.8 min was further purified using an isocratic method consisting of 63% methanol and 37% 0.3% v / v acetic acid with a 10-ml/min flow rate. The peak of interest eluted at 12.5 min and the yield was 1.4 mg.

Nuclear Magnetic Resonance Spectroscopy

Nuclear magnetic resonance (NMR) spectral studies for ononilactone were carried out on Avance III HDX spectrometer from Bruker BioSpin GmbH (Rheinstetten, Germany): 800 MHz (equipped with a 1 H& 19 F/ 13 C/ 15 N TCI CryoProbe 1 H: 799.7 MHz, 13 C: 201.0 MHz) and 500 MHz (with a 500 S2 1 H/ 13 C/ 15 N TCI Extended Temperature CryoProbe, 1 H: 499.9 MHz, 13 C: 125.7 MHz). Standard pulse sequences available in the TopSpin 3.5 pl 7 software were used for spectral acquisition, while the spectra were processed in MestreNova (Mestrelab Research). The complete resonance assignments were established from scalar and through-space 1 H- 1 H, direct 1 H- 13 C, and long−range 1 H- 13 C connectivities on the basis of 1D 1 H, 13 C as well as 2D COSY, ROESY (CW spinlock for 250 ms), 1 H- 13 C multiplicity-edited HSQC ( 1 J CH = 140 Hz), and 1 H- 13 C HMBC ( n J CH = 7 and 2.5 Hz) spectra, respectively. The sample temperature was maintained at 298 K, and standard 5-mm NMR tubes were used. The 1 H and 13 C chemical shifts were referenced to the solvent signal of CHD 2 SOCD 3 at δ H = 2.500 ppm and the resonance line of CD 3 SOCD 3 at δ C = 39.520 ppm, respectively. The sample was dissolved in 600 μl DMSO- d 6 (VWR International L.L.C.) and acidified by two drops of neat trifluoroacetic acid (TFA). NMR experiments for the structural analysis of bulatlactone 2″- O -β- D -glucoside were carried out in D 2 O on a 600-MHz Varian DDR NMR spectrometer (Agilent Technologies, Palo Alto, CA, United States) equipped with a 5-mm inverse-detection gradient (IDPFG) probehead. Standard pulse sequences and processing routines available in VnmrJ 3.2C/Chempack 5.1 were used for the structure identification. The complete resonance assignments were established from scalar and through-space 1 H- 1 H, direct 1 H- 13 C, and long−range 1 H- 13 C connectivities as described above. The probe temperature was maintained at 298 K, and standard 5-mm NMR tubes were used. The 1 H chemical shifts were referenced to the residual solvent signal δ H = 4.790 ppm.

Preparation of Stock Solutions, Calibration Standards, and Quality Control Samples

Individual stock solutions of the standards were prepared by dissolving the compounds in 70% methanol containing the internal standard (50 μl 2.0 mg/ml naringenin solution diluted to 25 ml) to obtain ∼1 mg/ml solutions. Equal parts of the standard solutions were mixed to gain the stock solution. Calibration standards were prepared by diluting the stock solution with the solution of the internal standard. The 10-point calibration curve was prepared using the following: 100, 60, 30, 10, 6, 3, 1, 0.6, 0.3, and 0.1 μg/ml concentration levels. QC samples were prepared separately from the stock solution at 50, 5, and 0.5 μg/ml nominal concentrations.

UHPLC-UV-DAD Conditions for the Quantitative Analysis of Ononis Samples

Quantitative measurements were executed on a Waters ACQUITY UPLC system (sample manager, binary solvent manager, and PDA detector) (Waters Corporation, Milford, MA, United States). The samples were analyzed using the same phenyl-hexyl column as mentioned at the qualitative studies. Aiming at the determination of isoflavone derivatives, the same eluents were used, with the following gradient program: 0 min, 10% B; 15 min, 30% B; 17 min, 100% B; and 19 min, 10% B with 0.4 ml/min flow rate and 5 μl injected volume, and the column was heated to 40°C. For quantification of the isoflavanone and pterocarpan derivatives, the following gradient was used: 0 min, 25% B; 5 min, 25%; 6 min, 29% B; 15 min, 29%; 17 min, 100% B; and 19 min, 25% B with 0.4 ml/min flow rate and 5 μl injected volume, and the column was heated to 27°C ( Gampe et al., 2020 ).

Characterization of Hairy Root Growth

For both Ononis species, the complete and stable transformation status of the isolated hairy root cultures (HRCs) was confirmed by PCR on their genomic DNA for the detection of the presence of the proto-oncogene rolB . In general, the HRCs of O. spinosa showed a more robust phenotype and darker color, than O. arvensis ( Figure 1 ). Investigating the change in biomass, a very similar trend could be observed. The biomass of both species increased until the 3rd week. In the first 2 weeks, the increase was lighter in the case of O. arvensis . During the 3rd week, both cultures reached their maximum in biomass production and their growth did not differ significantly. Reaching the 4th week, the biomass started to decrease, showing the aging of the cultures. The dry masses showed a similar pattern; however, O. spinosa cultures showed a significantly higher dry mass. O. arvensis HRCs followed a similar trend, but the change in the dry mass is not significant ( Figure 2 ).

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Figure 1. The relative change in the two cultures’ biomass production with their standard deviation ( n = 3) and the cultures after 21 days.

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Figure 2. The dry mass of the cultures expressed as mass percentage with their standard deviation ( n = 3).

Qualitative Characterization of Phytochemical Composition of Ononis HRCs

The total ion chromatogram (TIC) recorded in positive ionization mode of the 70% aqueous-methanol extracts with the identified compounds of O. spinosa and O. arvensis HRCs can be seen in Figure 3 .

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Figure 3. Total ion chromatograms of the aqueous-methanolic extracts of O. spinosa and O. arvensis hairy root cultures.

The identified compounds along with their number, retention time, protonated pseudo-molecular ions, and most intense product ions are shown in Supplementary Table 1 . All identified compounds could be observed in both species.

The most characteristic peaks of the samples were isoflavonoid derivatives. Isoflavonoid derivatives were identified following the methods described in our previous publication ( Gampe et al., 2016 , 2018a , b ). In the HRCs, the isoflavonoid derivatives could be found in the forms of glucosides (10, 12, 13, 14, 16, and 17), glucoside malonates (17, 20, 21, and 22), aglycones (24, 25, 27, 28, and 29), and homopipecolic acid esters of glucosides (5, 6, 7, and 8) ( Supplementary Table 1 ). Based on solely HR-MS/MS studies, the type of hexoside and the position of the malonate moiety could not be deduced, so the compounds were identified tentatively as 7- O -glucosides and 7- O -glucoside 6″- O -glucoside malonates based on the works of Farag et al. (2007) and de Rijke et al. (2004) . Because of the racemic feature of the beta amino acid moiety, the homopipecolic acid derivatives can be found in the form of diastereomeric pairs separated to double peaks on the stationary phase ( Supplementary Table 1 ). The first two peaks were identified as the methyl-esters of homoproline and homopipecolic acid; the esterification of homoproline in methanol as extraction solvent has already been reported before ( Paßreiter, 1992 ). Although homoproline could be found in the form of methyl ester, the corresponding homoproline isoflavonoid glucoside esters could not be detected.

Beside isoflavonoid derivatives, special phenolic lactones (norneolignans) were detected in the forms of glucosides (9 and 11) and aglycones (15 and 23). These derivatives of puerol A and clitorienolactone B were known in Ononis species before ( Ghribi et al., 2015 ; Addotey et al., 2018 ; Gampe et al., 2018b ). Surprisingly, a compound (peak 26) with a different fragmentation profile and a rather unusual UV spectrum with two absorption maxima was also observed eluting with the aglycones ( Figure 4 ).

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Figure 4. The structure, UV, and MS/MS spectra of ononilactone.

The solution of this compound in aqueous-polar organic solvents and DMSO showed a strong blue fluorescence if irradiated with UV light of 366 nm. The most dominant fragment ions originated from the sequential losses of CO and H 2 O units. Based on HR-MS analysis, the observed protonated pseudo-molecular ion showed an m / z 295.0591 value, and the calculated formula was C 17 H 10 O 5 , suggesting the presence of 13 double bond equivalents. Since broadened aromatic 1 H NMR signals could be observed in DMSO- d 6 , hindering the observation of key 1 H- 13 C correlations in the heteronuclear 2D spectra, two drops of TFA was applied to overcome line broadening. NMR spectra of the acidified spectrum enabled the structure elucidation of compound 26 from only 49 μM material (ca. 9 μg in the NMR tube). In the 13 C NMR spectrum, the presence of a carbonyl group at δ C 170.5; six quaternary carbons at δ C 163.2, 160.0, 146.4, 135.4, 134.1, 120.0, and 107.4; one methine carbon at δ C 89.1; and nine aromatic methine carbons between δ C 103.0 and 129.8 were confirmed ( Table 1 ). The 1 H NMR contained spectrum seven aromatic and one methine proton (see Table 1 ).

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Table 1. 1 H, 13 C, and 2D NMR data of ononilactone in DMSO-d 6 acidified by TFA [δ (ppm), J (Hz)].

Investigating the early eluting peaks, peak 4 showed a UV spectrum very similar to that of chlorogenic acid or other caffeoyl acid derivatives ( Figure 5 ). Moreover, in the HR-MS spectrum, a peak at m / z 355.1054 could be detected, which could result in the same molecular formula (C 16 H 18 O 9 ) as chlorogenic acid, but the fragmentation profile did not match. The 1 H NMR spectrum revealed glucose resonances (δ H 3.54, 3.62, 3.70, 3.70, 3.80, 3.98, and 5.20) and two aromatic spin systems. Similarly to compound 26, a para -disubstituted phenyl ring gives the resonances at δ H 6.71 and 6.87 ppm, while the multiplets at δ H 6.67, 6.79, and 7.25 indicate a 1,2,4 trisubstituted phenyl ring ( Table 2 ). Three singlet signals were also recorded at δ H 5.19, 6.07, and 6.22. The lack of scalar coupling between the adjacent methine protons may be the consequence of their ca. 90 dihedral angle, according to the Karplus relationship.

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Figure 5. The structure, UV, and MS/MS spectra of bulatlactone 2″- O -β- D -glucoside.

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Table 2. 1 H, 13 C, and 2D NMR data of bulatlactone 2″- O -β- D -glucoside in D 2 O [δ (ppm), J (Hz)].

