- 1Department of Pediatric Pulmonology and Immunology, West China Second University Hospital, Sichuan University, Chengdu, China
- 2Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
- 3The Joint Laboratory for Lung Development and Related Diseases of West China Second University Hospital of Sichuan University and School of Life Sciences of Fudan University, West China Institute of Women and Children’s Health, West China Second University Hospital, Sichuan University, Chengdu, China
- 4Department of Gynecology and Obstetrics Nursing, West China Second University Hospital, Sichuan University/West China School of Nursing, Sichuan University, Sichuan, China
- 5NHC Key Laboratory of Chronobiology (Sichuan University), Chengdu, China
- 6Department of Pediatric Pulmonology and Immunology, WCSUH-Tianfu·Sichuan Provincial Children’s Hospital, Sichuan University, Meishan, China
- 7Sichuan Birth Defects Clinical Research Center, West China Second University Hospital, Sichuan University, Chengdu, China
Autism Spectrum Disorder (ASD) manifests as a group of neurodevelopmental disorders with high clinical and genetic heterogeneity, characterized by core features including social communication deficits, repetitive behaviors, and restricted interests. Current research primarily focuses on genetic variations, immune dysregulation, synaptic dysfunction, and gene–environment interactions. Nowadays, accumulating evidence indicates that maternal gut microbiota dysbiosis, induced by high-fat diets, antibiotic overuse, and urbanization, significantly correlates with abnormal fetal neurodevelopment and increased ASD risk. This review systematically delineates three transplacental mechanisms whereby maternal dysbiosis regulates fetal neurodevelopment: Metabolite-mediated pathways, Immune pathway activation, and Epigenetic reprogramming. Meanwhile, the key translational challenges are highlighted. At last, metagenomics-metabolomics-fetal neuroimaging, Development of microbiota metabolite-treated brain organoids, and Artificial Intelligence-driven (AI-driven) probiotic screening were proposed as research directions in future.
1 Introduction
Autism Spectrum Disorder (ASD) is a highly heterogeneous, complex neurodevelopmental disorder. Its core clinical manifestations consist of persistent deficits in social communication/interaction and restricted, repetitive behavioral patterns with narrow interests (Urbonaite et al., 2022). Notably, the global prevalence of ASD has increased significantly, exceeding 1% in some regions (Elvers et al., 2020). This underscores that etiological research impacts not only individual health but also public health.
For a long time, researches of ASD have focused on interactions between genetic susceptibility and environmental factors. There have been excellent reviews for the roles of genetic factors and the interaction of genetic and environmental factors in the pathogenesis in ASD (Grayson and Guidotti, 2016; Cheroni et al., 2020; Masini et al., 2020; Sandin et al., 2014), which are not the major topics of this review. Recently, the gut microbiota—a dynamic community of bacteria (predominant), archaea, viruses, fungi, and protists inhabiting the gastrointestinal tract—and its collective gene pool (the gut microbiome) have transformed ASD research paradigms. With a gene catalog surpassing the host genome, the microbiome is termed the “second genome” (Zhang et al., 2021). It maintains gastrointestinal homeostasis, regulates immune development/function, and reinforces intestinal barrier integrity (Winglee et al., 2017). Its composition is highly individualized, shaped by genetics, age, diet, medications (especially antibiotics), and environment.
Clinical observations frequently report comorbid gastrointestinal dysfunction, such as chronic constipation, diarrhea, and abdominal pain in ASD children. The severity of such symptoms often correlates with core behavioral deficits (Hassib et al., 2023). This association implicates the gut-brain axis—a bidirectional communication network linking the gut microbiome, intestinal mucosa, and nervous system via neural, endocrine, immune, and metabolic pathways [e.g., neurotransmitters, short-chain fatty acids (SCFAs, a type of fatty acid with less than six carbon atoms including acetic acid (acetate), propionic acid (propionate) and butyric acid (butyrate)) (Mann et al., 2024)], tryptophan metabolites, cytokines (Tian et al., 2023). This axis profoundly influences neurodevelopment and behavior.
Microbial colonization begins during critical windows of early-life through maternal-offspring microbial transmission (Amat et al., 2022). This process spans three phases: (1) Prenatal: Emerging evidence—though still debated—challenges the “sterile womb” hypothesis, suggesting potential low-biomass microbial transfer occurring through blood or placenta (Hernández-Martínez et al., 2022). (2) Intrapartum: Vaginal delivery exposes neonates to maternal vaginal/perianal microbes, such as Lactobacillus, Prevotella and Sneathia, whereas cesarean section results in colonization by skin and environmental taxa, including Staphylococcus, Streptococcus and Corynebacterium (Wu et al., 2017). (3) Postnatal: Breastfeeding [conveying microbes/HMOs (human milk oligosaccharides, consisting of multifunctional, unconjugated, and non-digestible glycans) (Dinleyici et al., 2023)], skin contact, and caregiving behaviors further shape the infant microbiome, influenced by maternal diet/health/antibiotics (Choi et al., 2016). These processes train immune development, metabolic programming, and neurodevelopment. The disruptions of these processes are implicated in ASD pathogenesis (Luoto et al., 2010).
In ASD research, the gut-brain-microbiota axis represents a critical frontier. Compelling evidence reveals widespread gut microbial dysbiosis in ASD, characterized by reduced diversity, decreased beneficial symbionts (the microbial organisms mutualistically interacting with the host and the interactions are beneficial to both the host and the microbial organisms, e.g., Bifidobacterium), and increased potential pathobionts (members of the microbiota associated with the development of clinical diseases, e.g., some Clostridium spp.) (Zhang et al., 2021). Such dysbiosis may drive ASD pathophysiology through: (i) Disruption of intestinal barrier integrity (intestinal hyperpermeability [“leaky gut”]), permitting systemic influx of pro-inflammatory mediators; (ii) Alterations in microbial metabolites (SCFAs, secondary bile acids, neuroactive compounds); (iii) Induction of local/systemic immune inflammation (e.g., elevated pro-inflammatory cytokines like IL-6); (iv) Epigenetic modulation of host gene expression (Tartaglione et al., 2022).
Although causality remains debated (e.g., dietary habits may influence microbiota), recent animal models and microbiota transplantation studies support pathogenic roles for gut microbes in ASD (Kang et al., 2013). This supports probiotic [live microorganisms conferring health benefits at adequate doses; e.g., Lactobacillus, Bifidobacterium, Saccharomyces boulardii (Sun et al., 2024)] and fecal microbiota transplantation (FMT) as translational interventions.
This review summarizes key advances linking gut microbiota to ASD. The conceptual frameworks (gut microbiome, gut-brain axis, microbial transmission) were outlined, the mechanistic hypotheses (barrier dysfunction, microbial metabolites, neuroimmune signaling, epigenetics) were analyzed, the clinical/experimental evidence was evaluated, and the challenges for microbial interventions were discussed. Genetic/epigenetic factors were discussed only where they interact with microbiota as they are not the major topics in this review. Future breakthroughs require multi-omics integration to elucidate microbiota-mediated neurodevelopmental mechanisms and enable personalized therapies.
2 The gut microbiome in autism spectrum disorder: the association hypothesis
2.1 Landmark historical events
During the clinical observation phase from the 1960s to the 1990s, healthcare workers noted that children with autism spectrum disorder (ASD) frequently exhibited feeding difficulties, constipation, diarrhea, and other gastrointestinal (GI) symptoms. These observations suggested potential dysfunctions within the digestive system in ASD patients (McElhanon et al., 2014). Although the 1998 study by Wakefield and colleagues—subsequently retracted due to scientific misconduct—erroneously linked the measles, mumps, and rubella (MMR) vaccine and intestinal inflammation to ASD onset, its invalid conclusions inadvertently heightened scientific and public awareness of gut-related factors in ASD (Sikora, 2015). This attention indirectly stimulated subsequent researches (Zheng et al., 2020).
The advent of high-throughput sequencing technologies, including 16S rRNA gene sequencing and metagenomics along with metabolomics in the 21st century provided the core technical foundation for large-scale, detailed characterization of gut microbiota composition and functional activity in ASD individuals (Xu et al., 2022; Chang et al., 2024). This facilitated the transition of this field into a period of rapid advancement (Wang X. Q. et al., 2018).
Driven by these technologies, some population studies revealed potential dysbiosis patterns in the gut microbiota of ASD patients. For instance, work by Finegold et al. in 2002 and 2010 reported early observations of increased Clostridium species abundance in fecal samples from ASD children (Lu and Claud, 2019; Finegold et al., 2002; Finegold et al., 2010). Subsequently, studies by Kang and colleagues in 2013 and 2017, utilizing large cohorts of ASD children, systematically identified several core features of the ASD gut microbiome: reduced alpha diversity, decreased relative abundance of Prevotella, and significant alterations in the Bacteroidota/Bacillota ratio (Kang et al., 2013; Grochowska et al., 2018). Associations between the abundance of specific bacterial genera and the severity of core ASD symptoms were also observed, providing early evidence for microbiota-behavior links. Critically, their research using the maternal immune activation (MIA) animal model offered experimental support for the biological mechanism whereby maternal infection or inflammatory states might alter offspring gut microbiota and thereby impact neurodevelopment (Liu et al., 2022).
To investigate the potential causal role of gut microbiota in ASD pathophysiology, functional validation approaches were employed. A landmark work by Kang et al. (2019) in 2019 demonstrated that transplantation of fecal microbiota from ASD donors into germ-free (GF) mice recapitulated certain ASD-like behavioral phenotypes, such as social deficits and increased repetitive behaviors. This was accompanied by altered expression of ASD-associated neurodevelopmental genes. This study provided the first direct evidence in a living organism that the ASD gut microbiota possesses functional activity sufficient to influence host neurobehavior, crucially establishing causal evidence for gut microbial regulation of central nervous system function.
As the microbiota compositional features have been increasingly delineated, the researches focus in this field have progressively shifted from descriptive analyses towards mechanistic dissection. Current investigations are deeply exploring several core subjects: the potential neuromodulatory effects of microbial metabolites (e.g., SCFAs, bacterial lipopolysaccharide [LPS], tryptophan derivatives) (Silva et al., 2020; Marć et al., 2022; Cox and Weiner, 2018); the role of compromised intestinal barrier integrity (often termed the “leaky gut” hypothesis) in facilitating systemic access for microbial products or inflammatory mediators, potentially impacting the central nervous system (Kelly et al., 2015); and the contribution of microbiota-dysbiosis-induced aberrant neuroimmune activation to neurodevelopmental disorders (Wang et al., 2023). Collectively, the results of these researches suggest a potential contributory role of complex microbiota-gut-brain axis (MGBA) mechanisms in ASD pathogenesis (Ho et al., 2020).
2.2 Key theories
2.2.1 Leaky gut syndrome
The increased intestinal permeability, often termed “leaky gut syndrome,” refers to a pathological state characterized by impaired integrity of the intestinal epithelial barrier (Fiorentino et al., 2016). This dysfunction permits the abnormal translocation of undigested food macromolecules, endotoxins (e.g., LPS), microbial metabolites, and other luminal contents into the systemic circulation (Ruiz-Rodríguez et al., 2022). Investigations within ASD researches have delineated biological features associated with this condition in subsets of affected children (Liu, 2022). Clinical evidence links autism spectrum disorder to gut microbiota dysbiosis and barrier dysfunction, with interventions targeting this axis showing therapeutic potential (Al-Ayadhi et al., 2021); concurrently, broader clinical and preclinical studies implicate this ‘leaky gut’ phenotype in the pathogenesis of diverse neurological disorders through neuroimmune mechanisms (Parodi and Kerlero de Rosbo, 2021). Multiple clinical studies reported aberrant serological markers in specific ASD subgroups (Al-Ayadhi et al., 2021). These frequently included elevated levels of zonulin, a protein regulating intestinal tight junctions (although contradictory findings exist in a minority of studies), and elevated levels of lipopolysaccharide-binding protein (LBP) or endotoxin antibodies (Nalbant et al., 2022). Urine metabolomic analyses further revealed significant alterations in concentrations of gut microbiota-associated metabolites, such as p-cresol sulfate and 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA) (Sanctuary et al., 2018). Collectively, this evidence suggests that the increased intestinal permeability and systemic translocation of gut microbial products occur in a subset of ASD individuals. Crucially, these alterations are not universally present across the ASD population, highlighting significant heterogeneity.
The potential biological mechanisms linking increased intestinal permeability to altered neurodevelopment in ASD patients remain to be incompletely elucidated and are subject to ongoing scientific discourse. The researches, primarily based on animal models, in vitro studies, and clinical observations, proposed two interconnected (yet unproven causal) pathways. The first is the immune-inflammation pathway hypothesis: translocation of microbial-associated molecular patterns such as LPS may activate circulating immune cells like monocytes and macrophages, eliciting a state of low-grade systemic inflammation (Tucureanu et al., 2018). Subsequently, pro-inflammatory cytokines such as IL-6, IL-1β and TNF-α released in this response could impact the central nervous system (CNS) via mechanisms including transport across the blood–brain barrier (BBB) and vagal nerve signaling (Morris and Maes, 2014). In animal models, such peripheral inflammation and the ensuing neuroinflammation, for example microglial activation, disrupt synaptic pruning, neurotransmitter balance, and neurogenesis. Within the ASD context, this mechanism represents a biologically plausible hypothesis; however, it is essential to emphasize that the precise multifactorial etiology of ASD remains undefined. The second is the neuroactive substance hypothesis: under conditions of intestinal hyperpermeability, gut microbiota-derived metabolites—certain organic acids—that potentially influence GABAergic signaling or tryptophan-serotonin metabolic pathways may gain aberrant access to the systemic circulation (Boccuto et al., 2013). This hypothesis, however, contains several points of contention. For instance, whether microbially produced GABA can efficiently cross the BBB and attain concentrations sufficient to directly modulate CNS function is questionable; indirect effects, such as modulation of host metabolic pathways, might be more relevant. Conversely, dysregulation of tryptophan metabolism is a hot research focus strongly implicated in neuropsychiatric disorders, including ASD.
Key limitations impede a deeper understanding of the role of intestinal hyperpermeability in ASD. The primary challenge is establishing causality: Existing evidence linking this condition to ASD largely derives from cross-sectional studies (Liu, 2022). Thus, it remains difficult to discern whether increased intestinal permeability and associated dysbiosis are primary drivers of ASD pathogenesis, or if they represent secondary consequences of other ASD-related factors such as restricted dietary patterns, inherent genetic variations, or downstream neurobehavioral alterations affecting gut function. A large-scale metagenomic study by Yap et al. (2021) (n = 247) revealed only a very weak direct association between ASD and gut microbial structure, with Romboutsia timonensis being the only species showing statistical significance, albeit with a small effect size. The study further indicated that the observed differences in the gut microbiome of children with ASD are more likely a consequence of their behavioral characteristics (such as restricted dietary preferences and low intake diversity) rather than a causal factor in the development of ASD. However, there are still other factors such as the mode of maternal delivery, breastfeeding, genetics, and drugs attributed to the changes of the gut microbiota in children (Takyi et al., 2025). Furthermore, a meta-analysis (Gao et al., 2025) suggested that microbiome-targeted interventions could have a mild positive effect on improving behavioral symptoms in individuals with ASD, but the overall improvement in gastrointestinal symptoms did not reach statistical significance. This conclusion is limited by the heterogeneity and methodological quality of existing studies, underscoring the need for more rigorously designed clinical trials for validation. Overall, alterations in the gut microbiota of children with ASD result from the interplay of multiple factors, including genetics, perinatal influences, dietary patterns, medication use, and immune-metabolic abnormalities. The current debate highlights the complexity of the etiological mechanisms underlying ASD and suggests that future research should place greater emphasis on the role of intrauterine environment and early developmental factors. Well-designed longitudinal studies, particularly cohorts initiated in early development, are critically needed to delineate the temporal sequence and potential causal relationships. Secondly, there is an issue of biomarker specificity: elevated serum levels of zonulin, LBP, and other markers are not unique to ASD (Heidt et al., 2023; Kim et al., 2023; Ohlsson et al., 2017). Similar elevations are observed in other gastrointestinal disorders (e.g., celiac disease, irritable bowel syndrome) and various systemic inflammatory states, limiting their utility as specific diagnostic or subtyping tools for ASD. Lastly, there is limited therapeutic evidence: interventions targeting gut microbiota modulation, such as specific probiotic formulations or FMT, have reported improvements on behavioral scales (e.g., Autism Behavior Checklist, ABC score) in some underpowered, non-blinded, or open-label studies (Prosperi et al., 2022). Nevertheless, these preliminary findings currently lack validation through high-quality, large-scale, randomized, double-blind, placebo-controlled trials (RCTs). The clinical efficacy, identification of responsive subgroups, and long-term safety of such interventions require rigorous evaluation (Zhang et al., 2022).
