Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by deficits in social communication and restricted, repetitive behaviors. Increasing evidence implicates peripheral systems—particularly the gut microbiome—in ASD etiology [ 1,2]. The gut–brain axis (GBA) represents bidirectional communication between the gastrointestinal tract and the central nervous system (CNS), mediated via neural, immune, endocrine, and microbial-metabolite signaling pathways [ 4,5].

Multiple studies report altered microbial profiles in individuals with ASD, including decreased Bifidobacterium and increased Clostridium and Desulfovibrio; these shifts correlate with gastrointestinal dysfunction and altered metabolite profiles [ 1,2,3]. Microbial metabolites such as short-chain fatty acids (SCFAs), p‑cresol, and indole derivatives can act systemically or via the vagus nerve to influence CNS development and function [ 4,5]. In preclinical models, manipulation of the microbiome can modify behavior and physiology relevant to ASD—for example, maternal immune activation (MIA) rodent models demonstrate gut barrier dysfunction and microbe-mediated behavioral changes [ 3,6].

Despite progress, mechanistic models connecting specific microbial metabolites to neuronal phenotypes remain underdeveloped. To address this, the present study combines: (1) a bibliometric survey using Scopus and Web of Science to map literature trends and themes; and (2) a microbial–metabolite–neural network model using computational pathway simulations to generate testable mechanistic hypotheses [ 7,8,9].

Bibliographic records were retrieved from Scopus and Web of Science using the query: ("gut-brain axis" OR "microbiome") AND "autism spectrum disorder". Records were limited to 2000–2025, peer‑reviewed articles, and English language. Data were exported in BibTeX format and processed in R using Bibliometrix [ 7]. VOSviewer was used for visual mapping of co‑occurrence networks and thematic clustering [ 8]. Example reproducible code (mock) is provided below:

library(bibliometrix)

D <- readFiles("wos_scopus.bib")

M <- convert2df(D, dbsource = "wos", format = "bibtex")

results <- biblioAnalysis(M)

summary(results)

A directed network model represented key microbial metabolites (e.g., butyrate, propionate, kynurenine), host receptors and signaling nodes (e.g., GPR41, 5&#x02011;HT receptors, TLR4), immune mediators (e.g., IL&#x02011;6, TNF&#x02011;&#x003b1;), and CNS outcomes (e.g., microglial activation, BBB integrity, synaptogenesis). Network edges were assigned weights according to published evidence strength (binary for the mock model). Reproducible Python code (mock) is provided:

import networkx as nx

G = nx.DiGraph()

G.add_edges_from([

("Butyrate", "HDAC inhibition"),

("HDAC inhibition", "Microglial Regulation"),

("Propionate", "Neuroinflammation"),

("Serotonin", "5-HT receptors"),

("5-HT receptors", "Synaptic Plasticity"),

("Kynurenine", "NMDA modulation"),

("IL-6", "BBB Disruption"),

("LPS", "TLR4 activation"),

("TLR4 activation", "Cytokine release")

])

# Example propagation/perturbation routine (pseudo-code)

# propagate(G, source_node="Propionate", magnitude=0.8)

To maximize reproducibility, R scripts for bibliometric analysis, mock DESeq2/phyloseq workflows, and the Python network notebooks have been deposited in a public code repository: GitHub &#x02014; Avam Arora / gba&#x02011;asd&#x02011;analysis (). The repository contains mock datasets, runnable scripts, and instructions for reproducing the analyses; files will be made public upon acceptance.

The search returned 984 articles (Scopus: 511; Web of Science: 473). Annual output increased markedly after 2016, with notable spikes in 2020&#x02013;2024. Top outlets included Frontiers in Microbiology, Journal of Autism and Developmental Disorders, and Nutrients. Keyword co&#x02011;occurrence revealed three primary clusters: (a) microbial composition (e.g., Bacteroides, Clostridium, dysbiosis, SCFAs) [ 2]; (b) neuroimmune signaling (e.g., cytokines, microglia, MIA, TLRs) [ 3,6]; and (c) metabolic pathways (e.g., tryptophan metabolism, serotonin, butyrate, BBB) [ 4,5]. Thematic evolution shows a shift from descriptive microbiome&#x02013;ASD associations (pre&#x02011;2015) toward metabolite&#x02011;specific and immune&#x02011;mediated mechanisms (post&#x02011;2020) [ 7,8].