Quantitative Analysis of Isoflavonoids in Ononis HRCs

Using the same UHPLC-UV-DAD method developed for the characterization of wild-grown Ononis species, the relative and absolute isoflavonoid contents of HRCs were evaluated ( Supplementary Tables 2 , 3 ). Firstly, the liquid media were investigated, but no isoflavonoid derivatives could be detected. In the in vitro cultures, the main compounds were sativanone glucoside and medicarpin glucoside followed by pseudobaptigenin glucoside and formononetin glucoside ( Figure 6 ). The aglycones could be observed in a magnitude lower quantity for sativanone and medicarpin, whereas in the case of isoflavones, they were under limit of detection ( Supplementary Tables 2 , 3 ). In O. spinosa samples, the isoflavonoid concentration (mg/100 mg) showed a constant regression from the 1st week, whereas in O. arvensis samples, up to the 2nd week the level increased, then dropped ( Figure 7 ). The O. spinosa cultures possessed a higher level of total isoflavonoid content than O. arvensis ( Figures 7 , 8 ). If the absolute quantities of isoflavonoids were investigated ( Figure 8 ), without the correction of the biomass, both samples reached their maximum at 3rd week (similarly to the biomass). In the 4th week, the isoflavonoid levels dropped, indicating their breakdown or transformation ( Figure 8 ).

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Figure 6. The most abundant structures found in Ononis HRC extracts.

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Figure 7. The relative isoflavonoid content of O. spinosa and O. arvensis HRCs in mg/100 mg (SG, sativanone glucoside; MedG, medicarpin glucoside; S, sativanone; Med, medicarpin; PG, pseudobaptigenin glucoside; FG, formononetin glucoside).

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Figure 8. The absolute isoflavonoid content of O. spinosa and O. arvensis HRCs in milligrams (SG, sativanone glucoside; MedG, medicarpin glucoside; S, sativanone; Med, medicarpin; PG, pseudobaptigenin glucoside; FG, formononetin glucoside).

Structural Identification of the New Compounds

The UV spectrum of compound 26 resembled to that of flavonoids; however, the bands did not completely overlap with that of standard compounds ( Markham and Mabry, 1975 ). The MS/MS fragmentation spectrum recorded in positive ionization mode did not show any specific patterns characteristic for flavonoids or isoflavonoids ( Cuyckens and Claeys, 2004 ). The presence of two phenyl rings with para (ring B) and ortho-para (ring A) substitution patterns could be deduced from the coupling constants of 1 H resonances and 1 H- 1 H COSY experiments. Based on the 1 H NMR chemical shifts and 2D HMBC correlation peaks, the protons at δ H 8.01 were shown to belong to H-2′ and 6′, while protons at δ H 7.00 to H-3′ and 5′ of ring B, respectively. The coupling pattern of the 1 H resonances at δ H 7.94, 7.02, and 6.98 assigns these chemical shifts to H-3″, H-6″, and H-5″ (ring A), respectively. These aromatic protons served as good entry points to assign the quaternary carbon atoms of ring B. Using the HMBC data, the C-1″, C-2″, and C-4″ atoms were assigned to peaks at δ C 107.4, δ C 152.7, and δ C 163.2 ppm, respectively. The sharp singlet at δ H 6.07 showed a HMBC correlation peak to C-1″, indicating a connection of a –C= CH– unit to ring A. The position of the H-2 proton has also been confirmed by the ROESY crosspeak of H-6″ and H-2. The same proton (δ H 6.07) showed a HMBC crosspeak to the carbonyl δ C 170.5, which permitted the deduction that this olefinic group is linked to an ester or lactone. The structural motif, which could be drawn up regarding the information gained from the NMR experiments, showed a close similarity with the structure of puerol A ( Ghribi et al., 2015 ), so that the presence of a δ-lactone was presumed, which could be confirmed by the HMBC correlations of H-2 with both C-3 and C-4. In the case of puerol derivatives or clitorienolactones ( Addotey et al., 2018 ; Gampe et al., 2018b ), one of the phenolic rings (ring A) is linked to the lactone ring through a methylene group. However, in our case, a CH 2 unit could not be observed, and the other phenolic ring (ring B) showed a linkage through a quaternary carbon atom based on HMBC experiments. Regarding the molecular formula and the structures of puerol derivatives, an ether bridge between C-2″ and C-4a was hypothesized. With this linkage, a furanoflavonoid-like structure is formed ( Figure 4 ), which can explain the similarity of the UV spectrum and the blue fluorescence. As this compound is described for the first time, the name ononilactone was chosen for this new skeleton.

Although the UV spectrum, HR-MS base peak, and the calculated formula strongly resembled to chlorogenic acid, looking at the MS/MS spectrum of compound 4, the most intense fragment rose at m / z 193.0491, which was in disagreement with the MS/MS data of chlorogenic acid registered by other research groups [ Metabocard of Chlorogenic Acid (HMDB0003164), 2006 ]. The mass difference (162 Da) between the two peaks ( Figure 5 ) led us to the assumption that this compound is a glycoside, which could lose a hexose unit as a neutral loss. With the same exact mass and molecular formula, scopolin is mentioned in the literature, as a glycosidic compound. Furthermore, scopolin and its aglycone scopoletin were isolated from O. arvensis ( Sichinava et al., 2014 ). Nevertheless, the UV spectrum of these compounds shows an absorption maximum at higher values ( Pina et al., 2019 ), and the fragmentation pathway does not match with that of coumarins ( Yi et al., 2014 ). As a consequence, the isolation of the compound was inevitable to elucidate its structure by NMR spectroscopy. The number of carbon and hydrogen resonances were not in agreement with that of the hypothesized one by the HR-MS measurements (C 16 H 18 O 9 ), but the presence of a glucose unit could be confirmed. The recorded NMR signals showed great similarity with that of puerol A ( Ghribi et al., 2015 ), except for the lack of a CH 2 signal at 4a position. The downfield shifts of H-4a and C-4a indicated the presence of a hydroxy group in geminal position ( Figure 5 ). Regarding the structure of the aglycone and the glucoside drawn from the NMR studies, the molecular formulas C 17 H 14 O 6 and C 23 H 24 O 11 could be calculated, resulting in calculated protonated quasi-molecular masses of 315.0863 and 477.1391. Revising the HR-MS spectrum of peak 4, none of these signals could be detected; however, the [M + Na] + , [2M + H] + , and [2M + Na] + ions were present at m / z 499.1224, 953.7456, and 975.6811, respectively. Instead of the aglycone as a product ion, the [M + H-ring B] + ion could be detected at m / z 355.1054, which indeed could lose a glucose moiety resulting in the peak at m / z 193.0491 ( Figure 9 ). Neither the aglycone nor the glucoside form of this compound have been described before in the plant kingdom; thus, it is named as bulatlactone 2″- O -β- D -glucoside.

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Figure 9. The tentative MS/MS fragmentation pathway of bulatlactone 2″- O -β- D -glucoside.

The presence and structure of bulatlactone is fascinating from the point of view that it can serve as an intermediate between the well-known puerol derivatives and the newly described structure, ononilactone ( Figure 10 ). As we assume, through a dehydration and a ring closure step, ononilactone could be formed from bulatlactone aglycone. Bulatlactone was only detected in the form of 2″- O -glucoside and not as an aglycone. As the glucosidation takes place through the 2″ hydroxy group (which is involved in the ring closure, as well), it prevents the transformation of bulatlactone to ononilactone and stabilizes this form.

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Figure 10. The putative synthetic pathway of ononilactone.

Investigating the isoflavonoid pattern of the HRCs of the species, a reduced spectrum could be observed compared to the native plants. Formononetin, 2′-methoxyformononetin, sativanone, and medicarpin beside their derivatives could be found in both wild-grown ( Ghribi et al., 2015 ) and genetically modified transformed samples abundantly. On the contrary, isoflavonoid derivatives with various skeletons (isoflavone, isoflavanone, and pterocarpan) but with a common methylenedioxy substituent (pseudobaptigenin, cuneatin, onogenin, and maackiain) could only be detected in the HRCs in trace quantities or not at all. Interestingly, pseudobaptigenin derivatives could be observed only in trace quantities in the samples for qualitative analysis; however, in the quantitative samples, their concentration was comparable with formononetin. This could be the consequence of that after lyophilization, quantitative samples were immediately measured, while the qualitative samples were kept airtight and analyzed only weeks after the harvest. The decomposition of pseudobaptigenin in aqueous medium was experienced by our research group during in vitro tests, but in this case, the HRC were kept sealed in a dry form. Moreover, in the qualitative samples, onogenin and maackiain derivatives could be observed, while in quantitative samples, their amounts were under limit of detection. Based on these observations, it is hypothesized that pseudobaptigenin is transformed to onogenin and maackiain. These results show that the measured isoflavonoid content of wild grown samples ( Gampe et al., 2020 ) does not necessarily reflect the isoflavonoid content of the living plant, as it changes with time after harvest. As different isoflavonoids can possess distinct biological effects, the age and the storage conditions can affect the medicinal value of the sample.

Proliferation of HRCs and Comparison of Biomass and Phenolic Compound Accumulation

The overall isoflavonoid yield of HRCs showed a somewhat higher level, than the wild-grown samples of O. spinosa , and was comparable of that of O. arvensis . However, the aim of this study was not to optimize the proliferation and isoflavonoid extractability, thus modifying the circumstances of cultivation can lead to better results. Moreover, the isoflavonoid profile of wild-grown samples markedly differed from the fresh and older samples, too ( Gampe et al., 2020 ). The most characteristic compound produced by the HRCs was medicarpin glucoside (2.23–2.89 mg/100 mg in O. spinosa and 1.69–1.87 mg/100 mg in O. arvensis ), followed by sativanone glucoside (0.56–1.14 mg/100 mg in O. spinosa and 0.13–0.20 mg/100 mg in O. arvensis ) and pseudobaptigenin glucoside (0.12–0.20 mg/100 mg in O. spinosa and 0.09–0.16 mg/100 mg in O. arvensis ) ( Supplementary Tables 2 , 3 ). In the wild-grown samples, the amount of methoxy and methylenedioxy derivatives are comparable, whereas in HRCs, the methoxy derivatives are the predominant, except for pseudobaptigenin. The observed decrease in the amount of the methylenedioxy compounds could be a result of the genetic modification by the Ri plasmid or the lack of some biotic or abiotic factors that could not be reproduced under in vitro circumstances, e.g., symbiotic Rhizobium strains and drought stress. Regarding the relative isoflavonoid content that decreased from the 1st ( O. spinosa ) or the 2nd week ( O. spinosa ), but the fresh and dry weight increased until the 3rd week, it can be assumed that the cultures use their sources mainly for growth and not accumulating specialized metabolites. If the aim is the isolation of isoflavonoid compounds, the 3rd week is optimal, since the absolute quantity of the isoflavonoids was the highest in those days. Usually, the aglycone forms are regarded as the biologically active forms, but unfortunately, fresh HRCs accumulate mainly glycosides. On the other hand, regarding the qualitative studies, upon storage, these can transform to aglycones or the intestinal flora can hydrolyze them to their aglycone form, too.

