Part 2 · Chapter 2.2

The Citric Acid Cycle (Krebs Cycle)

The mitochondrial matrix cycle that oxidizes acetyl-CoA to CO₂, harvesting high-energy electrons as NADH and FADH₂ that feed the electron transport chain. Eight enzymes, one GTP per turn, and a tight network of allosteric regulation respond to calcium signaling and cellular energy state.

Learning Objectives

▸List the 8 reactions, their enzymes, and mechanistic class (dehydration, oxidation, decarboxylation, etc.)
▸Explain the substrate-level phosphorylation catalyzed by succinyl-CoA synthetase
▸Identify isocitrate dehydrogenase as the rate-limiting enzyme and its allosteric regulators
▸Describe Ca²⁺ activation of IDH, \(\alpha\)-KG DH, and pyruvate DH
▸Distinguish oxidative versus anaplerotic roles of TCA intermediates

◆Overall Stoichiometry

Discovered by Hans Krebs in 1937 (Nobel 1953), the TCA cycle operates exclusively in the mitochondrial matrix (eukaryotes) or cytoplasm (prokaryotes). Per turn:

\[ \text{Acetyl-CoA} + 3\,\text{NAD}^+ + \text{FAD} + \text{GDP} + \text{P}_i + 2\,\text{H}_2\text{O} \longrightarrow \]\[ 2\,\text{CO}_2 + 3\,\text{NADH} + 3\,\text{H}^+ + \text{FADH}_2 + \text{GTP} + \text{CoA-SH} \]

The overall standard free energy change is \(\Delta G^{\circ\prime} \approx -40\;\text{kJ/mol}\)per turn, dominated by citrate synthase, isocitrate DH, and \(\alpha\)-KG DH. Since each glucose produces two pyruvate and each pyruvate one acetyl-CoA, the cycle runs twice per glucose.

◆Cycle Map

◆The Eight Reactions

1. Citrate Synthase

Irreversible Claisen condensation of acetyl-CoA with oxaloacetate. Active-site His/Asp residues deprotonate Ac-CoA generating a nucleophilic enolate that attacks OAA’s carbonyl. Hydrolysis of the CoA-thioester drives the reaction forward.

\[ \text{OAA} + \text{Acetyl-CoA} + \text{H}_2\text{O} \longrightarrow \text{Citrate} + \text{CoA-SH},\qquad \Delta G^{\circ\prime} = -32.2\;\text{kJ/mol} \]

2. Aconitase

Isomerizes citrate to isocitrate via a cis-aconitate intermediate. Remarkably, despite citrate being prochiral (stereochemically symmetric), aconitase is stereospecific and dehydrates only the pro-R arm. The enzyme uses a [4Fe–4S] cluster as a Lewis acid to polarize the OH group.

\[ \text{Citrate} \;\rightleftharpoons\; cis\text{-aconitate} \;\rightleftharpoons\; \text{Isocitrate} \]

3. Isocitrate Dehydrogenase (IDH) • Rate-Limiting

Oxidative decarboxylation: isocitrate is oxidized to oxalosuccinate, which then loses CO₂ to give \(\alpha\)-ketoglutarate. Mammals have three IDH isoforms: the matrix NAD⁺-dependent IDH3 (the TCA cycle enzyme), and cytosolic/mitochondrial NADP⁺-dependent IDH1/2 (biosynthesis, mutated in gliomas to produce the oncometabolite 2-hydroxyglutarate).

\[ \text{Isocitrate} + \text{NAD}^+ \longrightarrow \alpha\text{-KG} + \text{CO}_2 + \text{NADH} + \text{H}^+,\; \Delta G^{\circ\prime} = -20.9\;\text{kJ/mol} \]

Activated by: ADP, Ca²⁺, isocitrate (substrate). Inhibited by: ATP, NADH.

4. \(\alpha\)-Ketoglutarate Dehydrogenase

A multi-subunit complex mechanistically analogous to pyruvate dehydrogenase: E1 (TPP-dependent decarboxylase), E2 (lipoamide acyltransferase), E3 (FAD/NAD⁺-dependent dihydrolipoyl DH). Produces the high-energy thioester succinyl-CoA and a second CO₂.

\[ \alpha\text{-KG} + \text{NAD}^+ + \text{CoA-SH} \longrightarrow \text{Succinyl-CoA} + \text{CO}_2 + \text{NADH},\; \Delta G^{\circ\prime} = -33.5\;\text{kJ/mol} \]

Activated by Ca²⁺ (matrix concentration tracks cytosolic [Ca²⁺]); inhibited by NADH and succinyl-CoA (product inhibition).

