effect of caffeine on plant growth hypothesis

Efficiency of rooting of the Logan Thornless blackberry on MS medium with 1 mg/l of IBA at various concentrations of caffeine.

Root formation of the Logan Thornless blackberry’s microcuttings on MS medium with 1 mg/l of IBA at various caffeine concentrations.

Rhizogenesis of microcuttings of the Logan Thornless blackberry on MS medium with 1 mg/l of IBA at various concentrations of caffeine: a – control without caffeine; b – 1 mg/l of caffeine; c –10 mg/l of caffeine; d – 100 mg/l of caffeine.

Efficiency of rooting of the Boysenberry hybrid on MS medium with 1 mg/l of IBA at various concentrations of caffeine.

Root formation of the Boysenberry hybrid’s microcuttings on MS medium with 1 mg/l of IBA at various concentrations of caffeine.

Rhizogenesis of microcuttings of the Boysenberry hybrid on MS medium with 1 mg/l of IBA at various concentrations of caffeine: a – control without caffeine; b – 5 mg/l of caffeine; c – 10 mg/l of caffeine; d – 50 mg/l of caffeine.

Efficiency of rooting of the Logan Thornless blackberry on MS medium with 1 mg/l of IBA at various concentrations of caffeine.

Root formation of the Logan Thornless blackberry’s microcuttings on MS medium with 1 mg/l of IBA at various caffeine concentrations.

Rhizogenesis of microcuttings of the Logan Thornless blackberry on MS medium with 1 mg/l of IBA at various concentrations of caffeine: a – control without caffeine; b – 1 mg/l of caffeine; c –10 mg/l of caffeine; d – 100 mg/l of caffeine.

Efficiency of rooting of the Boysenberry hybrid on MS medium with 1 mg/l of IBA at various concentrations of caffeine.

Root formation of the Boysenberry hybrid’s microcuttings on MS medium with 1 mg/l of IBA at various concentrations of caffeine.

Rhizogenesis of microcuttings of the Boysenberry hybrid on MS medium with 1 mg/l of IBA at various concentrations of caffeine: a – control without caffeine; b – 5 mg/l of caffeine; c – 10 mg/l of caffeine; d – 50 mg/l of caffeine.

This is a great question. It's interesting to think about how various treatments could affect plant growth, both positively and negatively. Once you start thinking about exactly how to test and measure the effect it can get pretty complicated!

There is a lot of information ( , which means not a well controlled experiment) about how coffee grounds can be used as a or in composting in general, but , so it can be hard to separate out the effects of just the . I actually compost my coffee grounds, but they get mixed in with a lot of other plant matter.

itself is pretty interesting - when it's leaves drop onto the soil, they can - but You might be aware that our local pepper trees have this same capability by the way!

Luckily, with regard to your question, you can use purified caffeine and apply it to plants , everything else being equal. Scientists have done this experiment, monitoring mainly germination (sprouting from the seed) and growth (cell division) at the root tips. They do a test using various concentrations of caffeine and also "mock" (control) treatments for comparison. But, exact results depend on the plant species, how and when the caffeine is applied, and for how long.

Caffeine affects adventitious rooting and causes biochemical changes in the hypocotyl cuttings of mung bean (Phaseolus aureus Roxb.)

• Acta Physiologiae Plantarum 30(3):401-405
• 30(3):401-405

• Asian Institute of Technology

• Panjab University
• This person is not on ResearchGate, or hasn't claimed this research yet.

• Amity University Punjab Mohali

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• v.113(38); 2016 Sep 20

Convergent evolution of caffeine in plants by co-option of exapted ancestral enzymes

Ruiqi huang.

a Department of Biological Sciences, Western Michigan University, Kalamazoo, MI, 49008

Andrew J. O’Donnell

Jessica j. barboline, todd j. barkman.

Author contributions: R.H., A.J.O., J.J.B., and T.J.B. designed research; R.H., A.J.O., and J.J.B. performed research; R.H., A.J.O., J.J.B., and T.J.B. analyzed data; and T.J.B. wrote the paper.

Significance

Convergent evolution is responsible for generating similar traits in unrelated organisms, such as wings that allow flight in birds and bats. In plants, one of the most prominent examples of convergence is that of caffeine production, which has independently evolved in numerous species. In this study, we reveal that even though the caffeine molecule is identical in the cacao, citrus, guaraná, coffee, and tea lineages, it is produced by different, previously unknown, biosynthetic pathways. Furthermore, by resurrecting extinct enzymes that ancient plants once possessed, we show that the novel pathways would have evolved rapidly because the ancestral enzymes were co-opted from previous biochemical roles to those of caffeine biosynthesis for which they were already primed.

Convergent evolution is a process that has occurred throughout the tree of life, but the historical genetic and biochemical context promoting the repeated independent origins of a trait is rarely understood. The well-known stimulant caffeine, and its xanthine alkaloid precursors, has evolved multiple times in flowering plant history for various roles in plant defense and pollination. We have shown that convergent caffeine production, surprisingly, has evolved by two previously unknown biochemical pathways in chocolate, citrus, and guaraná plants using either caffeine synthase- or xanthine methyltransferase-like enzymes. However, the pathway and enzyme lineage used by any given plant species is not predictable from phylogenetic relatedness alone. Ancestral sequence resurrection reveals that this convergence was facilitated by co-option of genes maintained over 100 million y for alternative biochemical roles. The ancient enzymes of the Citrus lineage were exapted for reactions currently used for various steps of caffeine biosynthesis and required very few mutations to acquire modern-day enzymatic characteristics, allowing for the evolution of a complete pathway. Future studies aimed at manipulating caffeine content of plants will require the use of different approaches given the metabolic and genetic diversity revealed by this study.

Convergent evolution has resulted in the independent origins of many traits dispersed throughout the tree of life. Whereas some convergent traits are known to be generated via similar developmental or biochemical pathways, others arise from different paths ( 1 – 5 ). Likewise, similar (orthologous) or different (paralogous or even unrelated) genes may encode for the regulatory or structural proteins composing the components of pathways that build convergent traits ( 6 – 10 ). One of the most prominent examples of convergence in plants is that of caffeine biosynthesis, which appears to have evolved at least five times during flowering plant history ( 11 ). The phylogenetic distribution of caffeine, or xanthine alkaloids more generally, is highly sporadic and usually restricted to only a few species within a given genus ( 12 , 13 ). Caffeine accumulates in various tissues, where it may deter herbivory ( 14 , 15 ) or enhance pollinator memory ( 16 ). Numerous studies over the past 30 y have indicated that although several possible routes exist, the same canonical pathway to caffeine biosynthesis has evolved independently in Coffea (coffee) and Camellia (tea) involving three methylation reactions to sequentially convert xanthosine to 7-methylxanthine to theobromine to caffeine ( Fig. 1 ) ( 17 , 18 ). In Coffea , three xanthine methyltransferase (XMT)-type enzymes from the SABATH (salicylic acid, benzoic acid, theobromine methyltransferase) family ( 19 ) are used to catalyze the methylation steps of the pathway, whereas Camellia uses a paralogous, convergently evolved caffeine synthase (CS)-type enzyme ( 20 – 22 ) ( Fig. 1 ). Because most SABATH enzymes catalyze the methylation of oxygen atoms of a wide diversity of carboxylic acids such as anthranilic, benzoic, gibberellic, jasmonic, loganic, salicylic, and indole-3-acetic acid for floral scent, defense, and hormone modulation ( 23 – 25 ), methylation of xanthine alkaloid nitrogen atoms by XMT and CS is likely a recently evolved activity.

Caffeine biosynthetic network has 12 potential paths. The only path characterized from plants is shown by solid black arrows and involves sequential methylation of xanthosine at N-7, 7-methylxanthine at N-3, and theobromine at N-1 of the heterocyclic ring. Each methylation step is performed by a separate xanthine alkaloid methyltransferase in Coffea . In contrast, Camellia employs the distantly related caffeine synthase enzyme, TCS1, for both the second and third methylation steps, whereas the enzyme that catalyzes the first reaction remains uncharacterized. Other potential biochemical pathways to caffeine are shown by dashed arrows, but enzymes specialized for those conversions are unknown. Cleavage of ribose from 7-methylxanthosine is not shown, but may occur concomitantly with N-7 methylation of xanthosine. CF, caffeine; PX, paraxanthine; TB, theobromine; TP, theophylline; X, xanthine; 1X, 1-methylxanthine; 3X, 3-methylxanthine; 7X, 7-methylxanthine; XR, xanthosine.

Although convergence has been documented at multiple hierarchical levels, fundamental questions remain unanswered about the evolutionary gain of traits such as caffeine that are formed via a multistep pathway. First, although convergently co-opted genes, such as XMT or CS , may evolve to encode enzymes for the same biosynthetic pathway, it is unknown what ancestral functions they historically provided that allowed for their maintenance over millions of years of divergence. Second, it is unknown how multiple protein components are evolutionarily assembled into an ordered, functional pathway like that for caffeine biosynthesis. Under the cumulative hypothesis ( 26 ), it is predicted that enzymes catalyzing earlier reactions of a pathway must evolve first; otherwise, enzymes that perform later reactions would have no substrates with which to react. Subsequently, duplication of the gene encoding the first enzyme would give rise to enzymes catalyzing later steps. This hypothesis assumes that, initially, the intermediates in a pathway are advantageous, because it is unlikely that multiple enzymatic steps in a pathway could evolve simultaneously. Alternatively, the retrograde hypothesis ( 27 ) states that enzymes catalyzing reactions that occur at the end of a pathway evolved first. Gene duplication of the sequence encoding the first-evolved enzyme would eventually result in new enzymes that perform the preceding pathway steps. This hypothesis assumes that the intermediates of a given pathway would be produced nonenzymatically and be available for catalysis; as such, it may have less general explanatory application. Finally, the patchwork hypothesis ( 28 , 29 ) explains the origins of novel pathways by the recruitment of enzymes from alternative preexisting pathways. This hypothesis assumes that the older, recruited enzymes were ancestrally promiscuous with respect to the substrates catalyzed such that they were exapted for the activities that they later become specialized for in the novel pathway. Unlike the cumulative and retrograde hypotheses, there is no prediction for the relative ages of enzymes performing each step of the novel pathway under the patchwork hypothesis. The patchwork hypothesis is compatible with the innovation, amplification, and duplication model of protein functional change ( 30 ) and those that have emerged from protein engineering studies ( 31 ) in that promiscuous enzyme activities are nearly universal properties of modern-day enzymes and have been shown to serve as the basis for evolution of specialized, novel enzyme activities.

Here we report on a comparative molecular and biochemical approach that dissects how caffeine convergence occurred at the level of the genes involved and biochemical pathways catalyzed in the five economically important plants Theobroma (chocolate), Paullinia (guaraná), Citrus (orange), Camellia , and Coffea . We further use the Citrus lineage to test hypotheses related to the mechanisms allowing for the convergent evolution of caffeine by using paleomolecular biology coupled with experimental mutagenesis, thereby demonstrating how multistep pathways may independently evolve.

Results and Discussion

Novel biosynthetic pathways for caffeine in modern-day plants..

To uncover the genes and pathways used by modern-day plants to synthesize caffeine, bioinformatic and phylogenetic analyses were used to reveal that both Theobroma cacao (Tc) (Malvales) and Paullinia cupana (Pc) (Sapindales) express multiple CS -type sequences in their caffeinated leaves and/or fruits that are orthologous to those used by Camellia sinensis (Ericales) in leaves and shoots ( Fig. 2 A and Figs. S1 and ​ andS2) S2 ) ( 32 , 33 ). However, contrary to expectations from Camellia ( Fig. 1 ), heterologous expression and assays of the Theobroma and Paullinia CS enzymes indicate that they catalyze a different pathway to synthesize xanthine alkaloids. Specifically, both species possess one enzyme (CS1) that preferentially methylates xanthine to produce 3-methylxanthine, as well as a second enzyme (CS2) that preferentially methylates 3-methylxanthine to produce theobromine ( Fig. 2 B and C ). Surprisingly, even though the four enzymes are part of the CS lineage, TcCS1 is more closely related to TcCS2 rather than the enzymatically similar PcCS1, which, in turn, is more closely related to PcCS2 ( Fig. 2 B and C and Fig. S1 ). This pattern of relationships indicates that the relative methylation preferences of these enzymes, although similar, have convergently evolved in Theobroma and Paullinia , likely after gene duplication independently occurred in each lineage ( Fig. 2 B and C and Fig. S1 ). No enzymes have been previously reported to specialize in the methylation of xanthine or 3-methylxanthine, and the biochemical route to caffeine implied by these enzyme activities ( Fig. 2 B and C ) has not been implicated as the primary pathway in these or any other plants. However, there is evidence for this pathway in Theobroma fruits and leaves from metabolomic analyses and radiolabeled tracer studies that showed 3-methylxanthine as an intermediate formed during theobromine accumulation when xanthine or various purine bases and nucleosides are provided as substrates ( 34 , 35 ). Analyses of fruit and leaf extracts and isotope tracer studies also report the accumulation of 7-methylxanthine to a lesser extent ( 34 , 35 ). Our liquid chromatography-mass spectrometry (LC-MS) results indicate that this metabolite can be formed by methylation of xanthine by TcCS2 as a secondary activity ( Fig. 2 B and Fig. S3 ). Thus, in Theobroma , it is possible that theobromine is produced via methylation of this intermediate by BTS ( 36 ) and/or TcCS1 ( Fig. 2 B ), in addition to 3-methylxanthine by TcCS2 ( Fig. 2 B ). It is not yet clear which enzyme contributes to the low levels of caffeine accumulation in Theobroma , but this is not surprising, because its biosynthesis is reported to be very slow ( 35 ). In Paullinia , a third enzyme, PcCS, is reported to convert theobromine to caffeine ( 37 ) ( Fig. 2 C ). Theobromine is reported from Paullinia tissues ( 37 , 38 ), which is consistent with the pathway shown in Fig. 2 C , but no analyses have surveyed for the presence of intermediates such as 3-methylxanthine or others. For Camellia , bioinformatic and phylogenetic analyses show that TCS1 and TCS2 are expressed in leaves and recently duplicated ( Fig. 2 E and Figs. S1 and S2 C ) ( 36 ). Although the biochemical role of TCS1 is clear ( 39 ), an associated activity for TCS2 has remained elusive ( 36 ); however, we were able to demonstrate maximal methyl transfer activity at N-7 of xanthosine ( Fig. 2 E ), consistent with the reported pathway ( Fig. 1 ).

Caffeine has convergently evolved in five flowering plant species using different combinations of genes and pathways. ( A ) Phylogenetic relationships among orders of Rosids and Asterids show multiple origins of caffeine biosynthesis. Lime-green lineages trace the ancient CS lineage of enzymes that has been independently recruited for use in caffeine-accumulating tissues in Theobroma , Paullinia , and Camellia . Turquoise lineages trace the ancient XMT lineage that was independently recruited in Citrus and Coffea . ( B and C ) Theobroma and Paullinia have converged upon similar biosynthetic pathways catalyzed by CS-type enzymes. ( D ) Citrus has evolved a different pathway catalyzed by XMT-type enzymes, despite its close relationship to Paullinia . ( E and F ) Camellia and Coffea catalyze the same pathway using different enzymes. Proposed biochemical pathways are based on relative enzyme activities shown by corresponding bar charts that indicate mean relative activities (from 0 to 1) with eight xanthine alkaloid substrates. CisXMT1 and TCS1 catalyze more than one reaction in the proposed pathways. XMT and CS have recently and independently duplicated in each of the five lineages (see Fig. S1 for a detailed gene tree). # Data taken from the literature; *substrate not assayed.

Phylogenetic relationships among 356 SABATH protein sequences. Sequences were extracted from 11 complete genomes of land plants in addition to selected CS and XMT transcriptome sequences from the oneKP database. Lineages with functionally characterized sequences are labeled by enzyme name, whereas those without known functions are arbitrarily numbered from MT1 to MT6. Bootstrap support values are shown for selected nodes that define major enzyme lineages. Enzymes from Camellia (CS) or Coffea (XMT) known to be involved in caffeine biosynthesis are shown in lime-green and turquoise, respectively. Sequences expressed in Theobroma and Paullinia fruits are clearly orthologous to CS sequences from Camellia . Sequences expressed in Citrus flowers are clearly orthologous to XMT sequences of Coffea . Arrows point to CS and XMT lineages to show recent duplication events within Theobroma , Paullinia , Camellia , Citrus , and Coffea . Nodes for which ancestral resurrected proteins were studied are labeled A–C. Accession numbers for the oneKP and GenBank databases are shown before and after relevant sequences, respectively.