In conclusion, Ononis hairy root cultures contain some special phenolic lactones beside isoflavonoids. O. spinosa can serve as rich sources of methoxylated isoflavonoids, as it produced them in higher quantities compared to wild-grown plants. Considering that most isoflavonoids with methylenedioxy substituent are missing, the isoflavonoid spectrum of HRCs is less complicated, providing an easy possibility to realize the isolation of the present compounds.

Data Availability Statement

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

Author Contributions

ZS recorded and analyzed the NMR spectra for ononilactone and AD recorded the ones for bulatlactone. IB recorded the Orbitrap-MS chromatograms. IK and ÉS started and maintained the hairy root cultures. LK and SB contributed to the design of the experiments and assisted in the interpretation of the data. NG performed all other experiments, analyzed and interpreted the data, and drafted the manuscript with the assistance of SB. All authors read and approved the final version of the manuscript.

The financial support from the Bolyai fellowship for SB and IB and the support of EFOP-3.6.3-VEKOP-16-2017-00009 for NG are gratefully acknowledged. This work was supported by the ÚNKP-18-3-III-SE-30 New National Excellence Program of the Ministry of Human Capacities, by the Bolyai+ ÚNKP-20-5-SE-31 New National Excellence Program of the Ministry of Human Capacities, by the National Research Development and Innovation Office (projects: VEKOP-2.3.3-15-2017-00020, OTKA 135712), and by the ELTE Institutional Excellence Program (1783-3/2018/FEKUTSRAT) supported by the Hungarian Ministry of Human Capacities. The authors would like to acknowledge the financial support from the Central Library of Semmelweis University supporting the Open Access publication.

Conflict of Interest

ZS was employed by the company Gedeon Richter Plc.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

NG gratefully acknowledges the help of Andrea Nagyné Nedves and Tamás Czeglédi for their valuable help in sample preparation, Katalin Koller Béláné for managing the in vitro cultures, Rita Könye and Regina Bertóti-Gond for their help in the PCR studies, and Máté Kemecsei for naming the new structures. The authors thank Prof. Csaba Szántay Jr. for supporting this project by NMR spectrometer time at Gedeon Richter Plc.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.622585/full#supplementary-material

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Tůmová, L., Tůma, J., and Dolezal, M. (2011). Pyrazinecarboxamides as potential elicitors of flavonolignan and flavonoid production in Silybum marianum and Ononis arvensis cultures in vitro. Molecules 16, 9142–9152. doi: 10.3390/molecules16119142

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Keywords : Ononis , hairy root, biotechnology, isoflavonoid, bulatlactone, ononilactone

Citation: Gampe N, Szakács Z, Darcsi A, Boldizsár I, Szőke É, Kuzovkina I, Kursinszki L and Béni S (2021) Qualitative and Quantitative Phytochemical Analysis of Ononis Hairy Root Cultures. Front. Plant Sci. 11:622585. doi: 10.3389/fpls.2020.622585

Received: 28 October 2020; Accepted: 30 November 2020; Published: 13 January 2021.

Reviewed by:

Copyright © 2021 Gampe, Szakács, Darcsi, Boldizsár, Szőke, Kuzovkina, Kursinszki and Béni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Szabolcs Béni, [email protected] ; [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Discovering Health Benefits of Phytochemicals with Integrated Analysis of the Molecular Network, Chemical Properties and Ethnopharmacological Evidence

Sunyong yoo.

1 Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; rk.ca.tsiak@ooyys (S.Y.); rk.ca.tsiak@tsiak628ylno (K.K.)

2 Bio-Synergy Research Center, Daejeon 34141, Korea

Kwansoo Kim

3 School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea

Associated Data

Identifying the health benefits of phytochemicals is an essential step in drug and functional food development. While many in vitro screening methods have been developed to identify the health effects of phytochemicals, there is still room for improvement because of high cost and low productivity. Therefore, researchers have alternatively proposed in silico methods, primarily based on three types of approaches; utilizing molecular, chemical or ethnopharmacological information. Although each approach has its own strength in analyzing the characteristics of phytochemicals, previous studies have not considered them all together. Here, we apply an integrated in silico analysis to identify the potential health benefits of phytochemicals based on molecular analysis and chemical properties as well as ethnopharmacological evidence. From the molecular analysis, we found an average of 415.6 health effects for 591 phytochemicals. We further investigated ethnopharmacological evidence of phytochemicals and found that on average 129.1 (31%) of the predicted health effects had ethnopharmacological evidence. Lastly, we investigated chemical properties to confirm whether they are orally bio-available, drug available or effective on certain tissues. The evaluation results indicate that the health effects can be predicted more accurately by cooperatively considering the molecular analysis, chemical properties and ethnopharmacological evidence.

1. Introduction

Plants provide not only essential nutrients needed for life, but also other bioactive phytochemicals that contribute to health promotion and disease prevention. While the macro- and micronutrients in plants were long thought to be one of the essential components for human health, phytochemicals have recently emerged as modulators of key cellular signaling pathways [ 1 , 2 ]. Phytochemicals, often called secondary metabolites, are non-nutritive chemical compounds produced by plants via several chemical pathways. Recent studies have demonstrated that a large number of phytochemicals can be beneficial to the function of human cells [ 3 , 4 , 5 ]. With several studies indicating the effects of phytochemical-rich foods on health, it is strongly suggested that ingesting these phytochemicals can help to improve health [ 6 , 7 , 8 ]. Based on such evidence, many researchers have previously conducted studies to investigate the roles of phytochemicals in health improvements.

Despite the efforts, studies on the precise roles of phytochemicals were faced with various limitations. To begin with, most of the studies were performed through in vitro assessment [ 9 , 10 , 11 ]. For example, in vitro screening methods were used to confirm biological activities of extracted phytochemicals. However, large-scale experiments are required for a large number of considered phytochemicals and potential health effects, a process which is costly and yet very unproductive. Therefore, in silico approaches, mostly based on molecular or ethnopharmacological information, have been proposed to identify the potential health effects of phytochemicals from numerous candidates. Molecular-based approaches focus on the similarity between phytochemicals and approved drugs, such as the molecular structure, mechanisms of the molecular network or target protein similarity, to predict potential effects of phytochemicals [ 12 , 13 , 14 ]. However, these approaches are designed to predict the specific effect of phytochemicals on specific phenotypes, or vice versa. Therefore, it is difficult to analyze the systemic health effects on the human body. Alternatively, some ethnopharmacological knowledge-based approaches have been developed [ 15 , 16 , 17 , 18 ]. These studies focused only on the ethnopharmacological information as a preliminary tool to select plants or phytochemicals for a certain disease, followed by molecular analysis or in vitro assessment. Although this process is useful to filter out phytochemicals from a large number of candidates, the productivity is still low because plants contain hundreds of phytochemicals. Moreover, it is difficult to find plants that are highly related to a particular health effect, since effect terms are closely related to each other [ 19 , 20 ]. For example, when extracting plants associated with urination, we need to consider the phenotypes associated with urination, such as dysuria, urethral stone, and urinary tract abnormalities, to achieve more relevant results. These problems make it difficult to perform large-scale analysis of phytochemicals.

In this study, we apply an integrated in silico analysis to identify the potential health benefits of phytochemicals. Our previous study demonstrated that phenotypic effects of drugs can be identified by investigating the propagated drug effects from a molecular network, and mapping these results to phenotypes [ 21 ]. Therefore, we inferred the potential health effects of phytochemicals by adapting our previous method. However, this approach does not provide detailed information about the effects, such as whether they are beneficial, harmful, or associated. To solve this problem, we utilized the ethnopharmacological evidence of plants. Our underlying hypothesis is that if a predicted health effect of a certain phytochemical agrees with the ethnopharmacological use of a large number of plants which contain the phytochemical, then we can reasonably argue that the effect of phytochemical is beneficial to health. To measure the association between the predicted effects of phytochemicals and ethnopharmacological evidence of plants, we calculated the semantic similarity between phenotype pairs on the Unified Medical Language System (UMLS) network. Moreover, we investigated the chemical properties of phytochemicals to confirm whether they are orally bio-available, drug available or effective on certain tissues. Finally, we inferred the health effects of 591 phytochemicals for 3832 phenotypes based on the integrated analysis of the molecular network, chemical properties and ethnopharmacological evidence. When we assessed the results, we found that our predictions cover many results which were reported in previous work. To conclude, the novelty of our method is threefold: (i) it is the first in silico method which identifies systemic health effects of phytochemicals by analyzing molecular properties, chemical properties and ethnopharmacological evidence; (ii) the large-scale analysis can be performed based on the integrated and structured molecular and phenotype information; and (iii) it can be used as a preliminary tool to screen medicinal agents from numerous phytochemicals.

2. Materials and Methods

2.1. materials.

Information about phytochemicals and the chemical composition of plants was collected from KTKP [ 22 ], TCMID [ 23 ] and FooDB [ 24 ]. Plants and their ethnopharmacological use were collected from KTKP, TCMID and Kampo [ 25 ]. Molecular targets of phytochemicals were collected from the DrugBank, the Drug Combination Database (DCDB) [ 26 ], the Comparative Toxicogenomics Database (CTD) [ 27 ], MATADOR [ 28 ], STITCH [ 29 ] and TTD [ 30 ] databases, and gene-phenotype associations were collected from the CTD database. A protein-protein interaction (PPI) network, including 19,093 nodes and 270,970 edges, was obtained from BioGrid version 3.4.136 [ 31 ] and the Context-Oriented Directed Associations (CODA) system [ 32 ]. A phenotypic network was collected from UMLS in the 2017AA version [ 33 ]. UMLS provides integrated information of various terminologies pertaining to biomedicine. The Metathesaurus is the main component of the UMLS, which is organized by biomedical concepts where each distinct concept is assigned to a concept unique identifier (CUI). We collected CUIs with broader (RB), narrower (RN) and other-related (RO) relationships among 11 types of UMLS relationships from related concept lists (File=MRREL.RRF), resulting in total 220,104 CUIs and 663,018 relationships.