5. Succinyl-CoA Synthetase (SCS)

The only substrate-level phosphorylation of the TCA cycle. Hydrolysis of succinyl-CoA's thioester bond couples with phosphorylation of GDP (heart/skeletal muscle) or ADP (liver’s isoform), producing GTP or ATP respectively. The mechanism proceeds through a phospho-histidine intermediate.

\[ \text{Succinyl-CoA} + \text{GDP} + \text{P}_i \longrightarrow \text{Succinate} + \text{CoA-SH} + \text{GTP} \]

6. Succinate Dehydrogenase (Complex II)

The only TCA enzyme embedded in the inner mitochondrial membrane; it is also Complex II of the electron transport chain. FAD is covalently bound to a subunit via a histidyl linkage. Two electrons from succinate reduce FAD to FADH₂, which then transfers them through [Fe–S] clusters to ubiquinone (Q).

\[ \text{Succinate} + \text{FAD} \;\rightleftharpoons\; \text{Fumarate} + \text{FADH}_2 \]

Succinate dehydrogenase is competitively inhibited by malonate (the historical proof by Krebs that this was an intermediate in the cycle).

7. Fumarase

Stereospecific trans addition of water to fumarate, producing only L-malate. The mechanism passes through a carbanion intermediate, resolved stereospecifically by an active-site base that controls proton delivery.

\[ \text{Fumarate} + \text{H}_2\text{O} \;\rightleftharpoons\; \text{L-Malate} \]

8. Malate Dehydrogenase

The final oxidation regenerates oxaloacetate with production of the third NADH. The reaction is strongly unfavorable at standard conditions (\(\Delta G^{\circ\prime} = +29.7\) kJ/mol) but proceeds in cells because OAA is kept extraordinarily low (~1 \(\mu\)M) by citrate synthase pulling it forward.

\[ \text{L-Malate} + \text{NAD}^+ \;\rightleftharpoons\; \text{OAA} + \text{NADH} + \text{H}^+ \]

Energy Yield per Turn

Direct products

3 NADH (steps 3, 4, 8)
1 FADH₂ (step 6)
1 GTP (step 5, substrate-level)
2 CO₂ (steps 3, 4)

ATP equivalents (via ETC)

3 NADH × 2.5 = 7.5 ATP
1 FADH₂ × 1.5 = 1.5 ATP
1 GTP = 1.0 ATP
≈ 10 ATP per turn
≈ 20 ATP per glucose (2 turns)

◆Simulation 1: ODE Model of the Krebs Cycle

Fourteen coupled ODEs capture each intermediate along with NAD⁺/NADH, FAD/FADH₂, and GDP/GTP pools. Product inhibition is implemented on citrate synthase (NADH, succinyl-CoA), IDH, and \(\alpha\)-KG DH. External sink terms emulate ETC regeneration of NAD⁺/FAD.

Python

script.py149 lines

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint

# ODE model of the 8-reaction TCA cycle with product inhibition
# State: [OAA, Citrate, Isocit, aKG, SuccCoA, Succ, Fum, Mal, NAD, NADH, FAD, FADH2, GDP, GTP]
y0 = [0.05, 0.45, 0.02, 0.12, 0.01, 0.25, 0.08, 0.60,
      3.0, 0.08, 1.0, 0.05, 0.20, 0.03]

p = {
    # Vmax in mM/s, Km in mM
    'V_cs': 0.40, 'Km_cs_oaa': 0.002, 'Km_cs_accoa': 0.005,
    'Ki_cs_cit': 1.6, 'Ki_cs_nadh': 0.03, 'Ki_cs_succoa': 0.8,
    'V_aco': 3.0, 'Km_aco_cit': 0.5, 'Keq_aco': 0.067,
    'V_idh': 0.25, 'Km_idh_icit': 0.08, 'Km_idh_nad': 0.2,
    'Ka_idh_adp': 0.10, 'Ki_idh_atp': 0.9, 'Ki_idh_nadh': 0.015,
    'n_idh': 2.5,
    'V_kgdh': 0.22, 'Km_kgdh_akg': 0.08, 'Km_kgdh_nad': 0.1,
    'Ki_kgdh_nadh': 0.01, 'Ki_kgdh_succoa': 0.01,
    'V_scs': 1.0, 'Km_scs_succoa': 0.04, 'Km_scs_gdp': 0.2,
    'V_sdh': 0.8, 'Km_sdh_succ': 0.3, 'Km_sdh_fad': 0.05,
    'V_fum': 5.0, 'Km_fum_fum': 0.05, 'Keq_fum': 4.4,
    'V_mdh': 1.5, 'Km_mdh_mal': 0.3, 'Km_mdh_nad': 0.1,
    'k_etc_nadh': 0.8, 'k_etc_fadh': 0.8, 'k_gtpase': 1.0,
    'ca_signal': 1.0, 'atp': 1.5, 'adp': 0.1, 'accoa': 0.03,
}

