Structural and compositional dimensions of phytochemical diversity in the genus Piper reflect distinct ecological modes of action

Context: An increasing number of ecological studies have used chemical diversity as a functionally relevant, scalable measure of phytochemical mixtures, demanding more rigorous attention to how chemical diversity is estimated. Most studies have focused on the composition of phytochemical mixtures and have largely ignored structural concerns, which may have greater importance for ecological function. Here we explore the development of structural complexity and compositional diversity resulting from different biotic and abiotic interactions in Piper kelleyi Tepe (Piperaceae). We also describe how variation in structural complexity and compositional diversity differ between two congeners, P. kelleyi and Piper reticulatum. To better interpret these results, we have developed a hypothesis-driven framework for interpreting these dimensions of chemical diversity in phytochemical mixtures.

Approach: We used the tropical shrub, P. kelleyi, as a model system to examine interactions between ecological factors and dimensions of phytochemical diversity. We also compared compositional diversity and metabolic complexity in P. kelleyi and P. reticulatum using liquid chromatography and 1H NMR respectively to examine tradeoffs between compositional diversity and structural complexity. A framework is provided to generate meaningful estimates of the structural complexity of phytochemical mixtures as measured by 1H NMR.

Results and Conclusions: Piper is an abundant plant genus that supports diverse insect communities throughout the tropics. Subtle changes in understory forest light were associated with increases in herbivory that directly increased compositional diversity and indirectly decreased structural complexity in P. kelleyi. This was attributed to the production of oxidation products resulting from herbivory-driven decomposition of structurally complex defense compounds. This type of complex result would remain undetected using standard chemical ecology approaches and accounts for the detailed molecular changes that are likely to affect species interactions.

Synthesis: Our quantitative framework provides a method for considering tradeoffs between structural complexity and compositional diversity and the interpretation of analytical approaches for each. This methodology will provide new theoretical insights and a more sophisticated model for examining the ecology and evolution of chemically mediated interactions.

Methods
Piper reticulatum (Piperaceae) foliar samples were collected from individual plants > 10 m apart along both sides of the Sendero Surá (55-255 m), Sendero Arriera-Zampopa (110-804 m) and Sendero Tres Rios (155-2450 m) trails in La Selva Biological Station (LS, 10°25’ N 84°00' W), Costa Rica. In a prior experiment (Glassmire et al., 2019), we collected P. kelleyi from experimental plots of clones in Yanayacu Biological Station (YBS, 00°36′ S 77°53′ W) in Ecuador grown at high and low canopy height along an elevational gradient (2000-2400 m) of the Eastern Andes mountains. Canopy cover and direct light transmittance of P. kelleyi plants were measured using a Canon EOS Rebel-T4 camera with a hemispherical fisheye lens and processed using Gap Light Analyzer software (GLA software methods, Frazer et al., 1999). Herbivory was estimated from actual and estimated (pre-damage) leaf area using Image J before arcsine square root transformation (Glassmire et al., 2019). All Piper samples were finely ground using a tissue lyser (TissueLyser II, Quiagen; Hilden, Germany) and 100 mg portions from each individual plant were weighed before adding 10 mL of methanol (Fisher, Optima). Methanolic suspensions were sonicated for 10 min and extracted overnight with wrist-action shaking before filtering over a cotton plug and concentrating to dryness in vacuo. 

Nuclear magnetic resonance spectroscopy

Dried Piper extracts were re-dissolved in 1.00 mL d1-methanol for deuterium exchange in order to minimize water signal (as HOD) in 1H NMR spectra. An aliquot of this solution was diluted 1:10 using protonated methanol for LC-MS analysis before the remainder was dried in vacuo. A second deuterium exchange was performed before dissolving in d4-methanol containing 0.05% tetramethylsilane (TMS) as an internal standard and acquiring 1H NMR (32 scans) on a Varian MR-400 NMR. Raw FIDs were processed using MestreNova (Mestrelab Research, S.L. Santiago de Compostela, Spain), including auto-phasing, ablative baseline correction and global spectral deconvolution (GSD) peak picking. Spectra were exported to csv files and normalized to total peak area after removing solvent and TMS regions (0-0.5, 3.3-3.32, 4.79-4.92, and 7.25-7.28 ppm) before binning at 0.04 ppm. Bins with an area below 10-4 were set to zero before calculating diversity measures.