For a gold-standard set, phytochemical derived drugs were collected from DrugBank version 4.3 [ 34 ]. Drug-phenotype associations were collected from DrugBank, CTD, ClinicalTrials.gov [ 35 ] and DCDB databases by exploiting the MetaMap tool to extract phenotype-related terms [ 36 ]. With inputs such as narrative text, MetaMap returns a ranked list of Metathesaurus concepts associated with each word of the input text. Among the Metathesaurus concepts categorized in semantic types, we used Metathesaurus concepts assigned to 20 semantic types out of 135 semantic types, which have related phenotypes such as “Disease or syndrome”, “Sign or symptom” and “Clinical attribute” ( Table 1 ).

Health effects-related UMLS semantic types. Among 135 semantic types, the following 20 semantic types were selected as related to health effects.

2.2. Method Overview

We designed a systematic pipeline to predict the potential health benefits of phytochemicals based on molecular and chemical properties of the phytochemicals, and ethnopharmacological evidence of the plants that contain the phytochemicals. For a query phytochemical, the algorithm works in three steps ( Figure 1 ): (i) Inferring the systemic health effects of phytochemicals by calculating propagated effects on the molecular network and filtering statistically significant phenotypes; (ii) investigating bioavailability based on the physicochemical properties and physiological effects; and (iii) finding ethnopharmacological evidence.

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A systematic pipeline for the prediction of the health effects of phytochemicals. ( a ) Phenotype values of a phytochemical were obtained by calculating the propagated effects on the molecular network. In the molecular network, the random walk with restart (RWR) algorithm was performed based on direct targets (star) and indirect targets (triangle) of a phytochemical, in which the RWR results are shown as colored nodes. Based on gene-phenotype associations, sums of gene values are mapped to phenotypes. ( b ) For all phytochemicals, chemical properties, including physicochemical properties and physiological effects, were calculated. ( c ) Plants containing the phytochemical were extracted. For each extracted plant, we calculated the semantic similarity between the predicted health effect of the phytochemical and the ethnopharmacological effects of the plant. To do this, we constructed phenotypic network and calculated the shortest path length between phenotype pairs and depth of the phenotypes. Plants with the similarity score larger than the user-defined threshold were selected.

In our previous study, we found that the phenotypic effects of drugs can be identified by calculating propagated drug effects on the molecular network [ 21 ]. We applied this method to phytochemicals to infer their systemic health effects. For this, we constructed phenotype vectors of phytochemicals (PVPs). Each vector contains health effects or disease-related terms for 3832 phenotypes defined by Medical Subject Headings (MeSH) and Online Mendelian Inheritance in Man (OMIM). The PVPs were generated by the following three steps. In the first step, propagated phytochemical effects were calculated by using the random walk with restart (RWR) algorithm on the molecular network ( Figure 1 a). The effects of phytochemicals are not limited to direct targets, but they are further propagated to interacting proteins. Therefore, initial values of the molecular network were assigned to the known and inferred targets of the phytochemicals, and the propagated effects of phytochemicals were calculated by applying the RWR algorithm. Consequently, this approach addresses the problem of the relatively small number of known targets of phytochemicals, compared to synthetic drugs. In the second step, phenotype values were calculated by combining propagated phytochemical effects based on gene-phenotype associations. Accordingly, phenotypes have high values when a drug directly binds to phenotype-associated genes or when drug targets are closely located to phenotype-associated genes. In the third step, PVPs were constructed by filtering statistically significant phenotypes from the inferred list of phenotypes, which were calculated from the second step. We then calculated the chemical properties, including physicochemical properties and physiological effects, to predict the bioavailability of phytochemicals ( Figure 1 b). Based on this result, we found phytochemicals which can be orally absorbed or can be delivered to certain tissues. Finally, ethnopharmacological evidence of phytochemicals was investigated ( Figure 1 c). For a query phytochemical, we first found plants containing the phytochemical. We then calculated the semantic similarity between the predicted health effects of the phytochemical and the ethnopharmacological use of the plants. If the semantic similarity score is significantly high, then we determine that the ethnopharmacological use of the plant is highly associated with the phytochemical. This process is meaningful since it implies that ethnopharmacological evidence can help to select more relevant results. Also, it can be used as an additional filtering criterion. We provide all predicted health effects, chemical properties and ethnopharmacological use evidence of the phytochemicals ( Supplementary Data 1 and Data 2 ).

2.3. Inferring Health Effects of Phytochemicals on the Molecular Network

We constructed a molecular network based on PPI information and performed the RWR algorithm to investigate the propagated effects of phytochemicals. RWR simulates the random walker from its seed nodes and iteratively transmits the node values to the neighbor nodes, with the probabilities proportional to the corresponding edge weights [ 37 , 38 ]. To apply the RWR algorithm, we assigned initial values to seed nodes in the molecular network based on the target information of the phytochemicals. Target information of phytochemicals can be divided into two groups: direct and indirect associations. The direct associations contain binding information between phytochemicals and target proteins, while the indirect associations involve interactions caused by the changes in the expression of a protein, compound-induced phosphorylation, or influences of active metabolites of the phytochemicals. Information from both types of associations had to be taken into consideration, since the biological activity of a phytochemical can be changed from complex interactions within the molecular network, and the binding target information of phytochemicals is largely hidden compared to that of synthetic drugs. The initial values of a direct and indirect association were assigned as 1 and 0.3, respectively [ 21 , 27 ]. Second, the transition probability from a node to the neighbor node was calculated. We assumed that the transition probability represents the propagated drug effects on the molecular network. The transition probability vector of each node at time step t + 1 is defined as following equation:

where r represents the restarting probability of the random walker at each time step, set to 0.7 in this study [ 38 , 39 , 40 , 41 ]. W represents the normalized adjacency matrix of the molecular network, p t is the probability vector of each node at time step t , and p 0 represents the initial probability vector. The RWR algorithm simulates the random walker until all nodes reach the steady state ( p t +1 − p t < 10 −8 ). We then mapped the RWR results to phenotypes based on the gene-phenotype associations. In this step, we found all genes which are therapeutic targets or biomarkers of certain phenotypes and mapped the sum of these gene values, which were obtained from RWR results, to the corresponding phenotypes. Through this process, we obtained a list of phenotype values for each phytochemical.

These phenotype values calculated from propagated effects do not necessarily represent the extent of the relationship between a phytochemical and phenotype. Even if a phenotype value calculated from propagated effects is high, it may not mean that the phytochemical is highly related to the phenotype. In cases where there are many phenotype-associated genes, or when there is a large number of target proteins for a phytochemical, overall phenotype values increase stochastically. To overcome this problem, we generated random PVPs and compared them to a list of inferred phenotype values to select phenotypes with significant values. A random PVP was generated by randomly selecting targets of a phytochemical from a fixed number of target proteins. For each phytochemical, 1000 random PVPs were generated, and phenotypes with an empirical p -value lower than 0.01 were selected. The p -value was calculated from the following equation:

where n is the number of random PVPs and r is the number of PVP values that are larger than the phenotype value [ 42 ]. The raw values of PVPs were then replaced with binary values, where only those with p -values lower than 0.01 are given the value one. From this process, PVPs, which consist of binary values, were generated with filtered statistically significant phenotypes from a large number of inferred candidates of phytochemical effects.

2.4. Calculating Chemical Properties of Phytochemicals

The chemical properties of phytochemicals were calculated to provide an understanding of the physicochemical properties and physiological effects ( Figure 1 b). Physicochemical properties include molecular weight, log of the octanol-water partition coefficient (AlogP), hydrogen-bond donors, hydrogen-bond acceptors, and rotatable bond count. Physiological effects include human intestinal absorption (HIA), Caco-2 permeability, blood-brain barrier (BBB) permeability and Lipinski’s rule of five (RO5). By utilizing the physiological effects of phytochemicals, we can predict various functional activities of the compounds on the human body. For example, we can predict the in vivo absorption of phytochemicals across the gut wall based on the Caco-2 permeability [ 43 ]. Phytochemicals are required to cross the BBB to have a neuroactive function. In this study, HIA and BBB values are calculated with Shen’s work [ 44 ], while Caco-2 permeability is calculated through the quantitative structure–activity relationship (QSAR) model [ 45 ]. RO5 and other physicochemical properties are calculated with the Chemistry Development Kit (CDK) Descriptor Calculator [ 46 ].

2.5. Finding the Ethnopharmacological Use of Phytochemicals

We investigated the ethnopharmacological use of plants to provide further evidence of the predicted health effects of phytochemicals. The ethnopharmacological information, such as efficacy or indications collected in articles from scientific journals and documents of traditional medicine, is generally described in narrative text. Moreover, there are complex associations between phenotypes, such as synonyms and symptoms of diseases. Therefore, it is difficult to determine whether certain ethnopharmacological evidence is associated with the phenotype of interest. To extract plants which have ethnopharmacological evidence of the predicted effects of a phytochemical, (i) phenotype terms should be extracted and structuralized from the narrative text, and (ii) the complex relationship between phenotypes should be quantified. To solve this problem, we first extracted phenotype-associated terms from the narrative text by applying the MetaMap tool ( Figure 2 a). Next, plants containing the queried phytochemical were found based on external database information ( Figure 2 b). Next, the phenotypic network was constructed based on the hierarchical relationship of UMLS [ 33 ], and the semantic similarities between phenotypes were calculated ( Figure 2 c). A relationship between two general phenotype concepts, such as neoplasms and cardiovascular diseases, would result in a reasonably large difference, while one between two closely related concepts such as coronary stenosis and coronary vasospasm would result in a small difference. Semantic similarity can measure the quantitative relatedness between phenotypes by considering the distance and depth of phenotypes in the network. We applied the semantic similarity measure proposed by Wu & Palmer (wup) and defined as [ 47 ]:

where lcs ( c 1 , c 2 ) is the lowest common subsumer of concepts c 1 and c 2 . Based on this method, we can calculate the distance between the inferred effect of a phytochemical and the ethnopharmacological use of a plant. In this study, we assumed that the phenotype pair is highly associated when a semantic similarity score is larger than 0.8. Therefore, we calculated semantic similarities between all possible pairs of predicted health effects of a phytochemical and ethnopharmacological effects of the plant, and plants with the similarity score larger than 0.8 were selected. We showed that the effect type of predicted health effects is likely to be beneficial by investigating the evidence of the ethnopharmacological use of plants based on semantic similarity.