def mm(S, Km):
    return S/(Km+S)

def tca(y, t):
    OAA,CIT,ICIT,AKG,SCOA,SUCC,FUM,MAL,NAD,NADH,FAD,FADH2,GDP,GTP = y

v1 = p['V_cs']*mm(OAA,p['Km_cs_oaa'])*mm(p['accoa'],p['Km_cs_accoa'])          * 1.0/(1+CIT/p['Ki_cs_cit'])          * 1.0/(1+NADH/p['Ki_cs_nadh'])          * 1.0/(1+SCOA/p['Ki_cs_succoa'])
    v2 = p['V_aco']*(CIT - ICIT/p['Keq_aco'])/(p['Km_aco_cit']+CIT)
    alpha = ICIT/p['Km_idh_icit']
    act = p['ca_signal'] * (1 + p['adp']/p['Ka_idh_adp'])
    inh = (1 + p['atp']/p['Ki_idh_atp'])*(1 + NADH/p['Ki_idh_nadh'])
    v3 = p['V_idh']*act*mm(NAD,p['Km_idh_nad'])*alpha**p['n_idh']/(inh+alpha**p['n_idh'])
    v4 = p['V_kgdh']*p['ca_signal']*mm(AKG,p['Km_kgdh_akg'])*mm(NAD,p['Km_kgdh_nad'])          / (1 + NADH/p['Ki_kgdh_nadh'] + SCOA/p['Ki_kgdh_succoa'])
    v5 = p['V_scs']*mm(SCOA,p['Km_scs_succoa'])*mm(GDP,p['Km_scs_gdp'])
    v6 = p['V_sdh']*mm(SUCC,p['Km_sdh_succ'])*mm(FAD,p['Km_sdh_fad'])
    v7 = p['V_fum']*(FUM - MAL/p['Keq_fum'])/(p['Km_fum_fum']+FUM)
    v8 = p['V_mdh']*mm(MAL,p['Km_mdh_mal'])*mm(NAD,p['Km_mdh_nad'])

v_etc_n = p['k_etc_nadh']*NADH
    v_etc_f = p['k_etc_fadh']*FADH2
    v_gt    = p['k_gtpase']*GTP

dOAA   = v8 - v1
    dCIT   = v1 - v2
    dICIT  = v2 - v3
    dAKG   = v3 - v4
    dSCOA  = v4 - v5
    dSUCC  = v5 - v6
    dFUM   = v6 - v7
    dMAL   = v7 - v8
    dNAD   = -v3 - v4 - v8 + v_etc_n
    dNADH  =  v3 + v4 + v8 - v_etc_n
    dFAD   = -v6 + v_etc_f
    dFADH2 =  v6 - v_etc_f
    dGDP   = -v5 + v_gt
    dGTP   =  v5 - v_gt
    return [dOAA,dCIT,dICIT,dAKG,dSCOA,dSUCC,dFUM,dMAL,
            dNAD,dNADH,dFAD,dFADH2,dGDP,dGTP]

t = np.linspace(0, 100, 2000)
sol = odeint(tca, y0, t)

names = ['OAA','Citrate','Isocitrate','a-KG','Succinyl-CoA',
         'Succinate','Fumarate','Malate',
         'NAD+','NADH','FAD','FADH2','GDP','GTP']
colors = ['#67e8f9','#38bdf8','#60a5fa','#818cf8','#a78bfa',
          '#c084fc','#e879f9','#f472b6',
          '#86efac','#f87171','#fde047','#fdba74','#94a3b8','#fbbf24']

fig, axes = plt.subplots(2, 2, figsize=(14, 9))

