Chapter 4: Glycolysis & TCA Cycle

Part II — Core Metabolism

4.1 Glycolysis: 10 Steps

Glycolysis converts glucose (6C) to 2 pyruvate (3C) via 10 enzymatic reactions in the cytosol. Plants have both cytosolic and plastidial isoforms of many glycolytic enzymes, allowing carbon entry into plastidial biosynthesis directly. Net yield: 2 ATP + 2 NADH per glucose.

Step	Enzyme	Reaction	ΔG°′ (kJ/mol)
1	Hexokinase	Glucose + ATP → G-6-P + ADP	−16.7
2	Phosphoglucose isomerase	G-6-P ⇌ F-6-P	+1.7
3	Phosphofructokinase-1 (PFK)	F-6-P + ATP → F-1,6-BP + ADP	−14.2
4	Aldolase	F-1,6-BP ⇌ DHAP + G3P	+23.8
5	Triose phosphate isomerase	DHAP ⇌ G3P	+7.5
6	G3P dehydrogenase	G3P + NAD⁺ + Pᵢ ⇌ 1,3-bPGA + NADH	−18.8
7	Phosphoglycerate kinase	1,3-bPGA + ADP ⇌ 3-PGA + ATP	+1.1
8	Phosphoglycerate mutase	3-PGA ⇌ 2-PGA	+4.4
9	Enolase	2-PGA ⇌ PEP + H₂O	−3.2
10	Pyruvate kinase	PEP + ADP → pyruvate + ATP	−31.4

Plant-specific: PPi-dependent PFK

Many plant tissues express pyrophosphate-dependent phosphofructokinase (PFP) alongside ATP-PFK. PFP is near-equilibrium, reversible, and may facilitate gluconeogenesis or glycolytic flexibility:

\[\text{F-6-P} + PP_i \rightleftharpoons \text{F-1,6-BP} + P_i\quad (\Delta G^{\circ\prime} \approx 0)\]

4.2 Pyruvate Dehydrogenase Complex (PDC)

The PDC bridges glycolysis and the TCA cycle, irreversibly oxidizing pyruvate to acetyl-CoA. Plants have both mitochondrial and plastidial PDC isoforms; the plastidial form is essential for de novo fatty acid synthesis.

\[\text{Pyruvate} + \text{CoA} + NAD^+ \xrightarrow{PDC} \text{Acetyl-CoA} + CO_2 + NADH\quad \Delta G^{\circ\prime} = -33.4\text{ kJ mol}^{-1}\]

PDC is a multi-enzyme complex (E1: pyruvate decarboxylase + TPP; E2: dihydrolipoamide acetyltransferase; E3: dihydrolipoamide dehydrogenase). Regulated by product inhibition (NADH, acetyl-CoA) and phosphorylation (PDC kinase inactivates E1; PDC phosphatase reactivates).

4.3 TCA Cycle: 8 Reactions

Citrate synthase

Acetyl-CoA + OAA → Citrate + CoA

ΔG°′ = −32.2 kJ/mol

Aconitase

Citrate ⇌ Isocitrate

ΔG°′ = +5.0 kJ/mol

Isocitrate DH

Isocitrate + NAD⁺ → α-KG + CO₂ + NADH

ΔG°′ = −21.0 kJ/mol

α-KG DH complex

α-KG + NAD⁺ + CoA → Succinyl-CoA + CO₂ + NADH

ΔG°′ = −33.0 kJ/mol

Succinyl-CoA synthetase

Succinyl-CoA + GDP + Pᵢ → Succinate + GTP + CoA

ΔG°′ = −2.1 kJ/mol

Succinate DH (Complex II)

Succinate + FAD → Fumarate + FADH₂

ΔG°′ = +0.0 kJ/mol

Fumarase

Fumarate + H₂O ⇌ Malate

ΔG°′ = −3.8 kJ/mol

Malate DH

Malate + NAD⁺ ⇌ OAA + NADH

ΔG°′ = +29.7 kJ/mol

Net per acetyl-CoA:

\[2\,CO_2 + 3\,NADH + FADH_2 + GTP + \text{(regenerated OAA)}\]

Glycolysis Pathway Overview

Simulation: Glycolysis & TCA Energetics

Free energy profile across glycolysis steps and cumulative cofactor production in the TCA cycle.