( A ) The closely related TcCS1 and TcCS2 of Theobroma cacao are highly represented in fruits where theobromine and caffeine accumulate. EST counts in various tissues are shown for all full-length SABATH genes from the genome of T. cacao (Matina). Relationships among the sequences are shown below the chart. GenBank accession numbers are as follows: IAMT (Thecc1EG030787), FAMTa (Thecc1EG019318), FAMTb (Thecc1EG019315), FAMTc (Thecc1EG019314), MT4a (Thecc1EG011287), MT4b (Thecc1EG011286), MT4c (Thecc1EG011290), MT4d (Thecc1EG011291), MT1a (Thecc1EG045368), MT1b (Thecc1EG045372), MT1c (Thecc1EG045370), MT5a (Thecc1EG012604), MT5b (Thecc1EG031006), BAMTa (Thecc1EG000331), BAMTb (Thecc1EG000328), BAMTc (Thecc1EG040854), XMT (Thecc1EG006850), MT3 (Thecc1EG000336), JMTa (Thecc1EG034091), JMTb (Thecc1EG034089), SAMTa (Thecc1EG000326), SAMTb (Thecc1EG000324), BTS (Thecc1EG042576), TcCS1 (Thecc1EG042578), and TcCS2 (Thecc1EG042587, Thecc1EG042590). The two accession numbers for TcCS2 are indistinguishable in the ORFs and are therefore classified together in this chart. ( B ) The closely related PcCS1 to PcCS5 are highly represented in fruits of Paullinia cupana var. sorbilis , where caffeine accumulates. EST counts in fruit tissues are shown for all full-length SABATH genes. SABATH sequences from the Citrus genome were used to BLAST for ESTs from Paullinia , because a genome is not yet characterized for it. Relationships among the sequences are shown below the chart. Only CS-type sequences were found from Paullinia ESTs. GenBank accession numbers are as follows: GAMT ( Citrus : 1g014333m), IAMT ( Citrus : 1g016644m), FAMT ( Citrus : 1g017702m), MT4 ( Citrus : 1g040129m), MT2 ( Citrus : 1g018119m), XMT ( Citrus : 1g044727m), SAMT ( Citrus : 1g017514m), JMT ( Citrus : XM_006478399), MT3 ( Citrus : 1g017747m), PcCS3 (EC763988, EC777706, EC777629, EC777248, EC774687, EC768101, EC769415, EC769184, EC769690, EC773071, EC764367), PcCS1 ( 59 ), PcCS4 (EC765512, EC766603, EC766748, EC777652, EC769966, EC775308, EC766876), PcCS5 (EC765614, EC764433, EC776114, EC764220, EC778019, EC768317, EC764348, EC774623, EC765624, EC772805), and PcCS2 (EC774880, EC768886, EC775438, EC772015, EC764433, EC771794, EC774462, EC772993, EC770731, EC773205, EC765633, EC764023, 776855, EC767182, EC770596). Enzyme activities of PcCS 1 and 4 were highly comparable, as were PcCS2 and 5, so we only present data for one of each in the manuscript. PcCS3 had no detectable activity with any xanthine alkaloid substrate tested. ( C ) The closely related TCS1 and TCS2 of Camellia sinensis are highly represented in leaves and buds where caffeine accumulates. EST counts in various tissues are shown for representatives of each SABATH gene lineage. Full-length genes from each major lineage of the SABATH family in the Mimulus genome were used to BLAST for ESTs from Camellia , because its genome is not yet characterized. Relationships among the sequences are shown below the chart. Sequences used for BLAST/phylogenetic analysis are as follows: Mimulus IAMT (Phytozome accession no. M01704), Camellia sinensis MT4 (GenBank accession no. GB-GBBZ01008401), Mimulus MT1 (Phytozome accession no. H02148), Mimulus FAMT (Phytozome accession no. H00254), Camellia sinensis SAMT (GenBank accession no. KA284044), Camellia sinensis JMT (GenBank accession no. KA286401), Camellia sinensis MT3 (GenBank accession no. FS950428), Mimulus BAMT (Phytozome accession no. N01694), Camellia sinensis TCS2 (GenBank accession no. AB031281), and Camellia sinensis TCS1 (GenBank accession no. AB031280). No XMT ortholog is known from Mimulus or Ericales.

TcCS2 converts xanthine to 7-methylxanthine. Eight mass spectrometry scans show parent ion–fragment ion pairs that are largely unique for each xanthine alkaloid product detected in enzyme assays supplied with 100 µM xanthine. Scans for authentic standards are shown below to verify product identity.

In contrast, bioinformatic and phylogenetic analyses revealed that Citrus sinensis (Cis) (Sapindales) expresses two recently duplicated XMT -type enzymes in caffeinated flowers orthologous to those found in Coffea arabica (Gentianales) tissues ( Fig. 2 A and Figs. S1 and S4 A ). Surprisingly, assays of the Citrus XMT enzymes imply yet another pathway, different from that catalyzed by Coffea XMT enzymes ( Fig. 1 ), which has led to the convergent evolution of caffeine. Specifically, CisXMT1 not only methylates xanthine to produce 1- and 3-methylxanthine; it also methylates both 1- and 3-methylxanthine to produce theophylline ( Fig. 2 D and Fig. S5 ). A second enzyme, CisXMT2, preferentially methylates theophylline to produce caffeine ( Fig. 2 D ). LC-MS analyses of flower buds ( Fig. S6 ) and assays of crude enzyme extracts from Citrus x limon stamens ( 40 ) are consistent with the pathway shown in Fig. 2 D . Furthermore, in Citrus , theophylline is a conspicuous metabolite in developing flower buds that accumulates early and decreases in concentration as caffeine levels increase ( 41 ), suggesting that it is involved in the accumulation of caffeine. On the other hand, theobromine, the long-assumed universal precursor to caffeine in plants ( 11 ), is undetectable or present only at low levels in developing buds ( 41 ). These findings are particularly intriguing, given that no enzymes have been previously reported to be specialized for these methylation reactions to form theophylline or caffeine and because theophylline is usually considered a degradation product of caffeine ( 42 , 43 ). We expected Citrus to use the same gene family members and pathway as Paullinia because both are members of Sapindales ( Fig. 2 A ). However, we could neither detect in vitro activity with xanthine alkaloid substrates by the single Citrus CS-type enzyme ( Fig. S1 ) nor is it represented by ESTs in flowers ( Fig. S4 A ), the principal site of caffeine accumulation ( 41 ).

( A ) The closely related CisXMT1 and CisXMT2 of Citrus sinensis are highly represented in flowers where caffeine accumulates. EST counts in various tissues are shown for all full-length SABATH genes from the genome of Citrus sinensis . Relationships among the sequences are shown below the chart. GenBank accession numbers are as follows: GAMT (1g014333m), IAMT (1g016644m), MT4a (1g040129m), MT4b (1g044174m), MT2 (1g018119m), FAMTa (1g017702m), FAMTb (1g037735m), FAMTc (1g018250m), FAMTd (1g017363m), FAMTe (017439m), CS (1g18139m), MT3 (1g017747m), CisXMT5 (1g036911m), CisXMT4 (XM_006469416), CisXMT3 (1g045960m), CisXMT2 (1g047625m), CisXMT1 (1g044727m), JMT (XM_006478399), and SAMT (1g017514m). ( B ) Relative enzyme activity profile for SAMT from Citrus sinensis . Bar charts show relative enzyme activities (from 0 to 1) for three substrates. AA, anthranilic acid; BA, benzoic acid; SA, salicylic acid.

Mass spectrometry scans show that Citrus CisXMT1 can form 1-methylxanthine, 3-methylxanthine, and theophylline from xanthine alone. Citrus CisXMT1 converts xanthine to both 1-methylxanthine and 3-methylxanthine, as indicated by the presence of parent ion–fragment ion peaks unique for both compounds. The presence of the theophylline peak likely is a result of methylation of both 1-methylxanthine and 3-methylxanthine, although which may be preferred cannot be discerned from these traces alone. However, it should be noted that the catalytic efficiency by which 3-methylxanthine is converted to theophylline is ca. four times lower than that of 1-methylxanthine ( Table S1 ). Scans for authentic standards are shown below to confirm product identities.

Citrus flower buds appear to accumulate xanthine, 1-methylxanthine, theophylline, and caffeine as well as minor amounts of 3-methylxanthine. Mass spectrometry scans show parent ion–fragment ion pairs unique for each xanthine alkaloid product detected in Citrus flower buds. The presence of these metabolites is consistent with the enzyme assays ( Fig. 2 D and Fig. S5 ) and suggests that the primary pathways by which caffeine is produced in Citrus flowers are those shown in Fig. 2 D . Table S2 shows parent ion–fragment ion masses for xanthine alkaloid standards.

For more than 30 y, published studies have indicated that caffeine is produced via a single canonical pathway in plants ( 44 , 45 ). Our results show that flowering plants have a much broader biochemical repertoire whereby at least three pathways lead to caffeine biosynthesis catalyzed by enzymes that derive from one of two methyltransferase lineages. These enzymes have substrate affinities (as measured by K M ) comparable to XMT and CS in Coffea and Camellia , respectively, as well as those of other SABATH family members, which are in the 10–1,000 µM range ( 21 , 23 , 46 , 47 ) ( Table S1 ). Additionally, although xanthine alkaloids may not be homogeneously distributed at the subcellular and tissue level ( 48 ), reported concentrations of the relevant intermediates in Theobroma , Paullinia , and Citrus may be conservatively estimated to be in the 10–1,000 µM range ( 34 , 38 , 41 ), which are comparable to the K M values we obtained ( Table S1 ). Although it is apparent that three analogous pathways for caffeine biosynthesis have evolved in flowering plants, it is unclear what historical genetic and biochemical conditions facilitated this convergence.

Enzyme kinetic parameter estimates for modern-day and ancestral xanthine alkaloid-producing enzymes with selected substrates

Historical Maintenance of Ancestral XMT Enzymes Allowed for Convergence.

In the case of convergent caffeine production in Citrus and Coffea , XMT needed to be maintained for more than 100 My from their common ancestor ( 49 ) to then independently give rise to xanthine alkaloid-methylating enzymes, because it is unlikely that their progenitor was producing caffeine given that they currently use completely different biosynthetic pathways ( Fig. 2 D and F ). To understand how this long-term maintenance occurred, we resurrected ancestral enzymes ( 50 ) for the XMT lineage at nodes A–C ( Fig. 3 and Fig. S1 ). Surprisingly, both the putative ca. 100-My-old Rosid-Asterid ancestral enzyme, RAAncXMT (node A), as well as its descendant, CisAncXMT1 (node B), exhibit high relative activity with benzoic acid and salicylic acid (to form methyl benzoate and methyl salicylate, respectively) but very little with xanthine alkaloids ( Fig. 3 ). Ancestral O-methylation of benzoic acid is maintained in a modern-day XMT from Mangifera , which is a relative of Citrus in Sapindales but is not known to synthesize xanthine alkaloids ( Fig. 3 and Fig. S1 ). On the other hand, ancestral activities with benzoic and salicylic acid were completely lost in the modern-day descendant enzymes of Citrus , CisXMT1 and CisXMT2, and they now appear specialized for only N-methylation of xanthine alkaloids ( Fig. 3 ). These specialized modern-day enzymes were most recently derived from CisAncXMT2 (node C), which exhibits both O- and N-methylation activities and seems to be a transitional enzyme associated with the gain of xanthine alkaloid production ( Fig. 3 ). Although CisAncXMT2 appears to have low levels of activity with benzoic and salicylic acid compared with that of 1-methylxanthine, the specific activity with these substrates (0.6 and 2.3 pkat/mg, respectively) is comparable to heterologously expressed, modern-day SAMT- and BSMT-type enzymes ( 51 ). Today, modern-day Citrus possesses an SAMT that is capable of methylating both benzoic and salicylic acid, thereby compensating for the eventual loss of those activities from ancestral XMT enzymes ( Fig. S4 B ). Ancestral O-methylation of the carboxyl moiety of benzoic and salicylic acid might have promoted the evolution of N-methylation of xanthine alkaloids because of common attributes of the active sites that would need to accommodate the largely planar rings of both classes of substrates. Indeed, a paralogous SABATH methyltransferase specialized for methylation of the carboxyl group of nicotinic acid (which is also an N-heterocyclic substrate) also recently arose from ancestral enzymes that exhibited activities with benzoic and salicylic acid ( 46 ). Although these data indicate that ancestral activity with benzoic and salicylic acid in the XMT lineage allowed for subsequent co-option of the descendant enzymes to form caffeine, how recruitment of the enzymes into a functional pathway occurred remains unknown.

Resurrected ancestral XMT proteins reveal the historical context for convergent evolution of caffeine biosynthesis. Bar charts show mean relative enzyme activities (from 0 to 1) for 10 substrates. BA, benzoic acid; SA, salicylic acid; all others are as in Fig. 1 . Node A shows the resurrected enzyme of the >100-My-old ancestor of Rosids and Asterids that exhibits high relative activity with benzoic and salicylic acid. Although those ancestral activities were maintained in CisAncXMT1 at node B and modern-day Mangifera , they were eventually replaced by increased relative preference for xanthine alkaloid methylation as seen at node C and its descendants. CisAncXMT2 mutants (P25S and H150N indicated on lineages C′ and C′′, respectively) show that very few amino acid replacements are necessary to re-evolve modern-day enzyme activity patterns and form a complete caffeine biosynthetic pathway. Product formation from assays and implied pathway connections are shown by color-coded dots ( Insets ). For example, a connection between TP (green) and CF (black) implies that the enzyme in question converts theophylline to caffeine. Unshaded rectangles exhibit complete metabolic connections to caffeine, whereas shaded rectangles do not. Average site-specific posterior probabilities are shown for each resurrected ancestral enzyme. Select substrate structures are shown to specify the atom to which a methyl group is transferred.

Exaptation Facilitates Multistep Pathway Evolution in a Cumulative Manner.

To understand how convergent caffeine production evolved via an entirely novel pathway in the Citrus lineage, we mapped ancestral XMT pathway connections of the caffeine biosynthetic network ( Fig. 3 , Insets , dot boxes). At nodes A and B, ancestral enzymes exhibited very low activity with xanthine alkaloids, such that quantities were too low to allow for product identification by HPLC, making it unlikely that a complete pathway existed at those times. Subsequently, the derived ancestral Citrus enzyme, CisAncXMT2 (node C), had activity with numerous xanthine alkaloids; in particular, highest relative activity with 1-methylxanthine resulted in paraxanthine formation, and 3-methylxanthine was methylated to form theophylline ( Fig. S7 A and B ). CisAncXMT2 could also convert theophylline to caffeine, such that it would have performed two of the three steps necessary to form caffeine from 3-methylxanthine ( Fig. S7 C ). However, this ancestral enzyme exhibits only a low level of activity and specificity with xanthine to form 1-methylxanthine ( Fig. 3 , Fig. S8 , and Table S1 ), which could have subsequently been converted to paraxanthine by CisAncXMT2, but not caffeine ( Fig. 3 , node C). Thus, if CisAncXMT2 was used for methylation of benzoic and salicylic acid, then it appears to have been exapted for several later reactions of the xanthine alkaloid biosynthetic network used by modern-day Citrus , because a complete pathway to caffeine was not likely catalyzed by this enzyme alone. Alternatively, it remains formally possible that the ancestor of Citrus possessed a different, now extinct, enzyme that could have converted xanthine to 3-methylxanthine, so that caffeine may have been produced by CisAncXMT2, yet only modern-day XMT- and CS-type enzymes ( Fig. 2 ) are capable of that conversion, making it unclear why an enzyme specialized for that reaction would subsequently be lost given its importance today.

( A ) CisAncXMT2 converts 1-methylxanthine to paraxanthine. Mass spectrometry scans show parent ion–fragment ion pairs largely unique for each xanthine alkaloid product detected in enzyme assays supplied with 100 µM 1-methylxanthine. Scans for authentic standards are shown below to confirm product identity. ( B ) CisAncXMT2 converts 3-methylxanthine to theophylline. Mass spectrometry scans show parent ion–fragment ion pairs largely unique for each xanthine alkaloid product detected in enzyme assays supplied with 100 µM 3-methylxanthine. Scans for authentic standards are shown below to confirm product identity. ( C ) CisAncXMT2 converts theophylline to caffeine. Mass spectrometry scans show parent ion–fragment ion pairs largely unique for each xanthine alkaloid product detected in enzyme assays supplied with 100 µM theophylline. Scans for authentic standards are shown below to confirm product identity.

HPLC analyses show the evolution of product formation in ancestral and modern-day Citrus XMT enzymes. CisAncXMT2 converts xanthine to 1-methylxanthine but not 3-methylxanthine ( Insets ). In contrast, CisAncXMT2 P25S and CisXMT1 methylate xanthine to both 1-methylxanthine and 3-methylxanthine, which are further converted into theophylline. Enzyme assays were conducted using 2 mM xanthine, and products were measured by absorbance at 272 nm. mAU, milliabsorption units.

Next, to recapitulate the evolutionary steps required to generate complete caffeine biosynthetic pathway linkages, we performed experimental mutagenesis of CisAncXMT2 (node C), which was duplicated to give rise to the two modern-day enzymes of Citrus , CisXMT1 and CisXMT2. In the lineage leading to CisXMT1, 17 amino acids were replaced and resulted in the evolution of increased activity with xanthine as well as specialization with 1- and 3-methylxanthine ( Fig. 3 ). We experimentally replaced Pro25 by Ser in CisAncXMT2 ( Fig. 3 ; lineage C′), because this site is predicted to be part of the active site of Coffea DXMT ( 52 ) and differs in CisXMT1 and CisXMT2 ( Fig. S9 ). This single mutation resulted in the evolution of three important biochemical changes. First, near-complete loss of ancestral activity with theophylline as well as with benzoic and salicylic acid occurred such that CisAncXMT2 P25S acquired a relative activity profile very similar to modern-day CisXMT1 ( Fig. 3 ). Second, CisAncXMT2 P25S exhibited a 2.5-fold increased catalytic efficiency with xanthine compared with CisAncXMT2 ( Table S1 ) to produce both 1- and 3-methylxanthine ( Fig. 3 and Fig. S8 ). Third, activity of CisAncXMT2 P25S changed such that 1-methylxanthine is methylated to form theophylline instead of paraxanthine ( Fig. S10 ). The importance of this single amino acid replacement is that a connected biosynthetic network from xanthine to theophylline via both 1- and 3-methylxanthine would have rapidly evolved, in part, due to exaptation of CisAncXMT2 ( Fig. 3 ). The existence of exapted ancestral enzymes such as CisAncXMT2 resolves one of the fundamental problems of the cumulative hypothesis because multiple steps of a pathway could evolve simultaneously, thereby avoiding the need to assume the existence of selectively advantageous intermediates. These results also point to a crucial role for ancestral promiscuous activities postulated as part of the patchwork hypothesis and protein engineering studies ( 31 ) and reported previously for other SABATH enzymes ( 46 ).