ax = axes[0,0]
for i,lbl in zip([0,1,2,3], names[:4]):
    ax.plot(t, sol[:,i], color=colors[i], lw=2, label=lbl)
ax.set_title('First half: C-6 intermediates + CO2 release',
             color='white', fontsize=12, fontweight='bold')
ax.set_xlabel('Time (s)', color='white'); ax.set_ylabel('[mM]', color='white')
ax.legend(facecolor='#0a0a1a', edgecolor='#67e8f9', labelcolor='white')
ax.set_facecolor('#0a0a1a'); ax.tick_params(colors='white')
ax.grid(alpha=0.2, color='#67e8f9')
for s in ax.spines.values(): s.set_color('#67e8f9')

ax = axes[0,1]
for i,lbl in zip([4,5,6,7], names[4:8]):
    ax.plot(t, sol[:,i], color=colors[i], lw=2, label=lbl)
ax.set_title('Second half: C-4 intermediates regenerate OAA',
             color='white', fontsize=12, fontweight='bold')
ax.set_xlabel('Time (s)', color='white'); ax.set_ylabel('[mM]', color='white')
ax.legend(facecolor='#0a0a1a', edgecolor='#67e8f9', labelcolor='white')
ax.set_facecolor('#0a0a1a'); ax.tick_params(colors='white')
ax.grid(alpha=0.2, color='#67e8f9')
for s in ax.spines.values(): s.set_color('#67e8f9')

ax = axes[1,0]
ax.plot(t, sol[:,8],  color='#86efac', lw=2, label='NAD+')
ax.plot(t, sol[:,9],  color='#f87171', lw=2, label='NADH')
ax.plot(t, sol[:,10], color='#fde047', lw=2, label='FAD')
ax.plot(t, sol[:,11], color='#fdba74', lw=2, label='FADH2')
ax.set_title('Redox cofactor dynamics',
             color='white', fontsize=12, fontweight='bold')
ax.set_xlabel('Time (s)', color='white'); ax.set_ylabel('[mM]', color='white')
ax.legend(facecolor='#0a0a1a', edgecolor='#86efac', labelcolor='white')
ax.set_facecolor('#0a0a1a'); ax.tick_params(colors='white')
ax.grid(alpha=0.2, color='#86efac')
for s in ax.spines.values(): s.set_color('#86efac')

ss_range = t > 50
integrated_flux = np.trapezoid(sol[ss_range,1], t[ss_range])
ss = sol[-1]
flux_ss = p['V_mdh']*mm(ss[7], p['Km_mdh_mal'])*mm(ss[8], p['Km_mdh_nad'])

ax = axes[1,1]
names_short = ['NADH\nx3 = 7.5', 'FADH2\nx1 = 1.5', 'GTP\nx1 = 1.0']
atp_eq = [7.5, 1.5, 1.0]
ax.bar(names_short, atp_eq, color=['#f87171','#fdba74','#fbbf24'], alpha=0.85,
       edgecolor='white', linewidth=1.5)
ax.set_ylabel('ATP equivalents per turn', color='white')
ax.set_title('Per-turn ATP yield = 10 | 2 turns per glucose = 20 ATP eq.',
             color='white', fontsize=11, fontweight='bold')
ax.set_facecolor('#0a0a1a'); ax.tick_params(colors='white')
ax.grid(alpha=0.2, color='#fbbf24', axis='y')
for s in ax.spines.values(): s.set_color('#fbbf24')
for i,v in enumerate(atp_eq):
    ax.text(i, v+0.2, f'{v}', ha='center', color='white', fontsize=11, fontweight='bold')

fig.suptitle('Coupled ODE model of the Krebs cycle',
             color='white', fontsize=15, fontweight='bold')
fig.patch.set_facecolor('#0a0a1a')
plt.tight_layout()
plt.savefig('output.png', dpi=120, bbox_inches='tight', facecolor='#0a0a1a')

print("=== TCA steady-state intermediates (mM) ===")
for n,v in zip(names[:8], ss[:8]):
    print(f"  {n:14s} = {v:.4f}")
print()
print(f"  NAD+/NADH = {ss[8]/ss[9]:.2f}   FAD/FADH2 = {ss[10]/ss[11]:.2f}")
print(f"  Steady-state cycle flux (malate DH) = {flux_ss*1000:.1f} uM/s")
print(f"  Integrated citrate exposure (50-100s) = {integrated_flux:.3f} mM*s")
print()
print("Per turn: 3 NADH + 1 FADH2 + 1 GTP + 2 CO2  (net oxidation of 1 acetyl-CoA)")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

◆Regulation: Ca²⁺ and the NAD⁺/NADH Ratio

The cycle is controlled at three enzymes by a trio of physiological signals: NAD⁺/NADH ratio, ATP/ADP ratio, and matrix [Ca²⁺]. Crucially, calcium serves as the master activator: when a cell is stimulated to do work (muscle contraction, hormone signaling), cytosolic Ca²⁺ rises, some enters the matrix through the MCU (mitochondrial calcium uniporter), and simultaneously activates pyruvate DH, IDH, and \(\alpha\)-KG DH—pouring reduced cofactors into the ETC to match the increased ATP demand.

