Module 7

Insect-Microbe Symbiosis

From obligate endosymbionts to reproductive parasites — the molecular biology of insect-microbe partnerships

7.1 Buchnera aphidicola: The Aphid's Essential Partner

Aphids feed exclusively on phloem sap, a diet rich in sugars but severely deficient in essential amino acids. To survive on this unbalanced diet, aphids harborBuchnera aphidicola, an obligate intracellular gamma-proteobacterium that has been vertically transmitted for ~160-280 million years.

Genome Reduction

Buchnera's genome is one of the smallest known for any cellular organism: only ~640 kb encoding ~580 genes, compared to its free-living relative E. coli (~4,600 kb, ~4,300 genes). This represents a ~86% reduction in genome content. The organism has lost genes for:

Cell surface structures (no lipopolysaccharide, reduced cell wall)
Regulatory systems (most transcription factors lost)
DNA repair (elevated mutation rate, AT-biased genome ~74% AT)
Amino acid catabolism (can synthesize but not degrade amino acids)

Despite this extreme reduction, Buchnera retains genes for synthesizing all essential amino acids (tryptophan, leucine, isoleucine, valine, threonine, methionine, phenylalanine, lysine, histidine) — precisely those absent from phloem sap.

Metabolic Complementarity

The partnership is a textbook case of metabolic complementarity. The host provides precursors that Buchnera cannot make (due to gene loss), and Buchnerareturns essential amino acids the host cannot synthesize:

\(\text{Host} \xrightarrow{\text{Glu, Asp, ATP, NADPH}} \textit{Buchnera} \xrightarrow{\text{Trp, Leu, Ile, Val...}} \text{Host}\)

Tryptophan biosynthesis exemplifies this interdependence. The pathway from chorismate to tryptophan requires 5 enzymatic steps. In some aphid-Buchnera systems, the pathway is split:Buchnera encodes \(trpEG\) (anthranilate synthase) while the aphid genome has acquired a horizontally transferred copy of \(trpA\) (tryptophan synthase alpha subunit).

Bacteriocyte Biology

Buchnera cells reside in specialized host cells called bacteriocytes, which are clustered into an organ called the bacteriome. Each bacteriocyte contains ~100 Buchnera cells enclosed within host-derived membrane vesicles (symbiosomes). The host actively controls symbiont population through:

\(\frac{dN}{dt} = r \cdot N \left(1 - \frac{N}{K(T, \text{diet})}\right) - \mu_{\text{lysosome}} \cdot N\)

Host regulates carrying capacity \(K\) via autophagy/lysosomal degradation rate \(\mu\)

7.2 Wolbachia: The Reproductive Parasite

Wolbachia pipientis is an alpha-proteobacterium that infects an estimated 40% of all insect species — making it arguably the most successful intracellular parasite on Earth. Unlike Buchnera, Wolbachia is primarily a reproductive parasite that manipulates host reproduction to favor its own transmission.

Cytoplasmic Incompatibility (CI)

The most common manipulation is cytoplasmic incompatibility (CI): when an infected male mates with an uninfected female, the embryos die. This gives infected females a reproductive advantage because they can mate with any male (infected or not), while uninfected females can only successfully mate with uninfected males.

Compatible crosses:

\(\text{W}^+ \female \times \text{W}^+ \male \rightarrow \text{viable}\)

\(\text{W}^+ \female \times \text{W}^- \male \rightarrow \text{viable}\)

\(\text{W}^- \female \times \text{W}^- \male \rightarrow \text{viable}\)

Incompatible cross:

\(\text{W}^- \female \times \text{W}^+ \male \rightarrow \textbf{DEAD}\)

Sperm is “modified” by Wolbachia;
only Wolbachia in egg can “rescue”

Invasion Dynamics

The frequency of Wolbachia-infected individuals \(W\) in a population evolves according to:

\(\frac{dW}{dt} = W \cdot \left(\frac{f_{\text{CI}} \cdot (1 - W)}{1 - f_{\text{CI}} \cdot W \cdot (1 - W)} - c\right)\)

where \(f_{\text{CI}}\) = CI strength (0-1), \(c\) = fitness cost of infection

This equation has an unstable equilibrium (threshold frequency) at:

\(W^* \approx \frac{c}{f_{\text{CI}}}\)

Below \(W^*\), Wolbachia is lost; above \(W^*\), it sweeps to fixation

For typical values (\(f_{\text{CI}} = 0.9\), \(c = 0.05\)), the threshold is only \(W^* \approx 0.056\) — meaning Wolbachia can invade from very low initial frequencies.