Aligned protein sequences show that very few sites differ between ancestral and modern-day enzymes and that mutations at very few sites account for functional change. ( A ) Amino acid alignment of functionally characterized modern-day XMT and CS sequences. Also shown are resurrected ancestral XMT protein sequences. Amino acids that were experimentally replaced to recapitulate evolutionary changes in the Citrus lineage are highlighted in turquoise. Alternative ancestral alleles were generated by mutations shown in green. ( B ) Posterior probabilities of original and mutated sites are shown for the four alternative ancestral alleles generated and assayed. ( C ) Bar charts show relative enzyme activities of additional site-directed mutants made to recapitulate evolutionary changes from CisAncXMT2 to either CisXMT1 or CisXMT2. Mutated amino acid positions are highlighted in magenta.

HPLC analyses show that CisAncXMT2 P25S converts 1-methylxanthine to theophylline instead of paraxanthine, like its ancestor, CisAncXMT2. Enzyme assays were conducted using 200 µM 1-methylxanthine, and products were measured by absorbance at 272 nm. Unlabeled peaks in Middle and Lower are unidentified molecules found in both the enzyme assay and negative control to which no xanthine alkaloids were added.

Finally, although the immediate, postduplication daughter enzyme of lineage C′′ would have initially retained the ancestral activities of it progenitor, CisAncXMT2, it eventually gave rise to the modern-day descendant CisXMT2, which exhibits near-complete specialization with theophylline ( Fig. 3 ). A total of 16 amino acid replacements occurred along this lineage, one of which was His150, which was replaced by Asn ( Fig. S9 ). This residue is likely part of the active site and known to control substrate preference in other SABATH enzymes ( 46 ). Experimental mutagenesis of His150 to Asn in CisAncXMT2 ( Fig. 3 , node C′′) resulted in an enzyme that is similar to modern-day CisXMT2. Specifically, H150N nearly completely abolished methylation activity with every substrate except theophylline and, to a lesser extent, 1-methylxanthine ( Fig. 3 ). Because methylation of theophylline results in the formation of caffeine, the combined activities of CisAncXMT2 H150N and CisAncXMT2 P25S would have allowed for a complete caffeine biosynthetic pathway given xanthine as a starting substrate, much like the two modern-day XMT enzymes in Citrus . Although mutations other than H150N and P25S might have shifted ancestral enzymes toward the modern-day specialized activities of CisXMT1 and CisXMT2, we show that only these two replacements need to be implicated in specialization, because other mutations do not recapitulate the inferred relative activity changes ( Fig. S9 C ).

The results for the XMT lineage indicate that convergent evolution of caffeine biosynthesis was possible partly because ancient lineages of enzymes were maintained over 100 My for alternative biochemical functions. Furthermore, like the fortuitous roles of feathers for flight in birds ( 53 ) or ligand binding in ancestral hormone receptors ( 54 ), it appears that exapted activities of the ancestral XMT enzymes ultimately promoted their co-option for caffeine biosynthesis. These exaptations became biochemically relevant when, as predicted under the cumulative hypothesis ( 26 ), the initial reactions of the caffeine pathway evolved. The fact that very few substitutions to CisAncXMT2 were required to promote substrate preference switches suggests relatively facile mutational basis for the evolution of caffeine biosynthetic pathways. Therefore, it is likely that caffeine biosynthesis would evolve in flowering plants again if the evolutionary tape of life were to be replayed ( 55 ). What is more difficult to predict is which of the 12 potential biochemical pathways any particular lineage will use, which methyltransferase enzyme will be co-opted, or which amino acids will be substituted to provide for particular substrate preferences due to the role of historical contingency associated with any given evolutionary transition.

Materials and Methods

Heterologous expression and purification of enzymes..

Gene sequences for Theobroma and Paullinia were synthesized with codon use optimized for gene expression in Escherichia coli (GenScript). Gene sequences for Citrus and Camellia were cloned from fresh flowers or leaves, respectively, using primers designed from the EST and genomic sequences. cDNA was generated using the SuperScript II/Platinum Taq One-Step RT-PCR Kit (Invitrogen). Protein overexpression used either pET-15b (Novagen) or Expresso T7 SUMO (Lucigen) expression vectors, and induction of His 6 –protein was achieved in 50-mL BL-21(DE3) cell cultures with the addition of 1 mM isopropyl β- d -1-thiogalactopyranoside at 23 °C for 6 h. Purification of the His 6 -tagged protein was achieved by TALON spin columns (Clontech) according to the manufacturer’s instructions. To determine protein concentration, a standard Bradford assay was used. Recombinant protein purity was evaluated by SDS/PAGE.

Enzyme Assays.

All enzymes were tested for activity with the eight xanthine alkaloid substrates shown in Fig. 1 . In addition, all enzymes were tested with benzoic and salicylic acid, but we only report results for XMT enzymes, as shown in Fig. 3 , because CS enzymes do not show activity with those substrates. Xanthine alkaloid substrates were dissolved in 0.5 M NaOH, whereas benzoic and salicylic acid were in ethanol. Radiochemical assays were performed in 50-μL reactions with 0.01 μCi (0.5 μL) 14 C-labeled SAM, 100 μM methyl acceptor substrate, and 10–20 μL purified protein in 50 mM Tris⋅HCl buffer at 24 °C for 20 min. Negative controls were composed of the same reagents except that the methyl acceptor substrate was omitted and the corresponding solvent was added instead. Methylated products were extracted in ethyl acetate and quantified using a liquid scintillation counter. Raw disintegrations per min obtained from the scintillation counter were corrected using empirically determined extraction efficiencies of products in ethyl acetate. The highest enzyme activity reached with a specific substrate was set to 1.0, and relative activities with the remaining substrates were calculated. Each assay was run at least twice so that mean plus SD, could be calculated. All assays shown were performed on purified protein unless activity was abolished after purification. In such cases (only CisXMT2 and TCS2), we present total protein data. The specific activity for TCS2 with xanthosine was 0.025 pkat/mg, and the specific activity for CisXMT2 with theophylline was 0.12 pkat/mg.

Ancestral Sequence Resurrection and Mutagenesis.

CODEML ( 56 ) was used to estimate ancestral sequences for the XMT lineage of enzymes of the SABATH family assuming the Jones, Taylor, Thornton (JTT) + gamma model of amino acid substitution. Regions with alignment gaps were analyzed with parsimony to determine ancestral residue numbers. The estimated sequences were subsequently synthesized by GenScript with codons chosen for optimal protein expression in E. coli . Alternative ancestral alleles were generated by site-directed mutagenesis using the QuikChange Site-Directed Mutagenesis Kit (Stratagene) to change amino acids that differed among analyses using different subsets of sequences, trees, and models of substitution. Although posterior probabilities were high for most sites of most alleles (see average site-specific posterior probabilities in Fig. 3 ), different analyses did result in different estimated ancestral alleles in some cases. Therefore, at least two ancestral enzymes were characterized for each node A–C in Fig. 3 , and an alignment showing each resurrected allele is provided in Fig. S9 A . Amino acid sites differing between alternate alleles are shown in Fig. S9 B , with posterior probabilities listed for each amino acid that was mutated. The positions of mutations are shown in the alignment of Fig. S9 A . Because assays at each node were represented by at least two alleles, mean and SE were calculated for relative activity with each substrate assayed.

Detailed procedures for bioinformatic, phylogenetic, enzyme kinetics, HPLC, and LC-MS/MS analyses are provided in SI Materials and Methods . See Table S2 for MS/MS parameters and LC retention time for target xanthine alkaloids.

MS/MS parameters and LC retention time for target xanthine alkaloids

SI Materials and Methods

Bioinformatics..

For Citrus and Theobroma , we obtained all SABATH gene family members from their respective genomic sequences and used these for BLAST analyses of the EST database of GenBank. Only full-length SABATH genes predicted to encode more than 300 amino acids were included. All ESTs were assembled according to the reference sequence using Sequencher (Gene Codes), and the number of positive EST matches was tallied and the tissue origin was recorded. For Theobroma , only two genes were represented by >5 ESTs in caffeinated cocoa seeds/fruits relative to all other 23 SABATH family members: TcCs1 (Thecc1EG042578) (30 ESTs) and TcCS2 (Thecc1EG042587 and Thecc1EG04290) (>120 ESTs) ( Fig. S2 A ). Thecc1EG042587 and Thecc1EG04290 were tallied together because they encode identical ORFs. One gene, TcBTS, was previously isolated from leaves and reported to have activity with 7-methylxanthine ( 36 ). This gene is represented by <5 ESTs in fruits and its K M for that substrate is high (2 mM) ( 36 ). Thus, although it may participate in xanthine alkaloid production in leaves, any role in fruits must be relatively minor given the kinetics of the enzyme, particularly relative to those of TcCS1 and TcCS2 ( Table S1 ). In Citrus ( 57 ), three SABATH genes are represented by more than five ESTs in flowers, which accumulate both caffeine and theophylline ( Fig. S4 A ). Of these, one of the most abundant sets of ESTs represents an SAMT (1g017514m) that we have experimentally investigated for enzyme activity. It does not methylate any of the xanthine alkaloids at detectable levels; instead, it prefers to methylate salicylic, benzoic, and anthranilic acid ( Fig. S4 B ), the latter of which correlates with the presence of methylanthranilate in the flowers of Citrus ( 58 ). Because the in vitro activity of this enzyme appears to be irrelevant for xanthine alkaloid methylation, we do not discuss it further. CisXMT1 (1g044727m) was represented by >30 ESTs, whereas CisXMT2 (1g047625m) was represented by 7 ESTs ( Fig. S4 A ).

Because no genomic sequences exist for Camellia and Paullinia , we used a different approach to survey the EST sets. First, we chose full-length SABATH family sequences from an Asterid, Mimulus , to query Camellia ESTs and a Rosid, Citrus , to query Paullinia ESTs ( 32 ). The genomic reference sequences were then used for BLAST analyses of the EST database of GenBank. All Camellia and Paullinia matches were assembled using Sequencher, and phylogenetic analyses were performed to verify the orthology of each putative EST assembly to the query sequence. After orthology was established, EST number and tissue type were recorded. For Camellia , only two genes were represented by any ESTs in leaves and leaf buds, where caffeine accumulates ( Fig. S2 C ). These sequences correspond to the previously studied CS sequences TCS1 (AB031280) (9 ESTs) and TCS2 (AB031281) (12 ESTs) ( 36 ). For Paullinia , ESTs were assembled and hypothesized to represent the same gene if they possessed 98% identity over a 100-bp window of the coding sequence. Out of 105 ESTs, five contigs were assembled. These five putative genes were the only SABATH sequences represented by any ESTs, and all were expressed in fruits/seeds where caffeine is known to accumulate ( Fig. S2 B ). One of the contigs contained 18 ESTs and putatively represents the full-length CS sequence, grn006 (which we refer to as PcCS1), that was reported previously but not experimentally characterized ( 59 ). The contig with the highest coverage is referred to as PcCS2 (30 ESTs). One contig, PcCS3, was represented by 15 ESTs, but we could not demonstrate activity with any substrate. Therefore, it is not discussed further in the paper. Two other contigs, PcCS4 (27 ESTs) and PcCS5 (15 ESTs), are minor sequence variants of PcCS1 and PcCS2, respectively, and showed the same in vitro enzyme activities as they did and therefore are not discussed further. It should be noted that the recently reported PcCS ( Fig. 2 C ) that was reported to methylate theobromine to produce caffeine ( 37 ) was most similar to ESTs making up PcCS1 and PcCS4, but there were numerous differences in the 3′ end of the gene that did not match any ESTs in GenBank.

Estimation of Michaelis–Menten Parameters.

For kinetic measurements ( Table S1 ), enzyme assays were performed by varying methyl acceptor substrate concentrations while SAM was held at saturating levels. All kinetic studies were performed in two independent experiments with incubation times chosen so that reaction velocity was linear and less than 10% substrate depletion occurred. Initial velocities versus substrate concentrations were plotted using GraphPad Prism to fit the hyperbolic Michaelis–Menten equation for the calculation of V max and K M . V max was then converted to apparent k cat and expressed in units of s −1 .

High-Performance Liquid Chromatography.

Product identity was determined using HPLC on 500-μL scaled-up reactions using all of the same reagents as described above except that nonradioactive SAM was used as the methyl donor and reactions were allowed to progress for 2 h. Whole reactions were filtered through Vivaspin columns (Sartorius) to remove all protein before injection onto the HPLC. Mixtures were separated by HPLC using a two-solvent system with a 250 mm × 4.6 mm Kinetex 5 μm EVO C18 column (Phenomenex). Solvent A was 99.9% (vol/vol) water with 0.1% TFA and solvent B was 80% (vol/vol) acetonitrile, 19.9% (vol/vol) water, and 0.1% TFA, and a 0–10% gradient was generated over 16 min with a flow rate of 1.0 mL/min. Subsequently, buffer B was increased to 100% over 4 min and then held at that percentage for 20 min. Equilibration back to 0% buffer B was achieved over a 20-min period. Product identity was determined by comparing retention times and absorbance at 254 and 272 nm of authentic standards. All reactions were compared with negative controls in which no methyl acceptor substrates were added.

Liquid Chromatography–Tandem Mass Spectrometry.

Enzyme assays were analyzed by LC-MS tandem mass spectrometry to confirm product identity based on UV absorbance. LC-MS/MS analysis was performed using an Agilent 1100 HPLC inline with a Quattro Micro mass spectrometer using the established HPLC conditions described above except ( i ) mobile phase A was changed to 0.1% formic acid/0.01% trifluoroacetic acid/water and mobile phase B was 0.1% formic acid/0.01% trifluoroacetic acid/acetonitrile, ( ii ) the flow rate was reduced to 0.5 mL/min, and ( iii ) a postcolumn addition of 0.1% formic acid in acetonitrile was added via a PEEK tee at a flow rate of 100 μL/min. Compound elution was performed using a linear gradient of 0–16% mobile phase B over 16 min followed by 2 min of 95% B for a run time of 20 min. Under these conditions, mass spectrometry scans allowed for unambiguous identification of all eight xanthine alkaloid metabolites used or produced in the enzyme assays. First, xanthine is clearly separated from all other alkaloids and shows a unique 153.4 > 110 fragmentation pattern. Second, 7-methylxanthine and 3-methylxanthine may be identified from different retention times even though they both show a 167.4 > 124 fragmentation pattern. Third, 1-methylxanthine has a different retention time from all other alkaloids and a unique 167.4 > 110 fragment. Fourth, the 181.5 > 138 fragmentation pattern is specific for theobromine, which also has a clear retention time difference from other alkaloids. Fifth, paraxanthine and theophylline are difficult to separate under these LC conditions. In addition, both have a common 181.5 > 124 fragment. Therefore, to reliably identify each, we also scanned for 181.5 > 96, which is more abundant for theophylline whereas 181.5 > 55 is more abundant for paraxanthine. Finally, caffeine separated clearly from all other alkaloids and had a specific parent ion–fragment ion signature that allowed its unambiguous identification. Table S2 provides additional details about mass spectrometry fragmentation conditions. Detection was initially optimized using pure standards of the expected products diluted to 1 μM in 0.1% formic acid/50% (vol/vol) acetonitrile and infused directly onto a Waters Quattro Micro mass spectrometer via an electrospray ion source.

Phylogenetic Analyses.

Amino acid sequences from all enzymatically characterized SABATH gene family members and those from various land plant complete genomes were obtained from GenBank and the PlantTribes database ( 60 ). In addition, a limited sample of XMT and CS sequences was obtained from the oneKP database ( www.onekp.com ) to provide more detailed branching relationships of the recently evolved caffeine biosynthetic enzymes of Theobroma (Malvales), Camellia (Ericales), Coffea (Gentianales), as well as Paullinia and Citrus (Sapindales). Sequences were aligned using MAFFT version 7 ( 61 ) using the auto search strategy to maximize accuracy and speed. PhyML ( 62 ) was used to generate a maximum-likelihood phylogenetic estimate for the SABATH family members. We assumed the JTT model for amino acid substitution with an invariant and gamma parameter for among-site rate heterogeneity. Bootstrap support values were obtained from 100 replicates.

Acknowledgments

We thank Greg Cavey, Talline Martins, Andre Venter, James Kiddle, Matt Gibson, Logan Rowe, Rachel Steuf, and Kevin Blair for helpful discussions and assistance; Gane Ka-Shu Wong for providing access to the oneKP database to confirm placements of gene sequences shown in Fig. S1 ; the Chemistry Department at Western Michigan University for facilitating our HPLC analyses; and Thomas Baumann for allowing us to use his photos of Camellia and Paullinia in Fig. 2 . This study was supported by National Science Foundation Grant MCB-1120624 (to T.J.B.).

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1602575113/-/DCSupplemental .

Imperial Bioscience Review

The Role of Caffeine in Plants

By Ellie Fung

As wild as it may sound, caffeine was not designed to be guzzled upon by sleep-deprived university students. Its stimulant activity is exploited by millions worldwide, whether it be a morning coffee or a late-night Redbull, but much less is known about its function in the plants that produce them. These genera include Coffea (coffee), Camellia (tea) and Theobroma (chocolate), but guarana (Paullinia) and some citrus (Citrus) species are also natural sources of the ubiquitous stimulant (Huang et al., 2016).

