Chapter 6: Lipid Biosynthesis

Part II — Core Metabolism

6.1 Acetyl-CoA Carboxylase & Malonyl-CoA

De novo fatty acid synthesis begins with the carboxylation of acetyl-CoA to malonyl-CoA, catalyzed by acetyl-CoA carboxylase (ACCase). In plants, there are two forms: a multifunctional homomeric form in the cytosol and a prokaryotic-type heteromeric form in the plastid (where de novo synthesis occurs).

\[\text{Acetyl-CoA} + HCO_3^- + ATP \xrightarrow{ACCase} \text{Malonyl-CoA} + ADP + P_i\]

The committed step and regulatory control point. ACCase is regulated by: light (via thioredoxin reduction), phosphorylation status, and metabolite feedback. Herbicides targeting plastidial ACCase include aryloxyphenoxypropionates (APPs) and cyclohexanediones (CHDs).

6.2 Fatty Acid Synthase (FAS) Complex

Plants use a dissociable prokaryotic-type FAS (type II) in plastids, with individual enzymes unlike the animal type I multifunctional protein. The acyl carrier protein (ACP) shuttles intermediates. One elongation cycle adds 2 carbons via 4 reactions:

1. Condensation

KAS III/I/II

Acyl-ACP + Malonyl-ACP → β-keto-acyl-ACP + CO₂

2. Reduction

KAR

β-keto-acyl-ACP + NADPH → β-hydroxy-acyl-ACP

3. Dehydration

HAD

β-hydroxy-acyl-ACP → trans-enoyl-ACP + H₂O

4. Reduction

ENR

trans-enoyl-ACP + NADPH → acyl-ACP (2C elongated)

Net reaction for palmitate (16:0) synthesis:

\[8\,\text{Acetyl-CoA} + 7\,ATP + 14\,NADPH \rightarrow \text{Palmitate} + 8\,CoA + 14\,NADP^+ + 7\,ADP + 7\,P_i + 7\,CO_2\]

6.3 Desaturases & Plastidial vs Cytosolic Pathways

Plants synthesize polyunsaturated fatty acids via sequential desaturation. The pathway bifurcates from 18:1-ACP (oleoyl-ACP) in plastids:

Plastidial Pathway (Prokaryotic):

18:1 → incorporated into MGDG/DGDG/PG at sn-2 position

FAD5: 16:0-MGDG → 16:1-MGDG (Δ7 desaturase)
FAD6: 18:1/16:1 → 18:2/16:2 (ω6 desaturase)
FAD7/FAD8: 18:2 → 18:3 (ω3 desaturase)

ER Pathway (Eukaryotic):

18:1-CoA exported to ER, incorporated into PC/PE at sn-2

FAD2: 18:1-PC → 18:2-PC (ω6, Δ12 desaturase, ER)
FAD3: 18:2-PC → 18:3-PC (ω3, Δ15 desaturase, ER)
Products reimported to chloroplast as DAG

6.4 TAG Assembly & Galactolipids

Kennedy Pathway (TAG Assembly):

Glycerol-3-P + acyl-CoA → LPA (GPAT)
LPA + acyl-CoA → PA (LPAAT)
PA → DAG + Pᵢ (PAP)
DAG + acyl-CoA → TAG (DGAT) — committing step

DGAT1 and DGAT2 are key targets in oilseed engineering.

Galactolipids (Major Plastid Lipids):

MGDG (monogalactosyldiacylglycerol): ~50% thylakoid lipid
DGDG (digalactosyldiacylglycerol): ~25% thylakoid lipid
Enriched in 18:3 (α-linolenate) — essential for PSI/PSII function
MGDG synthase (MGD1) — critical for chloroplast biogenesis
DGDG exported to outer mitochondrial/peroxisomal membranes under Pi starvation

Fatty Acid Synthesis Cycle

Simulation: Lipid Synthesis Energetics

Cumulative ATP and NADPH costs per elongation round, and fatty acid melting point vs desaturation.

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=0.38)

# Panel 1: Palmitate synthesis energetics (16C fatty acid, 8 rounds)
ax1 = axes[0]
ax1.set_facecolor('#0d1a0d')
elongation_steps = list(range(1, 8))  # Rounds of elongation (start from 4C -> 16C)
atp_per_round = 1  # from activation via malonyl-CoA (1 ATP per bicarbonate fixation)
nadph_per_round = 2
cumul_atp   = np.cumsum([7] + [0]*6)  # 7 ATP to make 7 malonyl-ACP
cumul_nadph = np.cumsum([2]*7)
cumul_co2_release = np.cumsum([1]*7)  # CO2 released from each decarboxylation
rounds = np.arange(1, 8)

ax1.plot(rounds, cumul_atp, 'o-', color='#fbbf24', lw=2, ms=7, label='Cumul. ATP used')
ax1.plot(rounds, cumul_nadph, 's-', color='#4ade80', lw=2, ms=7, label='Cumul. NADPH used')
ax1.plot(rounds, cumul_co2_release, '^-', color='#f87171', lw=2, ms=7, label='Cumul. CO2 released')
ax1.set_xlabel('Elongation round (2C addition)', color='#a3e635', fontsize=9)
ax1.set_ylabel('Cumulative molecules', color='#a3e635', fontsize=9)
ax1.set_title('Palmitate (C16) Synthesis Costs', 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, 0.97, 'Total: 7 ATP + 14 NADPH', transform=ax1.transAxes,
    color='#d9f99d', fontsize=9, va='top')
for sp in ax1.spines.values(): sp.set_edgecolor('#4ade80')

# Panel 2: Desaturation comparison in plant fatty acids
ax2 = axes[1]
ax2.set_facecolor('#0d1a0d')
fa_names = ['18:0
(Stearate)', '18:1
(Oleate)', '18:2
(Linoleate)', '18:3
(Linolenate)', '16:0
(Palmitate)', '20:4
AA', '20:5
EPA']
melting_pts = [69.6, 13.4, -5.0, -11.0, 63.1, -49.5, -54.0]
double_bonds = [0, 1, 2, 3, 0, 4, 5]
is_plant = [True, True, True, True, True, False, False]

colors_fa = ['#4ade80' if p else '#fb923c' for p in is_plant]
bars = ax2.bar(fa_names, melting_pts, color=colors_fa, edgecolor='#0d1a0d', linewidth=1)
ax2.axhline(37, color='#f87171', lw=1.5, ls='--', label='Body temperature (37°C)')
ax2.axhline(0, color='gray', lw=0.8, ls=':')
for bar, db in zip(bars, double_bonds):
    ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1.5,
        f'{db} dbl', ha='center', color='#d9f99d', fontsize=8)
ax2.set_ylabel('Melting Point (°C)', color='#a3e635', fontsize=9)
ax2.set_title('Fatty Acid Desaturation: Melting Points', color='#d9f99d', fontsize=10)
ax2.legend(fontsize=8.5, labelcolor='#d9f99d', facecolor='#0d1a0d', edgecolor='#4ade80')
ax2.tick_params(colors='#a3e635', labelsize=8)
ax2.grid(True, alpha=0.2, color='#4ade80', axis='y')
patch1 = plt.Rectangle((0,0),1,1, fc='#4ade80', label='Plant FA')
patch2 = plt.Rectangle((0,0),1,1, fc='#fb923c', label='Animal FA')
ax2.legend(handles=[patch1, patch2], fontsize=8.5, labelcolor='#d9f99d', facecolor='#0d1a0d', edgecolor='#4ade80')
for sp in ax2.spines.values(): sp.set_edgecolor('#4ade80')

fig.suptitle('Plant Lipid Biosynthesis Energetics', 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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