Given this complex evidentiary landscape, the prevailing scientific consensus posits that, within the current knowledge framework, increased intestinal permeability is not considered a direct etiological factor for ASD. A more precise conceptualization positions it as a potential “environmental trigger/perpetuating factor” or “disease modifier,” acting primarily within specific ASD subgroups harboring particular genetic susceptibilities (Zheng et al., 2020). It may contribute to neurodevelopmental deviation or exacerbate behavioral symptoms through the aforementioned immune-inflammatory and neuroactive substance pathways, acting upon an existing genetic predisposition (Fiorentino et al., 2016; Rylaarsdam and Guemez-Gamboa, 2019; Zhu et al., 2020). To overcome current limitations and precisely evaluate its pathological significance and potential therapeutic relevance, future research could integrate multi-omics approaches along the gut-brain axis with rigorously designed, prospective, large-scale clinical cohort studies (Takyi et al., 2025). This integrated strategy is essential to definitively elucidate the causal mechanisms of increased intestinal permeability within specific ASD endophenotypes and provide an evidence base for personalized interventions.
2.2.2 Metabolite-neuronal pathway validation
The gut microbiota ecosystem produces diverse metabolites through fermentation of substrates like dietary fiber, some of which exhibit confirmed or suspected neuroactive potential (Miri et al., 2023). These metabolites can mediate gut-brain axis communication through multiple routes, such as the circulatory system, direct vagus nerve transmission, or the enteroendocrine cell-vagus nerve pathway, indirectly influencing central nervous system (CNS) function and development (Kasarello et al., 2023). Key microbial metabolic pathways and their potential roles in ASD are now widely investigated.
SCFAs, the core end-products of dietary fiber fermentation, are primarily generated by specific strains within the Bacteroidetes and Firmicutes phyla (e.g., Faecalibacterium prausnitzii, Roseburia spp. luojiarufa junshu—Luojiaru Bacteria Genus), predominantly including acetate, propionate, and butyrate (Chang et al., 2019; Al-Qadami et al., 2022). Butyrate plays multiple beneficial roles in gut health: serving as the preferred energy source for colonic epithelial cells, which is crucial for maintaining intestinal barrier integrity (Al-Qadami et al., 2022; Lee et al., 2021). Furthermore, butyrate’s anti-inflammatory properties and inhibition of histone deacetylases (HDACs) suggest potential regulation of neural plasticity via epigenetic mechanisms (McClung and Nestler, 2008; Dash et al., 2009). Notably, several studies reported decreased abundance of butyrate-producing microbiota with key functional roles (e.g., F. prausnitzii pulasuojun—Pulasou Bacteria) in fecal samples from individuals with ASD, implying impaired butyrate production might contribute to ASD pathogenesis (Retuerto et al., 2024). Conversely, the neurobiological effects of propionic acid (PPA) exhibit significant complexity and dose-dependence (Nankova et al., 2014; Le Poul et al., 2003). While PPA acts as an important energy substrate and signaling molecule at physiological concentrations, neurotoxic evidence from animal models—primarily involving non-physiological high-dose intracerebroventricular or intraperitoneal injections—clearly demonstrates its ability to induce core ASD-like behavioral phenotypes in rodents, including social deficits, increased stereotypy/stereotypic movements, and sensory processing abnormalities (Choi et al., 2018). Potential mechanisms identified in these models involve mitochondrial dysfunction, aberrant neurotransmitter release—dopamine and glutamate—exacerbated oxidative stress, microglial activation, enhanced neuroinflammation, and aberrant epigenetic regulation (Choi et al., 2018; Nankova et al., 2014; Csoka et al., 2024). Critical controversy remains, however, regarding whether endogenous physiological concentrations or the mild PPA elevation observed in some ASD cohorts suffice to reach significant neurotoxic thresholds within the human CNS (Le Poul et al., 2003). Crucially, the highly inconsistent findings on SCFA profiles in stool or plasma from ASD populations, reflected substantial sample heterogeneity and potential influences of sample type and analytical methodologies (Liu et al., 2019). Moreover, certain behavioral effects of injected PPA were not fully replicated in female rodent models, highlighting the need for caution regarding sex differences when extrapolating model data to humans, especially given ASD’s marked male bias (Kamalmaz et al., 2023).
Beyond SCFAs, the tryptophan metabolic pathway constitutes another critical node linking the gut microbiota to host neural function. As an essential amino acid, tryptophan serves as the precursor for the key CNS neurotransmitter serotonin, which regulates mood and cognition, and melatonin, which regulates for sleep–wake cycles. Gut microbiota (e.g., Clostridium spp.) profoundly influence tryptophan metabolism: they directly utilize it for bacterial protein synthesis and convert it to indole derivatives (Wang G. et al., 2024). Importantly, a substantial proportion of dietary tryptophan is metabolized via the host kynurenine pathway (KP) (Munn and Mellor, 2016). Under pro-inflammatory conditions, which are triggered by diverse etiologies, indoleamine 2,3-dioxygenase (IDO) activity is significantly upregulated in host cells—immune cells, enterocytes and hepatocytes—and shunts tryptophan towards kynurenine (KYN) synthesis (Munn and Mellor, 2016). This appears congruent with observations of reduced plasma tryptophan and elevated KYN/tryptophan ratios in some ASD cohorts, indicating potentially widespread IDO activation mediated by inflammatory status. CNS exposure to KP metabolites is critical, as distinct pathway metabolites exert mutually antagonistic neural effects: further KYN metabolism generates the excitotoxic quinolinic acid (QUIN), an N-methyl-D-aspartate (NMDA) receptor agonist inducing excitotoxicity and oxidative stress (Guillemin et al., 2007); concurrently, neuroprotective kynurenic acid (KYNA) is produced, acting as an antagonist at NMDA and α7-nicotinic acetylcholine receptors (Guillemin et al., 2007). Some ASD studies suggested the evidence for upregulated IDO pathway activity and metabolic imbalance, which potentially favors neurotoxicity or excitotoxicity, in affected individuals, and these alterations showed some correlation with clinical symptom severity. However, the core causal mechanisms by which tryptophan-KP alterations drive ASD neuropathology and their precise CNS targets require further elucidation (Savino et al., 2020).
Finally, other microbially-derived metabolites with potential neuroactivity are of growing research interest. For instance, p-cresol, produced by certain Clostridia, transforms into its sulfate derivative (p-Cresyl sulfate, PCS) upon host sulfation (Harrison et al., 2021); PCS has been reported enriched in urine from some ASD children (Osredkar et al., 2023). Although its toxicity mechanisms remain incompletely defined, PCS is hypothesized to potentially exert neuroactive effects via interference with critical sulfation pathways or mitochondrial impairment (Mueller et al., 2018); however, current evidence favors its influence on neurodevelopment primarily through complex immune modulation, with direct significant neurotoxicity currently lacking definitive support. Furthermore, secondary bile acids (SBA), generated from host primary bile acids by gut bacterial modification, act as the key signaling molecules (Tie et al., 2023). By activating receptors such as the farnesoid X receptor (FXR) and G protein-coupled bile acid receptor 1 (TGR5/GPBAR1), SBAs regulate host metabolism and inflammation (Tie et al., 2023; Wang Y. et al., 2024). Theoretically capable of mediating brain function via vagal afferent signaling or neuro-immune crosstalk, SBAs represent an emerging research area in gut-brain communication (Kim et al., 2016). Preliminary animal behavioral studies and limited human data suggest associations between altered SBA profiles and certain neurobehavioral traits; however, their specific roles and causal involvement in ASD await systematic investigation and robust evidence.
3 External factors influencing maternal gut microbiota and offspring neurodevelopment
Previous studies have suggested that several external factors could attend in offspring neurodevelopment by affecting maternal gut microbiota (Figure 1).
Figure 1. Integrative impact of external factors on maternal gut microbiome and offspring neurodevelopment. Environmental, maternal, genetic, and nutritional factors converge to shape the maternal gut microbiome, which in turn modulates fetal neurodevelopment through metabolic, immune, and epigenetic mechanisms, influencing ASD susceptibility (created in BioRender; Xie, 2025, https://BioRender.com/0xsjbrb).
3.1 Environmental factors
Epidemiological studies confirmed that prenatal exposure to environmental pollutants such as PM2.5 and bisphenol A and alterations in delivery modes significantly impacted maternal gut microbiota dynamics (Wang W. et al., 2018). This manifested as increased relative abundance of specific bacterial genera such as Ruminococcus and Staphylococcus and perturbations in key metabolic pathways such as arachidonic acid metabolism (Filardo et al., 2022). Observational data further revealed statistical associations between such dysbiosis and offspring outcomes including cognitive developmental delay and ASD risk, with pollutant effects exhibiting dose-dependency (Agathokleous et al., 2022). However, existing evidence cannot establish direct causal relationships between individual microbial taxa/metabolic pathways and neurodevelopmental outcomes (Matsuyama et al., 2022).
Biologically, maternal dysbiosis affects fetal neurodevelopment through three principal mechanisms: (i) Microbial metabolites (e.g., SCFAs) regulate brain-derived neurotrophic factor expression via vagus nerve activation or central nervous system GPR41/43 receptors (Kasarello et al., 2023); (ii) Immune-gut-brain axis-mediated Th1/Th2 imbalance induces maternal-fetal interface inflammation, disrupting fetal blood–brain barrier development and triggering neuroinflammatory pathways (Zha et al., 2022; El Ahdab et al., 2021); (iii) Gut microbial biosynthesis of neurotransmitter precursors—5-hydroxytryptamine and γ-aminobutyric acid—exerts developmental programming effects (Park and Im, 2022).
Animal models provide critical validation: Probiotic interventions enhanced offspring hippocampal neuroplasticity and improve cognitive performance (Romo-Araiza et al., 2023). Experimental simulations of PM2.5 or bisphenol A exposure successfully replicated human phenotypes including reduced maternal microbial diversity and offspring neurodevelopmental abnormalities (Liu, 2018; Senaldi and Smith-Raska, 2020). Current research faces methodological challenges: interindividual heterogeneity in baseline microbiota impedes intervention assessment (Louis et al., 2016); observational studies struggle to exclude confounders, with path analysis indicates that approximately 32% of PM2.5 effects may be microbiota-mediated, though weighting depends on model assumptions (Senaldi and Smith-Raska, 2020); additionally, traditional dose–response models inadequately resolve nonlinear exposure patterns, such as short-term pollutant peaks, thereby limiting the generalizability (Hoel and Portier, 1994).
Translational strategies prioritize optimizing environmental exposure thresholds and developing microbiota-targeted interventions. Cross-generational transplantation experiments demonstrated that maternal microbiota modulation partially rescued pollutant-induced teratogenic effects (Hassib et al., 2023). Given substantial interindividual variation in circulating SCFAs during pregnancy (coefficient of variation [CV] = 62%) and host genetic regulation, future researches could integrate exposomics with multi-omics technologies (Hsu et al., 2021). This integration will enable construction of interactive network models spanning environmental exposure dynamics, microbiota succession, and neurodevelopmental trajectories to advance precision prevention (Deek et al., 2024).
3.2 Maternal health and behavioral determinants
Epidemiological studies indicated that maternal metabolic status (Krakowiak et al., 2012), nutritional intake patterns, and stress exposure during gestation were associated with offspring risk of ASD. Specifically, maternal metabolic disturbances (e.g., obesity or diabetes) were found to increase ASD risk [reported odds ratios [ORs] = 1.6–2.3 (Krakowiak et al., 2012; Li et al., 2016)], with evidence suggesting a dose-dependent trend. These conditions may impact neurodevelopment through elevated circulating proinflammatory cytokines, such as IL-6 and TNF-α, and impaired fetal microglial differentiation (Li et al., 2016).
Animal models demonstrated that high-fat diet (HFD) exposure during pregnancy activated placental inflammatory pathways, leading to aberrant amygdala-prefrontal circuit development (Hill et al., 2015). In specific models, this correlated with an 89% increase in ASD-like behaviors. Maternal deficiencies in key nutrients, such as iron and folate, were linked to compromised hippocampal synaptic plasticity and dopaminergic dysfunction (Bastian et al., 2016). Additionally, prenatal stress-induced glucocorticoids might dysregulate offspring hypothalamic–pituitary–adrenal (HPA) axis reactivity via epigenetic modifications, potentially elevating comorbid anxiety risk in ASD (Hill et al., 2015).
The intricate gut microbiota-host metabolic-immune crosstalk is considered to be the biological basis. Microbiota-derived metabolites such as butyrate and kynurenine may indirectly influence fetal neurodevelopment through histone-modifying enzymes like HDACs by affecting glial differentiation (Yan et al., 2012). Animal evidence supports these mechanisms: antibiotic-induced maternal microbiota depletion caused cerebellar axonal deficits and motor dysfunction in offspring; HFD-exposed dams produced offspring with reduced hippocampal neurogenesis and social deficits; and maternal immune activation via polyinosinic-polycytidylic acid (poly(I: C)) reduced cortical synaptic density, with IL-17A identified as a key mediator.
Notably, targeted interventions (e.g., Bacteroides fragilis or B. thetaiotaomicron transplantation; multispecies probiotics) partially attenuated these abnormalities in models, supporting microbiota-mediated effects. A phase II trial reported potential language improvement after vaginal microbiota transplantation (VMT) (Wrønding et al., 2023), but long-term neurological safety requires systematic follow-up. Future research must establish ethnically diverse maternal-child cohorts with lifelong tracking to bridge mechanistic insights and clinical prevention.
Current evidence faces significant constraints. Human studies predominantly involve European-ancestry populations (representing approximately 78% of participants in major cohorts) (Pérez-Morales et al., 2024), leading to limitations in generalizability. Furthermore, critical confounders, such as environmental toxin exposure, remain inadequately controlled for in analyses (Keller, 2014; Hernandez et al., 2019). While emerging, robust dose–response data for specific probiotic or prebiotic strains are still scarce (Rees et al., 2002). Translation of research findings into clinical practice requires optimized strategies. Promising avenues include combinatorial approaches; for example, combining high-fiber diets with select prebiotics that modulate Roseburia abundance and glutamate transporters might enhance therapeutic efficacy. Additionally, the development and validation of novel biomarkers could significantly improve patient risk stratification, as evidenced by certain composite models achieving promising receiver operating characteristic (ROC)-area under the curve (AUC) values of 0.87 (Li et al., 2019).
3.3 Genetic and immunological factors
Epidemiological studies indicated that the maternal genetic background might interact with the gut microbiota, collectively influencing offspring risk for ASD (Rees et al., 2002; Li et al., 2019). Cohort studies revealed that maternal polymorphisms in IL10 can affect IL-10-mediated immunosuppressive function (Rees et al., 2002). Population-based research further identified that such polymorphisms were accompanied by altered abundance of specific maternal gut microbiota members, such as the family Alcaligenaceae and the genus Acinetobacter (Li et al., 2019). This gene-microbiota interaction pattern is hypothesized to potentially affect the fetal immune microenvironment via microbial metabolites.
A research on mother-infant microbiota vertical transmission demonstrated that the children later diagnosed with ASD frequently exhibited reduced abundance of gut bacterial genera involved in SCFAs production, such as Prevotella (Li et al., 2019). This reduction correlated with dysregulated Th17/Treg immune balance and was associated with neuroinflammation (Li et al., 2019; Blagonravova et al., 2021; Bik et al., 2018).
As to biological mechanisms, animal model studies suggested that maternal factors—including genetic variants like PTEN mutations or environmental exposures such as high-fat diet, or HFD might influence the bioavailability of aryl hydrocarbon receptor (AhR) ligands, for example, certain microbial metabolites derived from tryptophan metabolism through epigenetic reprogramming (Kirstein et al., 2021). This, in turn, could potentially impair fetal microglial functions, such as synaptic pruning. Preliminary research proposed the hypothesis that gut microbe-derived serotonin (5-HT) precursors might influence amygdala development via vagal nerve signaling pathways (Dinan and Cryan, 2017a; Spencer et al., 2024; Raskov et al., 2016). These cumulative findings underscore the important role of a “microbiota-immunity-neural” axis in neurodevelopment.
Animal models provided supportive evidence for exploring these potential mechanisms: the offsprings of GF dams exhibited deficits in axonal outgrowth, which could be ameliorated by butyrate supplementation in this model (Onyszkiewicz et al., 2019); in the maternal immune activation (MIA) model, blocking IL-17a signaling mitigated synaptic-associated gene expression abnormalities in offspring brains (Choi et al., 2016). However, these models and their derived conclusions face methodological limitations. Serum IL-8, a commonly studied systemic inflammation marker, showed weak correlations (R2 < 0.2) with local intestinal immune status, such as lamina propria lymphocyte composition (Bukys et al., 2024; Lammers-Lietz et al., 2022), challenging the direct extrapolation of peripheral markers to gut immunity. Furthermore, acute MIA models induced by high-dose LPS inadequately recapitulated the complexity of chronic, low-grade inflammation experienced during human pregnancy. Notably, in human studies, analyses primarily based on European populations may not capture potential heterogeneity in HLA (human leukocyte antigen)-microbiota interactions across different ethnic/racial groups (Van Dorp et al., 2014). Moreover, the influence of paternally derived epigenetic reprogramming on offspring immune system development remains insufficiently evidenced and is typically not systematically addressed in existing models (Soubry et al., 2014; Eggert et al., 2014). These methodological challenges complicate causal inference regarding specific metabolic pathways.
