Part VI — Chapter 17

Starch & Sucrose Metabolism

The central currency of carbon in plants: starch granules store photosynthate in leaves and sink organs, while sucrose transports carbon over long distances via the phloem, with trehalose-6-phosphate acting as a metabolic sensor.

Starch Granule Structure & Sucrose Cycle

Starch Synthesis — From G1P to Granule

AGPase (ADP-glucose pyrophosphorylase)

G1P + ATP → ADP-glucose + PPi

Allosterically activated by 3-PGA, inhibited by Pi. Key regulatory switch; redox-activated (Cys12 disulfide reduction in light). Small + large subunits (50+52 kDa).

Starch synthase (SS)

ADP-glucose + (alpha-glucan)n → alpha-glucan(n+1) + ADP

Multiple isoforms: granule-bound SS (GBSS/waxy, makes amylose) and soluble SS (SSI/II/III/IV, make amylopectin chains of different lengths).

Branching enzyme (BE)

alpha-1,4 chain cleavage + alpha-1,6 reattachment

Creates the alpha-1,6 branch points of amylopectin. BEI and BEII isoforms differ in preferred chain length to transfer (BEI: DP >11; BEII: DP 6-9).

Debranching enzyme (DBE)

Isoamylase: hydrolyse alpha-1,6 branch points

Required for proper amylopectin crystallisation. isoamylase (ISA1/2/3) and pullulanase. ISA1/2 form a heteromer essential for normal granule structure.

Starch Degradation

alpha-Amylase (endoamylase)

Cleaves internal alpha-1,4 bonds randomly; generates oligosaccharides. In germinating cereals, gibberellin induces alpha-amylase in aleurone for reserve mobilisation.

beta-Amylase (exoamylase)

Removes maltose from non-reducing ends. Major degradative enzyme in leaf starch breakdown at night. Produces maltose exported from chloroplast via MEX1 transporter.

Glucan phosphorylase

Requires glucan phosphorylation (by GWD, glucan water dikinase). Phosphorylation disrupts crystalline packing, allowing amylase access.

Maltose export (MEX1)

MEX1 (maltose excess 1) exports maltose from chloroplast. Mutations accumulate starch; maltose is metabolised to sucrose via cytosolic DPE2 (disproportionating enzyme).

AGPase regulation:

\( \frac{v}{V_{\max}} = \frac{[\text{G1P}][\text{ATP}]}{K_m^2 + K_m[\text{G1P}] + K_m[\text{ATP}] + [\text{G1P}][\text{ATP}]} \times \frac{1}{1 + \frac{[\text{P}_i]}{K_i}} \)

Sucrose Synthesis, Transport & Cleavage

Sucrose Phosphate Synthase (SPS)

\( \text{UDP-Glc} + \text{F6P} \xrightarrow{\text{SPS}} \text{S6P} + \text{UDP} \)

Then sucrose phosphate phosphatase (SPP) removes the phosphate → sucrose. SPS is activated by G6P and inhibited by Pi; phosphorylation by SnRK1 inactivates SPS in response to low energy status.

Invertase (cleaves sucrose)

\( \text{sucrose} + \text{H}_2\text{O} \xrightarrow{\text{invertase}} \text{glucose} + \text{fructose} \)

Three classes: cell wall-bound (CWINV, phloem unloading/sink import), vacuolar (VINV, osmoregulation), and neutral cytosolic. Constitutively irreversible; drives phloem unloading down concentration gradient.

Sucrose Synthase (SuSy)

\( \text{sucrose} + \text{UDP} \xrightarrow{\text{SuSy}} \text{UDP-Glc} + \text{Fructose} \)

Reversible; thermodynamically favours sucrose cleavage in vivo. Provides UDP-glucose directly for cellulose synthase (membrane-associated SuSy) and starch synthesis (soluble SuSy). Key enzyme in high-demand sinks.

