Part VII — Chapter 19

Stress Biochemistry

Reactive oxygen species are both toxic by-products and important signals. Plants deploy a multi-layered antioxidant network, together with osmolytes, chaperones, and metal chelators, to survive oxidative, thermal, osmotic, and heavy metal stress.

Foyer-Halliwell-Asada Pathway (Ascorbate-Glutathione Cycle)

Reactive Oxygen Species (ROS) in Plants

Superoxide anion (O₂•⁻)

t½ ~1 μs

Mehler reaction (PSI), NADPH oxidase, mitochondrial ETC

Cannot cross membranes; dismutated by SOD → H₂O₂ + O₂. Charged; stays in compartment of origin.

Hydrogen peroxide (H₂O₂)

t½ ~1 ms

SOD product, peroxisomes (glycolate oxidase, fatty acid β-oxidation)

Membrane-permeable; can act as a signalling molecule (redox signalling) at low concentrations. Detoxified by CAT and APX.

Hydroxyl radical (•OH)

t½ ~1 ns

Fenton reaction: Fe²⁺ + H₂O₂ → Fe³⁺ + •OH + OH⁻

Most reactive ROS; no enzymatic scavenger. Causes DNA strand breaks, protein oxidation, lipid peroxidation. Controlled by limiting free iron.

Singlet oxygen (¹O₂)

t½ ~200 ns

PSII triplet chlorophyll; carotenoid-sensitised reactions

Key trigger for chloroplast-to-nucleus retrograde signalling (EXECUTER1/2 pathway). Quenched by carotenoids (beta-carotene) and tocopherols.

Antioxidant Enzymes

SOD (superoxide dismutase)

2 O₂•⁻ + 2 H⁺ → H₂O₂ + O₂

Three isoforms: Cu/Zn-SOD (cytosol/chloroplast), Mn-SOD (mitochondria), Fe-SOD (chloroplast). First line of defence; k_cat ~10⁹ M⁻¹s⁻¹.

Catalase (CAT)

2 H₂O₂ → 2 H₂O + O₂

Peroxisomal; handles high H₂O₂ flux (photorespiration). No reductant required; iron porphyrin cofactor. Three isoforms in Arabidopsis (CAT1/2/3).

APX (ascorbate peroxidase)

H₂O₂ + 2 AsA → 2 H₂O + 2 MDHA

Chloroplastic (stromal + thylakoid-bound) and cytosolic. High affinity for H₂O₂ (Km ~80 μM). Central hub of the AsA-GSH cycle.

Key Equations

\( \text{AsA} + \text{H}_2\text{O}_2 \xrightarrow{\text{APX}} \text{MDHA} + 2\,\text{H}_2\text{O} \)

\( \text{DHA} + 2\,\text{GSH} \xrightarrow{\text{DHAR}} \text{AsA} + \text{GSSG} \)

\( \text{GSSG} + \text{NADPH} \xrightarrow{\text{GR}} 2\,\text{GSH} + \text{NADP}^+ \)

Osmolytes, Heat Shock, Cold & Heavy Metals

Osmolyte Accumulation (Drought/Salt)

Proline

Synthesised from glutamate (P5CS key enzyme) during drought/salinity. Acts as osmolyte, metal chelator, and ROS scavenger. Accumulates to 100+ mM in some halophytes. Proline dehydrogenase (ProDH) catabolises upon stress relief; NADH yields to mitochondria.

Glycine betaine

Quaternary ammonium compound; synthesised from choline by BADH (betaine aldehyde dehydrogenase) in chloroplasts. Stabilises membranes and PSII complex. Not all plants synthesise it (absent in Arabidopsis).

Trehalose

Disaccharide from two glucoses (TPS/TPP pathway). Low concentrations but powerful protection via water replacement hypothesis (vitrification). Also a signalling molecule (T6P, Ch 17).

