Module 5

Olfaction & Chemical Sensing

Vomeronasal organ biophysics, 200 million olfactory receptors, nepetalactone pharmacology, and the chemistry of feline scent marking

5.1 The Vomeronasal Organ (Jacobson's Organ)

In addition to the main olfactory epithelium, cats possess a vomeronasal organ (VNO) — a paired, tubular chemosensory structure located in the roof of the mouth, just above the hard palate and behind the upper incisors. Each VNO is approximately 1–2 cm long and connects to the nasal cavity via the incisive (nasopalatine) duct.

The VNO is specialized for detecting pheromones and non-volatile chemical signals dissolved in fluid. Unlike the main olfactory system, which samples volatile odorants from the airstream, the VNO analyzes heavier molecules carried in mucus or saliva. The sensory neurons of the VNO project not to the main olfactory bulb, but to the accessory olfactory bulb (AOB), which connects directly to the amygdala and hypothalamus — brain regions governing reproductive behavior, aggression, and social recognition.

Signal Transduction: TRPC2 Channel

The VNO uses a fundamentally different signal transduction cascade than the main olfactory epithelium. Instead of the classical G\(_{\text{olf}}\)/cAMP pathway, VNO neurons employ the TRPC2 (Transient Receptor Potential Cation Channel, subfamily C, member 2) ion channel as the primary transduction element.

The VNO transduction cascade proceeds as:

Pheromone binds to a vomeronasal receptor (V1R or V2R family)
Receptor activates G\(_i\) (for V1R) or G\(_o\) (for V2R) proteins
Phospholipase C (PLC) is activated, producing IP\(_3\) and diacylglycerol (DAG)
DAG (or a metabolite) opens the TRPC2 channel
Ca\(^{2+}\) and Na\(^+\) influx depolarizes the neuron
Action potentials propagate along the vomeronasal nerve to the AOB

Receptor-Ligand Binding Kinetics

The interaction between a pheromone ligand (L) and its vomeronasal receptor (R) follows a reversible bimolecular reaction:

\[ \text{R} + \text{L} \underset{k_{\text{off}}}{\overset{k_{\text{on}}}{\rightleftharpoons}} \text{RL} \]

At equilibrium, the dissociation constant is:

\[ K_d = \frac{k_{\text{off}}}{k_{\text{on}}} = \frac{[\text{R}][\text{L}]}{[\text{RL}]} \]

The fraction of receptors occupied at a given ligand concentration is described by the Hill-Langmuir equation:

\[ \theta = \frac{[\text{L}]^n}{K_d^n + [\text{L}]^n} \]

where \(n\) is the Hill coefficient (typically \(n \approx 1\) for single-site binding). For feline vomeronasal receptors, typical values are:

• \(K_d \sim 10^{-9}\text{--}10^{-7}\) M for pheromone ligands (nanomolar affinity)
• \(k_{\text{on}} \sim 10^6\text{--}10^8\) M\(^{-1}\)s\(^{-1}\)
• \(k_{\text{off}} \sim 10^{-2}\text{--}1\) s\(^{-1}\)

The Flehmen Response

The Flehmen response is the characteristic behavior where a cat curls its upper lip, partially opens its mouth, and inhales slowly. This behavior serves a specific biophysical function: it directs air and fluid containing pheromones through the incisive duct into the VNO lumen.

The fluid transport mechanism relies on a vascular pump: the VNO is surrounded by a large venous sinus. Sympathetic vasoconstriction shrinks the sinus, creating negative pressure that draws fluid into the VNO lumen (analogous to a suction pump). The pressure differential can be modeled as:

\[ \Delta P = P_{\text{atm}} - P_{\text{VNO}} = \frac{8 \mu L Q}{\pi r^4} \]

where \(\mu\) is the fluid viscosity, \(L\) is the duct length,\(Q\) is the volumetric flow rate, and \(r\) is the duct radius (Hagen-Poiseuille flow). The remarkably narrow incisive duct (\(r \approx 0.3\) mm) creates significant capillary forces that assist in fluid transport.

5.2 Main Olfactory Epithelium

The main olfactory epithelium (MOE) of the domestic cat contains approximately 200 million olfactory receptor neurons — 40 times more than humans (~5 million) but fewer than dogs (~300 million). The epithelium lines the ethmoturbinate bones, which create a complex, scroll-like architecture that maximizes surface area.

