# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 1 Introduction  
Since time immemorial, humans have relied on plants as the main source of therapeutic agents for the treatment of various kinds of diseases and maintaining their health. Precious ethnnodecinal knowledge of the diverse plant resources used by different cultures around the world has been passed on over many generations. Despite the exponential growth of synthetic drugs in modern medicine, the practice of using herbs and herbal formulations as alternative medicine has seen a significant upsurge globally due to their availability, cost efficiency, and safety claims [1]. Statistically, it has been estimated that four billion people living in developing countries (80% of the world's population) still depend on herbal medicine as their primary healthcare source [2]. In recent years, multiple product forms derived from plant materials, ranging from supplements, health drinks or tonics, teas, massage oil, and many other forms, have flooded the nutraceutical and herbal supplement markets across the world, catering to the demand and preferences of consumers. Malaysia is of no exception; the use of herbal-based products has become quite the norm [3], marketed as over-the-counter products in pharmacies or via direct selling and network marketing. Christia vespertilionis_ (L.f.) Bakh. f. (Family: Fabaceae) is a common ornamental plant, popular for its unique leaf shape and color. The vernacular names given to this small garden plant, \"rerama\", \"mariposa\", or \"butterfly wings\", allude to the unique shape of its terminal leaflet, which looks like the wings of a butterfly. The plant species, originally found in tropical South East Asia regions, exists as two varieties, viz., the red-leafed variety and the green-leafed variety. Other than being grown for ornamental purposes, there have been records of its use in traditional medicine for treating diverse ailments and injuries, including tuberculosis, bronchitis, cold, muscle weakness, poor blood circulation, snake bites, bone fractures, and scabies [4]. Several investigations have reported that the plant possesses antiproliferative, cytotoxic, antimalarial, antidiabetic, and antioxidant properties [5; 6; 7; 8; 9; 10; 11]. From the viewpoint of the phytochemistry of this plant in general, it has been reported to contain alkaloids, triterpenes, fatty acids, phenols, alkanes, and long-chained alcohols [4; 8; 10; 11]. Following up on the use of the plant to treat malarial fever, the bicyclic hydrocarbon 7-isopropylidene-1-methyl-1,2,6,7,8,9-hexahdronaphthalene (or christene) has been identified as the potential bioactive principle based on its in vitro antiplasmodial activity [9]. In Malaysia, the use of the green-leafed variety of this plant has risen in popularity, following several testimonials from consumers on the efficacy of the water decoction of the plant leaves in \"treating cancer\". Despite its popular use, very little is known about the phytochemical constituents of the leaf metabolome of this plant. Thus, considering the medical interest in the plant leaves and from the perspective of quality control, whether for product manufacturing or regulatory purposes, a better understanding of the chemical profile of the leaf metabolome is a logical and important extension to the research and development activities that should be conducted on this plant. Recent years have witnessed the application of many technological advances in the field of natural product research. This has accelerated and increased the success rate of the discovery of compounds from nature. In the context of untargeted tandem mass spectrometry analysis, various bioinformatics tools, such as iMet [12], MS\({}^{n}\) spectral trees [13], and MAGMa (MS annotation based on in silico generated metabolites) [14], have been introduced. These tools are continuously and increasingly being improved to meet the challenges of Big Data analysis, primarily to enable more and more comprehensive chemical interpretation of the vast amount of MS/MS datasets over time.