Explorations into gene-microbiota interactions are driving translational research on intervention strategies. Some studies suggested that combined assessment of maternal IL-10 levels and Alcaligenaceae abundance held potential for auxiliary ASD risk assessment models (Li et al., 2019), while maternal supplementation with AhR agonists such as indole-3-carbinol or specific probiotics aimed at promoting defined gut colonization has shown preliminary promise in early-phase clinical studies (Puccetti et al., 2022; Zhao et al., 2021). However, some bottlenecks must be overcome for effective personalized interventions: the development of higher spatiotemporal resolution immune cell profiling is needed to precisely distinguish functional IL-10 subtypes and their sites of action (Mangiola et al., 2024), and large-scale, multi-ethnic population cohorts are imperative to systematically validate the role of microbial metabolites within complex genetic contexts, such as HLA-restricted antigen presentation. Future intervention designs should rigorously incorporate multilayered interactions between individual genetic susceptibility backgrounds and environmental exposures to more effectively support fetal neuroimmune homeostasis (Dahoun et al., 2017; Suh et al., 2019).
3.4 Nutritional and metabolic factors
Epidemiological studies suggested a potential intergenerational link between maternal nutritional imbalance during pregnancy and altered offspring microbiota-gut-brain axis function. Maternal high-fat diet (MHFD) induces compositional shifts in gut microbiota, characterized by reduced abundance of Bacteroidetes phylum members associated with metabolic regulation, alongside over-representation of specific Clostridium species—C. bolteae and C. histolyticum—linked to metabolic dysfunction. This dysbiotic state correlates positively with increased risk of aASD in offspring. Diminished synthesis of SCFAs and concomitant increased LPS leakage potentially contribute, via induction of maternal-fetal interface inflammation, to the downregulation of key neurodevelopmental genes (e.g., BDNF, SHANK3). Evidence suggested this reduced expression might be attributed to epigenetic mechanisms (Esposito et al., 2023; Cermak et al., 2010). Notably, selective dietary preferences common in ASD children, such as low-fiber and high-sugar diets, may theoretically compound the effects of antenatal maternal dysbiosis, creating a cyclical interaction of metabolic and microbial imbalance that could exacerbate potential neurodevelopmental sequelae (Esposito et al., 2023; Cermak et al., 2010).
At the biological mechanism level, interactions between gut microbial metabolites and the maternal immune-fetal neural axis constitute a significant regulatory network. Butyrate deficiency, for instance, may impair blood–brain barrier integrity, indirectly activating microglia and disrupting synaptic pruning. MIA-induced inflammatory signals could inhibit differentiation of fetal dopaminergic neurons via Toll-like receptor 4 (TLR4)-dependent pathways (Islam et al., 2009). Concurrently, impairment of specific microbial functions, such as diminished folate biosynthesis capabilities of Bacteroides species and disruption of serotonin precursor metabolism primarily driven by Bifidobacterium (Engevik et al., 2021; Zheng et al., 2024), has been linked to impaired neocortical neuronal migration. Crucially, animal model studies indicated that the neurodevelopmental impact of dysbiosis was developmental-stage dependent: microbiota interventions administered prenatally, but not post-weaning, effectively reversed offspring autism-like behavioral phenotypes (Tartaglione et al., 2022), underscoring the importance of targeting critical developmental windows. However, mechanistic insights derived from models like germ-free mice warrant cautious extrapolation to the complex human physiological milieu.
Translational research indicated that antenatal supplementation with n-3 polyunsaturated fatty acids (PUFAs) or selected probiotics, in some studies, improved maternal microbiota diversity/composition alongside better offspring social behavior outcomes (Chen et al., 2019). Nevertheless, the significant methodological heterogeneity across existing clinical trials—concerning probiotic strains, intervention protocols, inclusion criteria, and outcome measures—substantially limits the generalizability of findings and their clinical translatability. For example, while germ-free murine models are vital for elucidating mechanisms such as microbiota-regulated synaptic protein expression, a critical limitation lies in their inability to fully recapitulate the multidimensional diet-microbiota-host genetics interactions inherent to humans. Furthermore, the absence of robust longitudinal data complicates efforts to disentangle the independent or synergistic effects of prenatal nutritional interventions from the influences of offspring postnatal microbial colonization dynamics. Future research necessitates integrating multi-omics approaches to precisely define key metabolic-epigenetic regulatory nodes. Utilizing more sophisticated animal models accommodating human microbiota, namely human microbiota-associated gnotobiotic models, is essential to overcome translational bottlenecks and move beyond broad-spectrum probiotics towards the development of strain-specific and function-targeted interventions.
4 Molecular mechanisms underlying the influence of maternal gut microbiota on offspring neurodevelopment
To understand the roles of maternal gut microbiota in the offspring neurodevelopment, an abundance of researches has been performed and several molecular mechanisms have been elucidated (Figure 2).
Figure 2. Multifaceted mechanisms of SCFAs in maternal gut microbiota-mediated programming of offspring neurodevelopment. Maternal gut microbiota-derived short-chain fatty acids (SCFAs) regulate offspring neurodevelopment through five core mechanisms: (A) Barrier enhancement: SCFAs (e.g., butyrate) strengthen blood–brain barrier (BBB) integrity by upregulating tight junction proteins (Claudin-5, Occludin) and inhibiting nuclear factor kappa-B (NF-κB) inflammatory signaling; (B) Immune modulation: They induce anti-inflammatory cytokines and regulatory T cell (Treg) differentiation, fostering a neuroprotective immune microenvironment; (C) Epigenetic regulation: As histone deacetylase inhibitors (HDACi) and one-carbon metabolic modulators, SCFAs control histone acetylation and DNA methylation of neurodevelopmental genes (e.g., brain-derived neurotrophic factor, BDNF); (D) Signaling activation: SCFAs trigger neuronal G protein-coupled receptor 41/43 (GPR41/43)-cyclic adenosine monophosphate (cAMP) and tropomyosin receptor kinase B (TrkB)-mechanistic target of rapamycin (mTOR) pathways, directing axonal outgrowth and synaptic pruning; (E) Placental transport: The illustration depicts transplacental SCFA transfer via specific monocarboxylate transporters (MCTs) from maternal circulation to the fetus. These interconnected mechanisms highlight the complexity of prenatal microbial programming (Created in BioRender; Xie, 2025, https://BioRender.com/2ksz0di).
4.1 Impairment of blood–brain barrier integrity as an underlying mechanism
Experimental models, particularly GF animal models, have demonstrated that the maternal gut microbiota fundamentally programs the development of the offspring’s BBB. Compared to offspring of normally raised dams, offspring exposed to a GF environment in utero exhibited significantly increased BBB permeability and a corresponding significant decrease of approximately 30–40% in the transcription of key tight junction (TJ) core proteins, such as occludin and claudin-5 (Gong et al., 2008). Studies found that gut microbiota status influenced BBB plasticity, as FMT into adult GF mice partially reversed BBB hyperpermeability, with leakage decreasing by approximately 25% (Gong et al., 2008). This microbial programming occurs, in part, via microbiota-host metabolic-immune crosstalk. Microbiota-derived SCFAs impacted downstream molecules of the NF-κB pathway by activating free fatty acid receptors (e.g., FFAR3), suppressing endothelial inflammation and potentially enhancing TJ stability. Conversely, pro-inflammatory microbial products, such as LPS, likely impaired TJ integrity via TLR4 signaling (Gong et al., 2008). The placenta acts as a critical barrier with selective transfer mechanisms. Direct evidence indicated that the human placenta efficiently transported SCFAs, such as acetate (Krajmalnik-Brown et al., 2015), while partially clearing larger molecules like LPS from the maternal circulation via CD14-mediated mechanisms (Faulk et al., 1987). This sculpts a relatively favorable intrauterine microenvironment for robust fetal BBB development.
Immune dysregulation and sustained inflammation driven by gut dysbiosis in the mother are key factors in disrupting BBB integrity. Animal models of maternal dysbiosis showed that dysbiosis potentiated T helper 17 (Th17) cell responses and differentiation, leading to significantly elevated circulating levels of pro-inflammatory cytokines. This subsequently induced phosphorylation and activation of signal transducer and activator of transcription 3 (STAT3), which suppresses expression of critical TJ proteins such as ZO-1and claudin-5 (Honda and Littman, 2012). Concurrent neutrophil activation enhanced matrix metalloproteinase-9 (MMP-9) activity and reactive oxygen species (ROS) production, with reported increases of around 70%, which contributed to vascular basement membrane degradation and endothelial injury (De Bondt et al., 2020). BBB dysfunction may link to neuropsychiatric disorders. While human studies reported inconsistent results, animal models supported the notion that BBB structural abnormalities could precipitate behavioral deficits. Specifically, developmental BBB defects caused by deficient Sonic hedgehog (Shh)/Wnt signaling pathway regulation in the endothelium induced ASD-like stereotyped behaviors in endothelial cell-specific Shh knockout models. This suggests a possible causal relationship between BBB structural abnormalities and neurobehavioral phenotypes, although its significance in humans requires further elucidation.
A multidimensional challenge for understanding microbiota-BBB interactions persists. First, SCFA effects are complex and concentration-dependent; for instance, propionate enhanced TJ protein expression at low concentrations via FFAR2 but may promote inflammation at high concentrations via FFAR3 (Han K. et al., 2024). Second, probiotic interventions often exhibit strain-specific effects, demanding systematic dissection of precise molecular networks. Clinical translation faces obstacles, including: significant species differences—despite high sequence homology for key TJ molecules like claudin-5—in regulatory networks and expression profiles; and limitations of in vitro BBB models whose transendothelial electrical resistance (TEER) values typically fall well below physiological levels (many achieve only ~50% or less). Furthermore, insufficient research addresses individual heterogeneity, such as sex differences—where BBB integrity decline in specific aging or disease models like Alzheimer’s models may occur earlier or faster in males compared to females—and critical window sensitivity, whereby the impact of perinatal interventions on neurodevelopmental risk often exceeds that of adult interventions. Future research necessitates integration of spatiotemporal dynamics and individualized variables, such as genetic background, sex and age, to build refined pathogenic mechanisms and interventional frameworks.
4.2 Neurodevelopmental regulation by microbial metabolites
The maternal gut microbiota can directly or indirectly regulate fetal brain development through the production of diverse metabolites (Tian et al., 2023). Core mechanisms primarily include three aspects: microbial synthesis or modulation of neurotransmitters and their precursors; mediation of epigenetic modifications; and regulation of maternal-fetal barrier function and its interactions (Brown et al., 2003; Bromer et al., 2013; Greene et al., 2019).
Taking SCFAs, as an example, these metabolites can cross the placenta via monocarboxylate transporters (MCTs) and enter the fetal circulation. Within the fetal brain, SCFAs activate G protein-coupled receptors (e.g., GPR41/43) on neurons, thereby modulating synaptic plasticity through the cAMP-PKA-CREB signaling pathway. Butyrate, as a HDAC inhibitor, upregulates the expression of key neurodevelopmental genes (e.g., BDNF) by elevating histone acetylation levels in their promoter regions. Animal model studies substantiated this: offspring of germ-free dams exhibited downregulated expression of axonogenesis-related genes (e.g., Ntn1, Dcc) and impaired function of BBB tight junction proteins (e.g., occludin). Exogenous SCFA supplementation partially rescued these phenotypes (Bromer et al., 2013; Greene et al., 2019), underscoring the potential significance of SCFAs in neurodevelopment.
Beyond SCFAs, other gut microbial metabolites participate in neurodevelopmental regulation. For instance, the tryptophan-derived metabolite indole-3-propionic acid (IPA) acted as a ligand for the AhR to modulate synaptic pruning by microglia. Certain microbiota-derived metabolites may also confer indirect neuroprotection by inhibiting β-amyloid aggregation (Dong and Perdew, 2020). The vertical transmission and functional impact of such metabolites depend on complex transport and signal integration mechanisms. While SCFAs can partially diffuse passively across the placenta (Ilyés et al., 2023), secondary bile acids regulated neuroinflammation by activating TGR5 receptors on placental and fetal tissues, synergizing with immune mediators at the maternal-placental interface to influence fetal neuroimmune homeostasis (Wang et al., 2011).
Epigenetically, butyrate-mediated histone acetylation alterations in the placenta promote neuronal differentiation in specific brain regions such as hippocampus (Jaju Bhattad et al., 2020; Levenson et al., 2004). Propionate may protect the developing BBB against oxidative stress by activating the Nrf2 antioxidant pathway (González-Bosch et al., 2021). Notably, rodent models indicated concentration-dependent effects of SCFAs on BBB permeability (Braniste et al., 2014). However, human analyses reveal that physiological SCFA concentrations in fetal brain tissue were significantly lower than exogenous doses required for interventions in animal models (Generoso et al., 2021). This pronounced species difference necessitates careful evaluation of metabolic kinetics in translational research.
Human cohort studies provide epidemiological support for associations between specific microbial metabolites and offspring neurodevelopmental outcomes (Moreau et al., 2019; Padilha et al., 2025). For example, prospective observational studies reported a positive correlation between maternal fecal butyrate levels during pregnancy and specific language scores in offspring (Hernández-Martínez et al., 2022; Barbian et al., 2022; Han W. et al., 2024); other studies describe abnormalities in levels of neuroactive metabolites and abundance of butyrate-producing bacteria in the gut microbiota of women at high risk of bearing children with ASD (Ahmed et al., 2022; Chen et al., 2020; Descamps et al., 2019). Nevertheless, establishing definitive causality remains challenging. Limitations arise from inherent constraints in metabolome-transcriptome correlation analyses, such as difficulties in distinguishing causal relationships from correlative changes or identifying dominant drivers (Sen et al., 2023; Paternain and Campion, 2013), as well as from confounding factors, and physiological or dosage differences between humans and animal models. Existing evidence also contains inconsistencies; for instance, the relationship between maternal plasma trimethylamine N-oxide (TMAO) levels and offspring ASD risk was debated across studies (Quan et al., 2020), reflecting the critical influence of confounders like ethnicity and diet on TMAO metabolic pathways and biological effects.
Future investigations necessitate integrative approaches to dissect causal mechanisms and guide interventions. Conditional gene knockout techniques in animal models, coupled with interventions during specific gestational time windows (Zhang et al., 2012; Nishimura et al., 2020), could precisely delineate the dynamic sensitivity of critical fetal brain developmental periods to distinct microbiota-derived metabolites (Faulk et al., 1987; Schirmbeck et al., 2022). Concurrently, quantifying the effects of sex dimorphism—such as the observed potential advantage in male fetal responsiveness to the microbial metabolite queuine (Queuine) (Faulk et al., 1987; Kokko, 2008)—on heterogeneity in neuroprotection or susceptibility mechanisms is required.
4.3 Dysregulated immune activation and neuroinflammation
Maternal gut probiotics such as Lactobacillus and Bifidobacterium may enhance gut barrier integrity and induce an anti-inflammatory milieu by activating pattern recognition receptors including TLRs in intestinal epithelial cells and through microbial metabolites such as SCFAs. SCFAs epigenetically modulate immune responses by inhibiting HDACs, thereby promoting anti-inflammatory cytokines and suppressing Th17 cell differentiation. Studies in germ-free mouse models indicated that supplementation with specific strains (e.g., Lactobacillus reuteri) elevated TGF-β and IgA levels in breast milk, subsequently inducing Treg differentiation in offspring (Alsharairi, 2023; Leser and Baker, 2024). However, this effect demonstrated strain-specific and population-dependent variations: some human studies report fluctuations in breast milk IgA levels, though strain-specific impacts require further clinical validation (Leser and Baker, 2024).
During MIA, hyperactivation of the Th17/IL-17A axis may increase placental permeability, facilitating fetal translocation of maternal proinflammatory cytokines such as IL-6. Current evidence suggested (though not directly confirms) that IL-6 suppressed fetal insulin-like growth factor 1 (IGF1) signaling via the placental JAK/STAT3 pathway while upregulating local proinflammatory proteins (Hsiao and Patterson, 2011). Animal experiments demonstrated that the blockade of placental IL-6 signaling mitigated MIA-associated offspring neurodevelopmental abnormalities; however, causal links between placental IL-6 and fetal brain pathology warrant further investigation (Wu et al., 2017). Clinical cohort studies revealed positive correlations between elevated maternal serum IL-6 and increased proinflammatory factors (e.g., IL-8, CXCL1) in umbilical cord blood, indicating a fetal proinflammatory microenvironment (Wu et al., 2017). Under extreme inflammation, such microenvironments may activate fetal microglia and increase neuronal injury risk (Yanowitz et al., 2002). Excess IL-6 may additionally inhibit brain-derived neurotrophic factor (BDNF) signaling, while IL-17A could disrupt post-translational modifications of neurodevelopmental proteins. Postmortem studies of ASD patients show elevated proinflammatory markers, microglial activation, and reduced neuronal density in the brain (Voineagu et al., 2011; Kim et al., 2017); however, causal attribution to prenatal immune exposure requires cautious interpretation.
Although specific probiotic formulations exhibit neuroprotective potential in animal models, translating these findings to humans shows substantial heterogeneity (Wang et al., 2016). For instance, Lactobacillus reuteri improves synaptic pruning defects in rodents but remains unconfirmed in human trials. Bacteroides fragilis ameliorated social behavior deficits only in subpopulations with elevated inflammatory markers, highlighting the importance of strain specificity and host immune context (Buffington et al., 2016; Hsiao et al., 2013). This translational gap likely arises from: (i) weak correlation between peripheral inflammatory markers and central nervous system pathology, and (ii) the complexities of host immunity represent a significant consideration; for instance, confounding factors involving a Th2 bias in type I diabetes pregnancies may contribute to the risk of ASD. Future research should integrate single-cell placental transcriptomics with functional cerebral organoids to dissect dynamic crosstalk between SCFAs and immune pathways, thereby defining spatiotemporally precise windows for probiotic interventions.