Liquid chromatography-UV/VIS-mass spectrometry

Piper extracts were characterized via liquid chromatography-mass spectrometry (LC-MS), using an Agilent (Santa Clara, CA) 1200 analytical HPLC equipped with a binary pump, autosampler, column compartment and diode array UV/Vis detector, coupled to an Agilent 6230 Time-of-Flight mass spectrometer via an electrospray ionization source (ESI-TOF; gas temperature: 325 °C, flow: 10 L/m; nebulizer pressure: 35 psig; VCap: 3500 V; fragmentor: 165 V; skimmer: 65 V; octopole: 750 V). Extracts (0.20 μL) for P. kelleyi were co-injected with anofinic acid internal standard (1.00 uL, 0.2 M) and eluted at 0.500 mL/min through a Kinetex EVO C18 column (Phenomenex, 2.1 x 100 mm, 2.6 μ, 100 Å) at 40 °C. The linear binary gradient was comprised of buffers A (water containing 0.1 % formic acid) and B (acetonitrile containing 0.1 % formic acid) changing over 20 minutes accordingly: 0-1 min 20% B, ramp to 50% B at 6 min, ramp to 100% B at 12 min, 12-16 min hold at 100% B, 16-17 min ramp to 20% B, 17-20 min hold at 20% B. Raw LC-MS data were converted to mzML format using ProteoWizard (Kessner et al., 2008) before processing using the XCMS package in the R statistical programming software (Team, 2014). Chromatographic features were retention time corrected and aligned using density grouping. LC-MS features were sum-aggregated based on CAMERA classification (Kuhl et al., 2012) as to not overstate compositional richness. LC-UV chromatograms were integrated (l = 254) using Agilent MassHunter. This wavelength was chosen because it is representative of the relative concentrations of the NMR-apparent molecules in P. kelleyi, which allows for valid inferences about the composition of molecules in crude 1H NMR spectra.

Compositional diversity vs. metabolic complexity

Compositional diversity (DC) or metabolic complexity (DM) can be calculated as Richness, Shannon or Simpson diversity, or beyond into higher q values using analogous methods for calculating Hill numbers for community data (Marion et al., 2015). Metabolic complexity arises in chemical mixtures, such as a crude plant extracts, from the aggregation of molecules, each with their own structural complexity, in proportion to their composition. Figure 1 outlines how the 1H NMR spectra of individual molecules combine to yield crude mixture spectra within our framework for understanding the properties of chemical mixtures and their constituent molecules that yield metabolic complexity. If one were to analyze a hypothetical crude extract, separation methods such as liquid chromatography (LC) and gas chromatography (GC) provide a compositional profile of this mixture wherein each peak represents a different molecule (Fig. 1, compositional diversity). If each of these compounds is isolated to obtain a 1H NMR spectrum, each spectrum reflects the complexity of that molecule (Fig. 1, structural complexity). The structural complexity index of a molecule is calculated in the same fashion as one would for compositional diversity, but instead of each peak representing a whole molecule, it represents a structural feature of that molecule; all peaks in a spectrum represent the spectral fingerprint of an entire molecule.

Recombining these individual 1H NMR spectra according to their relative abundance yields a 1H NMR spectrum that reflects the crude mixture before separation. Similar structural features of molecules will lead to overlapping 1H NMR signals that increase abundance for those signals, lowering signal evenness. Dissimilar structural features will have non-overlapping signals, leading to higher peak richness (Fig. 1, structural dissimilarity). One can therefore view the diversity of crude 1H NMR as a gross measure of metabolic complexity, incorporating the composition, complexity and dissimilarity of phytochemical mixtures.

Effective structural complexity (DSeff)

Once we have obtained compositional diversity (DC, from LC) and metabolic complexity (DM, from 1H NMR) as either Richness, Shannon or Inverse Simpson diversity, we can calculate effective structural complexity (DSeff) as follows:

(Eqn. 1)

Having removed the compositional contribution to metabolic complexity, effective structural complexity represents the remaining structural contribution. In terms of richness, this could be thought of as the average number of 1H NMR peaks per compound or as an abundance-weighted average for higher order Hill numbers. While seemingly simple, effective structural complexity represents both the structural complexity and dissimilarity of molecules in a mixture.