Other Manipulation Strategies

Male-killing: infected males die during development, freeing resources for infected sisters
Feminization: genetic males develop as functional females (e.g., in woodlice Armadillidium)
Parthenogenesis induction: unfertilized eggs develop as females (e.g., in parasitoid wasps)

7.3 Termite Gut Microbiome: A Three-Tier Consortium

Termites are among the most efficient cellulose digesters on Earth, thanks to a remarkable three-tier microbial consortium in their hindgut. Lower termites (e.g., Reticulitermes) harbor flagellate protists, bacteria, and methanogenic archaea that collectively convert recalcitrant cellulose into usable metabolic energy.

The Fermentation Pathway

The complete degradation pathway proceeds through multiple trophic levels:

Tier 1 — Flagellate Protists (cellulolysis):

\((\text{C}_6\text{H}_{10}\text{O}_5)_n + n\text{H}_2\text{O} \xrightarrow{\text{cellulase}} n\text{C}_6\text{H}_{12}\text{O}_6\)

Tier 2 — Bacteria (fermentation):

\(\text{C}_6\text{H}_{12}\text{O}_6 \rightarrow 2\text{CH}_3\text{COOH} + 2\text{CO}_2 + 4\text{H}_2\)

\(\Delta G^\circ = -216 \text{ kJ/mol}\)

Tier 3 — Methanogenic Archaea:

\(\text{CO}_2 + 4\text{H}_2 \rightarrow \text{CH}_4 + 2\text{H}_2\text{O}\)

\(\Delta G^\circ = -131 \text{ kJ/mol}\)

Thermodynamic Coupling

The system works because of interspecies hydrogen transfer. The fermentation step (Tier 2) is thermodynamically unfavorable at high H\(_2\) concentrations. Methanogens (Tier 3) consume H\(_2\), keeping \(P_{H_2}\) below ~10\(^{-4}\) atm, which makes the fermentation step exergonic:

\(\Delta G = \Delta G^\circ + RT \ln \frac{[\text{acetate}]^2 \cdot P_{\text{CO}_2}^2 \cdot P_{\text{H}_2}^4}{[\text{glucose}]}\)

Low \(P_{H_2}\) maintained by methanogens makes \(\Delta G\) sufficiently negative for fermentation to proceed

Carbon and Energy Budget

The termite absorbs acetate (the primary fermentation product) across the hindgut wall, which provides ~60-90% of the insect's metabolic energy. Methane is released as waste — termites collectively produce an estimated 20 Tg CH\(_4\)/year, about 3% of global methane emissions.

7.4 Tsetse Fly & Wigglesworthia: B-Vitamin Provisioning

The tsetse fly (Glossina spp.), vector of African sleeping sickness, feeds exclusively on vertebrate blood — a diet rich in protein but deficient in B-vitamins. The fly depends onWigglesworthia glossinidia, an obligate endosymbiont that provides essential B-vitamins (thiamine B\(_1\), riboflavin B\(_2\), biotin B\(_7\), folate B\(_9\)).

Vertical Transmission via Milk Glands

Tsetse flies are viviparous — the female retains a single larva in her uterus, nourishing it with “milk” secreted by modified accessory glands.Wigglesworthia is transmitted to offspring through these milk gland secretions, ensuring faithful vertical transmission.

Coevolutionary Stability

This system exhibits partner fidelity — the symbiont's fitness is perfectly aligned with the host's because transmission is strictly vertical. The stability condition for mutualism requires:

\(\frac{\partial W_{\text{host}}}{\partial B_{\text{symbiont}}} > 0 \quad \text{and} \quad \frac{\partial W_{\text{symbiont}}}{\partial B_{\text{host}}} > 0\)

Both partners' fitness \(W\) increases with the other's investment \(B\) — ensured by vertical transmission

Aposymbiotic tsetse flies (experimentally cleared of Wigglesworthia) are completely sterile, demonstrating the obligate nature of the symbiosis.