4.4 Epigenetic modifications and gene expression regulation
Maternal gut microbiota-derived metabolites have been demonstrated to possess the potential to modulate the activity of key host epigenetic enzymes. This modulation may consequently affect the spatiotemporal expression of genes critical for neurodevelopment. SCFAs are among the core effectors. In animal model studies, butyrate was shown to significantly inhibit the activity of histone deacetylases HDAC1, HDAC2, and HDAC3, leading to elevated acetylation levels at histone H3K9 and H4K16 residues in neuronal precursor cells (Korsten et al., 2023; Večeřa et al., 2018). This was concomitant with a marked increase in the transcriptional activity of the Bdnf gene (McClung and Nestler, 2008).
Conversely, propionate, a critical contributor to one-carbon metabolism, impacted DNA methylation by modulating methyl donor pools (Wu et al., 2023). Mechanistic studies suggested that it may enhance the activity of DNA methyltransferases (DNMTs), thereby driving differential methylation patterns of the imprinted gene Igf2 in placental and fetal brain tissue (Wang et al., 2013; Steegers-Theunissen et al., 2009), which probably disrupted the proliferation/differentiation balance in neural precursor cells (Latchney et al., 2011).
Other metabolites, including specific vitamins such as folic acid, vitamin B12, as well as bile acids, may interact synergistically or antagonistically with SCFA pathways. For instance, folic acid, synthesized by microbiota including specific Bifidobacterium strains, served as an essential cofactor in the DNA methylation cycle (Pompei et al., 2007). It helped maintain a hypomethylation state at promoters of genes vital for neural tube development such as Pax3 (Song et al., 2015). Observational evidence consistently indicated that low maternal folate levels during pregnancy significantly increased the risk of neural tube defects in offspring (pooled OR = 2.4, 95% CI: 1.6–3.7).
In contrast, the secondary bile acid deoxycholic acid (DCA) was observed in some experimental models to activate the FXR-nuclear receptor-mediated HDAC3 pathway, significantly suppressing Shh gene expression in the cerebellum (Struhl, 1998; Chiang, 2013). This effect may involve decreased histone acetylation at its promoter region, potentially impacting nervous system development. It is noted that the folic acid synthesis capability is strain-specific among Bifidobacteria (Sugahara et al., 2015).
Crucial neurodevelopmental genes such as BDNF and MECP2 are subject to highly dynamic and complex epigenetic regulation (Allison et al., 2021). Maternal microbial metabolites bidirectionally modulate these genes by altering DNA methylation and histone modification states. For example, maternal gut microbiota dysbiosis induced by a high-fat diet correlated significantly with hypermethylation at the CpG dinucleotide within exon IV of the Bdnf gene in the offspring hippocampus in mouse models (Kimura et al., 2020). Conversely, butyrate supplementation restored phosphorylation levels of Methyl-CpG-binding protein 2 (MeCP2) in specific animal models, potentially alleviating its repression of Bdnf transcription. The functional consequences of DNA methylation are highly context-dependent: MeCP2 binding to the methylated CpG island at the Dlx5 gene locus, involved in GABAergic neuronal differentiation, resulted in repression (Chen et al., 2003). Maternal probiotic intervention reduced methylation levels at specific Dlx5 CpG sites in mouse models, concomitant with mitigated impairment of GABAergic neuronal differentiation (Liao et al., 2023). Single-cell epigenomic analyses have further indicated that aberrant methylation at non-CpG sites, such as CHH trinucleotides, may be associated with alterations in chromatin spatial conformation—a phenomenon observed in approximately 30% of ASD animal models—and could independently influence synaptic pruning. Nevertheless, the precise regulatory mechanisms remain unvalidated.
Current research underscores sophisticated interactions between specific strain-derived metabolites and the host epigenetic system. Translating these findings presents substantial challenges. Clinical cohort analyses report that hypermethylation of the OXTR (Oxytocin Receptor) gene in peripheral blood of ASD children inversely correlated with maternal Prevotella abundance (Andari et al., 2020). This remains an observational association. Reference suggests the probiotic strain Lactobacillus reuteri may induce hypomethylation at the Foxp3 locus, a key transcription factor for regulatory T cells, potentially via secretion of bioactive folate forms like 5-methyltetrahydrofolate, thereby mitigating neuroinflammation risk.
However, significant limitations persist: foundational studies often rely on broad-spectrum antibiotic-induced microbiota depletion models or direct metabolite injection/gavage, poorly replicating the spatiotemporal heterogeneity and metabolic dynamics present within a natural gut microbiota network (Gheorghe et al., 2021; Mao et al., 2022). Furthermore, observed associations between specific histone modifications and metabolite abundances in human populations lack established causality. Future research must integrate data on placental and BBB transport dynamics of microbial metabolites with high-resolution single-cell epigenomics to systematically delineate strain-host interactions within specific tissues and developmental timepoints, overcoming mechanistic fragmentation and advancing microbiota-based epigenetic intervention strategies (Duck and Connor, 2016).
4.5 Neuronal connectivity and signaling pathway interference
Maternal gut microbiota could potentially regulate fetal neural circuit development—including neurite formation and refinement—through microbiota-derived metabolites acting on signaling pathways. Current evidence indicated limited placental penetration capacity of specific microbial metabolites (Kolahi et al., 2018). These compounds may modulate critical neuronal signaling pathways such as AKT/mTOR and Wnt/β-catenin in the developing fetal central nervous system (Li et al., 2023).
In GF mouse models, researchers observed impaired axonal development—manifested as reduced axonal length—compared to conventionally colonized controls. This impairment correlated with dysregulated expression of axon guidance molecules. Exogenous supplementation with microbiota metabolites (e.g., IPA or 4-ethylphenylsulfate [4-EPS]) was found to improve axonal branching complexity under specific conditions (MacKay et al., 2024). This effect may partially involve β-catenin stability regulation or nuclear translocation.
Furthermore, bifidobacterial colonization in murine models downregulated genes associated with excessive synaptogenesis (Wang et al., 2014). This colonization concomitantly alleviated abnormal hippocampal synapse density elevation observed in GF models. These findings suggest microbiota metabolites may regulate synaptic developmental processes and influence microglia-mediated synaptic pruning, though their effects demonstrate bidirectional modulation.
Maternal dysbiosis during gestation may disrupt key neurodevelopmental signaling pathways, impairing normal neural circuit establishment. Evidence suggested microbiota imbalances affect Wnt signaling activity. In specific models, altered expression of cell cycle-related genes was observed in the fetal prefrontal cortex, potentially disrupting neurodevelopmental trajectories (Li et al., 2023). Interventions with particular bifidobacterial strains were reported to restore Wnt/β-catenin pathway equilibrium in corresponding models.
The BDNF-tropomyosin receptor kinase B (TrkB) pathway also exhibited microbiota connections (Sudo et al., 2004). Maternal probiotic administration elevated BDNF expression in offspring hippocampi across multiple studies. This upregulation was postulated to enhance dendritic spine maturation via downstream signaling activation, coinciding with behavioral improvements such as reduced social avoidance in male offspring. Conversely, GF models demonstrated diminished cortical BDNF/mTOR axis activity, accompanied by synaptic transmission deficits (e.g., reduced synaptic vesicle release frequency). These anomalies align with inhibitory synaptic defects [e.g., decreased miniature inhibitory postsynaptic current (mIPSC) amplitude] in neurodevelopmental disorder models.
However, several key challenges persist in understanding the maternal microbiota-fetal neurodevelopment axis. The regulatory processes exhibit significant spatiotemporal heterogeneity, and interspecies physiological differences—placental barrier permeability and metabolic capacity—hinder clinical translation. Notably, reported discrepancies in indole metabolite processing between humans and rodents require rigorous validation. Future studies should integrate multi-omics data, including metagenomics, metabolomics, epigenomics, and employ high spatiotemporal-resolution techniques, for instance, single-cell multi-omics coupled with live imaging, to dissect key pathways like the vagus-immune-metabolic axis.
5 Role of probiotics in preventing offspring autism
5.1 Mechanistic links
Emerging evidence indicates characteristic gut microbial dysbiosis in children with ASD, typically manifesting as reduced microbial diversity (Kang et al., 2013). While multiple studies report decreased Prevotella abundance and enriched Clostridium populations, a 2024 conference abstract documented contrasting findings (Prevotella and Clostridium enrichment), suggesting microbiome profiles may be influenced by geographical variations, cohort heterogeneity, or methodological factors (Debelius et al., 2016; Chu et al., 2024; Sun et al., 2020). The generalizability of these signatures requires large-scale validation.
Animal models provide critical evidence for the gut-microbiota-brain axis: Fecal microbiota transplantation from ASD donors to germ-free mice induces social deficits in recipients, with the microbial metabolite p-cresol identified as a key mediator of such behavioral abnormalities (Bermudez-Martin et al., 2021). Mechanisms linking dysbiosis to neurodevelopmental disruption involve multi-pathway crosstalk. Immunologically, maternal dysbiosis promotes Th17 cell differentiation and amplifies IL-17a production; this cytokine—transferred placentally or locally produced—directly impairs fetal cortical neuron migration (Choi et al., 2016). Metabolically, SCFA deficiency inhibits hippocampal synaptic plasticity via suppressed HDAC activity, whereas reduced levels of TMAO, a positive neurodevelopmental regulator, correlate with developmental impairments (Tang and Li, 2021; Li et al., 2018). Epigenetically, maternal high-fat diets alter microbiota-derived metabolites, modulating DNA methylation patterns in the placenta and fetal brain, thereby regulating expression of neurodevelopmental genes like BDNF (Martinowich et al., 2003; D’Aquila et al., 2020; Keleher et al., 2018).
Maternal gut microbiota may influence offspring neurodevelopment through vertical transmission; although elevated Alcaligenaceae abundance occurs in ASD children, direct evidence establishing causal links between maternal transmission of this taxon and ASD risk is lacking, and the mechanistic role of maternal-specific microbial shifts requires further validation (Cook and Prinz, 2022). Hypotheses regarding conserved “core microbiota signatures” across populations face limitations, as stability observed in plant-microbe systems cannot be directly extrapolated to human ASD studies; human gut microbiota heterogeneity is dynamically shaped by diet, antibiotic exposure, and socioeconomic status, challenging the universality of putative core microbial features (West et al., 2022).
Maternal intervention studies offer critical insights: In MIA models, probiotic treatment (Bacteroides fragilis) ameliorates offspring neurobehavioral deficits, potentially via central GABA receptor modulation or SCFA metabolic pathway restoration (Hsiao et al., 2013; Sharon et al., 2019). Current evidence supports two non-mutually exclusive hypotheses, yet their robustness warrants cautious interpretation (Vuong and Hsiao, 2017; Cryan et al., 2019). The Vertical Transmission Hypothesis posits microbial inheritance, yet evidence remains limited in complex human microbiomes; microbes and metabolites may indirectly affect neurodevelopment via birth mode or breastfeeding, but causal chains linking specific taxa transmission to ASD pathogenesis have not been established (Ferretti et al., 2018; Aatsinki et al., 2019; Sordillo et al., 2019). The Critical Window Intervention Hypothesis suggests perinatal probiotic supplementation exerts neuroprotective effects in animals, and observational studies suggest potential neurodevelopmental benefits; nonetheless, human translation faces challenges, as animal models incompletely recapitulate gene–environment interactions, and human studies are confounded by unmeasured variables (Buffington et al., 2016; Slykerman et al., 2017; Dinan and Cryan, 2017b).
Future research should integrate large prospective cohorts with multi-omics approaches and develop humanized animal models or conditional knockout systems to address: (1) causality and molecular mechanisms of maternal-offspring microbial transmission; (2) reliability of ASD-associated “core microbiota signatures” across diverse populations; and (3) efficacy and safety of microbiota-targeted interventions in human clinical translation.
5.2 Clinical efficacy of prenatal probiotic intervention
5.2.1 Current evidence gap
Robust evidence from large-scale, high-quality RCTs or prospective cohort studies is currently lacking to support the universal efficacy of prenatal probiotic supplementation in reducing ASD risk in offspring.
5.2.2 Animal model findings
Rodent studies have indicated that specific probiotic strains, such as select Lactobacillus spp., may improve offspring neurobehavioral phenotypes, including reduced stereotyped behaviors and enhanced social interaction, while also decreasing neuroinflammatory markers; such effects are hypothesized to occur via modulation of the gut-brain axis, although the clinical translatability of these findings requires cautious interpretation.
5.2.3 Limitations in human studies
Direct investigation of prenatal probiotics for preventing offspring ASD is scarce, with existing studies typically limited by small sample sizes; most human data focus on probiotic administration to children with established ASD diagnoses, and results from such interventions show mixed efficacy and frequently lack statistical significance, indicating that further investigation is warranted (Zyoud et al., 2023). Current clinical research on autism spectrum disorder (ASD) primarily focuses on interventions targeting the gut microbiota, including probiotics, prebiotics, synbiotics, fecal microbiota transplantation (FMT), microbiota transfer therapy, dietary interventions, as well as antibiotic and antifungal therapies. Although many studies report symptomatic improvements following these interventions, their conclusions remain limited due to challenges in establishing causality, generally small cohort sizes, and a limited number of available studies. Thus, further large-scale and rigorously designed studies are warranted to validate the clinical efficacy and mechanisms of these interventions (Liu, 2022; Buffington et al., 2016; Burket et al., 2013; Callery and Geelhaar, 1985; Caporaso et al., 2010).
5.2.4 Indirect effects and heterogeneity
Indirect evidence suggests that prenatal probiotics may influence neurodevelopmental trajectories by mitigating maternal complications such as gestational diabetes mellitus (GDM) (Appleton, 2018; Barthow et al., 2016; Kijmanawat et al., 2019). Nevertheless, a definitive causal pathway linking GDM mitigation via probiotics to ASD risk reduction remains unestablished (Berding and Donovan, 2016; Cryan et al., 2019). Major sources of inconsistency include probiotic strain selection, dose variability, and timing and duration of administration (Kristensen et al., 2016; McFarland et al., 2018). Given the high genetic heterogeneity and multifactorial etiology of ASD, probiotics may offer benefits only in specific high-risk subpopulations, particularly individuals with baseline gut dysbiosis or carriers of neurodevelopmental disorder-associated genetic variants (Ronald and Hoekstra, 2011; Kang et al., 2019; Sharon et al., 2019).
5.2.5 Proposed mechanisms
Two mechanistic hypotheses potentially explain the neuroprotective effects. First, the Gut-Brain Axis Modulation hypothesis posits that maternal gut dysbiosis linked to ASD disrupts fetal neurodevelopment via altered production of microbial metabolites, modified synthesis of neuroactive compounds, and induction of systemic low-grade inflammation (Hsiao et al., 2012; Stilling et al., 2016; O’Mahony et al., 2015). Probiotics might remodel the maternal gut microbiota, thereby potentially influencing fetal gut-immune-neural circuit development (El Aidy et al., 2012; Vuong et al., 2020). Animal models further suggest that probiotics exert strain-specific effects on HPA axis regulation and maternal metabolic homeostasis, potentially mitigating adverse impacts on hippocampal neurogenesis (Sgritta et al., 2019; Möhle et al., 2016). Second, the Vertical Transmission Hypothesis proposes that maternal microbes and/or their metabolites program fetal epigenetic landscapes, subsequently shaping immune maturation and neural plasticity (Gomez de Agüero et al., 2016; Thion et al., 2018b). It is important to note that these mechanisms derive predominantly from preclinical models; rigorous experimental validation in humans remains essential (Needham et al., 2021).
5.2.6 Future research imperatives
Standardized study designs should incorporate uniform core parameters, including clinically validated probiotic formulations, defined dosages, standardized intervention windows, and consistent neurodevelopmental endpoints, while clinical trials should prioritize high-risk subgroups, such as carriers of neurodevelopmental copy number variants (CNVs) mutations or individuals with severe baseline dysbiosis, coupled with longitudinal cohorts for long-term offspring follow-up (Reid, 2016; McFarland et al., 2018; Zwaigenbaum et al., 2015; Vuong and Hsiao, 2017). Further, mechanistic studies should leverage advanced models, such as blood–brain barrier organoids and fetal brain organoid cocultures, to probe the direct effects of probiotic metabolites on blood–brain barrier function and synaptogenesis (Gabriel et al., 2020; Qian et al., 2019). Metagenomic-metabolomic correlative analyses may identify key maternal-fetal microbial and metabolite transfer hubs (Walker et al., 2017; Younge et al., 2019). Finally, integrating these mechanistic insights with real-world evidence will ultimately define clinical applicability, target populations, and risk–benefit ratios for ASD precision prevention (Geschwind and State, 2015; Kim et al., 2018; Courchesne et al., 2020).