Metabolic complexity (DM) of a mixture can be decomposed into beta dissimilarity (βD) from mean structural complexity (DS) of all constituent molecules in a mixture, weighted to their concentration, as described for mean alpha diversity using community data by (Jost, 2007):

(Eqn. 2)

Highly dissimilar mixtures will lead to high metabolic complexity, even when the constituents of those mixtures have low structural complexity. Structural dissimilarity (DD) can be further decomposed from βD and compositional diversity:

(Eqn. 3)

which yields a number between 0 and 1 that can be interpreted as the fraction of signal overlap between constituents of a phytochemical mixture per compound. Mixtures with high structural dissimilarity (near one) have very little signal overlap, while mixtures having low structural dissimilarity have high signal overlap per compound. While this decomposition provides theoretical insight into partitioning chemical diversity, it is impractical for experimental data where structures and spectra of individual phytochemicals within a mixture are unknown. However, it is possible to estimate compositional diversity (DC) of phytochemical mixtures using hyphenated analytical methods (GC-, LC-), which can then be used to decompose structural complexity from metabolic complexity as in equation 1. If we combine equations 1-3, they simplify to give effective structural complexity as:

(Eqn. 4)

Effective structural complexity results from both structural parameters of chemical mixtures: structural dissimilarity and mean structural complexity. High DSeff can result from highly dissimilar mixtures or highly complex individual compounds.

### 研究背景
越来越多的生态学研究将化学多样性作为植物化学混合物（phytochemical mixtures）的功能相关且可量化的测度指标，这对化学多样性的估算方法提出了更严谨的要求。现有多数研究多聚焦于植物化学混合物的组成特征，却极大程度忽略了其结构层面的考量——而结构特征可能对生态功能具有更关键的影响。本研究以胡椒科（Piperaceae）的凯利胡椒（Piper kelleyi Tepe）为对象，探究不同生物与非生物交互作用下植物化学混合物的结构复杂度与组成多样性的变化规律；同时比较同属的凯利胡椒与网脉胡椒（Piper reticulatum）在结构复杂度与组成多样性上的差异。为更好地阐释本研究结果，我们构建了一个基于假说的分析框架，用于解读植物化学混合物的化学多样性维度。

### 研究思路
本研究以热带灌木凯利胡椒为模式系统，探究生态因子与植物化学多样性各维度之间的交互关系；同时分别采用液相色谱（Liquid Chromatography, LC）与氢谱核磁共振（Proton Nuclear Magnetic Resonance, 1H NMR）技术，对凯利胡椒与网脉胡椒的组成多样性与代谢复杂度进行比较，以解析组成多样性与结构复杂度之间的权衡关系。此外，我们提出了一套分析框架，可基于1H NMR谱图对植物化学混合物的结构复杂度进行有效估算。

### 结果与结论
胡椒属（Piper）是一类分布广泛的植物类群，在全球热带区域支撑着多样的昆虫群落。林下光照的细微变化与植食性昆虫取食压力的升高相关联，该变化直接提升了凯利胡椒的植物化学组成多样性，同时间接降低了其结构复杂度——这一现象可归因于植食作用驱动下结构复杂的防御化合物发生降解并产生氧化产物。这类精细的研究结果无法通过常规化学生态学方法被检测到，其揭示了可能影响物种间相互作用的分子层面的细微变化。