Trehalose-6-Phosphate (T6P) Signalling

\( \text{UDP-Glc} + \text{G6P} \xrightarrow{\text{TPS}} \text{T6P} + \text{UDP} \xrightarrow{\text{TPP}} \text{trehalose} + \text{P}_i \)

T6P acts as a proxy signal for sucrose availability, inhibiting SnRK1 (sucrose non-fermenting-related kinase 1) activity when sucrose levels are high. This coordinates growth with carbon availability. T6P also promotes starch synthesis by inhibiting the AGPase-inhibitory state. TPS1 mutants in Arabidopsis are embryo-lethal, underscoring T6P’s essential role.

Python: Source-Sink Carbon Partitioning Simulation

Model diurnal sucrose dynamics and starch accumulation using Michaelis-Menten kinetics for AGPase, sucrose synthase, invertase, and phloem loading transporters over two 12h light/12h dark cycles.

Python

script.py132 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# Model source-sink carbon partitioning with Michaelis-Menten transport kinetics
# Source leaf: exports sucrose via phloem
# Sink (e.g., developing seed): imports sucrose and converts to starch

# Parameters
Km_phloem_loading = 0.5    # mM (SUT1 sucrose transporter, leaf-phloem loading)
Vmax_loading = 5.0          # nmol/cm2/s

Km_unloading = 0.3          # mM (sink phloem unloading via SWEET transporters)
Vmax_unloading = 4.0        # nmol/cm2/s

Km_invertase = 0.8          # mM (apoplastic invertase in sink)
Vmax_invertase = 3.0        # nmol/min/mg

Km_starch = 0.2             # mM (AGPase: glucose-1-P substrate)
Vmax_starch = 2.5           # nmol/min/mg (starch synthesis rate)

Km_susy = 0.4               # mM (sucrose synthase)
Vmax_susy = 6.0             # nmol/min/mg

# Simulate diurnal cycle (12h light / 12h dark, 24h)
time = np.linspace(0, 48, 500)  # 2 days, hours

# Source leaf sucrose concentration (mM): rises in light, falls in dark
def sucrose_source(t):
    phase = t % 24
    # Light period (0-12h): linear rise
    if phase < 12:
        return 5.0 + 8.0 * (phase / 12)
    else:  # Dark period: export + remobilisation
        return 13.0 - 6.0 * ((phase - 12) / 12)

suc_source = np.array([sucrose_source(t) for t in time])

# Phloem transport flux (Michaelis-Menten)
flux_phloem = Vmax_loading * suc_source / (Km_phloem_loading + suc_source)

# Sink sucrose concentration (mM): depends on import - utilisation
suc_sink = np.zeros(len(time))
suc_sink[0] = 2.0
starch_content = np.zeros(len(time))
starch_content[0] = 0.0

dt = time[1] - time[0]
for i in range(1, len(time)):
    s = max(suc_sink[i-1], 0.001)

# Import via SUT in phloem unloading
    v_import = Vmax_unloading * s / (Km_unloading + s) * 0.5

# Utilisation 1: invertase cleavage (apoplastic + vacuolar)
    v_inv = Vmax_invertase * s / (Km_invertase + s) * 0.1

# Utilisation 2: sucrose synthase (SuSy) -> UDP-glucose + fructose
    v_susy = Vmax_susy * s / (Km_susy + s) * 0.08

# AGPase: glucose-1-P -> ADP-glucose -> starch
    g1p = v_susy * 0.7  # most UDP-glucose -> G1P -> starch
    v_starch = Vmax_starch * g1p / (Km_starch + g1p) * 0.05

dsdt = (flux_phloem[i] * 0.3) - v_inv - v_susy
    suc_sink[i] = max(s + dsdt * dt, 0)
    starch_content[i] = starch_content[i-1] + v_starch * dt

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)

# Shade light/dark periods
for ax in axes.flat:
    for day in range(3):
        ax.axvspan(day*24, day*24+12, alpha=0.06, color='#fbbf24')
        ax.axvspan(day*24+12, day*24+24, alpha=0.03, color='#1e293b')

