Melatonin Biosynthesis, Receptors, and the Microbiota-Tryptophan-Melatonin Axis: A Shared Dysbiosis Signature Across Cardiac Arrhythmias, Epilepsy, Malignant Proliferation, and Cognitive Trajectories

Abstract

Melatonin, an indolic neuromodulator with putative oncostatic and proposed anti-inflammatory properties, primarily demonstrated in preclinical models, is produced at extrapineal sites-most notably in the gut. Its canonical actions are mediated by high-affinity GPCRs (MT1/MT2) and by NQO2, a cytosolic enzyme with a melatonin-binding site (historically termed "MT3"). A growing body of work highlights a bidirectional interaction between the gut microbiota and host melatonin. We integrated two lines of work: (i) three clinical cohorts-cardiac arrhythmias (n = 111; 46-75 y), epilepsy (n = 77; 20-59 y), and stage III-IV solid cancers (25-79 y)-profiled with stool 16S rRNA sequencing, SCFA measurements, and circulating melatonin/urinary 6-sulfatoxymelatonin and (ii) an age-spanning cognitive cohort with melatonin phenotyping, microbiome analyses, and exploratory immune/metabolite readouts, including a novel observation of melatonin binding on bacterial membranes. Across all three disease cohorts, we observed moderate-to-severe dysbiosis, with reduced alpha-diversity and shifted beta-structure. The core dysbiosis implicated tryptophan-active taxa (Bacteroides/Clostridiales proteolysis and indolic conversions) and depletion of SCFA-forward commensals (e.g., Faecalibacterium, Blautia, Akkermansia, and several Lactobacillus/Bifidobacterium spp.). Synthesised literature indicates that typical human gut commensals rarely secrete measurable melatonin in vitro; rather, their metabolites (SCFAs, lactate, and tryptophan derivatives) regulate host enterochromaffin serotonin/melatonin production. In arrhythmia models, dysbiosis, bile-acid remodelling, and autonomic/inflammatory tone align with melatonin-sensitive antiarrhythmic effects. Epilepsy exhibits circadian seizure patterns and tryptophan-metabolite signatures, with modest and heterogeneous responses to add-on melatonin. Cancer cohorts show broader dysbiosis consistent with melatonin's oncostatic actions. In the cognitive cohort, the absence of dysbiosis tracked with preserved learning across ages, and exploratory immunohistochemistry suggested melatonin-binding sites on bacterial membranes in ~15-17% of samples. A unifying microbiota-tryptophan-melatonin axis plausibly integrates circadian, electrophysiologic, and immune-oncologic phenotypes. Practical levers include fiber-rich diets (to drive SCFAs), light hygiene, and time-aware therapy, with indication-specific use of melatonin. Our conclusions regarding microbiota-melatonin crosstalk rely primarily on local paracrine effects within the gut mucosa (where melatonin concentrations are 10-400× plasma levels), whereas systemic chronotherapy conclusions depend on circulating melatonin amplitude and phase. This original research article presents primary data from four prospectively enrolled clinical cohorts (total n = 577).

Keywords: AANAT; ASMT; MT1/MT2; NQO2 (“MT3”); SCFA; arrhythmia; cancer; dysbiosis; epilepsy; melatonin; microbiota; tryptophan.

Figure 1

Microbiota–tryptophan–melatonin axis and the shared dysbiosis signature. Colored arrows indicate directional links between compartments (matching the module colors). Solid arrows indicate relationships assessed in this study. Up arrows (↑) indicate increase or upregulation; down arrows (↓) indicate decrease or downregulation. The dashed magenta arrow denotes decreased shared eubiotic features in disease states. The dashed yellow arrow indicates the reciprocal feedback from disease pathophysiology back to the microbiota–melatonin axis.

Figure 2

BMRDI across clinical states (means ± SD) (p-values are less than 0.005 for all comparisons).

Figure 3

Melatonin (urinary 6-sulfatoxymelatonin) vs. learning (p < 0.005). Each × represents an individual participant data point.

Figure 4

Age-stratified learning by microbiome status (p < 0.005).

Figure 5

Dysbiosis Index vs. melatonin amplitude showing a negative correlation (p < 0.005).

Figure 6

Age vs. learning, separate regressions (p < 0.005).

Figure 7

IL-6 by microbiome status × melatonin amplitude (p < 0.005). orange = Eubiotic/High Melatonin; blue = Eubiotic/Low Melatonin; teal = Dysbiotic/High Melatonin; yellow = Dysbiotic/Low Melatonin.

Figure 8

Exploratory ICC showing putative MT2-like immunoreactivity on select Bacteroides membranes. These findings require independent validation with orthogonal methods. Controls in Figures S1–S3. (A–C) B. fragilis; (D–F) B. thetaiotaomicron; (G–I) B. ovatus.

Figure 9

BMRDI dumbbell plot by clinical state (means ± SD) (p < 0.005). blue markers represent the adverse physiological state and orange markers represent the favorable physiological state; connecting lines pair the two states within each condition. Green lines indicate comparisons within neurological conditions (epilepsy, cognition), and yellow lines indicate comparisons within cardiometabolic conditions (arrhythmia, cancer).

Figure 10

Proposed mechanistic schematic including the bacterial receptor interface (BMRDI). Solid arrows indicate direct links between modules; dashed arrows indicate indirect/modulatory feedback (behavior/sleep ↔ microbiota; therapy timing ↔ outcomes). Arrow colors match the originating module shown in the schematic. Up arrows (↑) indicate increase or upregulation; down arrows (↓) indicate decrease or downregulation.