Caffeine, or 1,3,7-trimethylxanthine, is a purine alkaloid, a group of chemical compounds forming part of the wide arsenal of plant defenses against abiotic and biotic stresses (Kim, Yun-Soo, Choi & Sano, 2010). These are well-established to be directly toxic and unpalatable to pests, but it wasn’t until recently that the specific activity of caffeine and other methylxanthines was identified. In an early study, tobacco hornworm larvae exhibited dose-dependent inhibition of feeding activity when finely ground Camellia sinensis leaves and Coffea arabica beans were added to a liquid medium up to a concentration of 3% and 10% respectively. Beyond these concentrations, the larvae were killed within 24 hours. The study also found that the levels of purified caffeine naturally found in fresh tea leaves and coffee beans were lethal to most larvae, providing early evidence that caffeine may function as an insecticide. Other insect species were shown to be affected too, such as mealworm larvae, butterfly larvae and mosquitoes (Nathanson, 1984a).

Kim et al. (2006) supported these findings by creating transgenic tobacco plants that were able to synthesise caffeine naturally. Contrary to their wild counterparts, these plants were unpalatable to tobacco cutworms and lepidopteran caterpillars. The repellent effects seem to extend to pathogens as well. Later, Kim & Sano (2008) found that while wild-type and transgenic varieties constitutively expressed pathogenesis-related (PR)-1a and proteinase inhibitor II (PR-2) genes, the transgenic plants expressed much higher levels of these defense-related genes when infected with tobacco mosaic virus and Pseudomonas syringae. They suggested that caffeine elevates resistance against pathogens possibly by acting as a signalling molecule and increasing salicylic acid production immediately after infection, which stimulates a greater hypersensitive response. Caffeine that is applied exogenously to wild-type tobacco also resulted in higher PR-1a and PR-2 expression and could even disturb the reproductive potential of four moth species (Nathanson, 1984).

Further research has also demonstrated the toxicity of caffeine against insects and pathogens, but its exact physiological role in plants remains unclear. Surprisingly, caffeine may even be mildly toxic to the plant producer. By acting as a phosphodiesterase inhibitor, caffeine raises intracellular concentrations of cyclic AMP. In plants, accumulation of cAMP may impair signal transduction in pathways modulating stomatal closure, cell cycle regulation and cell channel guarding. It has been suggested that self-defence signalling pathways, such as salicylic acid production, have arisen to compensate for these detrimental effects, which performs double duty as this also provides protection against biotic stresses. In light of this, researchers have even suggested that caffeine could pave the way to developing a novel “plant vaccine”, as much like animal vaccines, the introduction of a slightly dangerous external agent would confer resistance against more deadly invaders (Kim, Choi & Sano, 2010).

With caffeine recognised as a defensive compound, its presence in the nectar of some Citrus and Coffea species came across as counterintuitive, leading researchers to believe that caffeine may also confer a reproductive advantage. Indeed, one group showed that individual honeybees rewarded with caffeine-containing nectar were three times more likely to remember the scent of the rewarding flower 24 hours later than those that were not fed with caffeine. Bees were twice as likely to retain scent memory after 72 hours. As their strongly aromatic flowers signifies, Citrus and Coffea reproduce in a pollinator-dependent manner. The group hypothesised that caffeine enhances long-term associative memory to increase the likelihood of pollinators returning to flowers with the same scent signals. Even so, the bitterness of caffeine is not lost upon bees: concentrations above 1mM was enough to be repellent, but, as testament to the remarkable ability of plants in manipulating their pollinators, caffeine concentrations in naturally-occurring nectar never exceeded 0.3mM, even if the quantity in other tissues is much higher (Wright et al., 2013). Though these findings are exciting, the experiment does not accurately reflect the real-world as it neglects the social interactions between bees, which exerts a strong influence on foraging behaviour (Thomson, Draguleasa & Tan, 2015). To date, studies regarding the role of caffeine in improving pollinator memory are very limited, thus more research needs to be conducted to confirm this hypothesis.

Given these advantages of caffeine, it is predictably produced in multiple lineages. In fact, the biosynthetic pathway seemed to have evolved independently at least 5 times in angiosperm history. Previously, it was believed that all these species utilised the same three-step biochemical pathway, but recent research has demonstrated that at least three distinct pathways exist, involving orthologous enzymes derived from ancestral methyltransferases. By reconstructing the ancestral xanthine methyltransferase (XMT) of the Citrus genus, they found that just a few amino acid substitutions allowed for substrate preference to switch to theophylline, the exact compound that is preferentially methylated by modern Citrus XMT to make caffeine. This suggests that ancient enzymes previously engaged in other biochemical pathways had undergone exaptation to synthesise caffeine in a relatively facile fashion, which could explain the many instances of convergence (Huang et al., 2016).

For something we humans so readily put into our bodies, the origin and specific role of caffeine in the context of its botanical sources is not well understood. One might wonder how many more cups of coffee it might take for scientists to come up with firm conclusions.

References :

Huang, R., O’Donnell, A.,J., Barboline, J. J. & Barkman, T. J. (2016) Convergent evolution of caffeine in plants by co-option of exapted ancestral enzymes. Proceedings of the National Academy of Sciences of the United States of America. 113 (38), 10613-10618. Available from: doi: 10.1073/pnas.1602575113.

Kim, Y. S. & Sano, H. (2008) Pathogen resistance of transgenic tobacco plants producing caffeine. Phytochemistry. 69 (4), 882-888. Available from: doi: S0031-9422(07)00614-0 [pii].

Kim, Y. S., Uefuji, H., Ogita, S. & Sano, H. (2006) Transgenic tobacco plants producing caffeine: a potential new strategy for insect pest control. Transgenic Research. 15 (6), 667-672. Available from: doi: 10.1007/s11248-006-9006-6.

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Home > EVENTS > SCJAS > 2018 > ALL > 108

The Effect of Different Concentrations of Caffeine in Coffee on the Growth of Wisconsin Fast Plants

Hailey Nicks , HHES

School Name

Heathwood Hall Episcopal School

Grade Level

Presentation topic, presentation type.

Non-Mentored

The purpose of this experiment was to study the effects of various concentrations of caffeine, found in coffee, on the growth of Wisconsin Fast Plants. Three different volumes of coffee were compared in the study, being 10 oz, 6 oz, and 2 oz, and all of which were Community Coffee Ground Dark Roast. The effects of the three liquids were compared to those of the control group. The plants were set up to absorb a mixture of water and the three respective ounces of the liquid coffee (or simply just water in the case of the control group), and were left to grow for a total of 15 days. The hypothesis was that the greatest concentration of caffeine, contained in the 10 oz of coffee, will have the greatest effect, and will cause the greatest acceleration of plant growth. In addition, the null hypothesis was that the control group would cause the greatest acceleration of plant growth, since it contained no caffeine. The results of the experiment supported neither the hypothesis nor the null hypothesis, since the 6 ounce group showed the most mean plant growth by the end of the fifteen day experiment period. In conclusion, this experiment could prove to be beneficial for plant growth, and determining if different caffeine concentrations could possibly act as a sort of stimulant for botanical growth.

Recommended Citation

Nicks, Hailey, "The Effect of Different Concentrations of Caffeine in Coffee on the Growth of Wisconsin Fast Plants" (2018). South Carolina Junior Academy of Science . 108. https://scholarexchange.furman.edu/scjas/2018/all/108

Neville 105

4-14-2018 9:00 AM

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Oral and Written

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Caffeine: the allelochemical responsible for the plant growth inhibitory activity of vietnamese tea ( camellia sinensis l. kuntze).

1. Introduction

2. materials and methods, 2.1. plant materials, 2.2. screening of phytotoxic potential of tea samples by the sandwich method, 2.3. high-performance liquid chromatography (hplc), 2.4. specific and total inhibitory activity bioassay, 2.5. effect of aqueous tea extracts of vinatea-green tea (v2) on the germination and growth of different species of plants, 2.5.1. extraction procedure, 2.5.2. germination bioassay, 2.5.3. seedling growth bioassay, 2.5.4. soil sampling for rhizosphere soil method, 2.5.5. analysis of caffeine residue in the soil, 2.6. statistical analysis, 3. results and discussion, 3.1. screening of phytotoxic potential of tea samples by the sandwich method, 3.2. determination of caffeine concentration in tea samples, 3.3. specific and total inhibitory activity, 3.3.1. specific activity, 3.3.2. total inhibitory activity, 3.4. effect of vinatea-green tea extracts and caffeine on the germination and growth of some crop and weed species, 3.5. phytotoxic potential of caffeine from vinatea-green tea in soil, 4. conclusions, author contributions, acknowledgments, conflicts of interest.

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Share and Cite

PHAM, V.T.T.; ISMAIL, T.; MISHYNA, M.; APPIAH, K.S.; OIKAWA, Y.; FUJII, Y. Caffeine: The Allelochemical Responsible for the Plant Growth Inhibitory Activity of Vietnamese Tea ( Camellia sinensis L. Kuntze). Agronomy 2019 , 9 , 396. https://doi.org/10.3390/agronomy9070396

PHAM VTT, ISMAIL T, MISHYNA M, APPIAH KS, OIKAWA Y, FUJII Y. Caffeine: The Allelochemical Responsible for the Plant Growth Inhibitory Activity of Vietnamese Tea ( Camellia sinensis L. Kuntze). Agronomy . 2019; 9(7):396. https://doi.org/10.3390/agronomy9070396

PHAM, Van Thi Thanh, Tamer ISMAIL, Maryia MISHYNA, Kwame Sarpong APPIAH, Yosei OIKAWA, and Yoshiharu FUJII. 2019. "Caffeine: The Allelochemical Responsible for the Plant Growth Inhibitory Activity of Vietnamese Tea ( Camellia sinensis L. Kuntze)" Agronomy 9, no. 7: 396. https://doi.org/10.3390/agronomy9070396

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The Effect of Different Concentrations of Caffeine in Coffee on the Growth of Wisconsin Fast Plants

• Hailey Nicks

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Furman University

This paper was published in Furman University .

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Affect of Caffeine on Plant Growth

Introduction: Affect of Caffeine on Plant Growth

Aim Of Project

Since certain plants struggle to grow due to a lack of resources and exposure to sunlight, my peers and I will perform an experiment to see if using coffee grounds will help the plant grow faster. Caffeine is a chemical stimulant that has an effect on both humans and plants. The ability to photosynthesize and absorb water/nutrients from the soil, as well as lower pH levels, are all part of the process.

Scientific Question

Our experiment is about stimulating the plant growth using caffeine as a supplement. Caffeine has a clear effect on humans’ energy and therefore we wanted to test it out on plants. We are experimenting if caffeine can speed the plant growth:

How is plant growth dependent on caffeine?

Our Hypothesis

If coffee mixture is added to plants then the plants will grow faster because coffee grounds contain a lot of nitrogen, you would assume that plants grown in soil containing coffee grounds would grow faster and look better than plants grown in normal soil.

Coffee grounds/mixture

Area with sunlight exposure

Step 1: Name Your Variables

Independent variable:

- Coffee mixed with water used to water the plant

- Plain water mixture used to water the plants

Dependent variable:

- The growth and length of the plant

Constant Variable:

- Coffee pot size

- Type of solutions

- The concentration of the solution

- Amount of sunlight the plant is exposed to

- The temperature of the environment (room temperature)

- Amount of solution and water added

Step 2: Perform the Experiment

To perform this experiment, follow the procedure below for accurate results:

1. Fill two pots with equal amounts of soil, planting 10 seeds in each as well.

2. In the first 5 days, water both pots with tap water only. This is for seeds to germinate within those 5 days.

3. After the 5 days pass, measure the height of each plant.

4. Prepare the coffee solution by dissolving 10g of caffeine in 100mL water in a beaker.

5. Label the pots with “coffee” and “regular.” This depends on which pot you plan to add coffee to.

6. Over the next 10 days, water the pot once with either the coffee solution or water solution only.

7. Measure and record the height and data of both plants for the next 10 days.

Step 3: Collect Your Data

In 15 days, we completed our experiment. We only watered each plant with water for the first five days. After the five days were up, we started watering one plant with the caffeine mixture we had made for ten consecutive days.

Step 4: Describe Your Graph

Plant 1 (Ward's):

In Ward's experiment the plant growth difference is shown, in day 6 til day 9, the caffeine plant had a speeder growth while the normal plant had an average growth. Afterwards, the caffeine plant began decelerating in growth hence why the normal plant is higher than the coffee one. However, they both are still healthy, the coffee plant seemed to dry faster.

Analyzing Graph : There is a positive relationship between the number of days and the length of the plant in both lines because the plant grows more over the days. The regular plant started to grow 1 centimeter in day 2 and continued growing unit it reached 12 centimeters in day 15. The caffeine plant also started to grow 0.7 centimeters in day 2 but slower than the regular plant then continued to grow until it reached 10 centimeters in day 15.

Step 5: Describe Your Graph

Plant 2 (Fatema H.'s):

In Fatema's experiment, the plant growth difference is shown, in day 3 the both plants were growing an average growth, in day 8 it was clear that the coffee plant started to have a faster growth where it increased in length in the last day of the experiment the plants didn't show much of a difference where only 7cm was the difference between the coffee plant and the regular plant. This shows that the coffee really did affect the plants growth.

Analyzing Graph : There is a positive relationship between the number of days and the length of the plant in both lines because the plant grows more over the days. The caffeine plant started to grow 3 centimeters on day 2 and continued growing unit it reached 30 centimeters in day 15. The regular plant also started to grow 2 centimeters in day 2 but slower than the caffeine plant then continued to grow until it reached 23 centimeters in day 15.

Step 6: Describe Your Graph

Plant 3 (Layan's):

In Layan's experiment, there was a difference between the length of both plants but it wasn't very obvious or visible. There was about a 2 cm difference between both plants by the end of the project or experiment. since day 1, they started growing almost at the same rate. On day 7, a difference in length started appearing and the caffeine plant started going faster up until the last day, day 15. Throughout this experiment and observations, I was able to conclude that caffeine does play a role in the plant's growth.

Analyzing Graph : There is a positive relationship between the number of days and the length of the plant in both lines because the plant grows more over the days. The regular plant started to grow 1 centimeter in day 2 and continued growing unit it reached 23.9 centimeters in day 15. The caffeine plant also started to grow 0.7 centimeters in day 2 but slower than the regular plant then continued to grow until it reached 19.6 centimeters in day 15.

Step 7: Describe Your Graph

Plant 4 (Fatema J.'s):

In Fatima's experiment, there isn't a much difference in the length of the plants. We can see that in day 6, the caffeine plant on the right started to grow while the regular plant didn't show up. By the end of Day 15, the regular plant seems to be a little shorter than the caffeine plants which means that adding caffeine as a substitute to water to the plant fastens its growth.

Analyzing Graph : There is a positive relationship between the number of days and the length of the plant in both lines because the plant grows more over the days. The regular plant started to grow 1 centimeter in day 4 and continued growing unit it reached 9 centimeters in day 15. The caffeine plant started to grow 0.5 centimeters in day 6 and it is slower than the regular plant then it continued to grow until it reached 5 centimeters in day 15.

Step 8: Evaluating Hypothesis and Conclusion

After each student has observed the two of her plants, one with caffeine and the other without, we found out that our hypothesis was inaccurate. Our hypothesis was "If coffee mixture is added to plants then the plants will grow faster because coffee grounds contain a lot of nitrogen, you would assume that plants grown in soil containing coffee grounds would grow faster and look better than plants grown in normal soil." It was generalized, meaning all plants should have the same effect, however, it turned out that each plant reacted to it differently. Ward's caffeine plant decelerated its growth, while Fatema H.'s and Fatema J.'s caffeine plants grew drastically compared to the regular watered plant, on other hand Layan's plant had little to no effect.

Our hypothesis is inaccurate and we're unable to state whether or not our hypothesis was successful due to the variety in results. Therefore, we concluded that caffeine does indeed speed up the process, but it there are surrounding factors such as the amount, plant type and placement to sunlight.

Step 9: Finalized Graph of Experiment

The finalized graph shows all eight plants us four students have planted for the experiment.

Purple represents -> Ward Al-Jawad's plants (plant 1)

Blue represents -> Fatema Al-Helal's plants (plant 2)

Red represents -> Layan Al-Khushams plants (plant 3)

Green represents -> Fatima Al-Jamea's plants (plant 4)

Step 10: Reflection

The experiment was easy to conduct and all materials were provided at home. Furthermore, given the circumstances, our group was able to communicate properly and we were able to work through our project. A major skill we displayed is writing accurate information and using trust worthy websites. Finally, we were able to assign and distribute tasks amongst ourself to get great results.

Not all the plants had the same reaction, which means our hypothesis is inaccurate and incorrect. Additionally, having online school made it really difficult as it was harder and each girl had to grow her own plant instead of being able to meet in school and having one shared plant. We also faced a struggle with time management and ended up finalizing our experiment in the last 3 weeks rather than beginning it once assigned.

Improvement

The first improvement that our group should make was to manage our time better and start the task as soon as it is given instead of waiting and starting everything late and pressuring ourselves, in other words, improve our time management skills. Another improvement we could've done was to choose a more challenging experiment idea next time. Another improvement is to improve communication skills and try being more active with the group and updating each other more often.

Step 11: Application

To raise yields, organic farms resist using pesticides, fertilizers, and other chemicals. Yields are extra healthy, and berries ripen conveniently and easily within a few weeks. Farmers use coffee beans if they're assured it's organic, and to ensure so, the coffee must have been made without the use of pesticides or chemicals. When opposed to regular coffee beans, organic coffee commands a price. In Brazil, this is a common occurrence. This is how about 20% of farms that aren't owned by families are managed. Coffee farming has become commercialized in Mexico and Vietnam, and these farms produce a lot of coffee. Coffee grounds are commonly added to the garden or compost by several gardeners. The consistency of the soil increases as the grounds gradually decompose. They contain around 2% nitrogen by volume, and the nitrogen is released when they decompose.