Receptor Gene Repertoire

The feline olfactory receptor gene family includes:

~800

Olfactory receptor genes

(~400 in humans)

~30

V1R genes (functional)

(~5 in humans)

5.8%

Olfactory bulb / brain volume

(1.3% in humans)

Signal-to-Noise Ratio for Odorant Detection

The ability to detect a faint odorant amid background noise depends on the statistics of receptor activation. For \(N\) olfactory neurons of a given type, each with probability \(p\) of being activated by a single odorant molecule, the signal-to-noise ratio is:

\[ \text{SNR} = \frac{N \cdot p}{\sqrt{N \cdot p \cdot (1-p)}} = \sqrt{\frac{N \cdot p}{1-p}} \]

This follows from Poisson counting statistics. For threshold detection (SNR \(\geq\) 3), the minimum number of activated receptors is:

\[ N \cdot p \geq \frac{9(1-p)}{p} \approx \frac{9}{p} \quad \text{(for } p \ll 1\text{)} \]

With \(N = 200 \times 10^6\) receptors and assuming \(p \sim 10^{-6}\)per molecule per receptor, a cat can detect a single odorant molecule with:

\[ \text{SNR} = \sqrt{\frac{200 \times 10^6 \times 10^{-6}}{1 - 10^{-6}}} \approx \sqrt{200} \approx 14 \]

This extraordinarily high SNR explains why cats can detect certain odorants at concentrations as low as parts per billion. The detection threshold for a given odorant depends on its binding affinity (\(K_d\)), the number of cognate receptors, and the neural integration time.

Olfactory Coding

Each olfactory neuron expresses exactly one olfactory receptor gene (the one-receptor-one-neuron rule). All neurons expressing the same receptor converge on the same pair of glomeruli in the olfactory bulb. The identity of an odorant is encoded as a combinatorial pattern of activated glomeruli:

\[ \text{Odorant identity} = \{g_1, g_2, \ldots, g_k\} \subset \{G_1, G_2, \ldots, G_M\} \]

where \(g_i\) are the activated glomeruli and \(M \approx 800\) is the total number of glomerular types. The number of distinguishable odor patterns is therefore combinatorial: \(\binom{800}{k}\), where \(k\) is the typical number of glomeruli activated per odorant (~10–30). This gives on the order of \(10^{20}\)distinguishable odor identities — far more than a cat would ever encounter.

5.3 The Catnip Response

The behavioral response to Nepeta cataria (catnip) is one of the most dramatic chemosensory-driven behaviors in any mammal. Exposure to the plant — specifically its volatile terpenoid compound nepetalactone — triggers a stereotyped sequence: sniffing, licking, chin rubbing, cheek rubbing, head shaking, and rolling, often accompanied by drooling and apparent euphoria.

Nepetalactone: Structure and Pharmacology

Nepetalactone is a cyclopentanoid monoterpenoid with the molecular formula C\(_{10}\)H\(_{14}\)O\(_2\)(MW = 166.22 g/mol). It consists of a cyclopentane ring fused to a lactone (cyclic ester). Two diastereomers exist: cis-trans and trans-cis, both bioactive.

The mechanism of action involves binding to mu-opioid receptors in the olfactory epithelium. This activates the endogenous opioid system, producing effects similar to a brief, mild opiate response. The binding follows the standard receptor occupancy model:

\[ \text{Response} = \frac{E_{\max} \cdot [\text{Nepetalactone}]^{n_H}}{EC_{50}^{n_H} + [\text{Nepetalactone}]^{n_H}} \]

where \(EC_{50}\) is the half-maximal effective concentration and \(n_H\)is the Hill coefficient. Experimental data suggest \(EC_{50} \approx 10^{-7}\) M (nanomolar range) and \(n_H \approx 1.5\), indicating mild positive cooperativity.

Genetics of the Catnip Response

Approximately 70% of domestic cats respond to catnip. The response is inherited as an autosomal dominant trait with incomplete penetrance. Kittens under 6 months and very elderly cats typically do not respond, suggesting developmental regulation of the receptor or downstream signaling.

The response dynamics follow a characteristic temporal pattern:

• Onset: 5–15 seconds after initial sniffing
• Duration: 5–15 minutes of active behavioral response
• Refractory period: 30–120 minutes (receptor desensitization)
• Tachyphylaxis: Repeated exposure within the refractory period produces no response

The refractory period follows first-order receptor resensitization kinetics:

\[ R_{\text{available}}(t) = R_{\text{total}}\left(1 - e^{-t/\tau_{\text{resens}}}\right) \]

where \(\tau_{\text{resens}} \approx 30\text{--}40\) minutes is the resensitization time constant.