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An overview of the findings of the ethnopharmacological use of phytochemicals. ( a ) From public databases, we collected ethnopharmacological evidence of medicinal plants. We then extracted phenotype-associated terms from the narrative text of the collected information by applying the MetaMap tool. ( b ) For a queried phytochemical, plants containing the phytochemical were extracted. ( c ) For each extracted plant, we mapped its ethnopharmacological effects to the phenotypic network (blue circle). Then, we calculated semantic similarities between all possible pairs of predicted health effects of phytochemicals and ethnopharmacological effects of the plant. In this example, the semantic similarity between stroke and nephrosis is 0.57, based on the semantic similarity formula, because the depth of lcs is 2, the shortest path length between nephrosis and lcs is 1 and the shortest path length between stroke and lcs is 2. Plants with a similarity score larger than 0.8 were selected.

3.1. Inferred Health Effects of Phytochemicals

From public databases, we were able to collect information for 2136 phytochemicals found in 1212 plants. However, the information on chemical structures was only available for 512 of the phytochemicals (23.9%), while the molecular target was known for only 591 of them (27.6%). Hence, we predicted the potential health effects of 591 phytochemicals by investigating their propagated effects on the molecular network based on molecular target information and mapping the effects to phenotypes. From the results, an average of 415.6 ± 27.3 (confidence interval = 0.95) health effects were predicted for each phytochemical ( Figure 3 ). Since there are many candidate health effects in the molecular network analysis, and their detailed effect types are unknown, we further investigated the intersection between the predicted health effects of the phytochemicals and the ethnopharmacological use of the plant containing the phytochemicals. The results indicated that 31% of the predicted health effects had ethnopharmacological evidence (129.1 out of 415.6 health effects).

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The distribution of the number of predicted health effects. The distribution of the number of predicted health effects by molecular network analysis (red violin plot). The mean of predicted health effects is 415.6 ± 27.3. Next, we investigated the intersection between predicted health effects of the phytochemicals and ethnopharmacological use of the plant containing the phytochemicals. The distribution of the number of predicted health effects by molecular network analysis and ethnopharmacological use evidence (blue violin plot). The mean of predicted health effects is 129.1 ± 11.4.

Next, the physiological effects of phytochemicals were confirmed ( Table 2 ). To do this, we investigated RO5, HIA, Caco-2 permeability and BBB permeability for 512 phytochemicals ( Supplementary Data 2 ). For example, 446 phytochemicals were found to satisfy RO5. Additionally, 401 phytochemicals were confirmed to satisfy both RO5 and HIA.

The number of phytochemicals which satisfy RO5, HIA, Caco-2 and BBB. We also investigated the number of phytochemicals which satisfy two physiological effects.

3.2. Performance Evaluation

Our method predicts the potential health effects of phytochemicals from the integrated analysis ( Supplementary Data 1 ). The core information used in such a prediction is the propagated effects of phytochemicals obtained from the molecular network. Therefore, we evaluated the performance of the prediction by calculating precision ( p ) and recall ( r ) values [ 48 ]. To do this, we collected the experimentally validated information as the gold-standard positive set. Indications from DrugBank were used as the set for therapeutic effects, while information from Side Effect Resource (SIDER) was used as that for side effects. Furthermore, considering that information on phytochemicals is limited for DrugBank and SIDER, we additionally collected potential candidate effects from CTD as the silver standard positive set to address a large number of phytochemicals.

In the prediction of phytochemical effects, large class skew and large changes in class distributions are common, because the negative set is not available. Therefore, many studies have excluded gold-standard positive sets from all possible health effects and used the remaining as the gold-standard negative set [ 49 , 50 , 51 ]. To see the effect due to class skew, we calculated precisions for different positive/negative ratios to evaluate the precision performance in the various skewness of datasets ( Table 3 ) [ 52 ]. To do this, we generated a negative set by random sampling without replacement of the phytochemical and phenotype associations in different ratios. In each ratio, the negative set was generated ten times, and the performance for each case was evaluated by averaging the results. Moreover, since we predict an average of 415.6 potential health effects per phytochemical, the precision is very low ( p = 0.006 ± 0.001 and 0.049 ± 0.010, respectively). This is natural, because the correct answer in DrugBank or SIDER is only a fraction of all health effects of phytochemicals. Therefore, we evaluated the precision performance by adjusting skewness between the positive set and negative set, and we confirmed that molecular network analysis predicts health effects with high precision. Next, we checked the recall performance. Out of 270 therapeutic effects of 61 phytochemicals, our method covered 191 phenotypes ( r = 0.738 ± 0.062). Similarly, for side effect prediction, our method covered 1059 phenotypes among the total 1784 phenotypes of 60 phytochemicals ( r = 0.576 ± 0.061). In potential candidate effect prediction, our method covered 119,233 phenotypes among the total 136,862 phenotypes of 453 phytochemicals ( r = 0.909 ± 0.011). Overall, the prediction of health effects with molecular network analysis shows a good performance.

Precision and recall performance of molecular network analysis in predicting therapeutic effects, side effects and potential candidate effects.

Next, we compared the prediction performance with and without considering ethnopharmacological evidence ( Table 4 ). To consider the ethnopharmacological evidence, predicted health effects were filtered based on the presence of the ethnopharmacological use of the plant containing the phytochemical. The results show that the precision performance is increased when the ethnopharmacological evidence is considered, in terms of predicting therapeutic ( p = 0.014 ± 0.003) and potential candidate effects ( p = 0.563 ± 0.059). Interestingly, we found that the precision value of the side effects prediction was reduced because we only used the therapeutic use case information in the ethnopharmacological evidence. This indicates that the ethnopharmacological evidence helps distinguish between types of phytochemicals effects, such as therapeutic or side effects, which is one of the disadvantages of molecular network analysis.

Precision performance of the method, which uses molecular network analysis and ethnopharmacological use evidence in predicting therapeutic effects, side effects and potential candidate effects.

Lastly, we confirmed the performance improvement using chemical properties. Because phytochemicals must pass through the BBB to regulate a neuroactive function, we compared the performance of the prediction of neurological disorder for the two independent sets by selecting phytochemicals based on BBB permeability. From the results, we found that the precision and recall values of the set that crosses the BBB ( p = 0.611 ± 0.046, r = 0.725 ± 0.033) are much higher than the set that does not cross the BBB ( p = 0.312 ± 0.052, r = 0.558 ± 0.042). Overall, our results indicate that the integrated analysis can predict health benefits of phytochemicals more accurately than analysis using individual information.

3.3. External Literature Validation

In this section, we aimed to provide additional performance evaluations using the external data set that has not been used in our prediction method. We evaluated the reliability of our prediction results that are phytochemical-phenotype associations by checking the frequency of co-occurrence of phytochemicals and phenotypes in the PubMed abstract. Our basic assumption here is that if our method predicts reliable associations, then those phytochemical-phenotype associations would have higher probability of co-occurrence in previous studies than random phytochemical-phenotype associations would. Therefore, we made two independent sets based on the predicted health effects of phytochemicals. First, the predicted association set was generated by selecting phytochemical-phenotype associations, which were predicted as positive by molecular analysis, oral-availability and ethnopharmacological evidence. Second, for the control, the random association set was generated by randomly sampling the same number of samples from phytochemical-phenotype associations, without the aforementioned predicted association set.

We used 13,200,786 PubMed abstracts that were published from 1950–2013 for external literature validation. For the predicted phytochemical-health effects, we counted co-occurrences of phytochemical-phenotype terms ( n c ) from PubMed abstracts, calculated the Jaccard index (JI), and conducted the Fisher’s exact test ( n p ) and false discovery rate (FDR) test ( n q ) ( Table 5 ). We also performed the Mann-Whitney U test and calculated the corresponding p -values to check the statistical difference of the literature evidence between the predicted and random association sets [ 53 ]. A p -value of the Mann-Whitney U test lower than 0.05 was considered statistically significant.

Literature validation was performed by comparing co-occurrence, the Jaccard index and Fisher’s exact test values between predicted and random association sets. Statistical significance was calculated by the p -value of the Mann-Whitney U test.

1 The number of phytochemical-health effects associations which satisfy the p -value of Fisher’s exact test is lower than 0.001. 2 The number of phytochemical-health effects associations which satisfy q -value of FDR test is lower than 0.05.

The co-occurrence value was calculated by counting the number of PubMed abstracts where a phytochemical and its corresponding phenotype were in a same sentence. The average number of co-occurrence of the predicted association set ( n c = 1.25) were 13.8 times larger than that of the random association set ( n c = 0.09). Also, we normalized the co-occurrence value by the Jaccard index to correct the differences in the frequency of phytochemicals and phenotypes [ 54 ]. To do this, we additionally calculated an occurrence value ( n o ) by counting the number of PubMed abstracts that contain both or either of a phytochemical or a phenotype. For each phytochemical-phenotype association, the Jaccard index was calculated by dividing n c by n o . For example, assume that we calculate the Jaccard Index for the phytochemical-phenotype pair “quercetin—stroke”. If there are 50 abstracts that mention both quercetin and stroke and there are 200 abstracts that mention either or both, then the Jaccard Index value for this pair would be 0.25. From the results, the average Jaccard index value of the predicted association set (JI = 1.8 × 10 −4 ) was 18.9 times higher than that of the random association set (JI = 9.5 × 10 −6 ). Furthermore, we performed Fisher’s exact test to find the significant associations ( p -value < 0.001). To get the Fisher’s test value of each association, we counted the number of PubMed abstracts based on whether they included the phytochemical and target health effect. The results indicate that the number of significant associations of the predicted association set ( n p = 2984) was 4.8 times higher than that of the random association set ( n p = 612). However, when a large number of associations are evaluated, multiple testing problems arise and lead to many false positive results. Therefore, we additionally performed the FDR test and found associations satisfying a q -value lower than 0.05 [ 55 ]. The results indicate that the number of associations satisfying the q -value criteria from the predicted association set ( n q = 1341) was 4.9 times higher than that from the random association set ( n q = 274). In addition, the p -values of the Mann-Whitney U test indicated that the difference in the literature evidence among the predicted and random association sets was significant. These results showed that our method can be used as a tool to identify the health effects of phytochemicals.