Heat Shock, Cold & Heavy Metals

Heat shock proteins (HSPs)

Hsp70 (DnaK-type), Hsp90 (HtpG-type), Hsp101 (ClpB-type), and small HSPs (sHSP) are upregulated by heat shock factors (HSF-A1). sHSPs form oligomeric shields protecting unfolded proteins; Hsp101 disaggregates protein aggregates. RNA thermometers (secondary structures in 5-UTRs) sense heat directly.

Cold acclimation

FAD desaturases (FAD2/3/7/8) increase unsaturated fatty acids at low temperature, maintaining membrane fluidity. LEA (late embryogenesis abundant) proteins stabilise cellular glasses during desiccation. ICE1-CBF-COR transcription cascade: ICE1 activates CBF1/2/3, which induce COR (cold-regulated) genes within 1-3 h.

Heavy metal detoxification

Phytochelatins (PCs: (Gly-Cys)n peptides synthesised by PC synthase from GSH) bind Cd²⁺, As, Pb in the cytosol; PC-metal complexes transported into vacuole by HMT1/ABC transporters. Metallothioneins (MTs): small Cys-rich proteins induced by Cu, Zn, and Cd; bind metals via thiolate clusters.

Python: ROS Dynamics Under Light Stress

Numerically integrate ODE model of the ascorbate-glutathione cycle (APX, MDHAR, DHAR, GR, SOD, CAT) under low and high light stress conditions, tracking H₂O₂, AsA/DHA pool, and GSH/GSSG redox status.

Python

script.py149 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from scipy.integrate import odeint

# Model ROS dynamics under light stress (production vs scavenging)
# State variables: [O2_rad, H2O2, OH_rad, AsA, GSH, DHA, GSSG]
# Units: micromolar (uM) for all species, time in seconds

# Rate constants (literature-derived, approximate)
k_SOD = 1e9        # M^-1 s^-1  SOD catalysis: 2O2.- + 2H+ -> H2O2 + O2
k_CAT = 1e7        # M^-1 s^-1  Catalase: 2H2O2 -> 2H2O + O2
k_APX = 3e7        # M^-1 s^-1  APX: H2O2 + 2AsA -> 2H2O + 2MDHA
k_MDHAR = 2e5      # M^-1 s^-1  MDHAR: MDHA + NADPH -> AsA + NADP+
k_DHAR = 1e6       # M^-1 s^-1  DHAR: DHA + GSH -> AsA + GSSG
k_GR = 5e5         # M^-1 s^-1  GR: GSSG + NADPH -> 2GSH
k_Fenton = 76      # M^-1 s^-1  Fenton: Fe2+ + H2O2 -> Fe3+ + OH. + OH-

# Initial conditions (uM)
SOD_conc = 10.0     # uM (enzyme)
CAT_conc = 5.0      # uM
APX_conc = 8.0      # uM
AsA_0 = 2000.0      # uM (ascorbate pool)
GSH_0 = 500.0       # uM (glutathione pool)
Fe_conc = 0.05      # uM (free iron, catalytic for Fenton)

y0 = [0.1, 0.5, 0.001, AsA_0, GSH_0, 50.0, 10.0]
# y = [O2.-_uM, H2O2_uM, OH._uM, AsA_uM, GSH_uM, DHA_uM, GSSG_uM]

# Production rates (stress-induced)
def production_rate(t, stress_level=1.0):
    # ROS production increases at onset of high light
    base = stress_level * (2.0 + 5.0 * np.exp(-t / 100))
    return base  # uM/s O2.-

def ros_odes(y, t, stress_level):
    O2r, H2O2, OHr, AsA, GSH, DHA, GSSG = y
    O2r = max(O2r, 0); H2O2 = max(H2O2, 0)
    OHr = max(OHr, 0); AsA = max(AsA, 1.0)
    GSH = max(GSH, 0.1); DHA = max(DHA, 0)
    GSSG = max(GSSG, 0)

prod = production_rate(t, stress_level)