Caffeine is a chemical stimulant that boosts biological processes in both humans and plants. The ability to photosynthesize and absorb water and nutrients from the soil are among these processes. In addition, it reduces the pH of the soil. Some plants are poisoned by this rise in acidity, while others, such as blueberries, thrive on it.

Biology SL 's Sample Internal Assessment

Effects of coffee & caffeine on the growth of plants.

Table of content

Introduction, research question, background information, literature review, independent variable, dependent variable, preparation of solutions, preparation of seeds, setting up the plants, completion of growth, qualitative data, sample calculation, data processing and analysis, processed data, discussion of figure 10, discussion of the graph, statistical analysis, inference of t-test, limitations, bibliography.

Use of tea grounds for the growth of plants is a common practice in horticulture. Tea contains tannic acid and many other nutrients. When added to soil, they decompose to liberate many useful organic compounds which are essential for the growth of plants. Coffee is used as a fertilizer in kitchen gardens and I wanted to check the extent to which growth of plants is affected by the presence of coffee in the soil. I will be comparing the growth in the presence of coffee and the growth in presence of pure caffeine to check whether only caffeine stimulates the plant growth or other factors of the coffee also help stimulate growth within plants. Caffeine has been reported to be a potential stimulant for both central nervous system and cardiac system. It has an ability to prevent cancer caused by the carcinogens present in environment and foods. Mutagenic potential of caffeine has also been proved against bacterium E . coli. It has also been found to show a major synergistic effect in mutation of chromosomes. Effect of caffeine on the quantitative and qualitative parameters of plant has been a significant area of research. The current investigation deals with the study of effect of caffeine on the growth of plants. Thus, I have decided to narrow down my Internal Assessment in Biology based on the research question stated below-

How do different concentrations of 1,3,7 -Trimethylpurine- 2,6 -dione (caffeine) and Coffea (coffee) ( 0.3%,0.6%,0.9%,1.2%,1.5% and 1.8% ) effect the root growth in the plant Vigna radiata?

Vigna radiata is a widely grown plant. This plant is germinated overnight and then eaten. People generally refer Vigna radiata to green gram and sprouts. Green gram is very good for human health if consumed directly after germination when it has sprouted a radicle. Green gram is very rich in protein and other nutrients and also is a powerful antioxidant and is also rich in nutrients and protein. This particular plant belongs to the category of legume because of which they contain bacteria in their roots. This bacteria fixes the free nitrogen from the atmosphere into nitrogenous compounds in the plant body through the process of nitrogen fixation. Apart from a rich source of protein for vegetarian diets, it is also the source of trace amounts of Riboflavin, Vitamin-C and thyamine. It can also be used as a manure crop and a cattle feed.

Coffee is a widely used stimulant for human beings. It is used especially by people who have stress and long working hours. The component in coffee that helps people keep awake and calm their nerves is caffeine. Caffeine is naturally found in coffee in small amounts. Coffee is acidic in nature and contains nutrients like phosphorous, nitrogen and potassium. Coffee has its benefits to humans and has shown to have positive impacts on the growth of plants too. Coffee stimulates shoot and root growth in plants. Coffee grounds are a very rich source of Potassium, Phosphorous and Nitrogen for the plant. Phosphorous is very useful in the maturation process of the plant. Due to the presence of excess phosphorous the cells in the root of the plant there is a higher rate of cell division and the size of cells is larger. These effects of coffee are the reason for longer shoot and root growth. The coffee tree belongs to tropical evergreen shrub in the genus Coffea. It is mainly found in the regions of Tropics of Cancer and Capricorn. The main varieties are - Coffea arabica (Arabicas) and Coffea canephora (Robustas). One cup of coffee is reported to contain around 180 mg of caffeine. For an adult, it is safe to consume around 200 - 300 mg of caffeine per day. Decaf coffee is another variety of coffee which contains much less amount of caffeine in comparison to normal coffee. A cup of decaf coffee contains 7 mg of caffeine in comparison to 70-80 mg of caffeine in a normal cup of coffee.

1,3,7- Trimethylpurine -2,6- dione also known as caffeine in its pure form is a white powder. Pure caffeine has antibacterial properties. When coffee leaves fall to the ground they release caffeine as they decompose and the caffeine which is released acts like a natural pesticide to some of the insects. Caffeine is also very slightly basic in nature. Pure caffeine can also be used by humans as a stimulant by mixing with water to boost body processes to make people feel more alert and awake.

The independent variable of the investigation is the concentration of coffee and caffeine used during the growth of the plants. The concentration chosen were - 0.3%,0.6%,0.9%,1.2%,1.5% and 1.8% . The solutions were prepared by adding the requisite amount of solid in distilled water within a glass beaker. A 30% stock solution was prepared by adding 30 g of solid coffee powder and caffeine in 100 cc of distilled water. The other solutions were made by dilution of this stock solution. A research article- Caffeine: The Allelochemical Responsible for the Plant Growth Inhibitory Activity of Vietnamese Tea (Camellia sinensis L. Kuntze) reported that growth of plant (red clover) was inhibited by 1.00% by caffeine solution while 0.50% solution has been proved to be effective. Thus, the concentration range of this investigation has been kept within the range of 0.30% to 1.80% so that it also gives us an idea about the effect of the concentration both below and above the permissible range. A very high concentration could lead to possible stunting of the growth of the plant.

The dependent variable is the shoot growth because the coffee provides the nutrients like phosphorous which help with the maturation of the plant and increased growth in root. The caffeine prevents the plants from getting infected and boosts the growth due to its ability to stimulate the body processes. We take shoot (radicle) length because in seeds that is the first part to come out. The shoot is most affected by different factors and root helps absorption of nutrients and grows extremely fast in the initial stages and so it is easy to measure using a ruler.

Figure 2 - Table On List Of Controlled Variable

Figure 3 - Table On List of uncontrolled variables

Figure 4 - Table On List of apparatus used

30.00 ± 0.01 g of caffeine powder was weighed on a watch glass placed over a digital mass balance using a spatula to transfer the solid to the watch glass. The exact mass of the solid was transferred to a 250 cc glass beaker and dissolved in 100 cc tap water. A graduated measuring cylinder was used to transfer tap water to the beaker. 1.00 ± 0.05 cc of this stock solution was transferred to a 100 cc reagent bottle using a graduated pipette and diluted to 0.30% by adding tap water. The other solutions of caffeine were prepared in the same method. 2.00 ± 0.05 cc, 3.00 ± 0.05 cc, 4.00 ± 0.05 cc, 5.00 ± 0.05 cc and 6.00 ± 0.05 cc of the stock solutions were diluted to 100 cc in a reagent bottle to prepare 0.60% , 0.90% , 1.20 % , 1.50% and 1.80% solutions respectively.

The same process was followed to prepare coffee solutions of the same concentration.

• Seeds of Vigna radiata were soaked overnight in water to start the germination process.
• After preparing different concentrations of coffee and caffeine 2 petri plates per concentration were taken for each concentration including three for water ( 0.0%) which represents our control solution.
• In each petri plate 15 cc of their respective solutions were added to each petri dish using a measuring cylinder. The solution was soaked in cotton plugs and kept within the petri dish.
• Five germinated seeds were then added to each petri dish. A total of 140 seeds and 28 petri plates were used in total.
• The plants were let to grow for 5 days and measurements of the length of the roots were measured at the end of five days using a ruler.
• The results were taken down and average for every concentration of coffee and caffeine were taken.
• The use of safety gloves to avoid direct contact with caffeine as it is a possibly addicting substance and can impact body if ingested.
• Use of mittens to move the hot beakers containing boiled coffee grounds. Mittens help avoid burning of skin as beaker can be hot after preparing coffee.
• The use of lab coats as the stain of coffee is very tough to get rid of and can be permanent.
• Use of a well ventilated area or an area with an exhaust to remove fumes produced during boiling coffee and caffeine.

Figure 5 - Table On Ethical And Environmental Impacts

Figure 6 - Table On Showing The Observations Made In Each Petri Dish After The Experiment Was Carried Out

For control (0.0%)

Average length of root =   \(\frac{4.10\ +\ 3.30\ +\ 4.30\ +\ 3.00\ +\ 3.30\ +\ 3.70\ +\ 3.60\ +\ 3.00}{8}\)  = 2.83 ± 0.05cm

Standard deviation (SD) =   \(\frac{(4.10-4.63)^2+(3.30-4.63)^2+(4.30-4.63)^2+(3.00-4.63)^2+(3.30-4.63)^2+(3.70-4.63)^2+(3.60-4.63)^2(3.00-4.63)^2}{8}\)  = 1.47

The readings highlighted in red are anomalous and thus discarded.

The scattered plot above depicts the variation of mean length of root of the plant against the concentration of caffeine solution used. The mean length of the root has been plotted along the y axes as it is the dependent variable while the % concentration of caffeine solution along the x axes as it is the dependent variable. As it is visible from the graph, there is a gradual increase in the root height from 3.56 ± 0.05 cm to 5.73 ± 0.05 cm as the concentration of caffeine increases from 0.3% to 1.5% . At the concentration of 1.8%, the mean height is found to decrease to 4.55 ± 0.05 cm. The data displayed here are in support of the literature references mentioned. 0.5% to 1.5% is found to be the optimum range for the growth while the growth is found to be inhibited beyond that.

There is an initial increase in the growth of the plants with both coffee and caffeine till the concentration of  1.5% after which there is a decline to the 1.8% . This shows that both coffee and caffeine do boost growth of plants to some extent after which the act as inhibitors. The graphs show that there is more growth in the plants with coffee solutions than in caffeine solutions. The roots grew longer in coffee and caffeine solutions than in control. 0% coffee and caffeine solutions grew lesser than 1.5% coffee and caffeine solutions. The error bars for coffee are very big showing that variation in the plant lengths for coffee in the plants was a lot whereas the error bars for caffeine are smaller showing that lengths of plants are more or less similar and closer to each other. The overlapping error bars show that there is a possibility that lengths of some coffee plants of the plants could range down to the lengths of the caffeine plants and vice versa.There is an initial increase in the growth of the plants with both coffee and caffeine till the concentration of 1.5% after which there is a decline to the 1.8%. This shows that both coffee and caffeine do boost growth of plants to some extent after which the act as inhibitors. The graphs show that there is more growth in the plants with coffee solutions than in caffeine solutions. The roots grew longer in coffee and caffeine solutions than in control. 0% coffee and caffeine solutions grew lesser than 1.5% coffee and caffeine solutions. The error bars for coffee are very big showing that variation in the plant lengths for coffee in the plants was a lot whereas the error bars for caffeine are smaller showing that lengths of plants are more or less similar and closer to each other. The overlapping error bars show that there is a possibility that lengths of some coffee plants of the plants could range down to the lengths of the caffeine plants and vice versa.

A T-test is a statistical analysis performed on results to be able prove the significance of our results and checking the validity of our hypothesis. In this case the analysis is going to be performed on caffeine and coffee and compare their results to the control. I will be using the t-test to show that the data I have from my experiment is valid. I will also use this test to prove that the data is valid despite the large and overlapping error bars.

Null hypothesis (H0):

There is no significant difference between the growth of plants in between coffee and caffeine Alternate hypothesis:

H1: There is a positive correlation between the growth of plants and the % concentration of coffee and caffeine solutions used.

H2: There is a negative correlation between the growth of plants and the % concentration of coffee or caffeine solutions used.

As seen in Figure - 13 and Figure - 14 , the calculated value of t value exceeds the critical value (p value) which means that for both independent t tests of coffee versus control and independent t tests for caffeine versus control, it is evident that there is a significant statistical difference between the growth of plants in presence of coffee or caffeine and in absence of it. Thus, we can claim that presence of coffee or caffeine has a significant impact on the growth of plants. Thus, the null hypothesis is rejected and the alternate hypothesis is accepted. If we consider Figure - 10 and Figure - 12 along with the results of the statistical test, we can claim that till 1.5% the concentration of coffee or caffeine solution used has a positive impact on the growth of plants while beyond it, the growth gets inhibited. Thus, both the alternate hypothesis ( H 1 and H 2 ) are accepted.

The null hypothesis stated that Coffee and Caffeine will have no effect on the growth of the plants. The alternate hypothesis stated that Coffee and caffeine will initially increase the growth of the plant to some extent. The results support the alternate hypothesis. According to the graph, the growth in plants increased till concentration 1.5% and then reduced. The processed data table shows the trend in the length of the roots of Vigna radiata. With increasing concentrations the length of the roots also increases. The relationship between my independent and dependent variables is weak positive till 1.5% and then weak negative. The optimal growth is at 1.5% of both coffee and caffeine. The presence of nutrients, antibacterial properties and stimulating effect of coffee and caffeine on Vigna radiata should increase the growth as coffee and caffeine stimulate cell division, the antibacterial properties of coffee grounds prevent disease helping plants grow and the nutrients in coffee and caffeine help the plant to be nutrient rich and increase the growth rate of the plant in the root and the shoot.

The results obtained during the experiment are comparable to the control. The use of dilute concentrations of my solutions helped boost the growth of the plants. The growth in the plants was boosted using coffee and caffeine solutions. The plants grew till the 1.5% concentration and then stopped growing for both coffee and caffeine. The growth in caffeine was more than the growth in coffee solutions, although the growth in both solutions was comparably larger than the control setup. The root lengths were measured because radicles are the first part from a seed which is visible. The coffee solution helps the plant grow because it contains nutrients like phosphorous, nitrogen and potassium. Phosphorous is important in plants because it promotes cell division by helping in complex energy transformations during cell division. The nitrogen is also important because it helps in amino acid production and formation of proteins to help the plant grow faster. Nitrogen is also an important part of chlorophyll in the leaves and helps leaves trap sunlight and grow. The potassium helps keep the internal conditions of plants constant. The potassium helps keep the plant cells in an isotonic condition helping control entry of water and nutrients in and out of cell. Caffeine can help boost root growth because it has antibacterial properties which helps the plants grow better and prevents bacteria from stunting plant growth. Caffeine also acts like a pesticide for some insects keeping the plants safe from insects. Stimulating properties of caffeine such as boosting metabolic processes helps with boosting the growth of the roots.

• The quality of the experiment have been improved by taking more precautions making the experiment more accurate and improving the reliability of the results.
• Anomalous data in the experiment was chosen by looking at values that seem to have an extreme range and did not seem to fit into the shoot lengths. The anomalous data has been highlighted in the raw data table.
• The length of the roots has been measured with a ruler. There is a chance of reading the ruler at an angle leading to parallax error which will reduce the accuracy of the investigation. To avoid this, the ruler must always be kept at the eye level.
• The type and amount of soil will have an impact on the growth of the plant. Thus, variations in the type and mass of soil used will interfere with the growth of soil. To combat this source of systematic error, the seeds were grown in petri dish with the cotton soaked in solutions. Thus, use of soil was avoided to discard the other factors that could arise due to the presence of variation of nutrients.
• Intensity of light and temperature are important factors for the growth of plants. Both of these are limiting factors for the growth of plants. Thus, these two factors were not controlled. Again, it must be noted that how can we ensure that all plants have received the same intensity of light. Moreover, the study was conducted over a time period of 5 days and hence there is unavoidable variation of temperature for these days. This can be fixed by carrying out the investigation in potted plants and storing the pots in an incubation chamber where temperature can be controlled. To sort out the variations of light intensity, the position of the plants were changed at regular intervals so that all of them receive the same intensity of light.

For further investigation I would like to find how different daily use house hold effects affect the growth of plants in the garden. Plotting the graph to compare the growth of plants in household substances will help us see which of the substances are most harmful to the environment and should not be directly discarded into the garden. Substances which are extremes of pH will be more harmful to the plants than neutral substances. The neutral substances which are rich in nutrients and minerals is more likely to promote growth.

• Batema, Cara. “A Science Project for Growing Plants With Coffee Grounds or Soil.” Education, 21 Nov. 2017 , education.seattlepi.com/science-project-growing-plants-coffee-grounds-soil-5620.html. 29th July 2019 7:39pm
• “Caffeine In Coffee - How Bad Is It Really? | CoffeeScience.” Coffee Science, 17 Oct. 2018, https://www.coffeescience.org/caffeine-coffee/   27th July 2019 7:22pm
• “Caffeine.” National Center for Biotechnology Information. PubChem Compound Database, U.S . National Library of Medicine, pubchem.ncbi.nlm.nih.gov/compound/caffeine. 30th July 2019 11:07am
• “Caffeine: Uses, Side Effects, Interactions, Dosage, and Warning.” WebMD, WebMD , https://www.webmd.com/500?aspxerrorpath=%2Fdummy.aspx   31th July 2019 9:02pm
• Copp. “Caffeine Dissolution.” Chemistry Stack Exchange, chemistry.stackexchange.com/questions/ 38693 / caffeine-dissolution. 31th July 2019 7:58pm
• Dannie, Marie. “Green Gram Dal Health Benefits.” Healthy Eating | SF Gate, 27 Dec. 2018 , healthyeating.sfgate.com/green-gram-dal-health-benefits- 11957 . html . 24th July 2019 6:48pm
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• Sledz, Wojciech, et al. “Antibacterial Activity of Caffeine against Plant Pathogenic Bacteria.” Acta Biochimica Polonica, U.S . National Library of Medicine, 2015, https://pubmed.ncbi.nlm.nih.gov/26307771/   30th July 2019 3:20 pm
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Will Caffeine Affect Plant Growth – Tips On Fertilizing Plants With Caffeine

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Coffee contains caffeine, which is addictive. Caffeine, in the form of coffee (and mildly in the form of CHOCOLATE!), might be said to make the world go round, as many of us rely on its stimulating benefits. Caffeine, in fact, has intrigued scientists, leading to recent studies regarding caffeine use in gardens. What have they discovered? Read on to find out about caffeine uses in gardens.