3.4. Case Studies

To further illustrate the potential of this algorithm in finding phytochemicals with possible medicinal effects against specific diseases, we selected a few phytochemicals, such as choline, isoquercitrin and niacin, as case study subjects on whether there is evidence of their health benefits. To begin with, our method predicted that choline could be effective against 1151 phenotypes, such as hypertension, neurological diseases and hypoimmunity. Of these, 515 phenotypes were supported by ethnopharmacological evidence. From this list, the top 10 phenotypes with the most ethnopharmacological use evidence were selected. Then, we manually searched through the ClinicalTrials.gov database to check whether any clinical trials were performed regarding these phenotypes using choline. Interestingly, there were two phase four trials done with choline to treat neurological disorders (ethnopharmacological evidence n e = 48, rank = 2). The first trial was by Daewoong Pharmaceutical Co. LTD. in 2016, where the study was designed to test the efficacy of the choline alfoscerate on cognitive improvements of patients with cerebrovascular injury in Alzheimer’s disease. Along with this study, there are several animal studies [ 56 , 57 ] and another clinical trial that support the dietary supplement of choline for the possible prevention of dementia and memory loss [ 58 ]. The second trial related to neurological disorders was by Seoul National University Hospital in 2013, in which the study focused on the cognitive impairment of post-stroke patients treated with choline alfoscerate. Such an approach to cognitive damage with a choline supplement is also supported with various animal studies [ 59 , 60 ] and a clinical trial [ 61 ]. Although one may claim that the range of the term “neurological disorder” covers a wide spectrum of phenotypes, it is important to note that the specific phenotypes of the clinical trials mentioned above, which are Alzheimer’s disease ( n e = 31, rank = 81) and cognitive impairment ( n e = 3, rank = 419), were also on the list of possible target phenotypes of choline. Furthermore, there was also a phase four clinical trial for pain ( n e = 44, rank = 6) by Columbia University in 2016, where the researchers planned to study the effects of taking choline to decrease postoperative pain. The anti-nociceptive effects of choline have been suggested with various in vivo animal studies as well [ 62 , 63 ].

The evidence that illustrates the effectiveness of the algorithm in finding the effects of phytochemicals on potential phenotypes can be also found in the other two phytochemicals—isoquercitrin and niacin—as well. The results of these case studies are organized, along with those of the choline, in Table 6 . In each case it can be seen that, for the phenotypes that are found to have a high potential relationship to the phytochemical, there have been related clinical trials, many of which are already on phase three or four. As the algorithm discussed in this paper agrees with the conclusions from the aforementioned clinical trials and experiments, it is possible to clearly see that the algorithm can provide productive and plausible insights into potential therapeutic relationships between phytochemicals and diseases of interest.

Summary of evidence indicating potential health effects of exemplary phytochemicals: isoquercitrin, niacin and choline.

4. Discussion

In this study, we introduced an integrated analysis to predict the health benefits of phytochemicals. By investigating the propagated effects of 591 phytochemicals in the molecular network, we inferred potential health effects of those phytochemicals for 3832 phenotypes. For all phytochemicals, we investigated various physicochemical and physiological properties, such as HIA, Caco-2 permeability, BBB permeability and RO5, so that the results can be used in further studies, such as on oral bioavailability, drug availability and tissue specificity. Moreover, we provided evidence on the ethnopharmacological use of plants to support the predicted health effects. Herbal medicine has accumulated information on medicinal plants for thousands of years. Recent studies have demonstrated that herbal medicine information can be used as an important resource in drug or functional food development [ 64 , 65 , 66 ]. Therefore, we supported our results by investigating whether the predicted health effects of phytochemicals are also found in the ethnopharmacological use of plants containing the phytochemicals. For example, when we are looking for effective phytochemicals against neurological disorders, we first check the 3832 predicted health effects of phytochemicals which were inferred from molecular network analysis. Then, we select phytochemicals with positive physiological effects for RO5 and BBB permeability. Finally, we can find phytochemical candidates for neurological disorders by investigating whether the phytochemicals have ethnopharmacological evidence for neurological disorders. Performance evaluation revealed that the accuracy of predictions using all three types of information together was better than that using each individual type. Such improvement can be attributed to how each type of information fills in each other’s gap in content. Mere health effect candidates can be obtained with simple molecular network analysis, but the results would have been based on information with two major gaps for proper pharmacological studies: tissue specificity and effect type prediction. By considering the phytochemical’s physiological properties in the algorithm, it became possible to consider tissue specificity, thereby improving the overall prediction. Likewise, utilizing ethnopharmacological evidences allowed to overcome a major drawback of molecular network analysis, which is that the prediction does not consider effect types. The effect types, such as therapeutic or side effects from experience, help to narrow down possible medicinal influences of the phytochemicals on human body.

The strength of this algorithm is further highlighted by illustrating that there are several clinical trials already deep into phases three or four that are investigating the potential effects of the selected phytochemicals on their predicted phenotypes. This implies that the algorithm can be utilized to effectively predict the potential targets of the phytochemicals and vice versa. Also, this shows the application potential of the proposed method. Thus, productivity in such studies of medicinal effects can be expected to improve.

There are additional considerations that may improve our method. First, although this study utilized the ethnopharmacological use of the plants as important information to analyze the effects of phytochemicals, we did not consider the combination effects of phytochemicals. Because plants are composed of many phytochemicals, pharmacological effects of plants are often caused by the combined actions of multiple phytochemicals, as well as the individual actions of phytochemicals. However, this issue is very complex since the number of candidate combinations has increased exponentially with the increase of the number of considered phytochemicals. Second, the dosage of phytochemicals is not taken into an account in the method, although the health effects can be varied by different amount of chemicals taken. Until now, most studies have focused on the dose-response relationship for drugs, whereas only a few computational methods have calculated the expected content-response relationship for phytochemicals [ 67 , 68 ]. Lastly, current knowledge of phytochemicals is limited, and hence only a small proportion of phytochemicals could be analyzed [ 69 ]. In this study, we only consider 591 phytochemicals, since the information on chemical structure and molecular targets of phytochemicals are mostly hidden. Nevertheless, these limitations can be taken into an account for further experiments or improved computational methods. With these further improvements, our method can be used as an in silico screening tool to provide a list of health effects of phytochemicals in a cost-effective manner.

5. Conclusions

This study identified the health benefits of phytochemicals by utilizing various phytochemical properties, including molecular and chemical properties, along with ethnopharmacological evidence. Based on the known and inferred effects from gold and silver standard datasets, we confirmed that the health effects of phytochemicals could be successfully predicted with high coverage. We believe that the identification of the potential health benefits of phytochemicals may be a key factor to provide further insights into the discovery of drugs or functional foods.

Acknowledgments

We thank our colleagues from Bio-Information System Laboratory (BISL) who provided insight and expertise that greatly assisted the research, although they may not agree with all the interpretations/conclusions of this paper.

Supplementary Materials

The following are available online at http://www.mdpi.com/2072-6643/10/8/1042/s1 , Supplementary Data 1: Predicted health effects of phytochemicals, Supplementary Data 2: Chemical properties of phytochemicals.

Author Contributions

S.Y. and D.L. designed this research. S.Y. performed experiments and analysis. K.K. performed case studies. K.K. and H.N. provided comments that improve the introduction and method parts. S.Y. and D.L. wrote the paper. H.N. and D.L. supervised this work.

This work was supported by the Bio-Synergy Research Project (NRF-2012M3A9C4048758) of the Ministry of Science and ICT through the National Research Foundation.

Conflicts of Interest

The authors declare no conflict of interest.

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  • Published: 11 December 2019

Antioxidant, Anticancer Activity and Phytochemical Analysis of Green Algae, Chaetomorpha Collected from the Arabian Gulf

  • Samina Hyder Haq   ORCID: orcid.org/0000-0002-8617-3767 1 ,
  • Ghaida Al-Ruwaished 1 ,
  • Moudhi Abdullah Al-Mutlaq 1 ,
  • Sundus Ali Naji 1 ,
  • Maha Al-Mogren 1 ,
  • Sarah Al-Rashed 2 ,
  • Qura Tul Ain   ORCID: orcid.org/0000-0002-3651-2337 3 , 5 ,
  • Abir Abdullah Al-Amro 1 &
  • Adnan Al-Mussallam 4  

Scientific Reports volume  9 , Article number:  18906 ( 2019 ) Cite this article

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Seaweeds are a group of marine multicellular algae; the presence of antioxidant phytochemical constituents in Seaweed Chaetomorpha sp . extracts has received attention for their role in the prevention of human diseases. This study explores the phytochemical constituents, antioxidant, and anticancer properties of the Cladophoraceae, Chaetomorpha sp . Energy dispersive x-ray spectroscopy (EDX), and Gas chromatography-mass spectrometry (GC/MS) were performed to study the chemical structure and chemical formula. Different concentrations of ethanol and aqueous extracts of Chaetomorpha were used to estimate antioxidant activity by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and total flavonoid, phenolic, and tannins content assays. Anti-tumor activity against breast cancer cell lines (MCF-7 and MDA-MB-231) was assessed by 3-(4,5-Dimethylthiazol-2-cyl)-2,5-Diphenyltetrazolium Bromide (MTT) assay. The EDX analysis indicated the presence of oxygen, silicon, and calcium as dominant elements. Antioxidant assays indicated that the ethanol extracts of Chaetomorpha consisted of a total of 189.14 ± 0.99 mg QE/g flavonoid content, 21.92 ± 0.43 mg GAE/g phenolic content and 21.81 ± 0.04 mg GAE/g tannins content. The DPPH radical scavenging assay exhibited higher antioxidant activity IC 50 (9.41 ± 0.54 mg/mL) in the ethanol extract. Moreover, it showed high anticancer activity by growth inhibition in the MDA-MB-231 breast cancer cell line and low IC 50 (225.18 ± 0.61 µg/mL). GC/MS analysis revealed the presence of Dichloracetic acid (DCA) as the active antitumor constituent of Chaetomorpha sp .; other anticancer compounds identified were Oximes and L-α-Terpinol. The results revealed that the type of Chaetomorpha sp . studied here possesses very unique and novel constituents and active potent antitumor chemical constituents and it can act as a promising antioxidant and anticancer agent for future applications in pharmaceutical industries.