### 研究意义
本研究提出的量化分析框架，为解析结构复杂度与组成多样性之间的权衡关系以及各自对应的分析方法解读提供了可行路径。该方法将为探究化学介导的物种相互作用的生态学与进化机制提供全新的理论视角与更为精细的分析模型。

## 材料与方法
### 样品采集与前处理
网脉胡椒（Piper reticulatum，胡椒科）的叶片样品采自哥斯达黎加拉塞尔瓦生物站（La Selva Biological Station, LS, 10°25’ N 84°00' W）内的三条样线两侧：Sendero Surá（海拔55-255 m）、Sendero Arriera-Zampopa（海拔110-804 m）以及Sendero Tres Rios（海拔155-2450 m），采样植株间距均大于10 m。
此前的实验（Glassmire等，2019）中，我们从厄瓜多尔亚纳亚库生物站（Yanayacu Biological Station, YBS, 00°36′ S 77°53′ W）的克隆种植实验样地采集凯利胡椒样品：该样地沿东安第斯山脉海拔梯度（2000-2400 m）设置了不同冠层高度的处理组（高冠层与低冠层）。
采用搭载半球形鱼眼镜头的佳能EOS Rebel-T4相机测定凯利胡椒植株的冠层覆盖率与直接光照透射率，并通过冠层间隙光分析仪（Gap Light Analyzer, GLA，Frazer等，1999）完成数据处理。植食率通过Image J软件对实际叶面积与损伤前预估叶面积进行测算，之后采用反正弦平方根变换进行数据标准化（Glassmire等，2019）。
所有胡椒属植物样品均使用组织研磨仪（TissueLyser II, 凯杰Quiagen；德国希尔德）进行精细研磨，称取每份植株样品的100 mg粉末，加入10 mL色谱级甲醇（Fisher Optima品牌）。将甲醇悬浮液超声处理10 min，随后通过手腕式摇床振荡萃取过夜；萃取液经棉塞过滤后，采用真空旋转蒸发至干。

### 核磁共振波谱分析
将干燥后的胡椒提取物重新溶解于1.00 mL氘代甲醇（d1-methanol）中进行氘代交换，以尽可能抑制1H NMR谱图中水峰（以HOD形式存在）的信号。取部分该溶液用质子化甲醇以1:10比例稀释，用于后续液相色谱-质谱（Liquid Chromatography-Mass Spectrometry, LC-MS）分析；剩余溶液经真空旋转蒸发至干后，再次进行氘代交换，随后将样品溶解于含0.05%四甲基硅烷（Tetramethylsilane, TMS，内标）的氘代甲醇（d4-methanol）中，在Varian MR-400核磁共振仪上采集1H NMR谱图（扫描次数32次）。
原始自由感应衰减信号（Free Induction Decay, FID）采用MestreNova软件（Mestrelab Research S.L.，西班牙圣地亚哥德孔波斯特拉）进行处理，包括自动相位校正、自适应基线校正与全局谱图去卷积（Global Spectral Deconvolution, GSD）峰识别。将处理后的谱图导出为csv格式文件，去除溶剂与TMS对应的信号区域（0-0.5、3.3-3.32、4.79-4.92与7.25-7.28 ppm）后，将谱峰面积归一化至总峰面积，再以0.04 ppm为宽度进行谱峰分箱；将面积低于10^-4的分箱信号值设为0，随后用于多样性指标的计算。

### 液相色谱-紫外/可见-质谱联用分析
采用安捷伦（Agilent，美国加利福尼亚州圣克拉拉）1200型高效液相色谱（High Performance Liquid Chromatography, HPLC）仪（搭载二元泵、自动进样器、柱温箱与二极管阵列紫外/可见检测器）与安捷伦6230型电喷雾电离飞行时间质谱（Electrospray Ionization Time-of-Flight Mass Spectrometry, ESI-TOF）仪联用，对胡椒提取物进行定性分析：气体温度325 ℃，气流速率10 L/m，雾化压力35 psig，毛细管电压3500 V，碎裂电压165 V，skimmer电压65 V，八极杆电压750 V。
凯利胡椒样品的进样体积为0.20 μL，与1.00 μL、0.2 M的茴香酸内标（anofinic acid）混合后进样；流动相流速为0.500 mL/min，采用Kinetex EVO C18色谱柱（Phenomenex，2.1 × 100 mm，2.6 μm，100 Å），柱温维持在40 ℃。二元线性梯度洗脱程序如下：流动相A为含0.1%甲酸的水溶液，流动相B为含0.1%甲酸的乙腈溶液；0-1 min保持20% B，6 min时梯度升至50% B，12 min时升至100% B，12-16 min维持100% B，16-17 min梯度降至20% B，17-20 min维持20% B。
原始LC-MS数据采用ProteoWizard软件（Kessner等，2008）转换为mzML格式，随后通过R统计编程软件（Team，2014）中的XCMS包进行数据处理：采用密度分组法对色谱峰进行保留时间校正与峰对齐；基于CAMERA分类法（Kuhl等，2012）对LC-MS特征峰进行汇总聚合，以避免高估组成丰富度。采用安捷伦MassHunter软件对LC-UV色谱图在254 nm波长下进行峰积分，该波长的选择依据为其能够反映凯利胡椒中通过1H NMR检测到的分子的相对浓度，从而可对粗提物1H NMR谱图中的分子组成进行合理推断。