6 Future perspectives
6.1 Systematic resolution of spatiotemporal specificity in gut microbiota-mediated neural development
Although current studies reveal associations between gut microbial metabolites and fetal neuronal axonogenesis, their precise impact on ASD-related neural circuit development remains unclear (Needham et al., 2021). This gap pertains particularly to dose–response dynamics and temporal mechanisms during critical gestational windows (Dunlop et al., 2015). Existing models exhibit critical limitations: broad-spectrum antibiotic perturbation fails to recapitulate complex host-microbiota interactions in human pregnancy, while cross-sectional studies cannot establish causality (Dunlop et al., 2015). Addressing this will require spatiotemporally precise intervention and monitoring tools. We will integrate CRISPR-associated protein 9 (CRISPR-Cas9)-mediated targeted editing of gut commensals with in vivo two-photon microscopy to establish gestational-stage-specific models (Dunlop et al., 2015). Such models will enable real-time tracking of how concentration gradients of specific microbial metabolites or precursors regulate axon guidance signaling pathways in vivo (Thion et al., 2018a). Implementing this strategy will necessitate: (i) applying advanced spatial metabolomics coupled with high-resolution mass spectrometry to enhance tissue-level metabolite localization; and (ii) performing three-dimensional imaging of intestinal crypt architectures to resolve spatial distributions of commensal microbes and metabolites within intestinal niches at submillimeter resolution. This integrated platform will resolve whether key developmental processes—such as embryonic neural crest cell migration—depend on spatially patterned microbial metabolic activities.
6.2 Integrated multi-omics approaches confront dual challenges of data heterogeneity and causal complexity
Research on microbiome-host interactions faces dual challenges: (i) Highly nonlinear relationships between high-dimensional microbial features and host physiological pathways impede critical regulatory insights from conventional linear models (Lloyd-Price et al., 2019); (ii) The limited resolution of 16S rRNA gene sequencing frequently fails to distinguish functionally active strains, potentially yielding spurious associations (Jovel et al., 2016). Current understanding of Bifidobacterium-mediated serotonin biosynthesis regulation, for instance, remains correlative, lacking mechanistic validation (Yano et al., 2015). To overcome these limitations, two innovative strategies have been proposed: constructing heterogeneous biological networks integrating microbe-metabolite-host gene nodes, within which graph neural networks (GNNs) with attention mechanisms could quantify regulatory weights and directionality for keystone species acting on host pathways within complex ecologies (Faust et al., 2012); and combining variational autoencoders (VAEs) with microbial ecological constraints to infer biologically plausible absolute abundances from relative abundance data, thereby mitigating compositional bias effects (Faust et al., 2012). For robust causal inference, frameworks beyond univariate Mendelian randomization (MR) are essential (Bowden et al., 2017). Future studies should implement structural equation modeling (SEM) incorporating ecological network dynamics. Such approaches could quantitatively validate observed microbiome-host relationships—as exemplified by Butyricicoccus members, which have experimentally demonstrated HDAC inhibition activity affecting host pathways like GABAergic neuron differentiation.
6.3 Intergenerational mechanistic validation requires humanized research paradigms
Human cohort studies face ethical constraints in controlling maternal diet-environment toxin interactions (Hanrahan et al., 1992). To address this and the evolutionary limitations of rodent models for gut-brain axis translation—alongside the absence of systematic immune-microbiota simulation in current human brain organoids—innovative solutions target two domains: confounder control and cross-platform integration (Quadrato et al., 2017). For confounder control, researchers should prioritize prospective cohort designs or standardize interventions, applying high-dimensional multivariate models to correct biases (Magnus et al., 2016). Cross-platform integration requires: (i) engineering 3D-bioprinted microfluidic systems that incorporate placental barrier organoids/chips within BSL-2 containment integrated with humanized living bacterial biofilms (Blundell et al., 2018); and (ii) establishing computational-experimental feedback loops via exposome models derived from mother-infant multi-omics data—including metagenomic, metabolomic, and epigenomic profiles—to predict targets, with subsequent ex vivo validation (Wild, 2012). Locally validated targets warrant development of gut-restricted delivery systems using pH-responsive chitosan coatings for oral gavage with site-specific release. Current achievable goals must focus on localized intestinal interventions—excluding systemic OMV delivery or maternal metabolic gradient simulation. This integrated strategy spanning bioinformatic prediction, multi-dimensional organoids, and engineered delivery provides a systematic framework to resolve prenatal microbiota-host networks underlying ASD risk (Hsiao et al., 2013).
7 Summary
Maternal gut dysbiosis is recognized as a significant non-independent environmental factor contributing to the risk of offspring ASD. Current understanding of its pathological mechanisms, primarily derived from preclinical studies, supports a multi-pathway synergistic model: Strong preclinical evidence indicates that dysbiosis triggers aberrant maternal immune activation, disrupts homeostasis of key metabolites, and potentially induces epigenetic reprogramming.
Collectively, these changes impair critical fetal neurodevelopmental processes in animal models, encompassing synaptogenesis, synaptic pruning, and the establishment and functional maturation of the BBB.
However, translating probiotic interventions into effective ASD prevention or treatment presents substantial challenges. Key scientific bottlenecks include heterogeneity in intervention efficacy due to strain-specific effects of probiotics and complexity in host-microbiome interactions; unresolved questions regarding maternal gut microbiota-derived metabolites in humans—specifically, their efficient placental transfer, post-transfer metabolic transformations, effective target-site concentrations, and precise mechanisms of action on fetal neurodevelopment; and limitations in existing human evidence supporting the pathogenic chain, which relies heavily on observational studies frequently confounded by covariates such as maternal diet, antibiotic usage, and environmental exposures, thereby compromising causal inference.
Consequently, it must be unequivocally emphasized that conclusive causal evidence linking maternal dysbiosis to altered fetal neurodevelopment and subsequent ASD pathogenesis in humans remains insufficient, and probiotic efficacy for ASD prevention or treatment lacks consistent validation through adequately powered and rigorously designed human RCTs.
Probiotics and fecal microbiota transplantation (FMT) show potential in modulating gut microbiota and alleviating symptoms of autism spectrum disorder (ASD). However, due to limited evidence quality, lack of standardization, and potential risks, they remain experimental therapies at present. Future large-scale randomized controlled trials (RCTs) are warranted to elucidate their efficacy mechanisms, establish individualized protocols, and rigorously monitor long-term safety.
To overcome current limitations and shift from associative findings to actionable target identification, future research must elucidate the spatiotemporal dynamics of dysbiosis-induced neurodevelopmental perturbations during critical gestational windows; integrate multi-omics data (e.g., metagenomics, metabolomics, and epigenomics) and employ advanced computational approaches to reconstruct and decode the “microbiota-metabolite-host gene/pathway” interaction network, enabling precise identification of core drivers and causal pathways; develop human-relevant gut-brain axis organoid models that better recapitulate human physiology; and design rigorous clinical trials to systematically validate mechanistic hypotheses and intervention strategies.
Author contributions
DZ: Data curation, Conceptualization, Writing – review & editing, Writing – original draft. MZ: Investigation, Data curation, Writing – review & editing, Writing – original draft. YQ: Data curation, Writing – review & editing. HX: Data curation, Writing – review & editing. HL: Supervision, Writing – review & editing, Conceptualization, Funding acquisition, Writing – original draft. YL: Conceptualization, Writing – review & editing, Funding acquisition, Writing – original draft, Investigation. LX: Funding acquisition, Writing – original draft, Writing – review & editing, Conceptualization.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the National Natural Science Foundation of the China Joint Fund for Regional Innovation and Development (No. U21A20333), Science and Technology Projects of Xizang Autonomous Region (No. XZ202501ZY0116), China, and Sichuan Science and Technology Program (No.2023NSFSC0530).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
Aatsinki, A. K., Lahti, L., Uusitupa, H. M., Munukka, E., Keskitalo, A., Nolvi, S., et al. (2019). Gut microbiota composition is associated with temperament traits in infants. Brain Behav. Immun. 80, 849–858. doi: 10.1016/j.bbi.2019.05.035
Agathokleous, E., Peñuelas, J., Azevedo, R. A., Rillig, M. C., Sun, H., and Calabrese, E. J. (2022). Low levels of contaminants stimulate harmful algal organisms and enrich their toxins. Environ. Sci. Technol. 56, 11991–12002.
Ahmed, H., Leyrolle, Q., Koistinen, V., Kärkkäinen, O., Layé, S., Delzenne, N., et al. (2022). Microbiota-derived metabolites as drivers of gut-brain communication. Gut Microbes 14:2102878. doi: 10.1080/19490976.2022.2102878
Al-Ayadhi, L., Zayed, N., Bhat, R. S., Moubayed, N. M. S., Al-Muammar, M. N., and El-Ansary, A. (2021). The use of biomarkers associated with leaky gut as a diagnostic tool for early intervention in autism spectrum disorder: a systematic review. Gut Pathog. 13:54. doi: 10.1186/s13099-021-00448-y
Allison, J., Kaliszewska, A., Uceda, S., Reiriz, M., and Arias, N. (2021). Targeting DNA methylation in the adult brain through diet. Nutrients 13:3979. doi: 10.3390/nu13113979
Al-Qadami, G. H., Secombe, K. R., Subramaniam, C. B., Wardill, H. R., and Bowen, J. M. (2022). Gut microbiota-derived short-chain fatty acids: impact on cancer treatment response and toxicities. Microorganisms. 10:2048. doi: 10.3390/microorganisms10102048
Alsharairi, N. A. (2023). Therapeutic potential of gut microbiota and its metabolite short-chain fatty acids in neonatal necrotizing enterocolitis. Life. 13:561. doi: 10.3390/life13020561
Amat, S., Dahlen, C. R., Swanson, K. C., Ward, A. K., Reynolds, L. P., and Caton, J. S. (2022). Bovine animal model for studying the maternal microbiome, in utero microbial colonization and their role in offspring development and fetal programming. Front. Microbiol. 13:854453. doi: 10.3389/fmicb.2022.854453
Andari, E., Nishitani, S., Kaundinya, G., Caceres, G. A., Morrier, M. J., Ousley, O., et al. (2020). Epigenetic modification of the oxytocin receptor gene: implications for autism symptom severity and brain functional connectivity. Neuropsychopharmacology 45, 1150–1158. doi: 10.1038/s41386-020-0610-6
Appleton, J. (2018). The gut-brain axis: influence of microbiota on mood and mental health. Integr. Med. Clin. J. 17, 28–32. doi: 10.1021/acs.est.2c02763
Barbian, M. E., Owens, J. A., Naudin, C. R., Denning, P. W., Patel, R. M., and Jones, R. M. (2022). Butyrate supplementation to pregnant mice elicits cytoprotection against colonic injury in the offspring. Pediatr. Res. 92, 125–134. doi: 10.1038/s41390-021-01767-1
Barthow, C., Wickens, K., Stanley, T., Mitchell, E. A., Maude, R., Abels, P., et al. (2016). The probiotics in pregnancy study (PiP study): rationale and design of a double-blind randomised controlled trial to improve maternal health during pregnancy and prevent infant eczema and allergy. BMC Pregnancy Childbirth 16:133. doi: 10.1186/s12884-016-0923-y
Bastian, T. W., Von Hohenberg, W. C., Mickelson, D. J., Lanier, L. M., and Georgieff, M. K. (2016). Iron deficiency impairs developing hippocampal neuron gene expression, energy metabolism, and dendrite complexity. Dev. Neurosci. 38, 264–276. doi: 10.1159/000448514
Berding, K., and Donovan, S. M. (2016). Microbiome and nutrition in autism spectrum disorder: current knowledge and research needs. Nutr. Rev. 74, 723–736. doi: 10.1093/nutrit/nuw048
Bermudez-Martin, P., Becker, J. A. J., Caramello, N., Fernandez, S. P., Costa-Campos, R., Canaguier, J., et al. (2021). The microbial metabolite p-cresol induces autistic-like behaviors in mice by remodeling the gut microbiota. Microbiome. 9:157. doi: 10.1186/s40168-021-01103-z
Bik, E. M., Ugalde, J. A., Cousins, J., Goddard, A. D., Richman, J., and Apte, Z. S. (2018). Microbial biotransformations in the human distal gut. Br. J. Pharmacol. 175, 4404–4414. doi: 10.1111/bph.14085
Blagonravova, A. S., Zhilyaeva, T. V., and Kvashnina, D. V. (2021). Dysbiosis of intestinal microbiota in autism spectrum disorders: new horizons in search for pathogenetic approaches to therapy. Part 2. Gut–brain axis in pathogenesis of autism spectrum disorders. J. Microbiol. Epidemiol. Immunobiol. 98, 221–230. doi: 10.36233/0372-9311-62
Blundell, C., Yi, Y. S., Ma, L., Tess, E. R., Farrell, M. J., Georgescu, A., et al. (2018). Placental drug transport-on-a-chip: A microengineered in vitro model of transporter-mediated drug efflux in the human placental barrier. Adv. Healthc. Mater. 7:10.1002/adhm.201700786. doi: 10.1002/adhm.201700786
Boccuto, L., Chen, C. F., Pittman, A. R., Skinner, C. D., McCartney, H. J., Jones, K., et al. (2013). Decreased tryptophan metabolism in patients with autism spectrum disorders. Mol. Autism. 4:16. doi: 10.1186/2040-2392-4-16
Bowden, J., Del Greco, M. F., Minelli, C., Davey Smith, G., Sheehan, N., and Thompson, J. (2017). A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat. Med. 36, 1783–1802. doi: 10.1002/sim.7221
Braniste, V., Al-Asmakh, M., Kowal, C., Anuar, F., Abbaspour, A., Tóth, M., et al. (2014). The gut microbiota influences blood-brain barrier permeability in mice. Sci. Transl. Med. 6:263ra158. doi: 10.1126/scitranslmed.3009759
Bromer, C., Marsit, C. J., Armstrong, D. A., Padbury, J. F., and Lester, B. (2013). Genetic and epigenetic variation of the glucocorticoid receptor (NR3C1) in placenta and infant neurobehavior. Dev. Psychobiol. 55, 673–683. doi: 10.1002/dev.21061
Brown, A. J., Goldsworthy, S. M., Barnes, A. A., Eilert, M. M., Tcheang, L., Daniels, D., et al. (2003). The orphan G protein-coupled receptors GPR41 and GPR43 are activated by propionate and other short chain carboxylic acids. J. Biol. Chem. 278, 11312–11319. doi: 10.1074/jbc.M211609200
Buffington, S. A., Di Prisco, G. V., Auchtung, T. A., Ajami, N. J., Petrosino, J. F., and Costa-Mattioli, M. (2016). Microbial reconstitution reverses maternal diet-induced social and synaptic deficits in offspring. Cell 165, 1762–1775. doi: 10.1016/j.cell.2016.06.001