Fertilizing Plants with Caffeine

Many gardeners, including myself, add coffee grounds directly to the garden or into the compost . The gradually breaking down of the grounds improves the quality of the soil. They contain about 2% nitrogen by volume, and as they break down, the nitrogen is released. This makes it sound like fertilizing plants with caffeine would be an excellent idea but pay attention to the part about breaking down. Un-composted coffee grounds may actually stunt the growth of plants. It is better to add them to the compost bin and allow the microorganisms to break them down. Fertilizing plants with caffeine will definitely affect plant growth but not necessarily in a positive manner.

Will Caffeine Affect Plant Growth?

What purpose does caffeine serve, other than to keep us awake? In coffee plants , the caffeine building enzymes are members of N-methyltransferases, which are found in all plants and build a variety of compounds. In the case of caffeine, the N-methyltranferase gene mutated, creating a biological weapon. For instance, when coffee leaves drop, they contaminate the soil with caffeine, which curtails the germination of other plants, lessening competition. Obviously, that means too much caffeine can have a detrimental effect on plant growth. Caffeine, a chemical stimulant, increases the biological processes in not only humans but plants as well. These processes include the ability to photosynthesize and absorb water and nutrients from the soil. It also decreases the pH levels in the soil. This increase in acidity can be toxic to some plants, although others, like blueberries , enjoy it. Studies involving the use of caffeine on plants have shown that, initially, cell growth rates are stable but soon the caffeine begins to kill or distort these cells, resulting in a dead or stunted plant.

Caffeine as an Insect Repellent

Caffeine use in the garden isn’t all doom and gloom, however. Additional scientific studies have shown caffeine to be an effective slug and snail killer. It also kills mosquito larvae , hornworms , milkweed bugs , and butterfly larvae . The use of caffeine as an insect repellent or killer apparently interferes with food consumption and reproduction, and also results in distorted behavior by suppressing enzymes in the insects’ nervous systems. It is a naturally derived ingredient, unlike commercial insecticides that are full of chemicals. Interestingly, while high doses of caffeine are toxic to insects, the nectar of coffee blossoms has trace amounts of caffeine. When insects feed on this spiked nectar, they get a jolt from the caffeine, which helps etch the scent of the flowers into their memories. This ensures that the pollinators will remember and revisit the plants, thereby spreading their pollen. Other insects that feed on the leaves of coffee plants and other plants containing caffeine have, over time, evolved taste receptors that help them identify plants with caffeine and avoid them. A final word on the use of coffee grounds in the garden. Coffee grounds contain potassium, which attracts earthworms , a boon to any garden. The release of some nitrogen is also a plus. It isn’t the caffeine in the grounds that has any bearing on increased plant growth, but the introduction of other minerals available in the coffee grounds. If the idea of caffeine in the garden has you spooked, however, use decaf grounds and allow them to break down before spreading the resulting compost.

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Amy Grant has been gardening for 30 years and writing for 15. A professional chef and caterer, Amy's area of expertise is culinary gardening.

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When Is The Best Time To Drink Coffee? Experts Weigh In

What is the best time to drink coffee, and how much is too much? As a self-confessed coffee devotee, my answer would be whenever you want to. But recently, I’ve struggled with insomnia, which has prompted me to think about my coffee habits. Studies suggest that your daily cup of caffeine can support your heart and brain health and when consumed mindfully—without sugar— coffee can offer numerous health benefits, thanks to the antioxidants found in the coffee plant. However, it’s not just about how you take your coffee, but when you drink it. Experts advise that by timing it right, you can avoid any negative effects.

The best time to drink coffee

“Coffee contains caffeine, which is a natural stimulant,” explains nutritionist Mugdha Pradhan , founder of iThrive. “This means it can boost your metabolism by increasing the body’s heart rate and energy expenditure. That’s why drinking coffee in the morning, about 90 minutes after waking up, works well, because it syncs up with the body’s natural cortisol rhythm.” If you’re someone who likes to work out in the morning, having black coffee beforehand may enhance physical performance and support fat burning, while consuming it afterward could aid muscle recovery, too.

That said, it’s best not to have it first thing in the morning on an empty stomach, as it can trigger the production of stomach acid, which can potentially make you feel uncomfortable. Therefore, look to drink coffee after a light meal or snack – this also reduces the risk of spiking your cortisol levels, which will already be peaking in the early hours of waking up.

Your afternoon coffee run shouldn’t be late

“Post lunch, your energy levels can be lower and a cup of coffee can be a good pick-me-up for many,” notes food coach Anupama Menon . An early afternoon coffee is more effective than one later in the day, as cortisol levels naturally decline in the afternoon. The caffeine in coffee can enhance alertness and give you the energy lift you might be craving.

However, given its capacity to heighten alertness, refrain from drinking coffee after 4 pm—this is especially important for those who struggle to fall asleep at night. It can disrupt sleep patterns, as it has an approximate effect of eight hours and can still be present in your system come bedtime.

How much coffee is too much coffee?

But what is the right amount of coffee we should be consuming? “Coffee acts as a diuretic and tends to dehydrate the body. Therefore, if you drink too much coffee, you will experience dehydration and will need to go to the toilet more frequently,” warns Pradhan. To combat this, you can focus on staying hydrated with filtered and remineralized water, which helps replenish nutrients. Excessive caffeine consumption can also elevate estrogen levels, potentially leading to weight gain, so being mindful of your coffee intake is key. “Try and restrict your intake to no more than 500n mg a day ([no] more than four cups) to counter the possible negative effects of dehydration, but take advantage of the benefits,” says Menon.

Coffee alternatives experts recommend

If your caffeine cravings overpower your will to time your coffee right, Mugdha recommends some alternative options. Yerba mate, a traditional South American tea, provides a gentle energy boost without abrupt caffeine spikes and is rich in antioxidants. Hot cocoa made from clean cacao nibs and coconut milk can also enhance mood and motivation.

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Are these coffee alternatives worth the hype?

From the brain-boosting effects of guarana to the calming properties of matcha, these drinks offer both an energy lift and enhanced mental focus.

For many, the day doesn’t begin without that first sip of coffee—a ritual so ingrained it’s almost synonymous with morning itself. But as interest in health and wellness grows, caffeine alternatives offering a different pick-me-up are gaining traction. From the ancient ritual of matcha to the probiotic benefits of kombucha, a range of options promises to energize your morning without the usual coffee jitters. Here’s what you need to know.

Mushroom coffee

Mushroom coffee is emerging as an energy-boosting alternative to your regular cup of joe, says Jessica Gavin , a food scientist and author of Easy Culinary Science for Better Cooking .

This morning blend combines ground coffee beans with adaptogenic mushrooms like chaga , reishi , lion’s mane , and cordyceps . Unlike their psychedelic counterparts, these mushrooms won’t alter your mind, but they can help the body adapt to stress, improve immune function, and maintain steady focus, says Gavin. Adding mushrooms to your beverage also provides antioxidants, vitamins, and minerals such as potassium, selenium, and B vitamins.

According to Gavin, most mushroom coffee products use less caffeine—about 48 to 50 milligrams—compared to an eight-ounce cup of coffee which   contains 80 to 100 milligrams of caffeine.

( Do mushroom supplements really help you ?)

“Because mushroom coffee does not have the same taxing load on your central nervous system, one could reasonably assume it’s a better alternative than a cup of coffee or green tea,” says Yaa Boakye , a registered dietitian nutritionist and owner of Elite Body Data .

However, mushrooms like chaga can contain high levels of oxalates , which may increase the risk of kidney stones if consumed in excess.  

Matcha green tea

Matcha, a potent form of green tea, delivers more than just a caffeine kick. With around 70 milligrams of caffeine per serving—more than the 50 milligrams in black tea—this herbal brew offers sustained energy without the jitters. According to Gavin, matcha’s unique combination of caffeine and antioxidants boosts brain energy while promoting relaxation, thanks to the amino acid L-theanine , which is known to reduce stress and protect against neuronal injury .

Containing roughly as much caffeine as coffee, South America’s super brew, yerba mate , packs more   antioxidants than any other tea-based drink. Made from the leaves of holly trees native to the region, yerba mate offers a steady caffeine release, providing sustained energy without the jitters. According to Boakye, this tea is also rich in polyphenols, vitamins, and minerals not typically found in traditional caffeinated beverages.

Research shows that yerba mate can boost mental alertness and enhance physical performance. Additional benefits include antimicrobial properties , support for weight loss , and the potential to lower blood sugar , reduce cholesterol , and combat chronic inflammation .

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Yaupon tea, brewed from the leaves of the yaupon holly—the only caffeinated plant native to the United States—offers a mild yet energizing boost. Bryan Quoc Le, a food scientist and author of 150 Food Science Questions Answered , says that yaupon tea contains 0.1 to 2 percent caffeine, less than coffee’s 1 to 3 percent. However, it’s not just about caffeine; yaupon tea is rich in xanthines, including theobromine, which enhances mood and alertness without the jitters.

( You actually can consume too much caffeine. Here are the risks .)

Kombucha , a fermented tea, is celebrated for its gut health benefits and mild stimulant effects. According to Le, kombucha retains some of the tea’s bioactive compounds, including 3 to 6 percent caffeine , depending on how it’s brewed. But the real star is its probiotics—beneficial microorganisms that nourish the gut’s “good” bacteria. A healthy gut microbiome is linked to improved cognitive functions like attention, memory, and learning. Additionally, kombucha is rich in B vitamins, which Boakye says help the body metabolize nutrients into cellular fuel.

( Your gut health can affect the rest of your body. Here’s why .)

Guarana stands out for its impressive caffeine content, with berries containing 2 to 8 percent caffeine —far exceeding the 1 to 3 percent in coffee beans. In addition to caffeine, guarana is rich in stimulating compounds like theophylline and theobromine , which, according to Gavin, boost cognitive performance, reduce fatigue, and increase alertness. It also boasts antioxidants and anti-inflammatory properties.

Often found in energy drinks with doses ranging from 50 to 75 milligrams , guarana can also be added as a powder to smoothies or other beverages. However, Boakye cautions against excessive consumption to avoid caffeine crashes and jitteriness, noting that the FDA recommends a daily limit of 400 milligrams of caffeine .

( Can energy drinks really boost your metabolism? Here’s what the science says .)

“If someone is about to sit and do some office work, [guarana] is probably not a good idea,” says Boakye. “But if you’re about to go and do a workout or an intense boot camp, [it] might be helpful since it provides that quick burst of energy.”  

Turmeric lattes

For a caffeine-free morning boost, turmeric lattes, known as golden milk, deliver powerful antioxidant and anti-inflammatory benefits. Made with turmeric and curcumin , this golden drink can be enhanced with a touch of coconut oil and black pepper , which improve curcumin absorption, says Gavin. Ginger, another key ingredient, aids digestion and adds a spicy kick.

( Some vitamins and minerals work better when eaten together. )

“I prefer it in the morning because the combination of ingredients has a way of stimulating you,” says Gavin. She says the sensation of the gingery spices, pepper, and warm cinnamon on your tongue helps wake up the senses differently than caffeine.  

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• Published: 30 August 2024

Antimigraine activity of Asarinin by OPRM1 pathway with multifaceted impacts through network analysis

• Rapuru Rushendran 1 &
• Vellapandian Chitra 1  

Scientific Reports volume  14 , Article number:  20207 ( 2024 ) Cite this article

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• Drug discovery
• Neuroscience

Migraine is a debilitating neurological disorder impacting millions worldwide. Calcitonin Gene-Related Peptide (CGRP) has emerged as a key player in migraine pathophysiology, leading to the development of targeted therapies. This study reviews novel CGRP-targeted treatments, including monoclonal antibodies small molecule inhibitors/nutraceuticals and introduces Asarinin as a potential modulator of the pathway. Asarinin, a natural compound found in various plants, is examined for its pharmacological potential in migraine management. Pharmacokinetic assessments, toxicological modelling, molecular property analysis, and network pharmacology were conducted. Molecular docking and dynamics studies with CGRP reveal potential interactions, providing a foundation for understanding Asarinin's therapeutic effects. Asarinin's favourable pharmacokinetics, safety profile, and bioactivity, supporting its candidacy as a therapeutic agent. In-depth molecular docking studies with the CGRP receptor (PDB: 6ZHO) demonstrate strong binding affinity (− 10.3kcal/mol), while molecular dynamics simulations unveil the dynamic behavior of the Asarinin-CGRP complex, (− 10.53 kcal/mol) for Atogepant-CGRP complex. Network analysis highlights key proteins in migraine pathology, indicating Asarinin's potential efficacy. The groundwork for future investigations, suggests Asarinin as a promising candidate for migraine management by targeting OPRM1 pathway. The integration of diverse assessments provides a comprehensive understanding of Asarinin's potential and paves the way for further preclinical and clinical research.

Introduction

Migraine is a complex neurological disorder characterized by recurrent episodes of moderate to severe headaches, often accompanied by symptoms such as nausea, sensitivity to light and sound, and visual disturbances 1 , 2 , 3 . This debilitating condition significantly impacts the quality of life for millions of individuals worldwide. One promising avenue of research and treatment for migraines revolves around the role of Calcitonin Gene-Related Peptide (CGRP), a neuropeptide that plays a crucial role in the pathophysiology of migraines. CGRP is involved in the dilation of blood vessels and the transmission of pain signals in the nervous system 4 , 5 , 6 . Its elevated levels during migraine attacks have led researchers to focus on developing therapies that target CGRP to alleviate symptoms and prevent the onset of migraine episodes. In recent years, the development of CGRP-targeted therapies has gained substantial attention and shown promising results in migraine management 7 . Common adverse effects reported include mild to moderate injection-site reactions, such as pain or erythema. Systemic reactions like fatigue, constipation, and hypersensitivity reactions have also been noted, albeit infrequently. Severe adverse events are rare but can include anaphylaxis or severe hypersensitivity. The cost of CGRP antagonists can be a significant consideration in clinical practice. These biologic therapies are typically expensive, often ranging from several thousand to tens of thousands of dollars annually per patient. Cost-effectiveness analyses have been conducted to evaluate their economic impact compared to conventional migraine treatments. Factors such as potential reductions in healthcare utilization and improved quality of life are taken into account when assessing their value. In terms of tolerability, CGRP antagonists are generally well-tolerated by patients. They offer a notable advantage over traditional migraine treatments by minimizing systemic side effects commonly associated with oral medications, such as gastrointestinal disturbances or sedation. Patients often appreciate the predictable dosing schedule of these biologics, which may improve treatment adherence and overall therapeutic outcomes. These novel treatments include monoclonal antibodies and small molecule inhibitors designed to either block CGRP receptors or inhibit the release of CGRP itself. By specifically targeting the CGRP pathway, these therapies aim to disrupt the cascade of events leading to migraine, providing patients with more effective and targeted relief 8 . The intricate relationship between migraines and CGRP, delving into the latest advancements in research, potential treatment options, and the transformative impact these developments may have on the lives of those affected by migraines. While pharmaceutical interventions are commonly used to manage migraines, there is growing interest in natural products as complementary or alternative approaches.

Natural products, derived from plants and other sources, have been explored for their potential in alleviating migraine symptoms and preventing recurrent attacks 9 . Feverfew is an herb known for its anti-inflammatory properties. Some studies suggest that it may help reduce the frequency and severity of migraines 10 . Butterbur (Petasites) has been studied for its potential in preventing migraines. It may work by reducing inflammation and stabilizing blood vessels 11 , 12 . Lavender oil is often used in aromatherapy to promote relaxation and alleviate stress, which can be triggers for migraines 13 . Peppermint oil has been associated with headache relief, possibly due to its muscle-relaxing and analgesic properties. Some studies suggest that magnesium supplementation may help reduce the frequency of migraines, possibly by stabilizing blood vessels and reducing cortical spreading depression 14 . Riboflavin has shown promise in reducing the frequency and duration of migraines, possibly by improving cellular energy production. Mind–body practices, such as yoga and meditation, can help manage stress, a common trigger for migraines 15 , 16 . These techniques promote relaxation and may contribute to overall well-being. Some individuals find relief by identifying and avoiding specific trigger foods, such as chocolate, caffeine, and certain additives 17 , 18 . Acupuncture, a key component of Traditional Chinese Medicine, involves the insertion of thin needles into specific points on the body. Some studies suggest that acupuncture may help reduce the frequency and intensity of migraines 19 , 20 , 21 . In recent years, Asarinin exhibited significant anti-inflammatory effects, which could be beneficial for various inflammatory conditions and may contribute to alleviating the inflammatory processes associated with migraine attacks. Inflammation is a key component in the pathophysiology of migraines, and substances with anti-inflammatory properties can potentially mitigate the severity and duration of migraine symptoms. Its potent antioxidant properties help in combating oxidative stress, a factor implicated in migraine pathogenesis. Its antioxidant activity 22 helps neutralize harmful free radicals, potentially reducing oxidative damage to cells and tissues that may contribute to overall migraine prevention or attenuation. Additionally, the compound's analgesic properties provide pain relief, making it a valuable candidate for further research in pain management 23 could be crucial in mitigating the intense pain experienced during migraine attacks. Additionally, it also exhibits neuroprotective properties, which can be beneficial for individuals experiencing migraines. This multifaceted profile of Asarinin underscores its potential as a versatile therapeutic agent and may show promise in the treatment of migraines, offering potential relief where traditional methods fall short. Migraines are associated with neuronal hyperexcitability, and neuroprotective agents like Asarinin may help safeguard nerve cells from damage during migraine episodes. The purpose of docking and dynamic studies of Asarinin with CGRP in the context of migraine research is multifaceted and aims to advance our understanding of Asarinin's potential as a therapeutic agent for migraine management. The intricate relationship between migraines and CGRP provides a foundation for investigating how Asarinin may interact with CGRP and contribute to migraine relief.

