Chapter 8: Nucleotide Metabolism

Part III — Nitrogen & Amino Acids

8.1 Purine De Novo Synthesis: IMP Pathway

Purine nucleotides are synthesized de novo from small molecules including glycine, glutamine, CO₂, aspartate, formate (from the one-carbon pool via THF), and the ribose-5-phosphate backbone. The pathway builds the purine ring atom by atom on the ribose-5-phosphate scaffold, producing inosine 5′-monophosphate (IMP) in 10 steps.

Key Features:

10 enzymatic steps from PRPP (phosphoribosyl pyrophosphate)
Costs ~6 ATP equivalents per IMP
Requires glycine, glutamine (×3), aspartate, CO₂, 10-formyl-THF (×2)
IMP → AMP (adenylosuccinate synthetase + lyase) or GMP (IMP DH + GMP synthetase)
Regulated by end-product feedback: AMP/GMP inhibit PRPP amidotransferase

IMP → ATP / GTP:

\[\text{IMP} + \text{Asp} + \text{GTP} \xrightarrow{2\text{ steps}} \text{AMP}\]

\[\text{IMP} + NAD^+ + H_2O \xrightarrow{2\text{ steps}} \text{GMP}\]

Cross-regulation: GTP needed for AMP synthesis; ATP needed for GMP synthesis → balances adenine:guanine ratio

8.2 Pyrimidine De Novo Synthesis: UMP Pathway

Unlike purines, pyrimidines are synthesized as a free base before attachment to ribose-5-phosphate. The pathway produces UMP (uridine 5′-monophosphate) in 6 steps.

Step	Reaction	Enzyme / Location
1	Gln + CO₂ + 2ATP → Carbamoyl-P	CPS II (cytosol); CPS I (mitochondria in animals, plastid in plants)
2	Carbamoyl-P + Asp → N-carbamoyl-Asp	ATCase (aspartate transcarbamoylase)
3	N-carbamoyl-Asp → Dihydroorotate	DHOase (dihydroorotase)
4	Dihydroorotate → Orotate + FMNH₂	DHODH (inner mitochondrial membrane)
5	Orotate + PRPP → OMP	OPRT (orotate phosphoribosyltransferase)
6	OMP → UMP + CO₂	ODC (UMP synthase / OMP decarboxylase)

UMP → CTP, TMP:

UMP → UDP → UTP (kinases)
UTP + Gln + ATP → CTP (CTP synthetase)
UDP → dUDP → dUMP (RNR)
dUMP + 5,10-methylene-THF → dTMP (thymidylate synthase)

Regulation:

CPS II: inhibited by UTP (end-product feedback), activated by PRPP
ATCase in bacteria: inhibited by CTP, activated by ATP (not in plants)
UMP synthase: bifunctional enzyme — efficient channeling of OMP

8.3 Salvage Pathways & Deoxyribonucleotide Synthesis

Salvage Pathways:

Recycling preformed bases/nucleosides — metabolically cheaper than de novo:

APRT: Adenine + PRPP → AMP + PPᵢ
HGPRT: Hypoxanthine/Guanine + PRPP → IMP/GMP
Uridine/cytidine kinases: Ur + ATP → UMP; Cyd + ATP → CMP
Adenosine kinase: important in methionine recycling and cytokinin metabolism

Ribonucleotide Reductase (RNR):

Converts all four rNDPs → dNDPs using thioredoxin as electron donor:

\[\text{rNDP} + \text{Trx}_{red} \xrightarrow{RNR} \text{dNDP} + \text{Trx}_{ox}\]

Class I RNR: R1 + R2 subunits; tyrosyl radical mechanism
Activity site: controls overall activity (ATP = on; dATP = off)
Specificity site: substrate preference (ATP→dCDP/dUDP; dTTP→dGDP; dGTP→dCDP)

Purine & Pyrimidine Synthesis Overview

Simulation: De Novo vs Salvage Costs & RNR Kinetics

Energetic cost comparison of de novo vs salvage nucleotide synthesis, and ribonucleotide reductase activity as a function of reduced thioredoxin concentration.

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=0.4)

# Panel 1: De novo vs salvage pathway ATP costs
ax1 = axes[0]
ax1.set_facecolor('#0d1a0d')
pathways = ['Purine
De novo
(IMP)', 'Purine
Salvage
(hypoxanthine)', 'Pyrimidine
De novo
(UMP)', 'Pyrimidine
Salvage
(uracil)']
atp_cost     = [6, 2, 2, 1]  # Approximate ATP equivalents
nadph_cost   = [2, 0, 1, 0]
glutamine_cost=[4, 0, 2, 0]
colors_path  = ['#f87171', '#4ade80', '#fb923c', '#86efac']
x = np.arange(len(pathways))
w = 0.28
ax1.bar(x - w, atp_cost, w, label='ATP', color='#fbbf24', edgecolor='#0d1a0d')
ax1.bar(x, nadph_cost, w, label='NADPH', color='#4ade80', edgecolor='#0d1a0d')
ax1.bar(x + w, glutamine_cost, w, label='Glutamine', color='#60a5fa', edgecolor='#0d1a0d')
ax1.set_xticks(x)
ax1.set_xticklabels(pathways, color='#a3e635', fontsize=8.5)
ax1.set_ylabel('Molecules consumed per nucleotide', color='#a3e635', fontsize=9)
ax1.set_title('De Novo vs Salvage: Energetic 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', axis='y')
for sp in ax1.spines.values(): sp.set_edgecolor('#4ade80')

# Panel 2: Ribonucleotide reductase kinetics (ribose to deoxyribose)
ax2 = axes[1]
ax2.set_facecolor('#0d1a0d')
substrates = ['CDP', 'UDP', 'GDP', 'ADP']
# RNR requires two electrons from thioredoxin per ribonucleotide reduced
# Allosteric regulation: activity sites and specificity sites
thioredoxin = np.linspace(0, 200, 200)  # uM reduced thioredoxin
Km_trx = 15.0  # uM
Vmax_max = 50.0  # nmol/min/mg

cols_rnr = ['#4ade80', '#86efac', '#fbbf24', '#f87171']
for substrate, col in zip(substrates, cols_rnr):
    modifier = np.random.uniform(0.8, 1.2)  # slight variation per substrate
    v = modifier * Vmax_max * thioredoxin / (Km_trx + thioredoxin)
    ax2.plot(thioredoxin, v, color=col, lw=2, label=f'{substrate} -> d{substrate}')

ax2.set_xlabel('[Reduced Thioredoxin] (uM)', color='#a3e635', fontsize=9)
ax2.set_ylabel('RNR Activity (nmol/min/mg)', color='#a3e635', fontsize=9)
ax2.set_title('Ribonucleotide Reductase Kinetics
(Thioredoxin Dependence)', 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')
ax2.text(0.5, 0.1, 'All 4 rNDPs are substrates; product allosterically
controls specificity of next substrate',
    transform=ax2.transAxes, color='#d9f99d', fontsize=7.5, ha='center')
for sp in ax2.spines.values(): sp.set_edgecolor('#4ade80')

fig.suptitle('Nucleotide Biosynthesis in Plants', 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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