Introduction

Marine algae (Seaweeds) are a group of marine multicellular algae, plentiful in minerals, vitamins, and polysaccharides. They are considered as a potential source of bioactive substances such as proteins, lipids, and polyphenols possessing potent antibacterial, anticancer, antioxidant, antifungal, and antiviral properties 1 .

Recently, the evaluation of antioxidant phytochemicals constituents in macro-algae extracts has received attention for their important role in the prevention of human diseases. The presence of antioxidant substances such as alkaloids, flavonoids, phenols, tannins, phlorotannin, terpenoids, pigments, glycosides, and steroids in algae was thought to act as a defense mechanism, protecting them against reactive oxygen species (ROS) resulting from harsh environmental conditions 2 , 3 . The presence of antioxidants in macro-algae protected the species’ structural components from environmental oxidative damage 4 .

ROS are produced endogenously from metabolic activity in the human body or exogenously from smoking, air pollutants, radiation, ozone, and industrial chemicals. ROS are stabilized by reactions which in turn cause cellular damage and the formation of carcinogenic DNA adducts. ROS is a major cause of human diseases involving the heart, brain, and various cancers. The consumption of antioxidants has shown to reduce the risks of getting these diseases 5 .

Breast cancer is the leading cause of death in women globally. Cancerous breast cells express survival factors that inhibit apoptotic cell death 6 . As described by Moussavou et al ., (2014) “Employing natural or synthetic agents to prevent or suppress the progression of invasive cancers has recently been recognized as an approach with enormous potential”. Studies have shown that seaweed extracts could be powerful anticancer agents, apoptosis was detected in breast cancer cells that were treated by seaweed extracts thereby suggesting that seaweed could protect against breast cancer 6 . With the ever-increasing rate of Breast cancer incidence, there is a need to look for natural more effective cancer treatment that is not toxic to the normal cells. Developing a plant-based natural therapy for cancer treatment without harming the rest of the body is the greatest challenge in designing cancer drug therapy.

The green algae genus Chaetomorpha (Chlorophyte, Cladophorales) is characterized by unbranched heavy filaments. It includes about 70 species 7 , mostly rich in bioactive compounds, which makes them ideal for their use as dietary supplement and natural therapy for the treatment of diseases 8 . Some of these green macroalgae had exhibited cytotoxicity against number of cancer cell lines 9 . This study aimed to characterize and identify the active constituents of the green algae, Chaetomorpha sp . which was collected locally from the coast of the Arabian Gulf of Saudi Arabia. The study also explored the antioxidant and anticancer properties of this macroalgae.

Characterization techniques

Scanning electron microscopy.

Field emission scanning electron microscopy (FESEM) was used to observe the microscopic morphology of chaetomorpha sp . Random-shape of chaetomorpha sp .was viewed under FESEM (Fig.  1 ).

figure 1

Scanning electron microscopy of chaetomorpha sp .

Energy dispersive x-ray spectroscopy

Energy dispersive x-ray spectroscopy (EDX) found the elemental composition of chaetomorpha sp . Figure  2 shows the EDX spectra. The highest oxygen percentage among all was observed (45%) and may be due to oxygen linkage. Silicon (Si) was found as a dominant element in the chaetomorpha sp . (32%). Sodium (Na) and calcium (Ca) (8.3%, 4.45%) respectively were observed as second and third ascendent elements. Moreover, a small percentage of magnesium (Mg), aluminum (Al), chromium (Cr), indium (In), stannum (Sn), and titanium (Ti) was found. Radioactive element radium (Ra)(1.2%) was also observed.

figure 2

Energy dispersive x-ray spectroscopy of Chaetomorpha sp .

The total flavonoid, phenolic and tannin content

The total flavonoid, phenolic, and tannins content of aqueous and ethanol extract were estimated to evaluate phytochemicals content as shown in Table  1 . The ethanol extract was registered as having a higher amount of flavonoid, phenolic, and tannins (189.14 ± 0.99 mg QE/g, 21.92 ± 0.43 mg GAE/g, and 21.81 ± 0.04 mg GAE/g, respectively) compared with the aqueous extract.

2, 2-Diphenyl-1-Picrylhydrazyl (DPPH)˙radical scavenging activity

DPPH˙Radical Scavenging assay was performed to assist in evaluating the antioxidant activity of Chaetomorpha sp . Lower IC 50 indicates higher scavenging capacity. Ethanol extract of Chaetomorpha sp . showed higher antioxidant activity with lower IC 50 (9.41 ± 0.54 mg/mL) compared to aqueous extract as shown in Table  2 . The ethanol extract was significantly different compared with the aqueous extract with a p < 0.05. Ascorbic acid was used as the standard antioxidant which gave IC 50 at 0.03 ± 0.01 mg/mL which was significantly different compared to the Chaetomorpha extracts, with a p < 0.05.

Anticancer assay

MCF-7 and MDA-MB-231 breast cancer cell lines were used to study the cytotoxic effect of different concentrations (20–200 µg/mL) of ethanol and aqueous extracts of Chaetomorpha sp . on cell proliferation by 3-(4,5-Dimethylthiazol-2-cyl)-2,5-Diphenyltetrazolium Bromide (MTT) assay. Ethanol extract of Chaetomorpha sp . showed a significant effect on MDA-MB-231 but not on the MCF-7. It exhibited high anticancer activity by inhibition of cancer cell growth, with an IC 50 value of 225.18 ± 0.61 μg/mL as shown in Table  2 , which indicated the sensitivity of MDA-MB-231 breast cancer cell line against the ethanolic extract of Algae as shown in Fig.  3 . The aqueous extract of Chaetomorpha sp . however, did not show any significant effect on MDA-MB-231 and MCF-7 breast cancer cell lines when compared to the control.

figure 3

Effect of Different Concentration of Ethanol Extract of Chaetomorpha sp . on Breast Cancer Cell Lines. The blue line represents Cell viability percent of MCF-7 breast cancer cell line at a different concentration of Chaetomorpha ethanolic extract. Red line represents Cell viability percent of MDA-MB-231 breast cancer cell line at a different concentration of Chaetomorpha ethanolic extract.

GC/MS analysis

The ethanolic extract exhibiting significant antitumor activity was subjected to analysis by GC/MS technique. The major antitumor components found in that extract were Oxime, Lα-Terpinol and Dichloroacetic acid (DCA) as shown in Table  3 and Fig.  4 .

figure 4

GC-MS analysis of an ethanolic extract of Chaetomorpha sp .

Marine algae produce a diverse array of compounds and chemicals that facilitate their survival and metabolism in extremely harsh and competitive environments. Research into the natural and unique bioactive compounds produced as a result of their biosynthesis of secondary metabolites has generated a renewed interest in the pharmaceutical industry. Their biodiversity and biodistribution made them unique in chemical composition and mineral content. Hence, they are a promising source of various therapeutic bioactive substances for the treatment of various diseases including cancer. In this study, we attempted to identify and characterize the major active chemicals and compounds found in Chaetomorpha sp . as well as to study its antioxidant and antitumor activity. A previous study 10 found the major elements found in Chaetomorpha to be carbon and oxygen. Sulfur, potassium, sodium, and chlorine were found in minor amounts. In contrast, in this study, the major elements were found to be oxygen and silicon. The presence of silicon and oxygen as the major constituents of this algae make it unique from the point of view of its therapeutic and medicinal implications. Silicon bound to oxygen is soluble in water, can be easily absorbed and readily bioavailable to humans with biological activity 11 . However the exact biochemical and biophysical role of silicon is still unknown. As a result, there is a growing interest in the potential therapeutic effect of water-soluble silica on human health. Silicon has been shown to perform major roles in the structural integrity of nails, hair, skin, and total collagen synthesis and bone mineralization. There have been reports of direct involvement of silicon in reduced metal accumulation in Alzheimer’s disease, immune system health, and reduction in the risk of atherosclerosis 11 , 12 . The presence of radioactive elements, such as radium and titanium in Chaetomorpha sp .is noteworthy due to its therapeutic potential, especially in cancer treatment.

Chaetomorpha sp . is rich in polyphenolic compounds 13 , 14 . The presence of hydrophilic polyphenolic compounds such as phlorotannins, which are bipolar in nature could function as a major antioxidant, which helps the algae resist oxidative stress 15 . The presence of phenolic and flavanoids contributes to the antioxidants potential of Chaetomorpha 14 . The total free radical quenching DPPH assay revealed the antioxidant activity of IC 50 9.41 ± 0.54 mg/mL in ethanol extract and IC 50 15.44 ± 0.98 mg/mL for aqueous extract. These results were in agreement with previous studies that found higher antioxidants properties in ethanol solvents 15 , 16 .

There is a strong correlation between oxidative stress and the prevalence of cancer. Several in-vivo and in-vitro studies suggested that the administration of exogenous antioxidants may prevent free radical formation and damage to DNA and proteins thereby lowering the risk of developing cancer 17 , 18 . The prospects of using naturally occurring antioxidants alone or in combination with existing chemotherapy are an ideal strategy to combat tumor progression. Several studies regarding the cytotoxic efficiencies of various macroalgae and their potential anti-proliferative effect on the growth of cancer cells have been reported 18 , 19 . The antiproliferative and antitumor properties of Chaetomorpha sp . against two cancer cell lines MCF-7 and MDA-MB-231, observed in the results were of significance as it indicated its potential use as an antitumor therapy. Alpha-Terpineol was found to be a potential anticancer agent acting through suppressing NF-kb signaling in several breast cancer cell lines 20 . Steroidal oximes are gaining a lot of interest recently due to its antiproliferative and cytotoxic properties against cancer cell lines and considerable advances have been made in the development of an oxime functional group with the steroidal nucleus as an active anticancer molecule 21 . Most significantly the presence of DCA in the Chaetomorpha sp . has enormous potential to be used as anticancer drug therapy. Recent in-vitro / in-vivo rat study showed the efficacy of DCA in treating human lung, breast and brain cancer by inhibition of mitochondrial enzyme pyruvate dehydrogenase 22 . Recently oral DCA treatment for colon cancer has shown very promising results as a cytotoxic and cytostatic agent with an ability to maintain long term stability to advanced –stage cancer 23 . Our anticancer results supported the previous studies and further highlighted the importance of using algae as a therapeutic agent.