Bukys, T., Kurlinkus, B., Sileikis, A., and Vitkus, D. (2024). The prospect of improving pancreatic cancer diagnostic capabilities by implementing blood biomarkers: a study of evaluating properties of a single IL-8 and in conjunction with CA19-9, CEA, and CEACAM6. Biomedicine 12:2344. doi: 10.3390/biomedicines12102344
Burket, J. A., Benson, A. D., Tang, A. H., and Deutsch, S. I. (2013). D-Cycloserine improves sociability in the BTBR T+ Itpr3tf/J mouse model of autism spectrum disorders with altered Ras/Raf/ERK1/2 signaling. Brain Res. Bull. 96, 62–70. doi: 10.1016/j.brainresbull.2013.05.003
Callery, P. S., and Geelhaar, L. A. (1985). 1-Piperideine as an in vivo precursor of the γ-aminobutyric acid homologue 5-Aminopentanoic acid. J. Neurochem. 45, 946–948. doi: 10.1111/j.1471-4159.1985.tb04085.x
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., et al. (2010). QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. doi: 10.1038/nmeth.f.303
Cermak, S. A., Curtin, C., and Bandini, L. G. (2010). Food selectivity and sensory sensitivity in children with autism spectrum disorders. J. Am. Diet. Assoc. 110, 238–246. doi: 10.1016/j.jada.2009.10.032
Chang, C. J., Lin, T. L., Tsai, Y. L., Wu, T. R., Lai, W. F., Lu, C. C., et al. (2019). Next generation probiotics in disease amelioration. J. Food Drug Anal. 27, 615–622. doi: 10.1016/j.jfda.2018.12.011
Chang, X., Zhang, Y., Chen, X., Li, S., Mei, H., Xiao, H., et al. (2024). Gut microbiome and serum amino acid metabolome alterations in autism spectrum disorder. Sci. Rep. 14:4037. doi: 10.1038/s41598-024-54717-2
Chen, W. G., Chang, Q., Lin, Y., Meissner, A., West, A. E., Griffith, E. C., et al. (2003). Derepression of BDNF transcription involves calcium-dependent phosphorylation of MeCP2. Science 302, 885–889. doi: 10.1126/science.1086446
Chen, Y., Fang, H., Li, C., Wu, G., Xu, T., Yang, X., et al. (2020). Gut Bacteria shared by children and their mothers associate with developmental level and social deficits in autism Spectrum disorder. mSphere 5, e01044–e01020. doi: 10.1128/mSphere.01044-20
Chen, J., Xu, Q., Li, Y., Tang, Z., Sun, W., Zhang, X., et al. (2019). Comparative effects of dietary supplementations with sodium butyrate, medium-chain fatty acids, and n-3 polyunsaturated fatty acids in late pregnancy and lactation on the reproductive performance of sows and growth performance of suckling piglets. J. Anim. Sci. 97, 4256–4267. doi: 10.1093/jas/skz284
Cheroni, C., Caporale, N., and Testa, G. (2020). Autism spectrum disorder at the crossroad between genes and environment: contributions, convergences, and interactions in ASD developmental pathophysiology. Mol. Autism. 11:69. doi: 10.1186/s13229-020-00370-1
Chiang, J. Y. L. (2013). Bile acid metabolism and signaling. Compr. Physiol. 3, 1191–1212. doi: 10.1002/j.2040-4603.2013.tb00517.x
Choi, J., Lee, S., Won, J., Jin, Y., Hong, Y., Hur, T. Y., et al. (2018). Pathophysiological and neurobehavioral characteristics of a propionic acid-mediated autism-like rat model. PLoS One 13:e0192925. doi: 10.1371/journal.pone.0192925
Choi, G. B., Yim, Y. S., Wong, H., Kim, S., Kim, H., Kim, S. V., et al. (2016). The maternal interleukin-17a pathway in mice promotes autism-like phenotypes in offspring. Science 351, 933–939. doi: 10.1126/science.aad0314
Chu, X. J., Song, D. D., Zhou, M. H., Chen, X. Z., Chu, N., Li, M., et al. (2024). Perturbations in gut and respiratory microbiota in COVID-19 and influenza patients: a systematic review and meta-analysis. Front. Med. 11:1301312. doi: 10.3389/fmed.2024.1301312
Cook, J., and Prinz, M. (2022). Regulation of microglial physiology by the microbiota. Gut Microbes 14:2125739. doi: 10.1080/19490976.2022.2125739
Courchesne, E., Gazestani, V. H., and Lewis, N. E. (2020). Prenatal origins of ASD: the when, what, and how of ASD development. Trends Neurosci. 43, 326–342. doi: 10.1016/j.tins.2020.03.005
Cox, L. M., and Weiner, H. L. (2018). Microbiota signaling pathways that influence neurologic disease. Neurotherapeutics 15, 135–145. doi: 10.1007/s13311-017-0598-8
Cryan, J. F., O’Riordan, K. J., Cowan, C. S. M., Sandhu, K. V., Bastiaanssen, T. F. S., Boehme, M., et al. (2019). The microbiota-gut-brain axis. Physiol. Rev. 99, 1877–2013. doi: 10.1152/physrev.00018.2018
Csoka, A. B., El Kouhen, N., Bennani, S., Getachew, B., Aschner, M., and Tizabi, Y. (2024). Roles of epigenetics and glial cells in drug-induced autism spectrum disorder. Biomolecules. 14:437. doi: 10.3390/biom14040437
D’Aquila, P., Carelli, L. L., De Rango, F., Passarino, G., and Bellizzi, D. (2020). Gut microbiota as important mediator between diet and DNA methylation and histone modifications in the host. Nutrients 12:597. doi: 10.3390/nu12030597
Dahoun, T., Trossbach, S. V., Brandon, N. J., Korth, C., and Howes, O. D. (2017). The impact of disrupted-in-schizophrenia 1 (DISC1) on the dopaminergic system: a systematic review. Transl. Psychiatry 7, –e1015. doi: 10.1038/tp.2016.282
Dash, P. K., Orsi, S. A., and Moore, A. N. (2009). HDAC inhibition combined with behavioral therapy enhances learning and memory following traumatic brain injury. Neuroscience 163, 1–8. doi: 10.1016/j.neuroscience.2009.06.028
De Bondt, M., Hellings, N., Opdenakker, G., and Struyf, S. (2020). Neutrophils: underestimated players in the pathogenesis of multiple sclerosis (MS). Int. J. Mol. Sci. 21:4558. doi: 10.3390/ijms21124558
Debelius, J., Song, S. J., Vazquez-Baeza, Y., Xu, Z. Z., Gonzalez, A., and Knight, R. (2016). Tiny microbes, enormous impacts: what matters in gut microbiome studies? Genome Biol. 17:217. doi: 10.1186/s13059-016-1086-x
Deek, R. A., Ma, S., Lewis, J., and Li, H. (2024). Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data. eLife 13:e88956. doi: 10.7554/eLife.88956
Descamps, H. C., Herrmann, B., Wiredu, D., and Thaiss, C. A. (2019). The path toward using microbial metabolites as therapies. EBioMedicine 44, 747–754. doi: 10.1016/j.ebiom.2019.05.063
Dinan, T. G., and Cryan, J. F. (2017a). Microbes, immunity, and behavior: psychoneuroimmunology meets the microbiome. Neuropsychopharmacology 42, 178–192. doi: 10.1038/npp.2016.103
Dinan, T. G., and Cryan, J. F. (2017b). Gut instincts: microbiota as a key regulator of brain development, ageing and neurodegeneration. J. Physiol. 595, 489–503. doi: 10.1113/JP273106
Dinleyici, M., Barbieur, J., Dinleyici, E. C., and Vandenplas, Y. (2023). Functional effects of human milk oligosaccharides (HMOs). Gut Microbes 15:2186115. doi: 10.1080/19490976.2023.2186115
Dong, F., and Perdew, G. H. (2020). The aryl hydrocarbon receptor as a mediator of host-microbiota interplay. Gut Microbes 12:1859812. doi: 10.1080/19490976.2020.1859812
Duck, K. A., and Connor, J. R. (2016). Iron uptake and transport across physiological barriers. Biometals 29, 573–591. doi: 10.1007/s10534-016-9952-2
Dunlop, A. L., Mulle, J. G., Ferranti, E. P., Edwards, S., Dunn, A. B., and Corwin, E. J. (2015). Maternal microbiome and pregnancy outcomes that impact infant health: a review. Adv. Neonatal Care 15, 377–385. doi: 10.1097/ANC.0000000000000218
Eggert, H., Kurtz, J., and Diddens-de Buhr, M. F. (2014). Different effects of paternal trans-generational immune priming on survival and immunity in step and genetic offspring. Proc. R. Soc. B Biol. Sci. 281:20142089. doi: 10.1098/rspb.2014.2089
El Ahdab, N., Haque, M., Madogwe, E., Koski, K. G., and Scott, M. E. (2021). Maternal nematode infection upregulates expression of TH2/treg and diapedesis related genes in the neonatal brain. Sci. Rep. 11:22082. doi: 10.1038/s41598-021-01510-0
El Aidy, S., van Baarlen, P., Derrien, M., Lindenbergh-Kortleve, D. J., Hooiveld, G., Levenez, F., et al. (2012). Temporal and spatial interplay of microbiota and intestinal mucosa drive establishment of immune homeostasis in conventionalized mice. Mucosal Immunol. 5, 567–579. doi: 10.1038/mi.2012.32
Elvers, K. T., Wilson, V. J., Hammond, A., Duncan, L., Huntley, A. L., Hay, A. D., et al. (2020). Antibiotic-induced changes in the human gut microbiota for the most commonly prescribed antibiotics in primary care in the UK: a systematic review. BMJ Open 10:e035677. doi: 10.1136/bmjopen-2019-035677
Engevik, M. A., Luck, B., Visuthranukul, C., Ihekweazu, F. D., Engevik, A. C., Shi, Z., et al. (2021). Human-derived bifidobacterium dentium modulates the mammalian serotonergic system and gut–brain axis. Cell. Mol. Gastroenterol. Hepatol. 11, 221–248. doi: 10.1016/j.jcmgh.2020.08.002
Esposito, M., Mirizzi, P., Fadda, R., Pirollo, C., Ricciardi, O., Mazza, M., et al. (2023). Food selectivity in children with autism: guidelines for assessment and clinical interventions. Int. J. Environ. Res. Public Health 20:5092. doi: 10.3390/ijerph20065092
Faulk, W. P., Coulam, C. B., and McIntyre, J. A. (1987). Reproductive immunology: biomarkers of compromised pregnancies. Environ. Health Perspect. 74, 119–127. doi: 10.1289/ehp.8774119
Faust, K., Sathirapongsasuti, J. F., Izard, J., Segata, N., Gevers, D., Raes, J., et al. (2012). Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8:e1002606. doi: 10.1371/journal.pcbi.1002606
Ferretti, P., Pasolli, E., Tett, A., Asnicar, F., Gorfer, V., Fedi, S., et al. (2018). Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133.e5–145.e5. doi: 10.1016/j.chom.2018.06.005
Filardo, S., Di Pietro, M., Protano, C., Antonucci, A., Vitali, M., and Sessa, R. (2022). Impact of air pollution on the composition and diversity of human gut microbiota in general and vulnerable populations: a systematic review. Toxics. 10:579. doi: 10.3390/toxics10100579
Finegold, S. M., Dowd, S. E., Gontcharova, V., Liu, C., Henley, K. E., Wolcott, R. D., et al. (2010). Pyrosequencing study of fecal microflora of autistic and control children. Anaerobe 16, 444–453. doi: 10.1016/j.anaerobe.2010.06.008
Finegold, S. M., Molitoris, D., Song, Y., Liu, C., Vaisanen, M., Bolte, E., et al. (2002). Gastrointestinal microflora studies in late-onset autism. Clin. Infect. Dis. 35, S6–S16. doi: 10.1086/341914
Fiorentino, M., Sapone, A., Senger, S., Camhi, S. S., Kadzielski, S. M., Buie, T. M., et al. (2016). Blood–brain barrier and intestinal epithelial barrier alterations in autism spectrum disorders. Mol. Autism. 7:49. doi: 10.1186/s13229-016-0110-z
Gabriel, E., Ramani, A., Altinisik, N., and Gopalakrishnan, J. (2020). Human brain organoids to decode mechanisms of microcephaly. Front. Cell. Neurosci. 14:115. doi: 10.3389/fncel.2020.00115
Gao, X., Fu, N., Ben, Q., and Bu, X. (2025). A meta-analysis of the effects of gut microbiota–based interventions on gastrointestinal and behavioral symptoms in children with autism spectrum disorder. Nutr. Rev. :nuaf050. doi: 10.1093/nutrit/nuaf050
Generoso, J. S., Giridharan, V. V., Lee, J., Macedo, D., and Barichello, T. (2021). The role of the microbiota-gut-brain axis in neuropsychiatric disorders. Braz. J. Psychiatry 43, 293–305. doi: 10.1590/1516-4446-2020-0987
Geschwind, D. H., and State, M. W. (2015). Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. 14, 1109–1120. doi: 10.1016/S1474-4422(15)00044-7
Gheorghe, C. E., Ritz, N. L., Martin, J. A., Wardill, H. R., Cryan, J. F., and Clarke, G. (2021). Investigating causality with fecal microbiota transplantation in rodents: applications, recommendations and pitfalls. Gut Microbes 13:1941711. doi: 10.1080/19490976.2021.1941711
Gomez de Agüero, M., Ganal-Vonarburg, S. C., Fuhrer, T., Rupp, S., Uchimura, Y., Li, H., et al. (2016). The maternal microbiota drives early postnatal innate immune development. Science 351, 1296–1302. doi: 10.1126/science.aad2571
Gong, P., Angelini, D. J., Yang, S., Xia, G., Cross, A. S., Mann, D., et al. (2008). TLR4 signaling is coupled to SRC family kinase activation, tyrosine phosphorylation of zonula adherens proteins, and opening of the paracellular pathway in human lung microvascular endothelia. J. Biol. Chem. 283, 13437–13449. doi: 10.1074/jbc.M707986200
González-Bosch, C., Boorman, E., Zunszain, P. A., and Mann, G. E. (2021). Short-chain fatty acids as modulators of redox signaling in health and disease. Redox Biol. 47:102165. doi: 10.1016/j.redox.2021.102165
Grayson, D. R., and Guidotti, A. (2016). Merging data from genetic and epigenetic approaches to better understand autistic spectrum disorder. Epigenomics. 8, 85–104. doi: 10.2217/epi.15.92
Greene, C., Hanley, N., and Campbell, M. (2019). Claudin-5: gatekeeper of neurological function. Fluids Barriers CNS 16:3. doi: 10.1186/s12987-019-0123-z
Grochowska, M., Wojnar, M., and Radkowski, M. (2018). The gut microbiota in neuropsychiatric disorders. Acta Neurobiol. Exp. 78, 69–81. doi: 10.21307/ane-2018-008
Guillemin, G. J., Cullen, K. M., Lim, C. K., Smythe, G. A., Garner, B., Kapoor, V., et al. (2007). Characterization of the kynurenine pathway in human neurons. J. Neurosci. 27, 12884–12892. doi: 10.1523/JNEUROSCI.4101-07.2007
Han, K., Meadows, A. M., Rodman, M. J., Russo, A. C., Sharma, R., Singh, K., et al. (2024). Propionate functions as a feeding state-dependent regulatory metabolite to counter proinflammatory signaling linked to nutrient load and obesity. J. Leukoc. Biol. 115, 738–749. doi: 10.1093/jleuko/qiae006
Han, W., Wang, J., Yan, X., Liu, C., Huang, J., Zhang, L., et al. (2024). Butyrate and iso-butyrate: a new perspective on nutrition prevention of gestational diabetes mellitus. Nutr. Diabetes 14:24. doi: 10.1038/s41387-024-00276-4
Hanrahan, J. P., Tager, I. B., Segal, M. R., Tosteson, T. D., Castile, R. G., Van Vunakis, H., et al. (1992). The effect of maternal smoking during pregnancy on early infant lung function. Am. Rev. Respir. Dis. 145, 1129–1135. doi: 10.1164/ajrccm/145.5.1129
Harrison, M. A., Kaur, H., Wren, B. W., and Dawson, L. F. (2021). Production of p-cresol by decarboxylation of p-HPA by all five lineages of clostridioides difficile provides a growth advantage. Front. Cell. Infect. Microbiol. 11:757599. doi: 10.3389/fcimb.2021.757599
Hassib, L., De Oliveira, C. L., Rouvier, G. A., Kanashiro, A., Guimarães, F. S., and Ferreira, F. R. (2023). Maternal microbiome disturbance induces deficits in the offspring’s behaviors: a systematic review and meta-analysis. Gut Microbes 15:2226282. doi: 10.1080/19490976.2023.2226282
Heidt, C., Kämmerer, U., Fobker, M., Rüffer, A., Marquardt, T., and Reuss-Borst, M. (2023). Assessment of intestinal permeability and inflammation bio-markers in patients with rheumatoid arthritis. Nutrients 15:2386. doi: 10.3390/nu15102386
Hernandez, A. F., Buha, A., Constantin, C., Wallace, D. R., Sarigiannis, D., Neagu, M., et al. (2019). Critical assessment and integration of separate lines of evidence for risk assessment of chemical mixtures. Arch. Toxicol. 93, 2741–2757. doi: 10.1007/s00204-019-02547-x
Hernández-Martínez, C., Canals, J., Voltas, N., Martín-Luján, F., and Arija, V. (2022). Circulating levels of short-chain fatty acids during pregnancy and infant neurodevelopment. Nutrients 14:3946. doi: 10.3390/nu14193946
Hill, A. A., Anderson-Baucum, E. K., Kennedy, A. J., Webb, C. D., Yull, F. E., and Hasty, A. H. (2015). Activation of NF-κB drives the enhanced survival of adipose tissue macrophages in an obesogenic environment. Mol. Metab. 4, 665–677. doi: 10.1016/j.molmet.2015.07.005
Ho, L. K. H., Tong, V. J. W., Syn, N., Nagarajan, N., Tham, E. H., Tay, S. K., et al. (2020). Gut microbiota changes in children with autism spectrum disorder: a systematic review. Gut Pathog. 12:6. doi: 10.1186/s13099-020-0346-1
Hoel, D. G., and Portier, C. J. (1994). Nonlinearity of dose-response functions for carcinogenicity. Environ. Health Perspect. 102 Suppl 1(Suppl 1), 109–113.