### 组成多样性与代谢复杂度的定义
组成多样性（Compositional Diversity, DC）与代谢复杂度（Metabolic Complexity, DM）均可采用物种群落数据的Hill数计算方法，以丰富度、Shannon指数或Simpson指数（或更高阶的q值）进行计算（Marion等，2015）。代谢复杂度源于植物粗提物等化学混合物中各分子按其组成占比的聚集：每个分子均具有自身的结构复杂度，其在混合物中的相对丰度共同决定了整体的代谢复杂度。
图1阐释了本研究框架下，单个分子的1H NMR谱图如何叠加得到粗提混合物的谱图，从而揭示化学混合物及其组成分子的属性与代谢复杂度的关系。假设对某一假想的植物粗提物进行分析：液相色谱（LC）或气相色谱（Gas Chromatography, GC）等分离技术可得到混合物的组成谱图，其中每个色谱峰对应一种不同的分子（图1，组成多样性）；若将每种化合物分离并采集其1H NMR谱图，则每张谱图反映了该分子自身的结构复杂度（图1，结构复杂度）。分子的结构复杂度指数的计算方式与组成多样性类似，但此时每个谱峰代表的并非完整的分子，而是该分子的一个结构特征；整张谱图的所有峰共同构成了整个分子的光谱指纹。

将单个分子的1H NMR谱图按照其相对丰度进行叠加，即可得到分离前粗提混合物的1H NMR谱图。若不同分子具有相似的结构特征，其1H NMR信号会发生重叠，导致该信号的丰度升高、信号均匀度降低；反之，结构特征差异较大的分子则不会出现信号重叠，对应更高的峰丰富度（图1，结构差异性）。因此，粗提物1H NMR谱图的多样性可作为代谢复杂度的整体测度指标，整合了植物化学混合物的组成、复杂度与结构差异性。

### 有效结构复杂度（Effective Structural Complexity, DSeff）
当通过液相色谱得到组成多样性（DC）、通过1H NMR得到代谢复杂度（DM）后，可采用以下公式计算有效结构复杂度（DSeff）：
（公式1）
有效结构复杂度是从代谢复杂度中扣除组成多样性的贡献后剩余的结构层面贡献。以丰富度指标为例，有效结构复杂度可理解为每个分子对应的平均1H NMR峰数，或针对高阶Hill数的丰度加权平均值。尽管形式看似简洁，有效结构复杂度同时反映了混合物中分子的结构复杂度与结构差异性。

### 代谢复杂度与有效结构复杂度的分解
混合物的代谢复杂度（DM）可分解为基于各组成分子浓度加权的平均结构复杂度（DS）与β多样性差异（βD），该分解方法借鉴了群落数据下平均α多样性的分解逻辑（Jost，2007）：
（公式2）
即使混合物中各组成分子的结构复杂度较低，若混合物整体的结构差异性较高，仍可产生较高的代谢复杂度。结构差异性（DD）可进一步通过βD与组成多样性进行分解：
（公式3）
该公式的取值范围为0到1，可解释为植物化学混合物中每单位化合物对应的信号重叠比例：结构差异性较高的混合物（趋近于1）几乎不存在信号重叠，而结构差异性较低的混合物则具有较高的单化合物信号重叠率。尽管该分解框架为化学多样性的组分解析提供了理论视角，但对于未知单个植物化学成分结构与谱图的实验数据而言，该方法难以直接应用。不过，我们可通过联用分析技术（如GC、LC）估算植物化学混合物的组成多样性（DC），再结合公式1从代谢复杂度中分解得到结构复杂度。将公式1-3整合后，可简化得到有效结构复杂度的计算公式：
（公式4）
有效结构复杂度由化学混合物的两个结构参数共同决定：结构差异性与平均结构复杂度。较高的有效结构复杂度既可源于混合物中各分子的结构差异性较高，也可源于单个分子本身的结构复杂度较高。