Python

script.py54 lines

import numpy as np
import matplotlib.pyplot as plt

nad_ratio = np.logspace(-1, 2.5, 200)
ca_levels = [0.1, 1.0, 3.0, 10.0]
names = ['Citrate\nsynthase', 'Isocitrate DH', 'a-KG DH']

def cs_activity(r, ca):
    nadh = 1.0/(1+r)
    return 1.0/(1+nadh/0.03) * (1+0.3*np.log1p(ca))

def idh_activity(r, ca):
    nadh = 1.0/(1+r)
    return ca**1.5 / (ca**1.5 + 0.5) * 1.0/(1+nadh/0.015)

def kgdh_activity(r, ca):
    nadh = 1.0/(1+r)
    return ca**2 / (ca**2 + 0.8) * 1.0/(1+nadh/0.01)

activities = [cs_activity, idh_activity, kgdh_activity]

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

for idx, (ax, f, title) in enumerate(zip(axes, activities, names)):
    for ca in ca_levels:
        a = np.array([f(r, ca) for r in nad_ratio])
        a = a/a.max()
        ax.semilogx(nad_ratio, a, lw=2.3, label=f'Ca x {ca}')
    ax.axvline(10, color='white', ls='--', alpha=0.4)
    ax.text(12, 0.1, 'fed\nstate', color='white', fontsize=9)
    ax.set_xlabel('NAD+/NADH ratio', color='white')
    ax.set_ylabel('Relative activity', color='white')
    ax.set_title(title, color='white', fontweight='bold')
    ax.legend(facecolor='#0a0a1a', edgecolor='#67e8f9', labelcolor='white', fontsize=8)
    ax.set_facecolor('#0a0a1a'); ax.tick_params(colors='white')
    ax.grid(alpha=0.2, color='#67e8f9', which='both')
    for s in ax.spines.values(): s.set_color('#67e8f9')

fig.suptitle('TCA enzymes: Ca2+ activation, NADH feedback inhibition',
             color='white', fontsize=14, fontweight='bold')
fig.patch.set_facecolor('#0a0a1a')
plt.tight_layout()
plt.savefig('output.png', dpi=120, bbox_inches='tight', facecolor='#0a0a1a')

print("=== Regulatory summary ===")
print("Citrate synthase:  inhibited by NADH, citrate, succinyl-CoA, ATP")
print("Isocitrate DH:     rate-limiting; activated by Ca2+, ADP; inhibited by ATP, NADH")
print("a-KG dehydrogenase: Ca2+-activated; inhibited by NADH, succinyl-CoA")
print()
print("Physiological lesson: during muscle contraction, [Ca2+] spikes,")
print("simultaneously activating IDH and a-KG DH (and pyruvate DH), matching")
print("ATP demand with TCA flux. NADH accumulation (hypoxia) brakes the cycle.")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

◆Anaplerotic Reactions

TCA intermediates are constantly siphoned off for biosynthesis (heme, amino acids, gluconeogenesis, lipogenesis). Anaplerotic (from Greek “to fill up”) reactions replenish the pool:

Pyruvate carboxylase

Biotin + ATP → pyruvate + CO₂ → OAA (activated by acetyl-CoA)

Glutamate dehydrogenase

Glu + NAD(P)⁺ → \(\alpha\)-KG + NH₄⁺

Transamination

Asp ↔ OAA; Ala ↔ pyruvate (and thus to OAA)

Propionyl-CoA pathway

Odd-chain fatty acids + Val/Ile/Thr → succinyl-CoA (via B₁₂)

◆Pyruvate Dehydrogenase: The Gateway

Technically not a TCA enzyme, but functionally the gateway feeding it. Pyruvate dehydrogenase (PDH) is a giant multi-enzyme complex (~9 MDa in mammals) containing three catalytic subunits—E1 (decarboxylase, TPP-dependent), E2 (lipoamide acyltransferase), E3 (dihydrolipoyl DH, FAD/NAD-dependent)—plus two regulatory enzymes (PDH kinase, PDH phosphatase) bound to E2.