Python

script.py69 lines

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

fig, axes = plt.subplots(1, 2, figsize=(14, 8), facecolor='#0a0f0a')
fig.subplots_adjust(wspace=0.4)

# Panel 1: Glycolysis step-by-step free energy
ax1 = axes[0]
ax1.set_facecolor('#0d1a0d')
steps = ['Glc', 'G6P', 'F6P', 'F1,6BP', 'DHAP/G3P', 'G3P', '1,3bPGA', '3-PGA', '2-PGA', 'PEP', 'Pyr']
dG_prime = [0, -16.7, +1.7, -14.2, +5.7, 0, -18.8, +1.1, +4.4, -31.4, 0]
cumulative_G = np.cumsum([0] + dG_prime[1:])
colors_steps = ['#4ade80' if dg <= 0 else '#f87171' for dg in dG_prime[1:]]

ax1.step(range(len(steps)), cumulative_G, color='#a3e635', lw=2, where='post')
for i in range(1, len(steps)):
    col = '#4ade80' if dG_prime[i] <= 0 else '#f87171'
    ax1.annotate('', xy=(i, cumulative_G[i]), xytext=(i, cumulative_G[i-1]),
        arrowprops=dict(arrowstyle='->', color=col, lw=1.8))
irreversible = [0, 3, 9]  # steps with large -dG
for idx in irreversible:
    ax1.axvline(idx, color='#fbbf24', lw=0.8, ls=':', alpha=0.5)
ax1.set_xticks(range(len(steps)))
ax1.set_xticklabels(steps, rotation=45, ha='right', color='#a3e635', fontsize=8)
ax1.set_ylabel('Cumulative dG (kJ/mol)', color='#a3e635', fontsize=9)
ax1.set_title('Glycolysis: Free Energy Profile', color='#d9f99d', fontsize=10)
ax1.tick_params(colors='#a3e635', labelsize=8)
ax1.grid(True, alpha=0.2, color='#4ade80', axis='y')
ax1.text(0.5, 0.97, 'Yellow lines = irreversible steps', transform=ax1.transAxes,
    color='#fbbf24', fontsize=8, ha='center', va='top')
for sp in ax1.spines.values(): sp.set_edgecolor('#4ade80')

# Panel 2: TCA cycle intermediates and ATP/NADH/FADH2 production
ax2 = axes[1]
ax2.set_facecolor('#0d1a0d')
tca_steps = ['Acetyl-CoA
entry', 'Citrate
synthase', 'Aconitase', 'Isocitrate
DH', 'alpha-KG
DH', 'Succinyl-CoA
synthetase', 'Succinate
DH', 'Fumarase', 'Malate
DH']
nadh_cumul = np.cumsum([0, 0, 0, 1, 1, 0, 0, 0, 1])
fadh_cumul = np.cumsum([0, 0, 0, 0, 0, 0, 1, 0, 0])
atp_cumul  = np.cumsum([0, 0, 0, 0, 0, 1, 0, 0, 0])
x_tca = range(len(tca_steps))
ax2.plot(x_tca, nadh_cumul, 'o-', color='#4ade80', lw=2, ms=7, label='NADH')
ax2.plot(x_tca, fadh_cumul, 's-', color='#fbbf24', lw=2, ms=7, label='FADH2')
ax2.plot(x_tca, atp_cumul,  '^-', color='#60a5fa', lw=2, ms=7, label='GTP/ATP')
ax2.set_xticks(range(len(tca_steps)))
ax2.set_xticklabels(tca_steps, rotation=45, ha='right', color='#a3e635', fontsize=7)
ax2.set_ylabel('Cumulative production per acetyl-CoA', color='#a3e635', fontsize=9)
ax2.set_title('TCA Cycle: Cofactor Production', 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')
ax2.text(0.5, 0.97, 'Per turn: 3 NADH + 1 FADH2 + 1 GTP', transform=ax2.transAxes,
    color='#d9f99d', fontsize=9, ha='center', va='top')
for sp in ax2.spines.values(): sp.set_edgecolor('#4ade80')

fig.suptitle('Glycolysis & TCA Cycle 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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