7.5 Photorhabdus: Bioluminescent Bacteria in the Nematode-Insect Cycle

Photorhabdus luminescens is a bioluminescent gamma-proteobacterium that participates in a remarkable tripartite symbiosis with entomopathogenic nematodes (Heterorhabditis) and insect hosts. The bacteria:

Reside mutualistically in the nematode gut during the free-living stage
Are released into insect hemolymph when the nematode invades a larval host
Kill the insect within 24-48 hours via toxins and immune suppression
Produce antibiotics that prevent competing microbes from colonizing the cadaver
Generate bioluminescence (lux operon) that may attract new insect hosts

The Bioluminescence Chemistry

Photorhabdus uses the bacterial luciferase system (distinct from firefly luciferase):

\(\text{FMNH}_2 + \text{RCHO} + \text{O}_2 \xrightarrow{\text{luciferase}} \text{FMN} + \text{RCOOH} + \text{H}_2\text{O} + h\nu\)

Quantum yield \(\Phi \approx 0.05\) (much lower than firefly \(\Phi = 0.88\)), peak emission ~490 nm

Simulation: Symbiosis Dynamics

This simulation models (1) endosymbiont genome reduction over evolutionary time, (2) Wolbachia CI invasion dynamics at different starting frequencies, (3) genome size comparison across insect endosymbionts, and (4) energy yields in the termite gut fermentation pathway.

Python

script.py194 lines

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

# ══════════════════════════════════════════════════════════════════════
# Panel 1: Buchnera Genome Reduction Model
# ══════════════════════════════════════════════════════════════════════
# Model: genome size decays over evolutionary time as genes are lost
# to host or become pseudogenes
time_myr = np.linspace(0, 200, 500)  # millions of years

# Gene loss follows exponential decay with deceleration
# (essential genes are retained)
genome_initial = 3800  # genes (free-living ancestor ~E. coli)
genome_essential = 580  # minimum viable genome (Buchnera today ~580 genes)

# Multi-phase loss model
def genome_size(t, G0=3800, G_min=580, k1=0.04, k2=0.008, t_switch=50):
    """Two-phase genome reduction"""
    if isinstance(t, np.ndarray):
        result = np.zeros_like(t)
        phase1 = t < t_switch
        phase2 = ~phase1
        G_switch = G_min + (G0 - G_min) * np.exp(-k1 * t_switch)
        result[phase1] = G_min + (G0 - G_min) * np.exp(-k1 * t[phase1])
        result[phase2] = G_min + (G_switch - G_min) * np.exp(-k2 * (t[phase2] - t_switch))
        return result
    else:
        if t < t_switch:
            return G_min + (G0 - G_min) * np.exp(-k1 * t)
        G_switch = G_min + (G0 - G_min) * np.exp(-k1 * t_switch)
        return G_min + (G_switch - G_min) * np.exp(-k2 * (t - t_switch))

G_t = genome_size(time_myr)

# Comparison organisms
organisms_genome = ['E. coli\n(free)', 'Serratia\nsymbiotica', 'Buchnera\n(aphid)', 'Carsonella\n(psyllid)', 'Nasuia\n(leafhopper)']
genome_sizes_kb = [4600, 3500, 640, 160, 112]
gene_counts = [4300, 2800, 580, 182, 137]

# ══════════════════════════════════════════════════════════════════════
# Panel 2: Wolbachia CI Invasion Dynamics
# ══════════════════════════════════════════════════════════════════════
generations = np.arange(0, 200)

def wolbachia_invasion(W0, f_CI=0.9, cost=0.05, n_gen=200):
    """
    Wolbachia frequency dynamics with cytoplasmic incompatibility
    W(t+1) = W(t) * (1 - cost) / (1 - CI_effect)
    CI_effect = f_CI * W * (1 - W)  (incompatible crosses)
    """
    W = np.zeros(n_gen)
    W[0] = W0
    for t in range(1, n_gen):
        w = W[t-1]
        # Fitness: infected females have (1-cost) fitness
        # Uninfected females suffer CI when mating with infected males
        # CI penalty to uninfected: proportion of incompatible matings
        fitness_infected = (1 - cost)
        fitness_uninfected = 1 - f_CI * w  # CI from infected males
        w_bar = w * fitness_infected + (1 - w) * fitness_uninfected
        W[t] = min(1.0, max(0, w * fitness_infected / w_bar))
    return W

# Different starting frequencies
start_freqs = [0.05, 0.10, 0.20, 0.30, 0.50]
colors_start = ['#f87171', '#fbbf24', '#a3e635', '#84cc16', '#65a30d']

# Threshold frequency
# At equilibrium: W* where W*(1-cost) = (1-W*)(1 - f_CI*W*)... unstable at lower threshold
# Approximate threshold: cost / f_CI
f_CI_val = 0.9
cost_val = 0.05
W_threshold = cost_val / f_CI_val