\[ \text{Pyruvate} + \text{NAD}^+ + \text{CoA-SH} \longrightarrow \text{Acetyl-CoA} + \text{NADH} + \text{CO}_2 \]

The swinging lipoyl arm of E2 visits each of the three active sites in turn, channeling substrates through covalent intermediates. PDH is inhibited by phosphorylation(PDK1-4 kinases activated by NADH, acetyl-CoA, ATP) and activated by dephosphorylation (PDP phosphatase activated by Ca²⁺, Mg²⁺, insulin). Dichloroacetate inhibits PDK, shifting flux from lactate to acetyl-CoA—investigated for lactic acidosis and cancer.

◆Thermodynamics: Standard vs Cellular \(\Delta G\)

The standard free energy changes computed at 1 M concentrations of reactants and products often look unfavorable (step 8 malate DH: \(+29.7\;\text{kJ/mol}\)) but inside cells the mass-action ratios are far from equilibrium. The cellular \(\Delta G = \Delta G^{\circ\prime} + RT\ln Q\) incorporates real concentrations and shows every reaction of the cycle proceeds spontaneously forward under physiological conditions. Three steps are notably far from equilibrium—citrate synthase (\(-53.9\)), IDH (\(-17.5\)), and \(\alpha\)-KG DH (\(-43.9\))—and these are the three regulated, rate-limiting enzymes. Near-equilibrium steps (aconitase, fumarase, MDH) are freely reversible and serve primarily as chemical relays.

\[ \Delta G = \Delta G^{\circ\prime} + RT\,\ln\frac{[\text{products}]}{[\text{reactants}]} \]

◆Cellular Context: Compartmentalization and Transport

The TCA cycle operates in the mitochondrial matrix, but many of its intermediates serve dual roles in cytosolic pathways. The inner mitochondrial membrane is impermeable to most metabolites and specific transporters shuttle substrates in and out:

Pyruvate Carrier (MPC1/2)

Imports pyruvate from cytosol; drug target for diabetes (UK-5099). Pyruvate drives both TCA entry and gluconeogenesis via OAA.

Citrate Transporter (SLC25A1)

Exports citrate in exchange for malate; delivers acetyl-CoA equivalents to cytosol for fatty acid and cholesterol synthesis.

2-Oxoglutarate/Malate Exchanger

Central to the malate-aspartate shuttle that transfers cytosolic reducing equivalents (NADH) into the matrix.

Dicarboxylate Carrier

Symports malate or succinate with Pᵢ; important in gluconeogenesis and anaplerotic topping-up.

◆The TCA Cycle as a Biosynthetic Hub

Far from being merely a catabolic engine, the TCA cycle is a biosynthetic crossroads whose intermediates supply the building blocks of macromolecules:

Citrate: exported to cytosol for fatty acid and cholesterol synthesis via ATP-citrate lyase
\(\alpha\)-KG: precursor of glutamate, glutamine, proline, arginine; substrate for \(\alpha\)-KG-dependent dioxygenases (prolyl hydroxylases, TET2, JmjC)
Succinyl-CoA: precursor of heme (via \(\delta\)-aminolevulinate synthase), porphyrins, and ketone body uptake by peripheral tissues (SCOT)
Fumarate: byproduct of urea cycle and purine synthesis re-entry point
OAA: transaminates to aspartate for pyrimidine synthesis, urea cycle, and gluconeogenesis

Clinical Relevance

IDH1/2 Mutations in Gliomas

Neomorphic mutants reduce \(\alpha\)-KG to 2-hydroxyglutarate, an oncometabolite that inhibits \(\alpha\)-KG-dependent dioxygenases (TET, JmjC), altering the epigenome.

Fumarase / SDH Deficiency

Loss-of-function mutations cause paraganglioma, pheochromocytoma, and HLRCC; accumulated fumarate/succinate inhibit prolyl hydroxylases, stabilizing HIF-1\(\alpha\) (pseudohypoxia).

Arsenite Poisoning

Covalently inhibits lipoamide at PDH and \(\alpha\)-KG DH by chelating vicinal thiols, blocking TCA entry.

Thiamine Deficiency (Wernicke)

TPP is a cofactor of PDH and \(\alpha\)-KG DH. Deficiency blocks entry into TCA, elevating lactate and causing neurological damage.

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