Methodology

Pharmacokinetic assessment.

Extracting information from the PubChem server reveals the standard GRINs (Graphical Representation of the Chemical Structure) of Asarinin. Pharmacokinetic studies were completed utilizing pkCSM and Swiss ADME, with the ADMET profile retrieved from the host computer using canonical SMILES. Both pkCSM and Swiss ADME furnish details on the drug's pharmacokinetics (PK), pharmacodynamics (PD), and toxicology (Toxicity). The web-based pkCSM application facilitates the exploration of the pharmacokinetic properties of drugs, and their physicochemical properties was conducted using pkCSM software. Lipinski's rule of five, which identifies five physicochemical features influencing a molecule's efficacy, safety, or metabolism, was employed to analyze Asarinin 24 , 25 .

Predicting drug bioactivity: in silico analysis with MOLINSPIRATION

The Asarinin underwent in silico testing using MOLINSPIRATION software to assess drug similarity and predict bioactivity. The likelihood of a molecule being active increases with a higher score. A chemical with a bioactivity score exceeding 0.00 is deemed to possess notable biological activities. Scores ranging between -0.50 and 0.00 on the bioactivity scale indicate high activity, whereas values falling below -0.50 are indicative of inactivity 26 , 27 .

Toxicological modeling and simulation

To mitigate potential complications arising from drug withdrawal, such as organ system failure or damage, it is imperative to conduct toxicological testing. The OSIRIS property explorer software was employed, utilizing PubChem structures to assess the toxicity levels of the compounds. Substances were categorized on a color scale, indicating their potential to cause cancer, induce mutations, irritate, affect reproduction, and act as a potential drug. The computed based on specific toxicity criteria: high risk indicated in red, medium risk denoted in yellow, and low risk represented in green 28 , 29 .

• Molecular docking

Molecular docking is a computational technique used in structural biology, bioinformatics, and drug discovery to predict how two molecules, typically a small ligand and a larger protein interact with each other. The primary goal of molecular docking is to predict the preferred orientation and conformation of the ligand when bound to the target protein 30 .

Selection of CGRP protein and preparation of Asarinin ligand

Crystallographic or homology-based models of the CGRP protein were obtained from relevant databases such as the Protein Data Bank. Multiple structures, representing different conformations or states, were considered for a comprehensive analysis. The chemical structure of Asarinin was obtained from PubChem database, and its 3D structure was optimized using Chem3D Pro 12.0. The rationale behind selecting the specific PDB ID 6ZHO for the CGRP protein, related to migraine, involves several key factors. PDB ID 6ZHO represents a high-resolution crystal structure of the CGRP receptor, ensuring an accurate representation of the protein's conformation, including critical regions involved in ligand binding and receptor activation. During the ligand preparation process, we performed several optimization steps to ensure accuracy and reliability. These included energy minimizations using molecular mechanics and quantum mechanics methods, conformational analysis to explore the conformational space thoroughly, optimization of protonation states to reflect physiological pH conditions, consideration of different tautomeric forms, application of solvation models to simulate the aqueous environment, and filtering ligands using Lipinski's Rule of Five and other drug-likeness criteria. These steps collectively improved the quality and reliability of our ligand preparations, enhancing the accuracy of our in silico predictions. The ligand was prepared by assigning appropriate charges and minimizing energy to ensure a stable conformation 31 , 32 , 33 .

Molecular docking setup and validation

The binding interactions between asarinin and the CGRP receptor PDBID:6ZHO were investigated using AutoDockTool v1.5.6. Grid parameters were carefully chosen to include the ligand-binding site, considering ligand and receptor flexibility. The proteins in this study were designed, and the modified protein was optimized by stabilizing amino acid residue ionization and tautomeric states. This technique removed water molecules and added hydrogen. To keep the modified protein structure available for docking studies, a PDB file was created. This work optimized proteins using the Molegro molecular viewer to change bond order after water and covalently bound ligands were removed. The molecular mechanics force field decreased energy once charge and protonation states were assigned. AutoDock generated docking conformers by computing Gasteiger charges and assessing the ligand's rotatable bonds. The receptor grids were built from defined receptor locations, and the grid boxes were created with the receptor grid axes as the domain, centrally situating the macromolecule in the checkerboard. Maps were created using Autogrid 4. All molecular docking simulations used Lamarckian genetics. Docking involved 50 iterations, 150 subjects, 2.5 million assessments, and 27,000 generations. Biovia Discovery Studio 2021 imported docking snapshots and exported docked structures to pdbqt. The docked conformations were analyzed for asarinin-CGRP receptor binding affinity, modes, and critical interactions. Visual inspection and computational measures like binding energy and hydrogen bond analyses were used to assess complex stability using Biovia Discovery Studio 2021 34 , 35 , 36 , 37 .

Molecular dynamics simulations

Molecular dynamics simulations were used to study the Asarinin-CGRP receptor complex's dynamics. Solvation was performed, and force fields were used to observe the interaction's stability and flexibility over time. This helped us understand Asarinin's long-term effects on the CGRP receptor. MD simulations were done on top-docked Asarinin and CGRP protein conformations (PDB: 6ZHO) using the Schrodinger Desmond 5.6.1 modelling software in Linux. PRODRG generated critical files, including gro and Asarinin inhibitor files, before simulation. After the MMFF94 force field, solvation, ion addition, energy minimization, and system equilibration tests (NVT and NPT) were performed. After generating trajectories with a 10-nm MD simulation, RMSD, RMSF, and radius of gyration were examined. To investigate the protein dynamics when Asarinin binds to 6ZHO, MD simulation experiments examined the docked protein's protein–ligand complex. If the original structure was energetically unstable, Desmond was critical in equilibrating the system to a stable shape. Periodic boundary conditions (PBCs) and the single-point charge (SPC) water model TIP3P were used to solve each compound in a 10-member water box. For charge neutrality, Na+ and Cl− ions were introduced to the OPLS2005 force field, which represented the protein and ligand. Energy minimization took 2000 steps, followed by a 10 ns production cycle. The system was then manufactured in the NPT ensemble, progressively heating to 300 K and steady pressure. For this, the Nose–Hoover thermostatic algorithm and Martina–Tobias–Klein method were used. Long-range electrostatic interactions were simulated using particle mesh Ewald (PME) with 0.8 grid spacing. The Desmond package's Simulation Interaction Diagram tool was used to examine ligand–protein interactions. Similar methods were used to compare protein and ligand RMSD and RMSF values to reference values. Trajectories are used in molecular dynamics modelling to anticipate molecular variability. The best-posed bound complexes with the strongest binding energies from molecular docking were individually obtained and analyzed using Schrödinger Desmond MD simulation. The drug's topology and gro file were prepared using PRODRUG (an online service) before MD simulation. The protein–ligand combination was built using the OPLS2005 force field, followed by energy minimization and ion addition. NVT and NPT were used for system equilibration after energy minimization. A leap-frog integrator with a step size of 2 fs was used to run a ten-nanosecond MD run. Data were stored every 2 picoseconds for stability study 38 , 39 , 40 .

MMGBSA analysis

MMGBSA (Molecular Mechanics/Generalized Born Surface Area) is a method used to estimate the binding free energy of ligands to proteins, combining molecular mechanics energies with solvation energies for accurate predictions. The obtained trajectory file from MD simulation was subjected to the MMGBSA panel in Schrodinger Maestro 12.0 Software. Various parameters such as molecular mechanics energy (bonded, electrostatic, van der Waals interactions), solvation energy (polar solvation via the Generalized Born model), and nonpolar solvation energy (via surface area calculations), were calculated. The binding free energy (ΔG bind) is derived as the difference in free energy between the complex and the sum of the individual protein and ligand energies. Finally, the average binding free energy and its standard deviation were computed 41 , 42 .

• Network pharmacology

Asarinin targeted proteins were screened by the SwissADME prediction tool http://www.swisstargetprediction.ch/ with the help of specific compound SMILES. The STRING tool constructed the PPI network, Gene Ontology (GO) aspects, and KEGG pathway analysis (P < 0.01) focusing on common targets in "homo sapiens" with an interaction score threshold of 0.4 by searching “CGRP”. Cytoscape v_3.10.0 software was used by searching CGRP in protein query to analyze potential targets, and pathways to evaluate Asarinin efficacy against migraine. Network topological parameters (degree, betweenness, proximity) and primary targets were identified. Protein–Protein Interactions (PPI) were crucial and analyzed for understanding cellular mechanisms. CytoScape_v3.10.0 software was utilized to generate the network diagram and network analysis 43 , 44 , 45 , 46 , 47 , 48 .

Pharmacokinetic assessment of Asarinin

Asarinin serves as a substrate for the CYP3A4 enzyme and acts as an inhibitor for CYP1A2, CYP2C9, CYP2C19, and CYP3A4. Its clearance was determined to be − 0.126 ml/min/kg. According to the pkCSM database, the maximum safe daily intake for Asarinin in humans is reported to be 0.089 mg/kg, with a predicted LD 50 of 2.833 mol/kg. Table 1 provides a comprehensive overview of the pharmacokinetic profile of Asarinin, including its substrate and inhibitory actions on various cytochrome P450 enzymes, clearance, maximum safe daily intake, and LD 50 predictions. Notably, Asarinin does not induce skin sensitivity, liver damage, or renal damage. It achieves a favourable bioactivity score for therapeutic targets, establishing the reliability of this pharmacokinetic technique in drug development and analysis. SwissADME analysis of Asarinin's pharmacokinetic profile suggests high gastrointestinal absorption and identification as a P-glycoprotein I inhibitor. While Asarinin exhibits moderate absorption and distribution throughout the body, its intestinal absorption rate of 97.81% stands out significantly. The permeability of Asarinin through the blood–brain barrier is calculated to be − 0.862 (log BB), indicating effective passage, while its central nervous system permeability is − 2.939 (log PS), signifying enhanced penetration and the potential for pharmacological effects in the CNS. ADMET predictions underscore Asarinin's notable pharmacological properties, particularly its role as a P-glycoprotein I inhibitor, as evidenced by absorption properties below the threshold values. The physiochemical profile of Asarinin is appealing, with anticipated values falling within the acceptable range.

Toxicological assessment of Asarinin

The toxicity of the examined inhibitors was determined through in-silico methods, and the substance's physicochemical properties were assessed using the OSIRIS Property Explorer illustrated in Fig.  1 . It is imperative to scrutinize the toxicity profile of Asarinin before progressing to clinical trials. Asarinin demonstrated a lack of mutagenic, carcinogenic, irritant, or reproductive effects, aligning with the toxicity characteristics of the tested compounds. The majority of proposed inhibitors exhibited acceptable toxicity profiles, rendering them plausible therapeutic alternatives. The OSIRIS Property Explorer endorsed the substance with a clean health bill regarding the mentioned aspects, enhancing its safety. The Topological Polar Surface Area (TPSA) of a molecule is a critical determinant of its absorption and distribution within the body. OSIRIS Property Explorer computed Asarinin's TPSA to be 55.38. The recorded drug-likeness score for this molecule was 0.65. A positive result indicates that the molecule shares common features with commercially available drugs. The essence of a drug, derived from the combination of its molecular weight, toxicity risks, and cLogP, is crucial in determining its suitability for a given treatment. Asarinin's drug score was determined to be 0.65.

The Asarinin compound is moderately lipophilic, as indicated by its logP value of 3.22 and molecular weight of 354.36. Despite its low solubility in water (logS of − 4.39), its interactions with biological targets may be impacted by the fact that it lacks hydrogen bond donors and contains six hydrogen bond acceptors. The presence of two stereocenters and four rotatable bonds suggests that the molecule is quite flexible. There is little to no concern for carcinogenicity, irritating potential, reproductive toxicity, or tumorigenicity, as all of the toxicity forecasts are green. In general, the chemical shows promise as a potential drug development candidate due to its low projected toxicity, acceptable drug-likeness, and drug score.

Bioactivity Score Assessment of Asarinin

The Molinspiration online tool extends complimentary services to the internet chemistry community, encompassing the prediction of bioactivity scores for crucial drug targets such as GPCR ligands, ion channel modulators, kinase inhibitors, and nuclear receptors. Additionally, it facilitates the computation of essential molecular properties, including polar surface area, log P, and the count of hydrogen bond donors and acceptors, among others. Table 2 displays the bioactivity scores for Asarinin across crucial drug targets, as predicted by the Molinspiration online tool. The absence of PAINS warnings is also highlighted. Furthermore, the tool delves into medicinal chemistry aspects such as synthetic accessibility and the presence of PAINS warnings. Notably, none of the synthesized chemicals elicited a warning, as indicated by a PAINS alert value of 0.

Docking assessment and validation

Among various PDB IDs examined, we identified 6ZHO as the most suitable candidate for further docking analysis, supported by its commendable Ramachandran plot, indicating the structural integrity necessary for robust molecular docking studies. Asarinin showed the almost equal binding energy in CGRP (PDB: 6ZHO = − 10.03 kcal/mol) when compared with Atogepant (PDB: 6ZHO = − 10.53 kcal/mol). Since molecular docking persists as a computer simulation approach used for determining the shape of a receptor ligand complex, the hypothesis desires to be tested empirically in future studies, but the data presented here suggests that Asarinin might have a significant ability to directly connect with CGRP. The Asarinin and Atogepant formed a hydrogen bond with amino acid residues LYS2103, ARG2119, THR2120, and THR2122 was illustrated in Fig.  2 and Table 3 details the hydrogen bonding interactions between Asarinin and amino acid residues in the CGRP (PDB: 6ZHO) receptor. The distances of the hydrogen bonds are presented for each interaction. In most selected targets, a considerably increased number of amino acid residues engaged in hydrogen bonding and van der Waals interactions was related with shorter hydrogen bond lengths (less than 3.0). The docking findings for Asarinin and Atogepant, with binding affinities were corroborated by a thorough examination of their binding poses and important interactions inside the target protein's active region which demonstrate the docking protocol's reliability and precision. Therefore, Asarinin had significant hydrogen bonds and hydrophobic interactions with important residues, which were similar to those seen with Atogepant. The protocol's ability to estimate ligand–protein binding affinities is validated by its concordance with experimental results.

This figure shows a snapshot of a molecular docking of a ligand's interaction with a protein's binding site. Ribbons in purple and blue depict several secondary structural components of the protein. In the middle of the stick figure is the ligand, which is colored according to its type of atom. Important residues that interact with each other are highlighted in red. These include TRP2072, ASP2070, PRO1085, THR2120, and ARG2119. The interactions are shown by the dashed lines, which vary in color from purple for hydrogen bonds to green for hydrophobic or π–π stacking interactions. ( a ) Presents the molecular docking analysis of Asarinin with CGRP (PDB: 6ZHO), revealing a hydrogen bond formation with specific amino acid residues (LYS2103, ARG2119, THR2120, THR2122). Binding affinity = − 10.03 kcal/mol. ( b ) Presents the molecular docking analysis of Atogepant with CGRP (PDB: 6ZHO), revealing a hydrogen bond formation with specific amino acid residues (LYS2103, ARG2119, THR2120, THR2122). Binding affinity = − 10.53 kcal/mol. The ligand's possible effectiveness as a medication candidate is dependent on its strong and stable binding affinity, which is suggested by the presence of numerous hydrogen bonds and hydrophobic interactions.

Molecular dynamic assessment

Molecular dynamics simulations were carried out on the Asarinin and 6ZHO docked complexes. In the early phases, the root mean square deviation (RMSD) showed a correlation with the ligand RMSD (Asarinin) and persisted for up to 100 ns, suggesting the occurrence of multiple interactions with amino acid residues. The accompanying visual figure summarizes these interactions and the number of touchpoints. Key amino acids, such as ASN14, GLY15, and LYS44 were highlighted for their essential role in establishing interactions and contributing to the non-competitive binding site, as depicted in Fig.  3 . After MD simulation, the 2D critical geometry of Asarinin with 6ZHO revealed varied interactions with the optimal binding site of 6ZHO. Graphs illustrating the composition of secondary structure elements (SSE) for each orbital frame throughout the simulation were provided, and the lower chart in Fig.  4 depicted alterations in SSE assignment for each residue over time.

The stability of the complex's protein and ligand, as simulated by a 100-ns molecular dynamics run, is illustrated in the image. Root Mean Square Deviation (RMSD) plots of the protein (blue line) and ligand (red line) are displayed in the top panel. After an initial period of rise, the protein RMSD stabilizes at approximately 3–4 Å, suggesting that it attains a relatively stable shape. This indicates that there are no major changes to the protein structure during the simulation. Likewise, the ligand RMSD varies within the range of 3–4 Å, showing that the ligand maintains a constant binding position in relation to the protein. Since the ligand RMSD remains relatively constant throughout the simulation, it is safe to assume that it maintains a stable contact with the protein and does not undergo any substantial dissociation or changes to its binding conformation. Taking all of these findings into account, it appears that the protein and ligand both keep their conformations steady, suggesting that the protein–ligand combination remains stable during the simulation.