# Plot 1: Source sucrose concentration
axes[0, 0].plot(time, suc_source, color='#a3e635', linewidth=2.5, label='Source leaf [Sucrose]')
axes[0, 0].plot(time, suc_sink, color='#f59e0b', linewidth=2, linestyle='--', label='Sink [Sucrose]')
axes[0, 0].set_xlabel('Time (h)', color='#94a3b8', fontsize=9)
axes[0, 0].set_ylabel('[Sucrose] (mM)', color='#94a3b8', fontsize=9)
axes[0, 0].set_title('Diurnal Sucrose Concentration', color='#e2e8f0', fontsize=10)
axes[0, 0].legend(fontsize=8, facecolor='#1e293b', edgecolor='#475569', labelcolor='#e2e8f0')
axes[0, 0].text(2, 14, 'Light', color='#fbbf24', fontsize=8)
axes[0, 0].text(14, 14, 'Dark', color='#64748b', fontsize=8)

# Plot 2: Phloem transport flux
axes[0, 1].plot(time, flux_phloem, color='#34d399', linewidth=2.5)
axes[0, 1].fill_between(time, flux_phloem, alpha=0.2, color='#22c55e')
axes[0, 1].set_xlabel('Time (h)', color='#94a3b8', fontsize=9)
axes[0, 1].set_ylabel('Phloem flux (nmol/cm2/s)', color='#94a3b8', fontsize=9)
axes[0, 1].set_title('Phloem Loading Flux (SUT1)', color='#e2e8f0', fontsize=10)

# Plot 3: Starch accumulation
axes[1, 0].plot(time, starch_content, color='#a3e635', linewidth=2.5, label='Starch (cumulative)')
axes[1, 0].set_xlabel('Time (h)', color='#94a3b8', fontsize=9)
axes[1, 0].set_ylabel('Relative starch content', color='#94a3b8', fontsize=9)
axes[1, 0].set_title('Sink Starch Accumulation', color='#e2e8f0', fontsize=10)

# Plot 4: Michaelis-Menten comparison (AGPase, SuSy, Invertase)
sub_range = np.linspace(0, 5.0, 300)
enzymes_km = [('AGPase', Km_starch, Vmax_starch, '#a3e635'),
              ('SuSy', Km_susy, Vmax_susy, '#34d399'),
              ('Invertase', Km_invertase, Vmax_invertase, '#f59e0b'),
              ('SUT1 loading', Km_phloem_loading, Vmax_loading, '#60a5fa')]
for name, Km, Vmax, col in enzymes_km:
    v = Vmax * sub_range / (Km + sub_range)
    axes[1, 1].plot(sub_range, v, color=col, linewidth=2, label=f'{name} (Km={Km})')
    axes[1, 1].axvline(x=Km, color=col, alpha=0.25, linestyle=':')
axes[1, 1].set_xlabel('[Sucrose/substrate] (mM)', color='#94a3b8', fontsize=9)
axes[1, 1].set_ylabel('Velocity (nmol/min/mg or nmol/cm2/s)', color='#94a3b8', fontsize=9)
axes[1, 1].set_title('Kinetics: Starch/Sucrose Pathway Enzymes', color='#e2e8f0', fontsize=10)
axes[1, 1].legend(fontsize=7.5, facecolor='#1e293b', edgecolor='#475569', labelcolor='#e2e8f0')

fig.suptitle('Source-Sink Carbon Partitioning: Sucrose Transport & Starch Synthesis',
             color='#e2e8f0', fontsize=12, fontweight='bold')
plt.tight_layout()
plt.savefig('output.png', dpi=110, bbox_inches='tight',
            facecolor='#0f172a', edgecolor='none')
print("Peak source sucrose:", round(max(suc_source), 2), "mM")
print("Peak phloem flux:", round(max(flux_phloem), 3), "nmol/cm2/s")
print("Starch at 48h:", round(starch_content[-1], 3), "(relative units)")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

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