Chapter 11: Secondary Metabolites Overview

Part IV — Hormones & Signaling

11.1 What Are Secondary Metabolites?

Plant secondary metabolites (also called specialized metabolites) are compounds not required for primary growth and reproduction but that provide ecological advantages. Over 200,000 distinct structures have been identified — an astonishing chemical diversity generated by a relatively small number of core biosynthetic pathways combined with diverse tailoring enzymes (CYPs, MTs, GTs, ATs).

Defense

Against herbivores, pathogens, UV radiation, oxidative stress, competing plants

Attraction

Pollinator attraction (floral volatiles, pigments), seed dispersal (fruit flavors/colors)

Communication

Allelopathy (root exudates), priming signals (MeSA, MeJA), rhizosphere signaling

11.2 Three Major Classes

A. Terpenes & Terpenoids (~40,000 known)

Built from isoprene (C₅) units via IPP (isopentenyl pyrophosphate). Two biosynthetic routes:

MEP/DXP Pathway (Plastid):

Pyruvate + G3P → DXP → MEP → HMBPP → IPP/DMAPP

Products: Monoterpenes (C₁₀), diterpenes (C₂₀), carotenoids, gibberellins, chlorophyll side chain

MVA Pathway (Cytosol):

3 Acetyl-CoA → HMG-CoA → mevalonate → MVA-5-P → IPP

Products: Sesquiterpenes (C₁₅), triterpenes (C₃₀), sterols, brassinosteroids

Notable examples: limonene (citrus), pinene (conifers), artemisinin (antimalarial), taxol (anticancer), isoprene (emitted, ~500 Tg/yr globally), rubber (polyisoprenoid).

B. Phenolics & Polyphenols (~8,000 known)

All derived from phenylalanine via the phenylpropanoid pathway (PAL is the gateway enzyme). Characterized by one or more aromatic rings with hydroxyl substituents.

Simple phenolics:

Hydroxybenzoates, hydroxycin-namates (e.g., caffeic acid)

Flavonoids:

Flavones, flavonols, anthocyanins (floral pigments, UV protection)

Lignins & Tannins:

Cell wall structural polymer (30% of land plant carbon), condensed & hydrolyzable tannins

C. Alkaloids (~12,000 known)

Nitrogen-containing compounds derived from amino acids (Trp, Tyr, Lys, Phe, Orn, Asp). Often physiologically active, acting as feeding deterrents to herbivores.

From Trp:

Vinblastine/vincristine (anticancer), strychnine, ergot alkaloids, indole alkaloids

From Tyr:

Morphine, codeine (opium poppy), berberine, colchicine

From Orn/Lys:

Nicotine (pyrrolidine), cocaine (tropane), caffeine (purine alkaloid)

Secondary Metabolites from Primary Precursors

Simulation: Stress-Induced Metabolite Accumulation

Kinetic models of phenolic, terpene, alkaloid, and glucosinolate accumulation following biotic stress, and comparative chemical diversity across secondary metabolite classes.

Python

script.py70 lines

import numpy as np
import matplotlib.pyplot as plt
# output saved as output.png

fig, axes = plt.subplots(1, 2, figsize=(14, 7), facecolor='#0a0f0a')
fig.subplots_adjust(wspace=0.42)

# Panel 1: Secondary metabolite accumulation under stress (exponential model)
ax1 = axes[0]
ax1.set_facecolor('#0d1a0d')
t = np.linspace(0, 48, 300)  # hours post-stress
stress_onset = 6

# Different metabolite classes with different kinetics
def metabolite_model(t, onset, kmax, k_rise, k_fall, baseline):
    response = np.where(t < onset, baseline,
        baseline + kmax * (1 - np.exp(-k_rise*(t-onset))) * np.exp(-k_fall * np.maximum(0, t - onset - 12)))
    return response

phenolics  = metabolite_model(t, stress_onset, 4.0, 0.15, 0.03, 0.5)
terpenes   = metabolite_model(t, stress_onset, 2.5, 0.25, 0.05, 0.3)
alkaloids  = metabolite_model(t, stress_onset, 1.8, 0.10, 0.02, 0.2)
glucosins  = metabolite_model(t, stress_onset, 3.2, 0.20, 0.04, 0.4)

ax1.axvline(stress_onset, color='#f87171', lw=2, ls='--', label='Stress onset', alpha=0.8)
ax1.plot(t, phenolics, color='#4ade80', lw=2.5, label='Phenolics')
ax1.plot(t, terpenes,  color='#fbbf24', lw=2.5, label='Terpenes')
ax1.plot(t, alkaloids, color='#c084fc', lw=2.5, label='Alkaloids')
ax1.plot(t, glucosins, color='#f87171', lw=2.5, label='Glucosinolates')
ax1.fill_between(t, phenolics, 0.5, where=(t > stress_onset), alpha=0.06, color='#4ade80')
ax1.set_xlabel('Time post-elicitation (h)', color='#a3e635', fontsize=9)
ax1.set_ylabel('Relative metabolite level', color='#a3e635', fontsize=9)
ax1.set_title('Secondary Metabolite Accumulation
under Biotic Stress', color='#d9f99d', fontsize=10)
ax1.legend(fontsize=8.5, labelcolor='#d9f99d', facecolor='#0d1a0d', edgecolor='#4ade80')
ax1.tick_params(colors='#a3e635', labelsize=8)
ax1.grid(True, alpha=0.2, color='#4ade80')
for sp in ax1.spines.values(): sp.set_edgecolor('#4ade80')

# Panel 2: Chemical diversity - number of known compounds per class
ax2 = axes[1]
ax2.set_facecolor('#0d1a0d')
classes = ['Terpenes
& steroids', 'Phenolics
& polyphenols', 'Alkaloids', 'Glucosinolates', 'Cyanogenic
glycosides', 'Saponins']
counts = [40000, 8000, 12000, 200, 60, 600]  # approximate known compounds
colors_cls = ['#fbbf24', '#4ade80', '#c084fc', '#f87171', '#60a5fa', '#fb923c']

bars = ax2.bar(classes, counts, color=colors_cls, edgecolor='#0d1a0d', linewidth=1)
for bar, count in zip(bars, counts):
    ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 200,
        f'~{count:,}', ha='center', color='#d9f99d', fontsize=8, fontweight='bold')

ax2.set_ylabel('Approximate known compounds', color='#a3e635', fontsize=9)
ax2.set_title('Chemical Diversity of Plant
Secondary Metabolites', color='#d9f99d', fontsize=10)
ax2.tick_params(colors='#a3e635', labelsize=8)
ax2.grid(True, alpha=0.2, color='#4ade80', axis='y')
for sp in ax2.spines.values(): sp.set_edgecolor('#4ade80')
ax2.set_yscale('log')
ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{int(x):,}'))

fig.suptitle('Plant Secondary Metabolites: Diversity & Dynamics', color='#d9f99d', fontsize=13, fontweight='bold')

plt.savefig('output.png', bbox_inches='tight', facecolor=fig.get_facecolor(), dpi=120)
plt.close()
print('Simulation complete.')

Click Run to execute the Python code

Code will be executed with Python 3 on the server

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