Alternative Euphorics

Several other plants produce similar behavioral responses in cats, often in individuals that do not respond to catnip, suggesting partially overlapping but distinct receptor targets:

Silver vine (Actinidia polygama)

Active compound: Actinidine, dihydroactinidiolide

Response rate: ~80%

Tatarian honeysuckle (Lonicera tatarica)

Active compound: Unknown (wood shavings)

Response rate: ~50%

Valerian root (Valeriana officinalis)

Active compound: Actinidine (also present)

Response rate: ~45%

Indian nettle (Acalypha indica)

Active compound: Unknown terpenoids

Response rate: ~30%

Bol et al. (2017) found that silver vine elicited the strongest and most frequent response, with ~80% of cats responding. Notably, nearly all cats that did not respond to catnip did respond to silver vine, suggesting that offering multiple plant euphorics can stimulate essentially 100% of the domestic cat population.

5.4 Scent Marking Chemistry

Cats communicate extensively through chemical signals deposited during scent marking. The chemistry is complex, involving multiple glandular sources, unique felid-specific compounds, and sophisticated information encoding.

Facial Pheromone Fractions

When a cat rubs its face against objects or people, it deposits a mixture of fatty acids from sebaceous glands located around the chin, cheeks, and forehead. These have been chromatographically separated into five fractions (F1–F5), each with distinct behavioral significance:

Deposited on objects from the lip area. Composition not fully characterized.

Associated with sexual behavior. Contains oleic acid and related compounds.

The "familiarization" pheromone. Used commercially as Feliway. Contains C10-C18 fatty acids. Reduces anxiety and marking behavior.

Deposited during allorubbing (cat-to-cat or cat-to-human). Promotes social bonding.

Deposited from the perioral region. Function under investigation.

Felinine: The Unique Feline Amino Acid

Perhaps the most remarkable feature of feline scent chemistry is felinine (2-amino-7-hydroxy-5,5-dimethyl-4-thiaheptanoic acid), a sulfur-containing amino acid found exclusively in cat urine. Felinine is synthesized in the liver from cysteine and is excreted at concentrations of up to 95 mg/day in intact males (much less in females and neutered males).

The biosynthesis requires a unique enzyme called cauxin(carboxylesterase-like urinary excreted protein), which catalyzes the final step:

\[ \text{3-methylbutanol-cysteinylglycine} \xrightarrow{\text{cauxin}} \text{felinine} + \text{glycine} \]

Once excreted, felinine slowly degrades to the potent volatile thiol 3-mercapto-3-methylbutan-1-ol (MMB) — the compound primarily responsible for the characteristic pungent odor of cat urine:

\[ \text{Felinine} \xrightarrow{\text{bacterial enzymes}} \text{MMB (3-mercapto-3-methylbutan-1-ol)} \]

The decomposition kinetics are first-order:

\[ [\text{MMB}](t) = [\text{Felinine}]_0 \left(1 - e^{-k_{\text{decomp}} \cdot t}\right) \]

with \(k_{\text{decomp}} \approx 0.05\) h\(^{-1}\) at room temperature. This slow release creates a time-released chemical signalthat can persist for days to weeks.

Information Content of Urine Marks

A single urine mark encodes multiple pieces of information through different chemical channels:

• Sex: Felinine concentration (high in intact males, low in females/neutered)
• Reproductive status: Estrogen/progesterone metabolites
• Individual identity: MHC-associated volatile compounds
• Freshness (time since deposit): Felinine/MMB ratio (freshness clock)
• Health status: Ketone bodies (diabetes), protein (kidney disease)
• Territorial claim: Concentration and spray pattern

The felinine/MMB ratio acts as a chemical clock: \(\text{Age} = -\frac{1}{k_{\text{decomp}}} \ln\left(\frac{[\text{Felinine}]}{[\text{Felinine}]_0}\right)\). A cat encountering a urine mark can therefore assess not only who left it but when, enabling sophisticated temporal reasoning about territorial boundaries and competitor movements.

5.5 Vomeronasal Organ Anatomy

Cross-sectional view of the feline vomeronasal organ showing its relationship to the nasal cavity, the incisive duct, sensory epithelium, and neural connections to the accessory olfactory bulb.

Figure 5.1: Coronal cross-section of the feline nasal cavity showing the paired vomeronasal organs (VNO) positioned along the nasal septum above the hard palate. Purple arrows indicate neural connections from VNO sensory epithelium through the accessory olfactory bulb to the amygdala and hypothalamus. The incisive ducts connect the VNO to the oral cavity, allowing pheromone-laden fluid to enter during the Flehmen response.