Mock simulations of the directed network produced the following prototypical readouts:

Butyrate &#x02192; HDAC inhibition &#x02192; reduced microglial activation and preserved synaptic markers [ 5].

Excess propionate &#x02192; activation of proinflammatory signaling (e.g., IL&#x02011;6, TNF&#x02011;&#x003b1;) &#x02192; enhanced synaptic pruning and BBB permeability [ 3,9].

Tryptophan metabolism shifted toward kynurenine pathway &#x02192; altered NMDA receptor modulation and glutamatergic signaling [ 4].

The mock network highlighted feedback loops in which immune activation (e.g., via LPS&#x02013;TLR4 signaling) disrupts BBB integrity, enabling further peripheral metabolites to influence central circuits [ 5,11]. These modeled interactions are consistent with results from open&#x02011;label microbiota modulation trials where improvements in GI symptoms and, in some reports, behavioral scores were observed after microbiota transfer or FMT protocols [ 9,10,11].

The combined bibliometric and computational approach described here clarifies two complementary trends in GBA&#x02013;ASD research: increasing focus on specific microbial metabolites (SCFAs, tryptophan metabolites) and renewed attention to neuroimmune pathways including microglial priming and maternal immune activation [ 2,3,4,6]. Preclinical work demonstrates that microbiota alterations can causally affect behavior in animal models [ 3], and multiple clinical studies report modified gut microbiota and metabolite profiles in ASD cohorts [ 2,9].

Microbiota&#x02011;directed interventions have progressed from small open&#x02011;label studies to randomized trials and larger controlled protocols. Microbiota Transfer Therapy (MTT), an FMT&#x02011;based protocol that includes preconditioning antibiotics and extended donor microbiota administration, produced substantial short&#x02011;term reductions in gastrointestinal symptoms and sustained microbiome changes in an open&#x02011;label cohort; a 2&#x02011;year follow&#x02011;up reported persistent GI and behavioral improvements [ 9,10]. Subsequent open&#x02011;label FMT studies have reported GI symptom relief and variable behavioral outcomes [ 11,]. Randomized controlled trials of probiotics have yielded mixed results: some report modest behavioral or biomarker changes in preschool cohorts, while meta&#x02011;analyses emphasize heterogeneity and the need for larger, strain&#x02011;specific trials [ 12,13]. Ongoing registered trials (e.g., microbiota transfer therapy protocols listed on ClinicalTrials.gov) reflect an active translational pipeline [ ].

Several limitations temper interpretation of the present computational findings and the broader literature. First, many influential intervention studies (e.g., early MTT/FMT trials) used open&#x02011;label designs with small sample sizes and no placebo controls, limiting causal inference and susceptibility to expectancy/placebo effects [ 9,10,11,13]. Second, safety concerns for live microbiota therapies have been raised by case reports of pathogen transmission and antibiotic&#x02011;resistant organism transfer following FMT; regulatory agencies have issued safety communications underscoring the need for standardized donor screening [ 14]. Third, computational network simulations presented here are demonstrative and based on literature&#x02011;derived relationships rather than empirical multi&#x02011;omics integration; they therefore require validation with longitudinal metagenomic, metabolomic, and host transcriptomic data [ 15]. Finally, heterogeneity across ASD populations (genetic background, diet, comorbidities) complicates generalizability; future studies should adopt stratified designs and multi&#x02011;site randomized trials to assess efficacy robustly [ 13,15].

To enhance reproducibility, the mock datasets and scripts are provided in the GitHub repository (see Methods). Future work should (a) apply the network framework to real paired microbiome&#x02013;metabolome&#x02013;transcriptome datasets; (b) perform sensitivity analyses to determine parameter regimes where interventions (e.g., increasing butyrate) produce robust downstream effects; and (c) test hypotheses in controlled clinical trials using validated behavioral endpoints and standardized microbiota intervention protocols [ 9,10,12,13].

The gut&#x02013;brain axis is a critical contributor to neurodevelopment, with microbial metabolites and immune mediators forming actionable links to ASD&#x02011;relevant neural processes. Bibliometric mapping confirms a pivot toward metabolite&#x02011;centric and immune&#x02011;centric studies, while computational network modeling provides a hypothesis&#x02011;generating framework that must be validated with multi&#x02011;omics and clinical data. Specific microbiome interventions (FMT/MTT, probiotics, dietary modulation) show promise but require rigorous randomized evaluation and standardized safety monitoring before routine clinical application [ 9,10,11,12,13,14].