# ══════════════════════════════════════════════════════════════════════
# Panel 3: Termite Gut Fermentation Pathway Flux
# ══════════════════════════════════════════════════════════════════════
# Metabolic flux through the three-tier consortium
# Cellulose -> Glucose -> Pyruvate -> Acetate + CO2 + H2 -> CH4

stages = ['Cellulose', 'Glucose', 'Pyruvate', 'Acetate\n+ CO\u2082 + H\u2082', 'CH\u2084']
# Flux in mmol/day for typical termite
fluxes = [1.0, 6.0, 12.0, 10.5, 3.5]  # cellulose units -> 6 glucose -> 12 pyruvate -> etc.
# Carbon efficiency at each step
carbon_retained = [100, 100, 100, 75, 25]  # percentage of carbon retained

# Energy yield at each step (kJ/mol substrate)
energy_yields = {
    'Cellulose hydrolysis': -15,
    'Glycolysis': -73,
    'Pyruvate fermentation': -47,
    'Acetogenesis': -105,
    'Methanogenesis': -131,
}

# ══════════════════════════════════════════════════════════════════════
# Plotting
# ══════════════════════════════════════════════════════════════════════
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
fig.patch.set_facecolor('#0a0a1a')

colors_lime = ['#a3e635', '#84cc16', '#65a30d', '#4d7c0f', '#3f6212']

# Plot 1: Genome reduction over time
ax1 = axes[0, 0]
ax1.plot(time_myr, G_t, color='#a3e635', linewidth=2.5, label='Genome size trajectory')
ax1.axhline(genome_essential, color='#f87171', linewidth=1.5, linestyle='--', label=f'Buchnera today ({genome_essential} genes)')
ax1.axhline(genome_initial, color='#9ca3af', linewidth=1, linestyle=':', alpha=0.5)
ax1.fill_between(time_myr, genome_essential, G_t, alpha=0.1, color='#a3e635')
ax1.annotate('Rapid initial\nloss (redundant genes)', xy=(20, 2500), fontsize=10, color='#fbbf24')
ax1.annotate('Slow phase\n(essential genes retained)', xy=(120, 800), fontsize=10, color='#fbbf24')
ax1.set_xlabel('Time Since Symbiosis Onset (Myr)', color='white', fontsize=11)
ax1.set_ylabel('Number of Genes', color='white', fontsize=11)
ax1.set_title('Endosymbiont Genome Reduction\n(Buchnera-like trajectory)', color='white', fontsize=12, fontweight='bold')
ax1.legend(fontsize=9, facecolor='#1a1a2e', edgecolor='#a3e635', labelcolor='white')
ax1.set_facecolor('#0a0a1a')
ax1.tick_params(colors='white')
ax1.grid(True, alpha=0.15, color='#a3e635')
for sp in ax1.spines.values(): sp.set_color('#a3e635')

# Plot 2: Wolbachia CI invasion dynamics
ax2 = axes[0, 1]
for i, W0 in enumerate(start_freqs):
    W_t = wolbachia_invasion(W0, f_CI=f_CI_val, cost=cost_val)
    ax2.plot(generations, W_t, color=colors_start[i], linewidth=2, label=f'W\u2080 = {W0:.2f}')
ax2.axhline(W_threshold, color='#f87171', linewidth=1.5, linestyle='--', alpha=0.7, label=f'Threshold ({W_threshold:.3f})')
ax2.set_xlabel('Generations', color='white', fontsize=11)
ax2.set_ylabel('Wolbachia Frequency', color='white', fontsize=11)
ax2.set_title('Wolbachia CI Invasion Dynamics\n(f_CI=0.9, cost=0.05)', color='white', fontsize=12, fontweight='bold')
ax2.legend(fontsize=9, facecolor='#1a1a2e', edgecolor='#a3e635', labelcolor='white', ncol=2)
ax2.set_ylim(-0.05, 1.1)
ax2.set_facecolor('#0a0a1a')
ax2.tick_params(colors='white')
ax2.grid(True, alpha=0.15, color='#a3e635')
for sp in ax2.spines.values(): sp.set_color('#a3e635')