# O2.- dynamics
    v_SOD = k_SOD * 1e-6 * SOD_conc * O2r   # rate in uM/s
    dO2r = prod - v_SOD

# H2O2 dynamics
    v_CAT = k_CAT * 1e-6 * CAT_conc * H2O2
    v_APX = k_APX * 1e-6 * APX_conc * min(H2O2, AsA)
    v_Fenton = k_Fenton * 1e-6 * Fe_conc * H2O2
    dH2O2 = v_SOD / 2 - v_CAT - v_APX - v_Fenton

# OH. (Fenton product, very reactive, short-lived)
    k_OH_scavenge = 50.0  # uM/s scavenging by antioxidants
    dOHr = v_Fenton - k_OH_scavenge * OHr

# Ascorbate dynamics (AsA used by APX, regenerated by MDHAR/DHAR)
    DHA_form = v_APX  # APX oxidises 2 AsA to 2 MDHA, ~1 MDHA -> DHA spontaneously
    AsA_regen_DHAR = k_DHAR * 1e-6 * GSH * DHA * 0.5
    dAsA = -v_APX + AsA_regen_DHAR

# GSH dynamics (used by DHAR, regenerated by GR)
    dGSH = -AsA_regen_DHAR + 2 * k_GR * 1e-6 * GSSG

# DHA dynamics
    dDHA = DHA_form * 0.5 - AsA_regen_DHAR

# GSSG dynamics (formed by DHAR)
    dGSSG = AsA_regen_DHAR * 0.5 - k_GR * 1e-6 * GSSG

return [dO2r, dH2O2, dOHr, dAsA, dGSH, dDHA, dGSSG]

t = np.linspace(0, 600, 1000)  # 0 to 600 seconds

# Solve for two stress levels
sol_low = odeint(ros_odes, y0, t, args=(0.5,), rtol=1e-6, atol=1e-8)
sol_high = odeint(ros_odes, y0, t, args=(2.0,), rtol=1e-6, atol=1e-8)

sol_low = np.clip(sol_low, 0, None)
sol_high = np.clip(sol_high, 0, None)

fig, axes = plt.subplots(2, 2, figsize=(13, 9))
fig.patch.set_facecolor('#0f172a')
for ax in axes.flat:
    ax.set_facecolor('#1e293b')
    ax.tick_params(colors='#94a3b8')
    ax.spines['bottom'].set_color('#475569')
    ax.spines['left'].set_color('#475569')
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

# Plot 1: H2O2 dynamics under low and high stress
axes[0, 0].plot(t, sol_low[:, 1], color='#a3e635', linewidth=2.5, label='Low stress (x0.5)')
axes[0, 0].plot(t, sol_high[:, 1], color='#ef4444', linewidth=2.5, label='High stress (x2.0)')
axes[0, 0].set_xlabel('Time (s)', color='#94a3b8', fontsize=9)
axes[0, 0].set_ylabel('[H2O2] (uM)', color='#94a3b8', fontsize=9)
axes[0, 0].set_title('H2O2 Dynamics Under Light Stress', color='#e2e8f0', fontsize=10)
axes[0, 0].legend(fontsize=8, facecolor='#1e293b', edgecolor='#475569', labelcolor='#e2e8f0')

# Plot 2: Ascorbate pool dynamics
axes[0, 1].plot(t, sol_low[:, 3], color='#a3e635', linewidth=2.5, label='AsA (low stress)')
axes[0, 1].plot(t, sol_high[:, 3], color='#ef4444', linewidth=2.5, label='AsA (high stress)')
axes[0, 1].plot(t, sol_low[:, 5], color='#4ade80', linewidth=2, linestyle='--', label='DHA (low stress)')
axes[0, 1].plot(t, sol_high[:, 5], color='#fca5a5', linewidth=2, linestyle='--', label='DHA (high stress)')
axes[0, 1].set_xlabel('Time (s)', color='#94a3b8', fontsize=9)
axes[0, 1].set_ylabel('Concentration (uM)', color='#94a3b8', fontsize=9)
axes[0, 1].set_title('Ascorbate Pool (AsA vs DHA)', color='#e2e8f0', fontsize=10)
axes[0, 1].legend(fontsize=7.5, facecolor='#1e293b', edgecolor='#475569', labelcolor='#e2e8f0')