Further studies will be conducted against other types of cancer cell lines to understand the biochemical and molecular mechanisms of apoptosis of cancerous cells and the full therapeutic potential of the active ingredients present in this type of algae. As this is the first report of its bioactive constituents, the species characterization and the isolation of the bioactive compounds are currently in process.

The use of natural and plant-based anticancer products is a useful tool to fight against the cancer cells due to their few or no side effects. Marine algae have already been used as a food supplement and antioxidants and currently, research on the health benefits of various types of Algae is gaining huge interest. This study demonstrated that the ethanol extract of Chaetomorpha sp . possessed higher Antioxidant and Anticancer activity compared to aqueous extract. Moreover, when the extracts were screened for Antitumor activity, MDA-MB-231 breast cancer cell lines were significantly affected by different concentrations of ethanol extracts of Chaetomorpha sp . This study demonstrated the anticancer activity of chaetomorpha sp . is due to the presence of several active potent Antitumor chemicals such as DCA, Oximes, and terpinol. Furthermore, it’s chemical composition consisting of silicon, calcium, and other precious metals make it an ideal therapeutic agent in novel drugs as well as nutritional supplements.

Materials and Methods

In the present study, sodium nitrite, sodium hydroxide, isopropanol, and Folin-Ciocalteau reagent were purchased from Winlab, U.K. Sodium carbonate, aluminum chloride, and polyvinylpolypyrrolidone were purchased from Loba Chemie, India. Gallic acid and 3-(4, 5-Dimethylthiazole-2-yo)-2, 5-diphenyltetrazolium bromide (MTT) were purchased from Sigma, USA. Quercetin was purchased from Sterilin England. DPPH was purchased from Atlantic and ascorbic acid from Avonchem, U.K. Dulbecco’s Modified Eagle’s Medium (DMEM), Trypsin-EDTA, Fetal Calf Serum (FCS), and antibiotic solution were purchased from UFC Biotech, KSA. MCF-7 and MDA-MB-231 breast cancer cell lines were donated by King Faisal Specialist Hospital and Research Center (KFSH&RC).

Collection of algal material

The green alga, Chaetomorpha was collected in October 2017 at low tide time along the coast of the Arabian Gulf of Saudi Arabia. The algal material was washed under running tap water and allowed to dry in air. Finally, air-dried alga was powdered and stored at room temperature.

Extract preparation

The dried powdered algae samples ( Chaetomorpha sp .) were dissolved in either absolute ethanol or sterile autoclave water using a magnetic stirrer for 1 hour and then soaked at 25 °C and 4 °C respectively for 48 h with slow constant agitation. The mixture was sonicated 5 times for 30 seconds at (500 W, 25 kHz) using the Hielscher ultrasound sonicator and then filtered through Whatman No. 1 filter paper. The obtained filtrates were aliquoted and stored at −80 °C for further studies 24 . The stock solution of 10 mg/ml of Chaetomorpha sp . was used in all of the subsequent studies.

To study the surface morphology of Chaetomorpha and elemental percentage, a scanning electron microscope (JEOL JSM-7610F FEG-SEM) was used.

The GC-MS analysis of an ethanolic extract of marine algae Chaetomorpha was carried out on Agilent technologies model 7890B GC coupled with a mass detector Agilent 5977 A GC/MSD. The Analytic column was Agilent J&W nonpolar column DB-5MS ((5%-Phenyl)-methylpolysiloxane, 30 m × 250 µm, 0.25 µm). Carrier gas helium (1 mL/min) was used to separate components. The different GC conditions were standardized as follows, injector parameters were injection volume (1 μL), while injector temperature was set at 280 °C (mass analyzer). During GC extraction the program of oven temperature was 1 min at 60 °C, increased to a temperature of 110 °C at a rate of 10 °C/min. Mass parameters were the following; the solvent delay time was 5 min. Transfer line temperature was 270 °C, Mass spectra were taken at an ionization mode with an electron impact at 70 eV; Ion source temperature was 230 °C, mass scan range was 50–400 m/z.

Total flavonoid content

The total flavonoid content was estimated using the procedure described by Zhishen et al . 1999. A total of 1 mL of plant extracts were diluted with 200 µL of distilled water followed by the addition of 150 µL of sodium nitrite (5%) solution. This mixture was incubated for 5 minutes followed by the addition of 150 µL of aluminum chloride (10%) solution and then allowed to stand for 6 minutes. Next, 2 mL of sodium hydroxide (4%) solution was added and made up to 5 mL with distilled water. The mixture was shaken well and left for 15 minutes at room temperature. The absorbance of the reaction mixture was measured at 510 nm. The appearance of the pink color in the mixture showed the presence of flavonoids. The total flavonoids content was expressed as quercetin equivalent mg QE/g extract on a dry weight basis using the standard curve in the range of (0–200) mg/ml. 24

Total phenolic content

The total phenolic content was estimated using the Folin-Ciocalteau reagent. 500 µL of water and ethanol extracts were taken separately and it was made up to 1 mL of distilled water. Then 250 µL of diluted Folins- Ciocalteau reagent and 1.25 mL of 20% sodium carbonate (Na2CO3) was added. The mixture was shaken well and incubated in the absence of light for 20 minutes for a light pink color to develop. After incubation, the absorbance was measured at 735 nm. A calibration curve of gallic acid was constructed and linearity was obtained in the range of (0.25–10) mg/L. The total phenolic content in the plant extracts was expressed as mg of Gallic acid equivalent (mg GAE/g extract) by using the standard curve 24 , 25 .

Estimation of tannins content

Tannin’s content was estimated by the method described by Siddhuraju & Manian 2007. A total of 500 µL of the extracts were taken in a test tube separately and treated with 100 mg of polyvinylpolypyrrolidone and 500 µL of distilled water. This solution was incubated at 4 °C for 4 hours. Then the sample was centrifuged at 5,000 rpm for 5 minutes and 20 µL of the supernatant was taken. This supernatant has only a simple phenolic free of tannins (the tannins would have been precipitated along with the polyvinylpolypyrrolidone). The phenolic content of the supernatant was measured at 725 nm and expressed as the content of free phenolic on a dry matter basis. From the above results, the tannins content of the extract was calculated as follows:

Tannins ( mg GAE/g extract ) = Total phenolic ( mg GAE/g extract ) −  Free phenolic ( mgGAE/gextract ) 3

DPPH˙radical scavenging activity

The ability of algae extracts to scavenge the DPPH• radicals was assessed by using the method of Blois with some modifications 26 . About 0.2 mmol/L solution of DPPH• in ethanol was prepared, and 500 µL of this solution was added to different concentrations of the extracts (0.5–5 mg/mL). The mixture was shaken vigorously and allowed to stand for 30 minutes at room temperature. The control was prepared similarly but without the sample extracts and ethanol was used for the baseline correction. The changes in the absorbance of the algal samples were measured at 517 nm using the spectrophotometer. A lower absorbance value indicates a higher radical scavenging activity. Results were compared with different concentrations of standard antioxidant ascorbic acid (0.01–0.05 mg/mL). The ability of DPPH• radical scavenging activity was calculated by using the following formula:

where, A0 is the absorbance of the control, and A1 is the absorbance of the sample extracts. The IC 50 (the milligram of extract to scavenge 50% of the radicals) value was calculated using linear regression analysis. The lower IC 50 value indicates greater antioxidant activity 3 .

Breast cancer cell lines donated by King Faisal Specialist Hospital and Research Center (KFSH&RC) were used to test the activity of algal extract by MTT cell viability assay. The reduction of MTT was estimated by measuring the absorbance at 570 nm. The cells were cultured and maintained in DMEM supplemented with 2 mM L-glutamine, 10%FCS and 1% antibiotics (100 U/mL penicillin G and 100 mg/mL streptomycin). Both cell lines were plated separately in a flat-bottom 24-well plate (5 × 10 4 cells/well) and treated with different concentrations of algal extract (0–200 µg/mL), in a humidified 5% CO 2 atmosphere at 37 °C for 72 hours. After incubation 50 μL MTT solution (5 mg/mL MTT in PBS buffer)/well were added and the plate was shaken and incubated for 2 hours in a humidified 5% CO 2 atmosphere at 37 °C. After incubation, 100 μL 0.04 N HCl with isopropanol were added and absorbance was measured by using microplate ELISA reader at 570 nm. The average of triplicate repeats was calculated for each concentration. The data were expressed as the percentage of relative viability:

Then, the value IC 50 was calculated from the equation of the dose-response curve 5 , 27 , 28 .

Statistical analysis

All data were expressed as mean values ± SD of triplicate. The mean values were analyzed by one-way ANOVA. Significant differences between the means of parameters were determined (p < 0.05).

Data availability

All data generated or analyzed during this study are available.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the Undergraduate Student’s Research Support Program, Project no. (URSP-3-17-07). Also, the authors would like to show their gratitude to Fouziah Abdullah Al-Malki for her technical support.

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Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia

Samina Hyder Haq, Ghaida Al-Ruwaished, Moudhi Abdullah Al-Mutlaq, Sundus Ali Naji, Maha Al-Mogren & Abir Abdullah Al-Amro

Microbiology and Botany Department, College of Science, King Saud University, Riyadh, Saudi Arabia

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The Wellman Centre for Photomedicine, Harvard Medical School, Boston, Massachusetts, USA

Qura Tul Ain

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Adnan Al-Mussallam

Department of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

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S.H.: study design, writing, and editing. M.A.M.: experimental work, writing, and editing. Gh.R., S.N., M.M. and S.R., Ad.A.,: experimental work. Ab.A.: ideology and funding. Q.T.A.: data analysis and experimental work. All authors reviewed the manuscript.

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Haq, S.H., Al-Ruwaished, G., Al-Mutlaq, M.A. et al. Antioxidant, Anticancer Activity and Phytochemical Analysis of Green Algae, Chaetomorpha Collected from the Arabian Gulf. Sci Rep 9 , 18906 (2019). https://doi.org/10.1038/s41598-019-55309-1

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DOI : https://doi.org/10.1038/s41598-019-55309-1

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