Chapter 9: Nitrogen Fixation & Assimilation

Part III — Nitrogen & Amino Acids

9.1 Nitrogenase Complex

Biological nitrogen fixation is catalyzed exclusively by prokaryotes expressing nitrogenase — a two-component metalloenzyme that reduces atmospheric N₂ to NH₃ at the expense of substantial ATP and reductant. The overall reaction is:

\[N_2 + 8H^+ + 8e^- + 16\,ATP \rightarrow 2\,NH_3 + H_2 + 16\,ADP + 16\,P_i\]

Fe Protein (Component 2, dinitrogenase reductase):

Homodimer (γ₂), ~60 kDa
Contains one [4Fe–4S] cluster
Binds 2 MgATP; hydrolyzes to MgADP on each e⁻ transfer
Reduced by ferredoxin or flavodoxin
Extremely O₂-sensitive (t½ ~ 30 s in air)

MoFe Protein (Component 1, dinitrogenase):

Heterotetramer (α₂β₂), ~240 kDa
Contains 2 P-clusters [8Fe–7S] — electron transfer
Contains 2 FeMo-cofactor (FeMoco) [7Fe–9S–Mo–C–R-homocitrate] — N₂ binding site
N₂ binds to Fe atom (not Mo) at FeMoco
Alternating/distal mechanisms — debate ongoing

The obligate H₂ production:

Nitrogenase invariably co-produces H₂ (minimum 1 H₂ per N₂ — the Thorneley–Lowe model). In Bradyrhizobium, an uptake hydrogenase (Hup) recycles this H₂ to recover ~30% of the ATP cost, improving symbiosis efficiency. nifH, nifD, nifK encode the Fe and MoFe proteins.

9.2 Leghemoglobin & O₂ Regulation in Root Nodules

Nitrogenase is irreversibly inactivated by O₂ (K_i ~ 30 µM O₂), yet the bacteroid requires high respiratory O₂ flux to meet the 16 ATP demand. This paradox is solved by leghemoglobin, a plant-encoded monomeric hemoprotein that buffers free O₂ at nanomolar levels in the nodule cytoplasm.

Leghemoglobin properties:

High O₂ affinity: K_d ~ 10–40 nM (vs myoglobin ~1 µM)
Rapid association/dissociation kinetics → efficient O₂ facilitated diffusion
Maintains [O₂]free ~ 10–30 nM in nodule cytoplasm
Responsible for pink/red color of active nodules
Globin gene: plant-encoded; heme: bacteroid-synthesized

Symbiosis Carbon Supply:

The plant provides carbon as dicarboxylates (malate, succinate) to bacteroids via DctA transporters. Bacteroids oxidize these via TCA for ATP generation. Fixed N is exported as ureides (allantoin, allantoate) in tropical legumes or asparagine/glutamine in temperate species.

9.3 GS/GOGAT Cycle: Ammonia Assimilation

NH₃ (whether from fixation, nitrate reduction, or photorespiration) is assimilated primarily via the glutamine synthetase/glutamate synthase (GS/GOGAT) cycle:

Glutamine Synthetase (GS):

\[\text{Glu} + NH_4^+ + ATP \xrightarrow{GS} \text{Gln} + ADP + P_i\]

GS1 (cytosol, phloem); GS2 (plastid, main assimilation)

Glutamate Synthase (GOGAT):

\[\text{Gln} + \alpha\text{-KG} + NADPH \xrightarrow{Fd\text{-GOGAT}} 2\,\text{Glu}\]

Fd-GOGAT (plastid, light-dependent); NADH-GOGAT (root/non-photosynthetic)

Nitrate Reduction Pathway:

\[NO_3^- \xrightarrow{NR,\,2e^-} NO_2^- \xrightarrow{NiR,\,6e^-} NH_4^+\]

Nitrate reductase (NR): cytosolic, NADH-dependent, contains FAD + heme b + Mo-MPT cofactor. Inducible by NO₃⁻; regulated post-translationally by phosphorylation + 14-3-3 protein binding. Nitrite reductase (NiR): plastidial, ferredoxin-dependent, contains siroheme + [4Fe–4S]. Needs 6e⁻ to reduce NO₂⁻ to NH₄⁺ (most reduced N form).

Nitrogenase Mechanism Overview

Simulation: N Fixation Energy Budget & GS/GOGAT Dynamics

Comparative energy costs of N₂ fixation vs nitrate reduction, and GS/GOGAT cycle pool dynamics during NH₄⁺ assimilation.

Python

script.py75 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: Energy budget N fixation vs nitrate assimilation
ax1 = axes[0]
ax1.set_facecolor('#0d1a0d')
categories = ['N2 Fixation
(2 NH3)', 'NO3- Reduction
(2 NH3)', 'NH4+ Direct
(2 NH3)']
atp_cost   = [16, 10, 2]  # ATP molecules per 2 NH3
reductant  = [8,  16, 0]  # e- equivalents (NADPH/Fd)
glu_equiv  = [4,  4,  4]  # GS/GOGAT cost same

x = np.arange(len(categories))
w = 0.28
ax1.bar(x - w, atp_cost, w, label='ATP', color='#fbbf24', edgecolor='#0d1a0d')
ax1.bar(x, reductant, w, label='e- equivalents (NADPH)', color='#4ade80', edgecolor='#0d1a0d')
ax1.bar(x + w, glu_equiv, w, label='Glutamate (GS/GOGAT)', color='#60a5fa', edgecolor='#0d1a0d')
ax1.set_xticks(x)
ax1.set_xticklabels(categories, color='#a3e635', fontsize=9)
ax1.set_ylabel('Molecules per 2 NH3 produced', color='#a3e635', fontsize=9)
ax1.set_title('Energy Budget: N Acquisition Routes', 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: GS/GOGAT cycle - glutamine/glutamate pool dynamics
ax2 = axes[1]
ax2.set_facecolor('#0d1a0d')
t = np.linspace(0, 20, 300)  # minutes
# Simple model: NH4+ pulse assimilation
NH4_initial = 10.0  # mM
k_gs = 0.8   # GS rate constant
k_gogat = 0.5  # GOGAT rate constant

glu = np.zeros_like(t)
gln = np.zeros_like(t)
glu[0] = 5.0  # initial glutamate
gln[0] = 0.5  # initial glutamine
NH4 = NH4_initial * np.exp(-k_gs * t)

for i in range(1, len(t)):
    dt = t[i] - t[i-1]
    v_gs   = k_gs * glu[i-1] * NH4[i-1]
    v_gogat = k_gogat * gln[i-1] * 1.0  # alpha-KG assumed constant
    dglu = -v_gs + 2 * v_gogat
    dgln = v_gs - v_gogat
    glu[i] = max(0, glu[i-1] + dglu * dt)
    gln[i] = max(0, gln[i-1] + dgln * dt)

ax2.plot(t, NH4, color='#f87171', lw=2, label='NH4+ (input)')
ax2.plot(t, glu, color='#4ade80', lw=2, label='Glutamate')
ax2.plot(t, gln, color='#fbbf24', lw=2, label='Glutamine')
ax2.set_xlabel('Time (min)', color='#a3e635', fontsize=9)
ax2.set_ylabel('Concentration (mM)', color='#a3e635', fontsize=9)
ax2.set_title('GS/GOGAT Cycle Dynamics
(NH4+ assimilation)', 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')
for sp in ax2.spines.values(): sp.set_edgecolor('#4ade80')

fig.suptitle('Nitrogen Fixation & Assimilation 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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