# Plot 3: Genome size comparison (bar chart)
ax3 = axes[1, 0]
bar_colors = ['#9ca3af', '#84cc16', '#a3e635', '#fbbf24', '#f87171']
bars = ax3.barh(organisms_genome, genome_sizes_kb, color=bar_colors, edgecolor='white', linewidth=0.5, height=0.6)
for bar, gc in zip(bars, gene_counts):
    ax3.text(bar.get_width() + 50, bar.get_y() + bar.get_height()/2, f'{gc} genes', va='center', color='white', fontsize=10)
ax3.set_xlabel('Genome Size (kb)', color='white', fontsize=11)
ax3.set_title('Endosymbiont Genome Reduction\nAcross Insect Lineages', color='white', fontsize=12, fontweight='bold')
ax3.set_facecolor('#0a0a1a')
ax3.tick_params(colors='white')
ax3.grid(True, alpha=0.15, color='#a3e635', axis='x')
for sp in ax3.spines.values(): sp.set_color('#a3e635')

# Plot 4: Termite fermentation — energy yields
ax4 = axes[1, 1]
step_names = list(energy_yields.keys())
step_energies = list(energy_yields.values())
bar_cols = ['#65a30d', '#84cc16', '#a3e635', '#fbbf24', '#f87171']
bars4 = ax4.barh(step_names, [abs(e) for e in step_energies], color=bar_cols, edgecolor='white', linewidth=0.5, height=0.6)
for bar, e in zip(bars4, step_energies):
    ax4.text(bar.get_width() + 2, bar.get_y() + bar.get_height()/2, f'{e} kJ/mol', va='center', color='white', fontsize=10)
ax4.set_xlabel('|\u0394G| (kJ/mol substrate)', color='white', fontsize=11)
ax4.set_title('Termite Gut Fermentation:\nEnergy Yield per Step', color='white', fontsize=12, fontweight='bold')
ax4.set_facecolor('#0a0a1a')
ax4.tick_params(colors='white')
ax4.grid(True, alpha=0.15, color='#a3e635', axis='x')
for sp in ax4.spines.values(): sp.set_color('#a3e635')

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0a1a')

# ── Numerical Output ──
print("=== Insect-Microbe Symbiosis Simulations ===\n")

print("--- Buchnera Genome Reduction ---")
for t in [0, 10, 50, 100, 150, 200]:
    print(f"t = {t} Myr: ~{genome_size(t):.0f} genes")
print(f"Reduction: {genome_initial} -> {genome_essential} genes ({(1-genome_essential/genome_initial)*100:.0f}% lost)")

print("\n--- Wolbachia CI Invasion ---")
print(f"CI strength (f_CI): {f_CI_val}")
print(f"Fitness cost: {cost_val}")
print(f"Invasion threshold: W* = {W_threshold:.4f}")
for W0 in start_freqs:
    W_final = wolbachia_invasion(W0, f_CI=f_CI_val, cost=cost_val)[-1]
    outcome = "INVADES" if W_final > 0.5 else "FAILS"
    print(f"W0 = {W0:.2f}: final W = {W_final:.3f} [{outcome}]")

print("\n--- Termite Gut Fermentation ---")
print("Pathway: Cellulose -> Glucose -> Pyruvate -> Acetate + CO2 + H2 -> CH4")
total_energy = sum(abs(e) for e in step_energies)
print(f"Total energy extracted: {total_energy} kJ/mol cellulose unit")
for name, energy in energy_yields.items():
    pct = abs(energy) / total_energy * 100
    print(f"  {name}: {energy} kJ/mol ({pct:.1f}% of total)")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

Advanced: Metabolic Exchange & Wolbachia Prevalence

This simulation visualizes (1) the amino acid complementarity between Buchnera and its aphid host, and (2) Wolbachia prevalence and manipulation mechanisms across major insect orders.

Python

script.py91 lines

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

# ══════════════════════════════════════════════════════════════════════
# Panel 1: Metabolic Complementarity — Buchnera-Aphid Exchange
# ══════════════════════════════════════════════════════════════════════
# Amino acid production: host vs symbiont
amino_acids = ['Trp', 'Leu', 'Ile', 'Val', 'Thr', 'Met', 'Phe', 'Lys', 'His', 'Arg']
# Production capacity (relative flux, 0-1)
buchnera_production = [0.95, 0.90, 0.85, 0.88, 0.92, 0.70, 0.82, 0.87, 0.80, 0.15]
aphid_production =    [0.05, 0.10, 0.15, 0.12, 0.08, 0.30, 0.18, 0.13, 0.20, 0.85]

# What host provides to Buchnera
host_provides = ['Glu', 'Asp', 'Gln', 'Non-essential\nAAs', 'ATP', 'NADPH']
host_flux = [0.95, 0.90, 0.85, 0.80, 0.70, 0.75]

