Chapter 12: Phenylpropanoid Pathway

Part IV — Hormones & Signaling

12.1 PAL: The Gateway Enzyme

Phenylalanine ammonia-lyase (PAL) catalyzes the committed step of the phenylpropanoid pathway — the non-oxidative elimination of ammonia from L-phenylalanine to produce trans-cinnamic acid. This reaction requires a 4-methylideneimidazole-5-one (MIO) prosthetic group formed by autocatalytic cyclization of an Ala-Ser-Gly motif.

\[\text{L-Phe} \xrightarrow{PAL} \text{trans-Cinnamate} + NH_3\quad \Delta G^{\circ\prime} = -5\text{ kJ mol}^{-1}\]

PAL Properties:

Homotetrameric, ~330 kDa total
K_m(Phe) ≈ 0.1–0.5 mM; k_cat ≈ 0.5–1 s⁻¹
Competitively inhibited by trans-cinnamate (product inhibition)
Inhibited by aminooxy-PAP (herbicide research tool)
Induced by UV, wounding, pathogen, blue light, MeJA
Arabidopsis: 4 PAL genes (PAL1–4) with overlapping functions

MIO Mechanism:

The MIO group acts as electrophile in an E1cb elimination:

NH₂ of Phe attacks MIO electrophile → N-MIO adduct
Pro-S β-proton abstracted by Tyr nucleophile
E1cb elimination: N-MIO adduct breaks → NH₃ release + cinnamate
MIO regenerated for next cycle

12.2 Core Phenylpropanoid Pathway: PAL → 4CL

Enzyme	Reaction	Cofactors
PAL (Phe ammonia-lyase)	Phe → trans-Cinnamate + NH₃	MIO prosthetic group
C4H (Cinnamate 4-hydroxylase, CYP73A5)	Cinnamate → p-Coumarate	CYP450, NADPH, O₂
4CL (4-coumarate:CoA ligase)	p-Coumarate + CoA + ATP → p-Coumaroyl-CoA + AMP + PPᵢ	ATP, CoA, Mg²⁺

p-Coumaroyl-CoA is the central branching point, leading to: (1) flavonoids via chalcone synthase (CHS), (2) monolignols via HCT/CCoAOMT/CCR/CAD, (3) stilbenes via stilbene synthase (STS), (4) coumarins via F6H/scopoletin pathway.

12.3 Monolignol Pathway & Lignin Polymerization

Lignin, the second most abundant biopolymer on Earth, is a random radical polymer of monolignols. Three major monolignols with different degrees of methoxylation:

p-Coumaryl alcohol

H-lignin (0 methoxy groups)

Grasses, compression wood

Coniferyl alcohol

G-lignin (1 methoxy group, 3-position)

Gymnosperms (dominant), hardwood

Sinapyl alcohol

S-lignin (2 methoxy groups, 3,5-positions)

Angiosperms (co-dominant with G)

Lignin Polymerization (Radical Coupling):

Class III peroxidases and laccases oxidize monolignols to phenoxy radicals in the apoplast. Resonance-stabilized radicals couple non-enzymatically via several linkage types: β-O-4 (most common, ~50–65%), β-5, β-β, 5-5, 4-O-5.

\[\text{Monolignol} + H_2O_2 \xrightarrow{\text{peroxidase}} \text{Monolignol}^{\bullet} + H_2O\]

The β-O-4 linkage is most susceptible to chemical/enzymatic depolymerization — key target for lignocellulosic biofuel research. Lignin has no regular repeat unit — it is technically a racemic, random heteropolymer.

12.4 Chalcone Synthase, Coumarins & Stilbene Synthase

CHS (Chalcone Synthase)

\[\text{4-Coumaroyl-CoA} + 3\,\text{Malonyl-CoA} \xrightarrow{CHS} \text{Naringenin chalcone}\]

Iterative type III PKS; Claisen condensations. Product cyclized by CHI (chalcone isomerase) → naringenin (flavanone) → all other flavonoids (flavones, flavonols, anthocyanins, proanthocyanidins).

Coumarin Biosynthesis

p-Coumaroyl-CoA → (via HCT, C3H, CCoAOMT) → caffeic/ferulic acid → glucoside → F6H (feruloyl-6-hydroxylase, CYP450) → ortho-hydroxylation → spontaneous lactonization → scopoletin/umbelliferone

Scopoletin: iron-mobilizing fluorescent coumarin, root exudate under iron deficiency

STS (Stilbene Synthase) & Resveratrol

\[\text{4-Coumaroyl-CoA} + 3\,\text{Malonyl-CoA} \xrightarrow{STS} \text{Resveratrol}\]

Same substrates as CHS but different folding/decarboxylation → stilbene skeleton. Resveratrol: antifungal phytoalexin in grape/peanut; viniferins from oxidative dimerization. Induced by UV, Botrytis, and mechanical wounding.