5.6 Simulation: Odorant Detection & Receptor Kinetics

This simulation models receptor binding kinetics, dose-response curves for nepetalactone, and comparative olfactory sensitivity across cat, dog, and human.

Olfactory Biophysics: Binding Kinetics, Catnip Response & Species Comparison

Python

script.py170 lines

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

fig, axes = plt.subplots(2, 2, figsize=(14, 12))

# ======= Panel 1: Receptor-Ligand Binding Kinetics =======
ax1 = axes[0, 0]

# Time course of receptor binding
t = np.linspace(0, 60, 500)  # seconds
kon = 1e7     # M^-1 s^-1
koff = 0.1    # s^-1
Kd = koff / kon  # 10 nM
L_concs = [1e-9, 5e-9, 1e-8, 5e-8, 1e-7]  # Ligand concentrations (M)
colors_bind = ['#22c55e', '#84cc16', '#f59e0b', '#ef4444', '#dc2626']

Rtotal = 1.0  # Normalized

for L, color in zip(L_concs, colors_bind):
    # Analytical solution for pseudo-first-order binding
    kobs = kon * L + koff
    theta_eq = L / (Kd + L)
    theta_t = theta_eq * (1 - np.exp(-kobs * t))
    ax1.plot(t, theta_t * 100, color=color, linewidth=2, label=f'[L] = {L*1e9:.0f} nM')

ax1.set_xlabel('Time (seconds)', fontsize=11, color='white')
ax1.set_ylabel('Receptor Occupancy (%)', fontsize=11, color='white')
ax1.set_title('VNO Receptor Binding Kinetics', fontsize=13, color='#f59e0b', fontweight='bold')
ax1.legend(fontsize=9, facecolor='#1a1a1a', edgecolor='#f59e0b', labelcolor='white')
ax1.set_facecolor('#0a0a0a')
ax1.tick_params(colors='white')
ax1.grid(True, alpha=0.15, color='white')

# ======= Panel 2: Nepetalactone Dose-Response =======
ax2 = axes[0, 1]

conc = np.logspace(-10, -4, 500)  # Molar
EC50 = 1e-7   # 100 nM
nH = 1.5      # Hill coefficient
Emax = 100    # Arbitrary response units

# Hill equation dose-response
response = Emax * conc**nH / (EC50**nH + conc**nH)

ax2.semilogx(conc, response, color='#f59e0b', linewidth=2.5)
ax2.axhline(Emax / 2, color='white', linestyle=':', alpha=0.3)
ax2.axvline(EC50, color='white', linestyle=':', alpha=0.3)
ax2.annotate(f'EC50 = {EC50*1e9:.0f} nM', xy=(EC50, Emax/2),
             xytext=(EC50 * 10, Emax/2 + 15),
             fontsize=10, color='#fde68a',
             arrowprops=dict(arrowstyle='->', color='#fde68a', lw=1))

# Mark threshold and saturation
ax2.axhspan(0, 10, alpha=0.1, color='gray', label='Sub-threshold')
ax2.axhspan(90, 100, alpha=0.1, color='#22c55e', label='Saturating response')

# Refractory period inset annotation
ax2.annotate('Refractory period:\n30-120 min after\npeak response',
             xy=(1e-5, 85), fontsize=9, color='#fca5a5',
             bbox=dict(boxstyle='round,pad=0.3', facecolor='#1a0a00', edgecolor='#ef4444', alpha=0.8))

ax2.set_xlabel('Nepetalactone Concentration (M)', fontsize=11, color='white')
ax2.set_ylabel('Behavioral Response (%)', fontsize=11, color='white')
ax2.set_title('Catnip Dose-Response Curve', fontsize=13, color='#f59e0b', fontweight='bold')
ax2.legend(fontsize=9, facecolor='#1a1a1a', edgecolor='#f59e0b', labelcolor='white', loc='upper left')
ax2.set_facecolor('#0a0a0a')
ax2.tick_params(colors='white')
ax2.grid(True, alpha=0.15, color='white')