Depicts the 2D critical geometry of Asarinin with 6ZHO post-molecular dynamics simulation. Additionally, it presents graphs illustrating the composition of secondary structure elements (SSE) for each orbital frame throughout the simulation. Each amino acid is represented by a coloured circle with its corresponding code and sequence number. Here we may see the ligand-amino acid or water-molecule interactions graphically represented by dotted lines and arrows with percentages next to them. The ligand-ASP 67 (26%), ASN 14 (88%), GLU 113 (46%), LYS 44 (26%), GLY 15 (70%), and GLU 155 (15%) interactions. The bonding likelihood varies, with ASN 14 showing the highest interaction probability. Shown are both direct and water-mediated interactions, illustrating how dynamic and complicated the binding environment is. To comprehend the possible therapeutic uses of the ligand, it is essential to grasp the binding mechanism, and this comprehensive map of ligand–amino acid interactions, particularly the highlighted percentages, gives important insights in this regard.

The free energy calculations provide insight into the binding affinities of Asarinin and Atogepant. Asarinin exhibits a more negative total binding free energy (Δ G Bind =  − 39.4678 kcal/mol) compared to Atogepant (Δ G Bind =  − 34.84066 kcal/mol), indicating that Asarinin has a stronger binding affinity to the target. This is an important factor in drug design as a more negative binding free energy generally correlates with higher efficacy. Examining the individual energy contributions, Atogepant demonstrates stronger lipophilic interactions (Δ G Bind_Lipo =  − 26.90095 kcal/mol) and Asarinin (Δ G Bind_Lipo =  − 22.5455 kcal/mol); van der Waals forces (Δ G Bind_vdW =  − 40.94962 kcal/mol) compared to Asarinin (Δ G Bind_vdW =  − 39.1126 kcal/mol). Atogepant also shows a slightly stronger hydrogen bonding contribution on comparison with Asarinin (Δ G Bind_Hbond =  − 1.281017 kcal/mol and Δ G Bind_Hbond = − 0.31806 kcal/mol). However, these favourable interactions are offset by Atogepant's (Δ G Bind_Coulomb = − 1.351845 kcal/mol), higher ligand strain energy (15.564371 kcal/mol) and less favourable solvation energy (Δ G Bind_Solv_GB = 25.209839 kcal/mol), suggesting that Atogepant undergoes more structural deformation and experiences greater desolvation penalties upon binding. In contrast, Asarinin, while having an unfavourable electrostatic contribution (Δ G Bind_Coulomb = 2.255693 kcal/mol), benefits from significantly lower ligand strain energy (5.749303 kcal/mol) and less unfavourable solvation energy (Δ G Bind_Solv_GB = 16.3162 kcal/mol). These factors contribute to its overall more favourable binding energy. Additionally, the lower ligand efficiency value for Asarinin (− 1.51799) compared to Atogepant (− 0.810248) was listed in Table 4 . It suggests that Asarinin is less efficient in binding per non-hydrogen atom, but the overall binding energy remains more favourable. In summary, while Atogepant has strong individual interactions, its overall binding is less favourable due to higher strain and solvation penalties. Asarinin’s lower strain and solvation penalties contribute to its stronger binding affinity, making it potentially a more effective binder to the target.

Network analysis

To delve deeper into the complexities of PPI networks, we inputted data on the 42 common targets into the STRING database. This led to the creation of a network with 42 proteins as nodes connected by 223 PPI relationships, visually depicted in Fig.  5 . The graphical representation of protein interactions was accomplished using Cytoscape v_3.10.0. After establishing the PPI network, we utilized the network analyzer to calculate degree values. The analysis focused on Gene Ontology (GO) and KEGG pathways, yielding 594 GO enrichment outcomes, including cellular component analysis (39), biological process analysis (519), and molecular function analysis (36). Results related to the top ten GO characteristics were specifically extracted and visualized using a bioinformatics platform. Regarding KEGG analysis, a total of 86 pathways were identified, emphasizing the gene ontology of the top ten targets, which were subsequently inputted into the bioinformatics platform. We employed the degree centrality method within Cytoscape, a widely acknowledged tool for identifying highly interconnected nodes, which often serve as critical targets or hubs in biological networks such as OPRM1, GNB1, GNAS, RAMP1, RAMP2, RAMP3, CALCR, CALCB, ADM, IAPP, and SLC5A2 are serve as central hubs within the PPI network, crucial in connecting various nodes addressing migraine with aura. The Table 5 presents results from 86 KEGG pathway enrichment analysis, highlighting pathways potentially relevant to migraine. Each pathway is characterized by observed gene counts, background gene counts, statistical strength (False Discovery Rate, FDR), and specific proteins identified within the network. Key pathways such as "Neuroactive ligand–receptor interaction", "Serotonergic synapse", "cAMP signaling pathway", and others suggest involvement in neurochemical processes and signaling mechanisms that could influence migraine pathophysiology. Genes identified within these pathways, including GABRA, GNAS, ADORA, and others, are known to modulate neurotransmitter release, vascular tone, and neuronal excitability, all critical factors in migraine development and propagation.

The figure shows a cell's protein–protein interaction (PPI) network. Visually represents the results of Gene Ontology (GO) and KEGG pathway analyses based on the PPI network. OPRM1, GNB1, GNAS, and other key targets are identified, shedding light on potential pathways implicated in migraine with aura. PPI network based on cluster analysis using the MCODE plugin. MCODE1: Proteins like PIK3CA, MTOR, and EGFR are labeled at each node. The node edges show protein interactions. Nodes with high connection, such as PIK3CA, are crucial to the network. To help visualize their biological importance, the colors of the nodes probably signify distinct functional groups or routes. Some of the most important proteins in this network include EGFR, which plays a key role in cell formation and differentiation, and MTOR, which controls cell metabolism and growth. The development of better therapeutic treatments and a better understanding of the molecular mechanisms driving migraines and similar diseases depend on our ability to decipher these interactions. MCODE 2: An important component in migraine pathogenesis is the GABA-A receptor, which consists of several subunits including GABRA1, GABRA2, GABRA3, GABRA5, GABRA6, GABRB3, and GABRG2. In this complex network, the individual subunits are shown as nodes and the connections between them are shown as edges. The gamma (GABRG), alpha (GABRA), and beta (GABRB) subunits can be more clearly distinguished by using color labeling. The intricate structure and possible interactions of the GABA-A receptor are shown in this detailed map. It emphasizes the receptor's function in inhibiting neurons and its connection to the processes of migraines. MCODE 3: The proteins PIK3CD, SYK, and HCK may play important roles in inflammation and immune cell activity. These proteins may play a role in the inflammatory pathways that contribute to the pathophysiology of migraines, which have been linked to neuroinflammatory processes. Exploring the interplay between PIK3CD, SYK, and HCK may lead to the discovery of new molecular targets for the improvement of migraine treatments, with an emphasis on regulating inflammatory responses and immunological responses. MCODE 4: Proteins like ALOX15, ALOX5, and ALOX12 are essential for the inflammatory response and bioactive lipid mediator synthesis; they are involved in the metabolism of arachidonic acid. Fatty acid metabolism impacts inflammation and vascular functions; ALOX12 is engaged in this process; ALOX5 leads to the generation of leukotrienes; and ALOX15 metabolizes arachidonic acid to create lipid mediators. Due to inflammation's central role in migraine pathophysiology, these interactions take on added significance when considering migraines. We have utilized KEGG analysis for the respective gene annotation and utilized Cytoscape_v1.10.0 to create this figure.

The pharmacokinetic, toxicological, bioactivity score, docking, and molecular dynamic assessments presented in this research article collectively contribute to a comprehensive understanding of Asarinin's potential as a therapeutic agent, particularly in the context of migraine management. The findings provide valuable insights into Asarinin's pharmacological properties, safety profile, and potential interactions with the CGRP. The pharmacokinetic profile of Asarinin reveals that its clearance, enzyme interactions, and predicted safe daily intake contribute to its overall safety profile. The negative clearance value of − 0.126 ml/min/kg signifies that the elimination process is overshadowed by the absorption process. It indicates a scenario where the rate of drug absorption exceeds its elimination rate during the specified experimental conditions 49 . The high gastrointestinal absorption, P-glycoprotein I inhibition 50 , 51 , and enhanced central nervous system permeability suggest promising pharmacokinetic characteristics for Asarinin's effectiveness in addressing migraine. The absence of mutagenic, carcinogenic, irritant, or reproductive effects, as well as a clean bill of health from the OSIRIS property explorer 52 , establishes Asarinin as a potentially safe therapeutic option. Quantitative Estimate of Druglikeness values can range between 0 and 1 considered as safest compound 53 . The drug-likeness score of asarinin 0.65 further supports its compatibility with commonly available drugs, enhancing its potential for clinical development. The bioactivity score assessment, conducted through Molinspiration, adds another layer of evidence supporting Asarinin's therapeutic potential. It is having good bioactivity score across important drug targets, along with the absence of PAINS warnings, suggests that Asarinin possesses desirable medicinal chemistry features. This is a positive indicator for its effectiveness in addressing specific targets related to migraine pathology. The docking assessment, utilizing molecular docking simulations, provides a theoretical foundation for understanding how Asarinin interacts with the CGRP receptor. The formation of hydrogen bonds with specific amino acid residues such as LYS2103, ARG2119, THR2120, and THR2122 in CGRP, as evidenced by the docking results, suggests a potential mechanism for Asarinin's activity. The favourable binding energy strengthens the hypothesis that Asarinin (− 10.03 kcal/mol) could have a significant ability to interact with CGRP. The molecular dynamic assessment extends the understanding gained from docking studies by exploring the dynamic behavior of Asarinin-CGRP complexes over time. RMSD and interaction analysis shed light on the stability and nature of interactions between Asarinin and CGRP. Gibb’s free energy lower than − 25 kcal/mol is considered as good indicative of strong binding affinity. The results suggest that Asarinin (Δ G Bind =  − 39.4678 kcal/mol) is potentially a more effective binder to the target than Atogepant (Δ G Bind =  − 34.84066 kcal/mol) due to its lower overall binding free energy. This finding is particularly relevant for drug design, where binding affinity is a critical parameter. Atogepant's higher ligand strain energy indicates that it undergoes more structural deformation upon binding, which could affect its stability and efficacy. Additionally, its less favourable solvation energy suggests greater desolvation penalties, which can impact its binding affinity. On the other hand, Asarinin's lower strain energy and solvation penalties contribute significantly to its stronger binding affinity. Overall, this analysis highlights the importance of evaluating multiple energy contributions to understand the binding characteristics of potential drug candidates fully. Asarinin emerges as a promising candidate due to its favourable binding free energy profile, but there may be room for improvement in its molecular interactions to enhance its efficiency and overall therapeutic potential.

The network analysis suggested that the specific proteins such as OPRM1, GNB1, and GNAS are interlinked and interacted with the migraine with aura pathological targets like RAMP1 54 , RAMP2 55 , RAMP3 56 , CALCR 57 , CALCB 58 , ADM 59 , IAPP 60 , and SLC5A2 61 . OPRM1 (Opioid Receptor Mu 1) opioid receptors are involved in pain modulation 62 , and there is evidence suggesting that the endogenous opioid system may play a role in migraine pathophysiology. GNB1 (G Protein Subunit Beta 1) G proteins are signaling molecules involved in transmitting signals from the cell surface to the inside of the cell. Abnormalities in signaling pathways may contribute to migraine susceptibility 63 . GNAS (G Protein Subunit Alpha S) similar to GNB1 is involved in G protein signaling. Genetic variations in GNAS have been associated with certain migraine subtypes 64 . RAMP1, RAMP2, RAMP3 (Receptor Activity Modifying Proteins) proteins are involved in the modulation of receptors, such as the calcitonin receptor-like receptor (CALCR), which plays a role in vascular regulation. Abnormalities in vascular function are thought to contribute to migraine attacks. CALCR (Calcitonin Receptor) is associated with the calcitonin gene-related peptide (CGRP) receptor, which is implicated in migraine 65 . CGRP is a neuropeptide involved in vasodilation and neurogenic inflammation, both of which are thought to play a role in migraine attacks 66 . CALCB (Calcitonin-Related Polypeptide Beta) is another component related to CGRP, and variations in the CGRP pathway are being explored as potential targets for migraine treatment 67 . SLC5A2 (Sodium/glucose cotransporter 2) is involved in glucose and sodium transport. While there is not a direct known link to migraines, disturbances in ion transport can impact neuronal excitability and may have implications in migraine pathophysiology 68 . The analysis of protein interactions indicates a network where these proteins are interconnected. This network analysis revealed that OPRM1 serves as a central hub, initiating the activation of GNAS and GNB1 which is illustrated in Fig.  6 . Eight out of thirteen recognized brain circuits are significant triggers in migraines. Among these are the GABAergic synapse, the Dopaminergic synapse, the Serotonergic synapse, the cAMP signaling pathway, the Neurotrophin signaling pathway, the Retrograde endocannabinoid signaling, and the Circadian entrainment pathways. In the onset and maintenance of migraine headaches, these pathways emphasize the intricate interaction of neurotransmitters, receptors, and signaling pathways. This activation, in turn, influences other proteins like RAMP 1–3, CALCB, CALCR, and SLC5A2, which are implicated in migraine attacks. By directing Asarinin toward OPRM1, it has the potential to inhibit OPRM1, further reducing the synthesis of CGRP and the activation of pain stimulation from other biomarkers implicated in migraine attacks. This suggests that Asarinin could play a pivotal role as a potential future for individuals suffering from migraines, offering the prospect of alleviating symptoms without causing severe adverse effects. It's important to note that while natural products may offer relief for some individuals, their efficacy varies, and not all remedies work for everyone. Additionally, maintaining a healthy lifestyle, managing stress, and identifying and avoiding triggers are essential components of a holistic approach to migraine management.

The figure illustrates a detailed protein interaction network related to migraines, with Asarinin highlighted as a novel biomolecule targeting multiple proteins. Asarinin (green diamond) is connected to a wide array of proteins (blue rectangles) via green edges, indicating its multifaceted influence. Central to this network is OPRM1 (purple oval), serving as a key intermediary linking Asarinin to migraine (yellow diamond) through direct red dashed arrows. Additional proteins like GNB1 and GNAS (green rectangles) further connect OPRM1 to migraine-related pathways. The involvement of proteins such as MTOR, PIK3CA, EGFR, ALOX5, ALOX12, and ALOX15 underscores the network’s complexity and highlights the roles of cell growth, metabolism, and inflammatory responses in migraine pathophysiology. This comprehensive network demonstrates Asarinin’s potential as a multifunctional therapeutic agent, providing valuable insights into the molecular mechanisms of migraines and identifying new avenues for treatment. We utilized CytoScape_v3.10.0 software to create this figure.

Future implications

Looking ahead, the multifaceted assessments conducted in this research article pave the way for a promising future in harnessing Asarinin as a therapeutic agent, particularly in the realm of migraine management. The integration of pharmacokinetic, toxicological, bioactivity score, docking, and molecular dynamic analyses provides a holistic perspective that not only elucidates Asarinin's pharmacological properties but also underscores its potential as a novel and effective intervention for migraines. The amalgamation of diverse assessments sets the stage for future research, preclinical investigations, and potential clinical trials that would be instrumental in establishing the safety and efficacy of Asarinin in human subjects, marking Asarinin as a beacon of hope in the pursuit of more effective migraine therapies. A thorough toxicological assessment is crucial for determining the safety of Asarinin before clinical trials.

In conclusion, the comprehensive array of assessments conducted in this research affords a thorough understanding of Asarinin's potential as a therapeutic agent in the context of migraine management. The combined findings contribute valuable insights into its pharmacological properties, safety profile, and potential interactions with the CGRP receptor. Its favourable pharmacokinetic profile, safety in toxicological evaluations, and potent bioactivity against CGRP suggest effectiveness. These endeavours will be instrumental in establishing the safety and efficacy of Asarinin in human subjects, marking it as a beacon of hope in the pursuit of more effective migraine therapies. Asarinin emerges as a promising candidate, offering a multifaceted approach to addressing the complex landscape of migraine management. OPRM1 emerges as a pivotal hub, instigating the activation of GNAS and GNB1, subsequently influencing proteins such as RAMP1, RAMP2, RAMP3, CALCB, CALCR, and SLC5A2 all implicated in migraine attacks. Significantly, OPRM1 stands out as a crucial target in network analysis presenting a promising avenue for intervention. Asarinin holds promise as a therapeutic agent, offering a multifaceted approach to addressing the intricate landscape of migraine management by targeting OPRM1. These findings pave the way for further exploration and potential clinical applications, marking Asarinin as a beacon of hope in the pursuit of more effective migraine therapies.

Data availability

All data generated or analysed during this study are included in this published article.

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Acknowledgements

The authors collectively extend their appreciation to the administration of SRM College of Pharmacy, SRMIST, located in Kattankulathur, Chengalpattu, Tamil Nadu, India. Furthermore, R. Rushendran expresses sincere gratitude to The Ministry of Tribal Affairs of the Indian Government for the fellowship support extended. The fellowship is identified by the award number 202122-NFST-AND-00924.

Open access funding provided by SRM Institute of Science and Technology for SRMIST – Medical & Health Sciences.

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Department of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603 203, India

Rapuru Rushendran & Vellapandian Chitra

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Rapuru Rushendran, conceptualized and designed the study, gathered and structured the data, formulated the methodology, generated visual representations, conducted formal analysis and interpretation, drafted the article, and extracted as well as compiled all essential data. Vellapandian Chitra, offered valuable supervision throughout the research process, contributing to data analysis, formal analysis, interpretation, validation, and data compilation. Ultimately, all authors jointly reviewed and approved the final version of the article for publication.

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Rushendran, R., Chitra, V. Antimigraine activity of Asarinin by OPRM1 pathway with multifaceted impacts through network analysis. Sci Rep 14 , 20207 (2024). https://doi.org/10.1038/s41598-024-70933-2

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Received : 13 February 2024

Accepted : 22 August 2024

Published : 30 August 2024

DOI : https://doi.org/10.1038/s41598-024-70933-2

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