# Plot 3: GSH/GSSG ratio (redox status indicator)
gsh_ratio_low = sol_low[:, 4] / (sol_low[:, 6] + 0.01)
gsh_ratio_high = sol_high[:, 4] / (sol_high[:, 6] + 0.01)
axes[1, 0].plot(t, gsh_ratio_low, color='#a3e635', linewidth=2.5, label='Low stress')
axes[1, 0].plot(t, gsh_ratio_high, color='#ef4444', linewidth=2.5, label='High stress')
axes[1, 0].axhline(y=10, color='#fbbf24', linestyle=':', alpha=0.7, linewidth=1)
axes[1, 0].text(10, 11, 'Oxidative stress threshold (GSH:GSSG = 10)', color='#fbbf24', fontsize=7)
axes[1, 0].set_xlabel('Time (s)', color='#94a3b8', fontsize=9)
axes[1, 0].set_ylabel('GSH:GSSG ratio', color='#94a3b8', fontsize=9)
axes[1, 0].set_title('Glutathione Redox Status (GSH:GSSG)', color='#e2e8f0', fontsize=10)
axes[1, 0].legend(fontsize=8, facecolor='#1e293b', edgecolor='#475569', labelcolor='#e2e8f0')

# Plot 4: ROS production vs scavenging
t4 = np.linspace(0, 600, 500)
prod_high = np.array([production_rate(ti, 2.0) for ti in t4])
scavenging = 0.8 * k_SOD * 1e-6 * SOD_conc * np.clip(sol_high[:500, 0], 0, None) +              k_CAT * 1e-6 * CAT_conc * np.clip(sol_high[:500, 1], 0, None)

axes[1, 1].plot(t4, prod_high, color='#ef4444', linewidth=2.5, label='ROS production')
axes[1, 1].plot(t4, scavenging, color='#a3e635', linewidth=2.5, label='Enzymatic scavenging')
axes[1, 1].fill_between(t4, prod_high, scavenging,
                         where=(prod_high > scavenging), alpha=0.2, color='#ef4444', label='Net ROS accumulation')
axes[1, 1].fill_between(t4, prod_high, scavenging,
                         where=(prod_high <= scavenging), alpha=0.2, color='#a3e635', label='Excess scavenging capacity')
axes[1, 1].set_xlabel('Time (s)', color='#94a3b8', fontsize=9)
axes[1, 1].set_ylabel('Rate (uM/s)', color='#94a3b8', fontsize=9)
axes[1, 1].set_title('ROS Production vs Scavenging (High Stress)', color='#e2e8f0', fontsize=10)
axes[1, 1].legend(fontsize=7.5, facecolor='#1e293b', edgecolor='#475569', labelcolor='#e2e8f0', ncol=2)

fig.suptitle('Plant ROS Dynamics: Ascorbate-Glutathione Cycle Under Light Stress',
             color='#e2e8f0', fontsize=12, fontweight='bold')
plt.tight_layout()
plt.savefig('output.png', dpi=110, bbox_inches='tight',
            facecolor='#0f172a', edgecolor='none')
print("Peak H2O2 (high stress):", round(float(np.max(sol_high[:, 1])), 3), "uM")
print("Steady-state AsA (high stress):", round(float(sol_high[-1, 3]), 1), "uM")
print("Final GSH:GSSG (high stress):", round(float(gsh_ratio_high[-1]), 2))

Click Run to execute the Python code

Code will be executed with Python 3 on the server

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