# ======= Panel 3: Olfactory Sensitivity Comparison =======
ax3 = axes[1, 0]

species = ['Human', 'Cat', 'Dog']
n_receptors = [5e6, 200e6, 300e6]          # Number of receptors
n_OR_genes = [400, 800, 1100]              # OR gene count
bulb_pct = [1.3, 5.8, 10.0]               # Olfactory bulb % of brain
v1r_genes = [5, 30, 9]                     # V1R gene count

x = np.arange(len(species))
width = 0.2

# Normalize each metric to max for comparison
metrics = {
    'Receptor count': [r / max(n_receptors) * 100 for r in n_receptors],
    'OR genes': [g / max(n_OR_genes) * 100 for g in n_OR_genes],
    'Bulb volume': [b / max(bulb_pct) * 100 for b in bulb_pct],
    'V1R genes': [v / max(v1r_genes) * 100 for v in v1r_genes],
}
colors_met = ['#60a5fa', '#f59e0b', '#22c55e', '#a78bfa']

for i, ((label, vals), color) in enumerate(zip(metrics.items(), colors_met)):
    bars = ax3.bar(x + i * width, vals, width, label=label, color=color, alpha=0.8, edgecolor='white', linewidth=0.3)

ax3.set_xlabel('Species', fontsize=11, color='white')
ax3.set_ylabel('Relative to Maximum (%)', fontsize=11, color='white')
ax3.set_title('Olfactory System Comparison', fontsize=13, color='#f59e0b', fontweight='bold')
ax3.set_xticks(x + 1.5 * width)
ax3.set_xticklabels(species)
ax3.legend(fontsize=9, facecolor='#1a1a1a', edgecolor='#f59e0b', labelcolor='white')
ax3.set_facecolor('#0a0a0a')
ax3.tick_params(colors='white')
ax3.grid(True, alpha=0.15, color='white', axis='y')

# Annotate cat advantage in V1R
ax3.annotate('Cat: 30 V1R genes\n(6x human, 3x dog!)',
             xy=(1 + 3 * width, metrics['V1R genes'][1]),
             xytext=(1.5, 70),
             fontsize=9, color='#c4b5fd',
             arrowprops=dict(arrowstyle='->', color='#c4b5fd', lw=1))

# ======= Panel 4: SNR vs Number of Receptors =======
ax4 = axes[1, 1]

N_range = np.logspace(4, 9, 500)  # Number of receptors
p_values = [1e-7, 1e-6, 1e-5]    # Activation probability
colors_snr = ['#60a5fa', '#f59e0b', '#ef4444']

for p, color in zip(p_values, colors_snr):
    SNR = np.sqrt(N_range * p / (1 - p))
    ax4.loglog(N_range, SNR, color=color, linewidth=2, label=f'p = {p:.0e}')

# Detection threshold
ax4.axhline(3, color='white', linestyle='--', alpha=0.5, linewidth=1)
ax4.text(2e4, 3.5, 'Detection threshold (SNR = 3)', fontsize=9, color='white', alpha=0.7)

# Mark species
species_N = {'Human': 5e6, 'Cat': 200e6, 'Dog': 300e6}
species_colors = {'Human': '#60a5fa', 'Cat': '#f59e0b', 'Dog': '#22c55e'}
for name, N in species_N.items():
    p_typical = 1e-6
    snr_val = np.sqrt(N * p_typical / (1 - p_typical))
    ax4.plot(N, snr_val, 'o', color=species_colors[name], markersize=10, zorder=5)
    ax4.annotate(f'{name}\nSNR={snr_val:.1f}', xy=(N, snr_val),
                 xytext=(N * 2, snr_val * 1.5),
                 fontsize=9, color=species_colors[name],
                 arrowprops=dict(arrowstyle='->', color=species_colors[name], lw=0.8))

ax4.set_xlabel('Number of Olfactory Receptors', fontsize=11, color='white')
ax4.set_ylabel('Signal-to-Noise Ratio', fontsize=11, color='white')
ax4.set_title('Olfactory SNR vs Receptor Count', fontsize=13, color='#f59e0b', fontweight='bold')
ax4.legend(fontsize=9, facecolor='#1a1a1a', edgecolor='#f59e0b', labelcolor='white')
ax4.set_facecolor('#0a0a0a')
ax4.tick_params(colors='white')
ax4.grid(True, alpha=0.15, color='white')

fig.patch.set_facecolor('#0a0a0a')
plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0a0a')
plt.close()