Phenylpropanoid Pathway: Phe to Lignin & Flavonoids

Simulation: PAL Kinetics & Pathway Carbon Flux

PAL Michaelis-Menten kinetics with trans-cinnamate product inhibition at varying concentrations, and phenylpropanoid carbon flux partitioning under normal vs biotic stress conditions.

Python

script.py72 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: PAL enzyme kinetics with product inhibition (trans-cinnamate)
ax1 = axes[0]
ax1.set_facecolor('#0d1a0d')
Phe_conc = np.linspace(0, 2.0, 200)  # mM
Vmax = 50.0   # nmol/min/mg
Km   = 0.12   # mM
# Product inhibition (competitive by trans-cinnamate)
Ki   = 0.08   # mM (competitive)
cinnamate_concs = [0.0, 0.1, 0.3, 0.8]
cols_pal = ['#4ade80', '#86efac', '#fbbf24', '#f87171']

for cin, col in zip(cinnamate_concs, cols_pal):
    Km_app = Km * (1 + cin / Ki)
    v = Vmax * Phe_conc / (Km_app + Phe_conc)
    label = f'[Cinnamate] = {cin} mM' if cin > 0 else 'No product inhibition'
    ax1.plot(Phe_conc, v, color=col, lw=2, label=label)

ax1.set_xlabel('[Phenylalanine] (mM)', color='#a3e635', fontsize=9)
ax1.set_ylabel('PAL Activity (nmol/min/mg)', color='#a3e635', fontsize=9)
ax1.set_title('PAL Kinetics: Product Inhibition
by trans-Cinnamate', 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')
ax1.text(0.05, 0.55, f'Km = {Km} mM
Ki (cin.) = {Ki} mM', transform=ax1.transAxes,
    color='#d9f99d', fontsize=8.5)
for sp in ax1.spines.values(): sp.set_edgecolor('#4ade80')

# Panel 2: Carbon flux through phenylpropanoid branches
ax2 = axes[1]
ax2.set_facecolor('#0d1a0d')
branches = ['Lignin', 'Flavonoids
(CHS branch)', 'Coumarins', 'Stilbenes
(STS branch)', 'Hydroxycinnamic
acid esters']
flux_normal  = [55, 20, 10, 3, 12]   # % carbon flux under normal
flux_stress  = [35, 35, 12, 10, 8]   # % under biotic stress
x = np.arange(len(branches))
w = 0.35
bars1 = ax2.bar(x - w/2, flux_normal, w, label='Normal growth', color='#4ade80', edgecolor='#0d1a0d', alpha=0.9)
bars2 = ax2.bar(x + w/2, flux_stress, w, label='Biotic stress', color='#fbbf24', edgecolor='#0d1a0d', alpha=0.9)
ax2.set_xticks(x)
ax2.set_xticklabels(branches, color='#a3e635', fontsize=8.5)
ax2.set_ylabel('Carbon flux (% of phenylpropanoid pool)', color='#a3e635', fontsize=9)
ax2.set_title('Phenylpropanoid Carbon Flux
Normal vs Biotic Stress', color='#d9f99d', fontsize=10)
ax2.legend(fontsize=9, labelcolor='#d9f99d', facecolor='#0d1a0d', edgecolor='#4ade80')
ax2.tick_params(colors='#a3e635', labelsize=8)
ax2.grid(True, alpha=0.2, color='#4ade80', axis='y')
for bar, val in zip(bars1, flux_normal):
    ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
        f'{val}%', ha='center', color='#4ade80', fontsize=8)
for bar, val in zip(bars2, flux_stress):
    ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
        f'{val}%', ha='center', color='#fbbf24', fontsize=8)
for sp in ax2.spines.values(): sp.set_edgecolor('#4ade80')

fig.suptitle('Phenylpropanoid Pathway: PAL Kinetics & Carbon Flux', 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

Course Complete!

You have completed all 12 chapters of Plant Biochemistry — from water potential and photosynthesis through nitrogen fixation to phenylpropanoid secondary metabolites. The chemical mechanisms studied here underpin all of plant biology, agriculture, and our understanding of how plants shape the global carbon and nitrogen cycles.

← Ch 11: Secondary Metabolites Course Overview →