Honda, K., and Littman, D. R. (2012). The microbiome in infectious disease and inflammation. Annu. Rev. Immunol. 30, 759–795. doi: 10.1146/annurev-immunol-020711-074937
Hsiao, E. Y., McBride, S. W., Chow, J., Mazmanian, S. K., and Patterson, P. H. (2012). Modeling an autism risk factor in mice leads to permanent immune dysregulation. Proc. Natl. Acad. Sci. USA 109, 12776–12781. doi: 10.1073/pnas.1202556109
Hsiao, E. Y., McBride, S. W., Hsien, S., Sharon, G., Hyde, E. R., McCue, T., et al. (2013). Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 155, 1451–1463. doi: 10.1016/j.cell.2013.11.024
Hsiao, E. Y., and Patterson, P. H. (2011). Activation of the maternal immune system induces endocrine changes in the placenta via IL-6. Brain Behav. Immun. 25, 604–615. doi: 10.1016/j.bbi.2010.12.017
Hsu, C. N., Hou, C. Y., Hsu, W. H., and Tain, Y. L. (2021). Cardiovascular diseases of developmental origins: preventive aspects of gut microbiota-targeted therapy. Nutrients 13:2290. doi: 10.3390/nu13072290
Ilyés, T., Pop, M., Surcel, M., Pop, D. M., Rusu, R., Silaghi, C. N., et al. (2023). First comparative evaluation of short-chain fatty acids and vitamin-K-dependent proteins levels in mother–newborn pairs at birth. Life. 13:847. doi: 10.3390/life13030847
Islam, O., Gong, X., Rose-John, S., and Heese, K. (2009). Interleukin-6 and neural stem cells: more than gliogenesis. Mol. Biol. Cell 20, 188–199. doi: 10.1091/mbc.e08-05-0463
Jaju Bhattad, G., Jeyarajah, M. J., McGill, M. G., Dumeaux, V., Okae, H., Arima, T., et al. (2020). Histone deacetylase 1 and 2 drive differentiation and fusion of progenitor cells in human placental trophoblasts. Cell Death Dis. 11:311. doi: 10.1038/s41419-020-2500-6
Jovel, J., Patterson, J., Wang, W., Hotte, N., O’Keefe, S., Mitchel, T., et al. (2016). Characterization of the gut microbiome using 16S or shotgun metagenomics. Front. Microbiol. 7:459. doi: 10.3389/fmicb.2016.00459
Kamalmaz, N., Ben Bacha, A., Alonazi, M., Albasher, G., Khayyat, A. I. A., and El-Ansary, A. (2023). Unveiling sex-based differences in developing propionic acid-induced features in mice as a rodent model of ASD. Peerj. 11:e15488. doi: 10.7717/peerj.15488
Kang, D. W., Adams, J. B., Coleman, D. M., Pollard, E. L., Maldonado, J., McDonough-Means, S., et al. (2019). Long-term benefit of microbiota transfer therapy on autism symptoms and gut microbiota. Sci. Rep. 9:5821. doi: 10.1038/s41598-019-42183-0
Kang, D. W., Park, J. G., Ilhan, Z. E., Wallstrom, G., LaBaer, J., Adams, J. B., et al. (2013). Reduced incidence of prevotella and other fermenters in intestinal microflora of autistic children. PLoS One 8:e68322. doi: 10.1371/journal.pone.0068322
Kasarello, K., Cudnoch-Jedrzejewska, A., and Czarzasta, K. (2023). Communication of gut microbiota and brain via immune and neuroendocrine signaling. Front. Microbiol. 14:1118529. doi: 10.3389/fmicb.2023.1118529
Keleher, M. R., Zaidi, R., Shah, S., Oakley, M. E., Pavlatos, C., El Idrissi, S., et al. (2018). Maternal high-fat diet associated with altered gene expression, DNA methylation, and obesity risk in mouse offspring. PLoS One 13:e0192606. doi: 10.1371/journal.pone.0192606
Keller, M. C. (2014). Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution. Biol. Psychiatry 75, 18–24. doi: 10.1016/j.biopsych.2013.09.006
Kelly, J. R., Kennedy, P. J., Cryan, J. F., Dinan, T. G., Clarke, G., and Hyland, N. P. (2015). Breaking down the barriers: the gut microbiome, intestinal permeability and stress-related psychiatric disorders. Front. Cell. Neurosci. 9:392. doi: 10.3389/fncel.2015.00392
Kijmanawat, A., Panburana, P., Reutrakul, S., and Tangshewinsirikul, C. (2019). Effects of probiotic supplements on insulin resistance in gestational diabetes mellitus: a double-blind randomized controlled trial. J. Diabetes Investig. 10, 163–170. doi: 10.1111/jdi.12863
Kim, H. J., Cho, M. H., Shim, W. H., Kim, J. K., Jeon, E. Y., Kim, D. H., et al. (2017). Deficient autophagy in microglia impairs synaptic pruning and causes social behavioral defects. Mol. Psychiatry 22, 1576–1584. doi: 10.1038/mp.2016.103
Kim, K. C., Gonzales, E. L., Lázaro, M. T., Choi, C. S., Bahn, G. H., Yoo, H. J., et al. (2016). Clinical and neurobiological relevance of current animal models of autism spectrum disorders. Biomol. Ther. 24, 207–243. doi: 10.4062/biomolther.2016.061
Kim, H. S., Lee, S., and Kim, J. H. (2018). Real-world evidence versus randomized controlled trial: clinical research based on electronic medical records. J. Korean Med. Sci. 33:e213. doi: 10.3346/jkms.2018.33.e213
Kim, N. Y., Shin, E., Byeon, S. J., Hong, S. J., Kang, S. H., Lee, T., et al. (2023). Serum zonulin is a biomarker for severe asthma. Allergy Asthma Immunol. Res. 15, 526–535. doi: 10.4168/aair.2023.15.4.526
Kimura, I., Miyamoto, J., Ohue-Kitano, R., Watanabe, K., Yamada, T., Onuki, M., et al. (2020). Maternal gut microbiota in pregnancy influences offspring metabolic phenotype in mice. Science 367:eaaw8429. doi: 10.1126/science.aaw8429
Kirstein, A. S., Kehr, S., Nebe, M., Hanschkow, M., Barth, L. A. G., Lorenz, J., et al. (2021). PTEN regulates adipose progenitor cell growth, differentiation, and replicative aging. J. Biol. Chem. 297:100968. doi: 10.1016/j.jbc.2021.100968
Kokko, H. (2008). Sex, size and gender roles: evolutionary studies of sexual size dimorphism. Bioscience 58, 460–461. doi: 10.1641/B580514
Kolahi, K. S., Valent, A. M., and Thornburg, K. L. (2018). Real-time microscopic assessment of fatty acid uptake kinetics in the human term placenta. Placenta 72–73, 1–9. doi: 10.1016/j.placenta.2018.07.014
Korsten, S. G. P. J., Vromans, H., Garssen, J., and Willemsen, L. E. M. (2023). Butyrate protects barrier integrity and suppresses immune activation in a caco-2/PBMC co-culture model while HDAC inhibition mimics butyrate in restoring cytokine-induced barrier disruption. Nutrients 15:2760. doi: 10.3390/nu15122760
Krajmalnik-Brown, R., Lozupone, C., Kang, D. W., and Adams, J. B. (2015). Gut bacteria in children with autism spectrum disorders: challenges and promise of studying how a complex community influences a complex disease. Microb. Ecol. Health Dis. 26:26914.
Krakowiak, P., Walker, C. K., Bremer, A. A., Baker, A. S., Ozonoff, S., Hansen, R. L., et al. (2012). Maternal metabolic conditions and risk for autism and other neurodevelopmental disorders. Pediatrics 129, e1121–e1128. doi: 10.1542/peds.2011-2583
Kristensen, N. B., Bryrup, T., Allin, K. H., Nielsen, T., Hansen, T. H., and Pedersen, O. (2016). Alterations in fecal microbiota composition by probiotic supplementation in healthy adults: a systematic review of randomized controlled trials. Genome Med. 8:52. doi: 10.1186/s13073-016-0300-5
Lammers-Lietz, F., Akyuz, L., Feinkohl, I., Lachmann, C., Pischon, T., Volk, H. D., et al. (2022). Interleukin 8 in postoperative delirium – preliminary findings from two studies. Brain Behav. Immun. Health. 20:100419. doi: 10.1016/j.bbih.2022.100419
Latchney, S. E., Lioy, D. T., Henry, E. C., Gasiewicz, T. A., Strathmann, F. G., Mayer-Pröschel, M., et al. (2011). Neural precursor cell proliferation is disrupted through activation of the aryl hydrocarbon receptor by 2,3,7,8-tetrachlorodibenzo-p-dioxin. Stem Cells Dev. 20, 313–326. doi: 10.1089/scd.2009.0529
Le Poul, E., Loison, C., Struyf, S., Springael, J. Y., Lannoy, V., Decobecq, M. E., et al. (2003). Functional characterization of human receptors for short chain fatty acids and their role in polymorphonuclear cell activation. J. Biol. Chem. 278, 25481–25489. doi: 10.1074/jbc.M301403200
Lee, J. S., Wang, R. X., Alexeev, E. E., and Colgan, S. P. (2021). Intestinal inflammation as a dysbiosis of energy procurement: new insights into an old topic. Gut Microbes 13, 1–20. doi: 10.1080/19490976.2021.1880241
Leser, T., and Baker, A. (2024). Molecular mechanisms of Lacticaseibacillus rhamnosus, LGG® probiotic function. Microorganisms. 12:794. doi: 10.3390/microorganisms12040794
Levenson, J. M., O’Riordan, K. J., Brown, K. D., Trinh, M. A., Molfese, D. L., and Sweatt, J. D. (2004). Regulation of histone acetylation during memory formation in the hippocampus. J. Biol. Chem. 279, 40545–40559. doi: 10.1074/jbc.M402229200
Li, M., Fallin, M. D., Riley, A., Landa, R., Walker, S. O., Silverstein, M., et al. (2016). The association of maternal obesity and diabetes with autism and other developmental disabilities. Pediatrics 137:e20152206. doi: 10.1542/peds.2015-2206
Li, D., Ke, Y., Zhan, R., Liu, C., Zhao, M., Zeng, A., et al. (2018). Trimethylamine-N -oxide promotes brain aging and cognitive impairment in mice. Aging Cell 17:e12768. doi: 10.1111/acel.12768
Li, N., Yang, J., Zhang, J., Liang, C., Wang, Y., Chen, B., et al. (2019). Correlation of gut microbiome between ASD children and mothers and potential biomarkers for risk assessment. Genomics Proteomics Bioinformatics 17, 26–38. doi: 10.1016/j.gpb.2019.01.002
Li, Y., Zheng, C., Liu, Y., He, J., Zhang, Q., Zhang, Y., et al. (2023). Inhibition of wnt activity improves peri-implantation development of somatic cell nuclear transfer embryos. Natl. Sci. Rev. 10:nwad173. doi: 10.1093/nsr/nwad173
Liao, T., Xu, X., Wu, J., Xie, Y., and Yan, J. (2023). Increased expression levels of DLX5 inhibit the development of the nervous system. Int. J. Dev. Neurosci. 83, 728–739. doi: 10.1002/jdn.10300
Liu, Y. (2018). Investigation on the impact of differently treated PM2.5 on cell viability of human bronchial epithelial cells (Beas-2b) and the construction of bio-hazard index. Sci. Discov. 6:212. doi: 10.11648/j.sd.20180603.22
Liu, X. (2022). The interaction of gut microbiota, genetic variation, and diet in autism spectrum disorder. mLife 1, 241–244. doi: 10.1002/mlf2.12024
Liu, S., Li, E., Sun, Z., Fu, D., Duan, G., Jiang, M., et al. (2019). Altered gut microbiota and short chain fatty acids in Chinese children with autism spectrum disorder. Sci. Rep. 9:287. doi: 10.1038/s41598-018-36430-z
Liu, H., Liu, H., Liu, C., Shang, M., Wei, T., and Yin, P. (2022). Gut microbiome and the role of metabolites in the study of graves’ disease. Front. Mol. Biosci. 9:841223.
Lloyd-Price, J., Arze, C., Ananthakrishnan, A. N., Schirmer, M., Avila-Pacheco, J., Poon, T. W., et al. (2019). Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662. doi: 10.1038/s41586-019-1237-9
Louis, S., Tappu, R. M., Damms-Machado, A., Huson, D. H., and Bischoff, S. C. (2016). Characterization of the gut microbial community of obese patients following a weight-loss intervention using whole metagenome shotgun sequencing. PLoS One 11:e0149564. doi: 10.1371/journal.pone.0149564
Lu, J., and Claud, E. C. (2019). Connection between gut microbiome and brain development in preterm infants. Dev. Psychobiol. 61, 739–751. doi: 10.1002/dev.21806
Luoto, R., Laitinen, K., Nermes, M., and Isolauri, E. (2010). Impact of maternal probiotic-supplemented dietary counselling on pregnancy outcome and prenatal and postnatal growth: a double-blind, placebo-controlled study. Br. J. Nutr. 103, 1792–1799. doi: 10.1017/S0007114509993898
MacKay, M., Yang, B. H., Dursun, S. M., and Baker, G. B. (2024). The gut-brain Axis and the microbiome in anxiety disorders, post-traumatic stress disorder and obsessive-compulsive disorder. Curr. Neuropharmacol. 22, 866–883. doi: 10.2174/1570159X21666230222092029
Magnus, P., Birke, C., Vejrup, K., Haugan, A., Alsaker, E., Daltveit, A. K., et al. (2016). Cohort profile update: the Norwegian mother and child cohort study (MoBa). Int. J. Epidemiol. 45, 382–388. doi: 10.1093/ije/dyw029
Mangiola, S., Milton, M., Ranathunga, N., Li-Wai-Suen, C., Odainic, A., Yang, E., et al. (2024). A multi-organ map of the human immune system across age, sex and ethnicity. Biorxiv Prepr. Serv. Biol. :2023.06.08.542671.
Mann, E. R., Lam, Y. K., and Uhlig, H. H. (2024). Short-chain fatty acids: linking diet, the microbiome and immunity. Nat. Rev. Immunol. 24, 577–595. doi: 10.1038/s41577-024-01014-8
Mao, K., Gao, J., Wang, X., Li, X., Geng, S., Zhang, T., et al. (2022). Bifidobacterium animalis subsp. lactis BB-12 has effect against obesity by regulating gut microbiota in two phases in human microbiota-associated rats. Front. Nutr. 8:811619. doi: 10.3389/fnut.2021.811619
Marć, M. A., Jastrząb, R., and Mytych, J. (2022). Does the GUT microbial metabolome really matter? The connection between GUT metabolome and neurological disorders. Nutrients 14:3967. doi: 10.3390/nu14193967
Martinowich, K., Hattori, D., Wu, H., Fouse, S., He, F., Hu, Y., et al. (2003). DNA methylation-related chromatin remodeling in activity-dependent bdnf gene regulation. Science 302, 890–893. doi: 10.1126/science.1090842
Masini, E., Loi, E., Vega-Benedetti, A. F., Carta, M., Doneddu, G., Fadda, R., et al. (2020). An overview of the Main genetic, epigenetic and environmental factors involved in autism spectrum disorder focusing on synaptic activity. Int. J. Mol. Sci. 21:8290. doi: 10.3390/ijms21218290
Matsuyama, M., Murtaza, N., Morrison, M., Davies, P. S. W., Hill, R. J., and Loughman, A. (2022). Cross-sectional and longitudinal associations between gut microbiota composition and cognition in the second year of life: findings from the child health and resident microbes study. Am. J. Med. Genet. B Neuropsychiatr. Genet. 189, 285–292. doi: 10.1002/ajmg.b.32915
McClung, C. A., and Nestler, E. J. (2008). Neuroplasticity mediated by altered gene expression. Neuropsychopharmacology 33, 3–17. doi: 10.1038/sj.npp.1301544
McElhanon, B. O., McCracken, C., Karpen, S., and Sharp, W. G. (2014). Gastrointestinal symptoms in autism spectrum disorder: a meta-analysis. Pediatrics 133, 872–883. doi: 10.1542/peds.2013-3995
McFarland, L. V., Evans, C. T., and Goldstein, E. J. C. (2018). Strain-specificity and disease-specificity of probiotic efficacy: a systematic review and meta-analysis. Front. Med. 5:124. doi: 10.3389/fmed.2018.00124
Miri, S., Yeo, J., Abubaker, S., and Hammami, R. (2023). Neuromicrobiology, an emerging neurometabolic facet of the gut microbiome? Front. Microbiol. 14:1098412. doi: 10.3389/fmicb.2023.1098412
Möhle, L., Mattei, D., Heimesaat, M. M., Bereswill, S., Fischer, A., Alutis, M., et al. (2016). Ly6C(hi) monocytes provide a link between antibiotic-induced changes in gut microbiota and adult hippocampal neurogenesis. Cell Rep. 15, 1945–1956. doi: 10.1016/j.celrep.2016.04.074
Moreau, G. B., Ramakrishnan, G., Cook, H. L., Fox, T. E., Nayak, U., Ma, J. Z., et al. (2019). Childhood growth and neurocognition are associated with distinct sets of metabolites. EBioMedicine 44, 597–606. doi: 10.1016/j.ebiom.2019.05.043
Morris, G., and Maes, M. (2014). Oxidative and nitrosative stress and immune-inflammatory pathways in patients with myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS). Curr. Neuropharmacol. 12, 168–185. doi: 10.2174/1570159X11666131120224653
Mueller, J. W., Idkowiak, J., Gesteira, T. F., Vallet, C., Hardman, R., Van Den Boom, J., et al. (2018). Human DHEA sulfation requires direct interaction between PAPS synthase 2 and DHEA sulfotransferase SULT2A1. J. Biol. Chem. 293, 9724–9735. doi: 10.1074/jbc.RA118.002248
Munn, D. H., and Mellor, A. L. (2016). IDO in the tumor microenvironment: inflammation, counter-regulation, and tolerance. Trends Immunol. 37, 193–207. doi: 10.1016/j.it.2016.01.002
Nalbant, K., Erden, S., Yazar, A., and Kılınç, İ. (2022). Investigation of the relation between epithelial barrier function and autism symptom severity in children with autism spectrum disorder. J. Mol. Neurosci. 72, 741–747. doi: 10.1007/s12031-021-01954-z
Nankova, B. B., Agarwal, R., MacFabe, D. F., and La Gamma, E. F. (2014). Enteric bacterial metabolites propionic and butyric acid modulate gene expression, including CREB-dependent catecholaminergic neurotransmission, in PC12 cells--possible relevance to autism spectrum disorders. PLoS One 9:e103740. doi: 10.1371/journal.pone.0103740
Needham, B. D., Adame, M. D., Serena, G., Rose, D. R., Preston, G. M., Conrad, M. C., et al. (2021). Plasma and fecal metabolite profiles in autism spectrum disorder. Biol. Psychiatry 89, 451–462. doi: 10.1016/j.biopsych.2020.09.025
Nishimura, K., Yamada, R., Hagihara, S., Iwasaki, R., Uchida, N., Kamura, T., et al. (2020). A super-sensitive auxin-inducible degron system with an engineered auxin-TIR1 pair. Nucleic Acids Res. 48:e108. doi: 10.1093/nar/gkaa748
O’Mahony, S. M., Clarke, G., Borre, Y. E., Dinan, T. G., and Cryan, J. F. (2015). Serotonin, tryptophan metabolism and the brain-gut-microbiome axis. Behav. Brain Res. 277, 32–48. doi: 10.1016/j.bbr.2014.07.027
Ohlsson, B., Orho-Melander, M., and Nilsson, P. M. (2017). Higher levels of serum zonulin may rather be associated with increased risk of obesity and hyperlipidemia, than with gastrointestinal symptoms or disease manifestations. Int. J. Mol. Sci. 18:582. doi: 10.3390/ijms18030582
Onyszkiewicz, M., Gawrys-Kopczynska, M., Konopelski, P., Aleksandrowicz, M., Sawicka, A., Koźniewska, E., et al. (2019). Butyric acid, a gut bacteria metabolite, lowers arterial blood pressure via colon-vagus nerve signaling and GPR41/43 receptors. Pflugers Arch. 471, 1441–1453. doi: 10.1007/s00424-019-02322-y
Osredkar, J., Baškovič, B. Ž., Finderle, P., Bobrowska-Korczak, B., Gątarek, P., Rosiak, A., et al. (2023). Relationship between excreted uremic toxins and degree of disorder of children with ASD. Int. J. Mol. Sci. 24:7078. doi: 10.3390/ijms24087078
Padilha, M., Keller, V. N., Normando, P., Schincaglia, R. M., Freitas-Costa, N. C., Freire, S. S. R., et al. (2025). Serum metabolome indicators of early childhood development in the brazilian national survey on child nutrition (ENANI-2019). eLife 14:e97982. doi: 10.7554/eLife.97982
Park, J. C., and Im, S. H. (2022). The gut-immune-brain axis in neurodevelopment and neurological disorders. Microbiome Res Rep. 1:23. doi: 10.20517/mrr.2022.11
Parodi, B., and Kerlero de Rosbo, N. (2021). The gut-brain axis in multiple sclerosis. Is its dysfunction a pathological trigger or a consequence of the disease? Front. Immunol. 12:718220. doi: 10.3389/fimmu.2021.718220
Paternain, L., and Campion, J. (2013). Metabolomics and transcriptomics of metabolic disorders. Curr. Nutr. Rep. 2, 199–206. doi: 10.1007/s13668-013-0062-2
Pérez-Morales, M., Bello-Medina, P. C., González-Franco, D. A., Díaz-Cintra, S., García-Mena, J., Pacheco-López, G., et al. (2024). Steering the microbiota-gut-brain axis by antibiotics to model neuro-immune-endocrine disorders. Neuroimmunomodulation 31, 89–101. doi: 10.1159/000538927
Pompei, A., Cordisco, L., Amaretti, A., Zanoni, S., Matteuzzi, D., and Rossi, M. (2007). Folate production by bifidobacteria as a potential probiotic property. Appl. Environ. Microbiol. 73, 179–185. doi: 10.1128/AEM.01763-06
Prosperi, M., Santocchi, E., Guiducci, L., Frinzi, J., Morales, M. A., Tancredi, R., et al. (2022). Interventions on microbiota: where do we stand on a gut-brain link in autism? A systematic review. Nutrients 14:462. doi: 10.3390/nu14030462
Puccetti, M., Pariano, M., Costantini, C., Giovagnoli, S., and Ricci, M. (2022). Pharmaceutically active microbial AhR agonists as innovative biodrugs in inflammation. Pharmaceuticals. 15:336. doi: 10.3390/ph15030336
Qian, X., Song, H., and Ming, G. L. (2019). Brain organoids: advances, applications and challenges. Dev. Camb. Engl. 146:dev166074.