print("=== Feline Olfaction & Chemical Sensing ===")
print(f"\nVNO Receptor Binding:")
print(f"  Kd = {Kd*1e9:.1f} nM")
print(f"  kon = {kon:.0e} M-1 s-1")
print(f"  koff = {koff} s-1")
print(f"\nNepetalactone Response:")
print(f"  EC50 = {EC50*1e9:.0f} nM")
print(f"  Hill coefficient = {nH}")
print(f"\nOlfactory System Comparison:")
print(f"  {'Species':<10} {'Receptors':<15} {'OR genes':<12} {'Bulb %':<10} {'V1R genes':<10}")
for i, sp in enumerate(species):
    print(f"  {sp:<10} {n_receptors[i]:<15.0f} {n_OR_genes[i]:<12} {bulb_pct[i]:<10.1f} {v1r_genes[i]:<10}")
print(f"\nSNR at p=1e-6:")
print(f"  Human (5M receptors): SNR = {np.sqrt(5e6 * 1e-6):.1f}")
print(f"  Cat (200M receptors): SNR = {np.sqrt(200e6 * 1e-6):.1f}")
print(f"  Dog (300M receptors): SNR = {np.sqrt(300e6 * 1e-6):.1f}")
print(f"\nCat advantage: 6x more V1R genes than humans")
print(f"  -> Superior pheromone detection via VNO")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

5.7 Advanced Topics

Evolutionary Loss of Sweet Taste

An interesting corollary to the feline chemosensory system is the loss of sweet taste perception. The sweet taste receptor is a heterodimer of T1R2 and T1R3 proteins. In all felids examined, the Tas1r2 gene (encoding T1R2) contains a 247-base-pair microdeletion that introduces a premature stop codon, creating a pseudogene:

\[ \text{Tas1r2}^{\text{cat}} = \text{pseudogene (247 bp deletion)} \implies \text{No T1R2 protein} \implies \text{No sweet receptor} \]

This deletion is shared by all tested felids (domestic cat, tiger, cheetah), indicating it occurred before the divergence of the cat family (~10–15 million years ago). The loss was selectively neutral because an obligate carnivore diet provided no benefit from detecting plant sugars. This exemplifies use-it-or-lose-it evolution: genes for unnecessary functions accumulate mutations without selective penalty.

Olfactory Lateralization

Recent studies have shown that cats exhibit olfactory lateralization — preferential use of one nostril for certain olfactory tasks. When investigating novel odors, cats tend to use the right nostril first (left-hemisphere processing), switching to the left nostril for familiar odors. This parallels handedness in humans and suggests hemispheric specialization in olfactory processing:

\[ \text{Laterality Index} = \frac{R - L}{R + L} \]

where \(R\) and \(L\) are the number of right- and left-nostril-first sniffs. Positive values indicate right-nostril preference. Studies report LI \(\approx +0.3\)for novel stimuli and LI \(\approx -0.2\) for familiar stimuli, suggesting that the right hemisphere (left nostril) may be specialized for processing emotionally salient or familiar odors.

References

Miyazaki, M., Yamashita, T., Suzuki, Y., Saito, Y., Soeta, S., Taira, H. & Suzuki, A. (2006). A major urinary protein of the domestic cat regulates the production of felinine, a putative pheromone precursor. Chemistry & Biology, 13(10), 1071–1079.
Bol, S., Caspers, J., Buckingham, L., Anderson-Shelton, G.D. & Bunnik, E.M. (2017). Responsiveness of cats to silver vine, Tatarian honeysuckle, valerian and catnip. BMC Veterinary Research, 13(1), 70.
Pageat, P. & Gaultier, E. (2003). Current research in canine and feline pheromones. Veterinary Clinics of North America: Small Animal Practice, 33(2), 187–211.
Li, X., Li, W., Wang, H., Cao, J., Maehashi, K., Huang, L., Bachmanov, A.A., Reed, D.R., Legrand-Defretin, V., Beauchamp, G.K. & Brand, J.G. (2005). Pseudogenization of a sweet-receptor gene accounts for cats' indifference toward sugar. PLoS Genetics, 1(1), e3.
Luo, M., Fee, M.S. & Katz, L.C. (2003). Encoding pheromonal signals in the accessory olfactory bulb of behaving mice. Science, 299(5610), 1196–1201.
Todd, N.B. (1962). Inheritance of the catnip response in domestic cats. Journal of Heredity, 53(2), 54–56.
Hendriks, W.H., Moughan, P.J., Tarttelin, M.F. & Woolhouse, A.D. (1995). Felinine: a urinary amino acid of Felidae. Comparative Biochemistry and Physiology Part B, 112(4), 581–588.
Shreve, K.R.V. & Udell, M.A.R. (2017). Stress, security, and scent: The influence of chemical signals on the social lives of domestic cats and implications for applied settings. Applied Animal Behaviour Science, 187, 69–76.

M4. Metabolism & Thermoregulation M6. Coat & Pigmentation