# ══════════════════════════════════════════════════════════════════════
# Panel 2: Wolbachia Prevalence Across Insect Orders
# ══════════════════════════════════════════════════════════════════════
orders = ['Coleoptera', 'Diptera', 'Hymenoptera', 'Lepidoptera',
          'Hemiptera', 'Orthoptera', 'Isoptera', 'Odonata']
prevalence = [28, 45, 52, 38, 35, 22, 15, 18]  # percentage infected
mechanisms = {
    'CI only': [65, 55, 40, 50, 45, 70, 60, 55],
    'Male-killing': [15, 25, 20, 30, 10, 5, 5, 10],
    'Feminization': [10, 10, 5, 8, 15, 10, 20, 15],
    'Parthenogenesis': [10, 10, 35, 12, 30, 15, 15, 20],
}

# ══════════════════════════════════════════════════════════════════════
# Plotting
# ══════════════════════════════════════════════════════════════════════
fig, axes = plt.subplots(1, 2, figsize=(15, 6))
fig.patch.set_facecolor('#0a0a1a')

# Plot 1: Metabolic complementarity
ax1 = axes[0]
x_pos = np.arange(len(amino_acids))
width = 0.35
bars1 = ax1.bar(x_pos - width/2, buchnera_production, width, color='#a3e635', label='Buchnera provides', edgecolor='white', linewidth=0.5)
bars2 = ax1.bar(x_pos + width/2, aphid_production, width, color='#f87171', label='Aphid provides', edgecolor='white', linewidth=0.5)
ax1.set_xlabel('Essential Amino Acid', color='white', fontsize=11)
ax1.set_ylabel('Relative Production Flux', color='white', fontsize=11)
ax1.set_title('Buchnera-Aphid Metabolic\nComplementarity', color='white', fontsize=12, fontweight='bold')
ax1.set_xticks(x_pos)
ax1.set_xticklabels(amino_acids, fontsize=10)
ax1.legend(fontsize=9, facecolor='#1a1a2e', edgecolor='#a3e635', labelcolor='white')
ax1.set_facecolor('#0a0a1a')
ax1.tick_params(colors='white')
ax1.grid(True, alpha=0.15, color='#a3e635', axis='y')
for sp in ax1.spines.values(): sp.set_color('#a3e635')

# Plot 2: Wolbachia prevalence stacked by mechanism
ax2 = axes[1]
x_pos2 = np.arange(len(orders))
bottom = np.zeros(len(orders))
mech_colors = ['#a3e635', '#f87171', '#fbbf24', '#67e8f9']
for (mech, values), color in zip(mechanisms.items(), mech_colors):
    # Scale to actual prevalence
    scaled = [p * v / 100 for p, v in zip(prevalence, values)]
    ax2.bar(x_pos2, scaled, 0.6, bottom=bottom, color=color, label=mech, edgecolor='white', linewidth=0.5)
    bottom += np.array(scaled)
ax2.set_xlabel('Insect Order', color='white', fontsize=11)
ax2.set_ylabel('Wolbachia Prevalence (%)', color='white', fontsize=11)
ax2.set_title('Wolbachia Prevalence & Mechanism\nAcross Insect Orders', color='white', fontsize=12, fontweight='bold')
ax2.set_xticks(x_pos2)
ax2.set_xticklabels(orders, fontsize=9, rotation=30, ha='right')
ax2.legend(fontsize=9, facecolor='#1a1a2e', edgecolor='#a3e635', labelcolor='white')
ax2.set_facecolor('#0a0a1a')
ax2.tick_params(colors='white')
ax2.grid(True, alpha=0.15, color='#a3e635', axis='y')
for sp in ax2.spines.values(): sp.set_color('#a3e635')

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0a1a')

print("=== Advanced Symbiosis Models ===\n")

print("--- Essential Amino Acid Complementarity ---")
for aa, bp, ap in zip(amino_acids, buchnera_production, aphid_production):
    provider = "Buchnera" if bp > ap else "Aphid"
    print(f"{aa}: Buchnera={bp:.2f}, Aphid={ap:.2f} -> Primary: {provider}")

print("\n--- Wolbachia Prevalence by Order ---")
for order, prev in zip(orders, prevalence):
    print(f"{order}: {prev}% infected")
print(f"\nOverall insect estimate: ~40% of species harbor Wolbachia")
print("Most common mechanism: Cytoplasmic Incompatibility (CI)")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

References

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