Quadrato, G., Nguyen, T., Macosko, E. Z., Sherwood, J. L., Min Yang, S., Berger, D. R., et al. (2017). Cell diversity and network dynamics in photosensitive human brain organoids. Nature 545, 48–53. doi: 10.1038/nature22047
Quan, L., Yi, J., Zhao, Y., Zhang, F., Shi, X. T., Feng, Z., et al. (2020). Plasma trimethylamine N-oxide, a gut microbe–generated phosphatidylcholine metabolite, is associated with autism spectrum disorders. Neurotoxicology 76, 93–98. doi: 10.1016/j.neuro.2019.10.012
Raskov, H., Burcharth, J., Pommergaard, H. C., and Rosenberg, J. (2016). Irritable bowel syndrome, the microbiota and the gut-brain axis. Gut Microbes 7, 365–383. doi: 10.1080/19490976.2016.1218585
Rees, L. E., Wood, N. A., Gillespie, K. M., Lai, K. N., Gaston, K., and Mathieson, P. W. (2002). The interleukin-10-1082 G/a polymorphism: allele frequency in different populations and functional significance. Cell. Mol. Life Sci. 59, 560–569. doi: 10.1007/s00018-002-8448-0
Reid, G. (2016). Probiotics: definition, scope and mechanisms of action. Best Pract. Res. Clin. Gastroenterol. 30, 17–25. doi: 10.1016/j.bpg.2015.12.001
Retuerto, M., Al-Shakhshir, H., Herrada, J., McCormick, T. S., and Ghannoum, M. A. (2024). Analysis of gut bacterial and fungal microbiota in children with autism spectrum disorder and their non-autistic siblings. Nutrients 16:3004. doi: 10.3390/nu16173004
Romo-Araiza, A., Picazo-Aguilar, R. I., Griego, E., Márquez, L. A., Galván, E. J., Cruz, Y., et al. (2023). Symbiotic supplementation (E. Faecium and agave inulin) improves spatial memory and increases plasticity in the hippocampus of obese rats: a proof-of-concept study. Cell Transplant. 32:9636897231177357. doi: 10.1177/09636897231177357
Ronald, A., and Hoekstra, R. A. (2011). Autism spectrum disorders and autistic traits: a decade of new twin studies. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 156B, 255–274. doi: 10.1002/ajmg.b.31159
Ruiz-Rodríguez, J. C., Plata-Menchaca, E. P., Chiscano-Camón, L., Ruiz-Sanmartin, A., and Ferrer, R. (2022). Blood purification in sepsis and COVID-19: what’s new in cytokine and endotoxin hemoadsorption. J. Anesth. Analg. Crit. Care 2:15. doi: 10.1186/s44158-022-00043-w
Rylaarsdam, L., and Guemez-Gamboa, A. (2019). Genetic causes and modifiers of autism spectrum disorder. Front. Cell. Neurosci. 13:385. doi: 10.3389/fncel.2019.00385
Sanctuary, M. R., Kain, J. N., Angkustsiri, K., and German, J. B. (2018). Dietary considerations in autism spectrum disorders: the potential role of protein digestion and microbial putrefaction in the gut-brain axis. Front. Nutr. 5:40. doi: 10.3389/fnut.2018.00040
Sandin, S., Lichtenstein, P., Kuja-Halkola, R., Larsson, H., Hultman, C. M., and Reichenberg, A. (2014). The familial risk of autism. JAMA 311, 1770–1777. doi: 10.1001/jama.2014.4144
Savino, R., Carotenuto, M., Polito, A. N., Di Noia, S., Albenzio, M., Scarinci, A., et al. (2020). Analyzing the potential biological determinants of autism spectrum disorder: from neuroinflammation to the kynurenine pathway. Brain Sci. 10:631. doi: 10.3390/brainsci10090631
Schirmbeck, G. H., Sizonenko, S., and Sanches, E. F. (2022). Neuroprotective role of Lactoferrin during early brain development and injury through lifespan. Nutrients 14:2923. doi: 10.3390/nu14142923
Sen, M. K., Bhattacharya, S., Bharati, R., Hamouzová, K., and Soukup, J. (2023). Comprehensive insights into herbicide resistance mechanisms in weeds: a synergistic integration of transcriptomic and metabolomic analyses. Front. Plant Sci. 14:1280118. doi: 10.3389/fpls.2023.1280118
Senaldi, L., and Smith-Raska, M. (2020). Evidence for germline non-genetic inheritance of human phenotypes and diseases. Clin. Epigenetics 12:136. doi: 10.1186/s13148-020-00929-y
Sgritta, M., Dooling, S. W., Buffington, S. A., Momin, E. N., Francis, M. B., Britton, R. A., et al. (2019). Mechanisms underlying microbial-mediated changes in social behavior in mouse models of autism spectrum disorder. Neuron 101, 246–259.e6. doi: 10.1016/j.neuron.2018.11.018
Sharon, G., Cruz, N. J., Kang, D. W., Gandal, M. J., Wang, B., Kim, Y. M., et al. (2019). Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell 177, 1600–1618.e17. doi: 10.1016/j.cell.2019.05.004
Sikora, C. M. (2015). A historical analysis of the pioneers and advocates of autism spectrum disorder (1980-2013): examining the evolution of the diagnosis and the influences that have shaped what the diagnosis is today [dissertations]. Chicago (lL): Loyola University Chicago.
Silva, Y. P., Bernardi, A., and Frozza, R. L. (2020). The role of short-chain fatty acids from gut microbiota in gut-brain communication. Front. Endocrinol. 11:25. doi: 10.3389/fendo.2020.00025
Slykerman, R. F., Hood, F., Wickens, K., Thompson, J. M. D., Barthow, C., Murphy, R., et al. (2017). Effect of lactobacillus rhamnosus HN001 in pregnancy on postpartum symptoms of depression and anxiety: a randomised double-blind placebo-controlled trial. EBioMedicine 24, 159–165. doi: 10.1016/j.ebiom.2017.09.013
Song, M., Garrett, W. S., and Chan, A. T. (2015). Nutrients, foods, and colorectal cancer prevention. Gastroenterology 148, 1244–1260.e16. doi: 10.1053/j.gastro.2014.12.035
Sordillo, J. E., Korrick, S., Laranjo, N., Carey, V., Weinstock, G. M., Gold, D. R., et al. (2019). Association of the infant gut microbiome with early childhood neurodevelopmental outcomes: an ancillary study to the VDAART randomized clinical trial. JAMA Netw. Open 2:e190905. doi: 10.1001/jamanetworkopen.2019.0905
Soubry, A., Hoyo, C., Jirtle, R. L., and Murphy, S. K. (2014). A paternal environmental legacy: evidence for epigenetic inheritance through the male germ line. BioEssays 36, 359–371. doi: 10.1002/bies.201300113
Spencer, N. J., Kyloh, M. A., Travis, L., and Hibberd, T. J. (2024). Mechanisms underlying the gut–brain communication: how enterochromaffin (EC) cells activate vagal afferent nerve endings in the small intestine. J. Comp. Neurol. 532:e25613. doi: 10.1002/cne.25613
Steegers-Theunissen, R. P., Obermann-Borst, S. A., Kremer, D., Lindemans, J., Siebel, C., Steegers, E. A., et al. (2009). Periconceptional maternal folic acid use of 400 microg per day is related to increased methylation of the IGF2 gene in the very young child. PLoS One 4:e7845. doi: 10.1371/journal.pone.0007845
Stilling, R. M., van de Wouw, M., Clarke, G., Stanton, C., Dinan, T. G., and Cryan, J. F. (2016). The neuropharmacology of butyrate: the bread and butter of the microbiota-gut-brain axis? Neurochem. Int. 99, 110–132. doi: 10.1016/j.neuint.2016.06.011
Struhl, K. (1998). Histone acetylation and transcriptional regulatory mechanisms. Genes Dev. 12, 599–606. doi: 10.1101/gad.12.5.599
Sudo, N., Chida, Y., Aiba, Y., Sonoda, J., Oyama, N., Yu, X. N., et al. (2004). Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice. J. Physiol. 558, 263–275. doi: 10.1113/jphysiol.2004.063388
Sugahara, H., Odamaki, T., Hashikura, N., Abe, F., and Xiao, J. Z. (2015). Differences in folate production by bifidobacteria of different origins. Biosci. Microbiota Food Health 34, 87–93. doi: 10.12938/bmfh.2015-003
Suh, Y., Noh, S. J., Lee, S., Suh, B. K., Lee, S. B., Choi, J., et al. (2019). Dopamine D1 receptor (D1R) expression is controlled by a transcriptional repressor complex containing DISC1. Mol. Neurobiol. 56, 6725–6735. doi: 10.1007/s12035-019-1566-6
Sun, J., Chen, S., Zang, D., Sun, H., Sun, Y., and Chen, J. (2024). Butyrate as a promising therapeutic target in cancer: from pathogenesis to clinic (review). Int. J. Oncol. 64:44. doi: 10.3892/ijo.2024.5632
Sun, S., Wang, H., Tsilimigras, M. C., Howard, A. G., Sha, W., Zhang, J., et al. (2020). Does geographical variation confound the relationship between host factors and the human gut microbiota: a population-based study in China. BMJ Open 10:e038163. doi: 10.1136/bmjopen-2020-038163
Takyi, E., Nirmalkar, K., Adams, J., and Krajmalnik-Brown, R. (2025). Interventions targeting the gut microbiota and their possible effect on gastrointestinal and neurobehavioral symptoms in autism spectrum disorder. Gut Microbes 17:2499580.
Tang, R., and Li, L. (2021). Modulation of short-chain fatty acids as potential therapy method for type 2 diabetes mellitus. Can. J. Infect. Dis. Med. Microbiol. 2021, 1–13. doi: 10.1080/19490976.2025.2499580
Tartaglione, A. M., Villani, A., Ajmone-Cat, M. A., Minghetti, L., Ricceri, L., Pazienza, V., et al. (2022). Maternal immune activation induces autism-like changes in behavior, neuroinflammatory profile and gut microbiota in mouse offspring of both sexes. Transl. Psychiatry 12:384. doi: 10.1038/s41398-022-02149-9
Thion, M. S., Ginhoux, F., and Garel, S. (2018a). Microglia and early brain development: an intimate journey. Science 362, 185–189. doi: 10.1126/science.aat0474
Thion, M. S., Low, D., Silvin, A., Chen, J., Grisel, P., Schulte-Schrepping, J., et al. (2018b). Microbiome influences prenatal and adult microglia in a sex-specific manner. Cell 172, 500–516.e16. doi: 10.1016/j.cell.2017.11.042
Tian, M., Li, Q., Zheng, T., Yang, S., Chen, F., Guan, W., et al. (2023). Maternal microbe-specific modulation of the offspring microbiome and development during pregnancy and lactation. Gut Microbes 15:2206505. doi: 10.1080/19490976.2023.2206505
Tie, Y., Huang, Y., Chen, R., Li, L., Chen, M., and Zhang, S. (2023). Current insights on the roles of gut microbiota in inflammatory bowel disease-associated extra-intestinal manifestations: pathophysiology and therapeutic targets. Gut Microbes 15:2265028. doi: 10.1080/19490976.2023.2265028
Tucureanu, M. M., Rebleanu, D., Constantinescu, C. A., Deleanu, M., Voicu, G., Butoi, E., et al. (2018). Lipopolysaccharide-induced inflammation in monocytes/macrophages is blocked by liposomal delivery of Gi-protein inhibitor. Int. J. Nanomedicine 13, 63–76. doi: 10.2147/IJN.S150918
Urbonaite, G., Knyzeliene, A., Bunn, F. S., Smalskys, A., and Neniskyte, U. (2022). The impact of maternal high-fat diet on offspring neurodevelopment. Front. Neurosci. 16:909762. doi: 10.3389/fnins.2022.909762
Van Dorp, C. H., Van Boven, M., and De Boer, R. J. (2014). Immuno-epidemiological modeling of HIV-1 predicts high heritability of the set-point virus load, while selection for CTL escape dominates virulence evolution. PLoS Comput. Biol. 10:e1003899. doi: 10.1371/journal.pcbi.1003899
Večeřa, J., Bártová, E., Krejčí, J., Legartová, S., Komůrková, D., Rudá-Kučerová, J., et al. (2018). HDAC1 and HDAC3 underlie dynamic H3K9 acetylation during embryonic neurogenesis and in schizophrenia-like animals. J. Cell. Physiol. 233, 530–548. doi: 10.1002/jcp.25914
Voineagu, I., Wang, X., Johnston, P., Lowe, J. K., Tian, Y., Horvath, S., et al. (2011). Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384. doi: 10.1038/nature10110
Vuong, H. E., and Hsiao, E. Y. (2017). Emerging roles for the gut microbiome in autism spectrum disorder. Biol. Psychiatry 81, 411–423. doi: 10.1016/j.biopsych.2016.08.024
Vuong, H. E., Pronovost, G. N., Williams, D. W., Coley, E. J. L., Siegler, E. L., Qiu, A., et al. (2020). The maternal microbiome modulates fetal neurodevelopment in mice. Nature 586, 281–286. doi: 10.1038/s41586-020-2745-3
Walker, R. W., Clemente, J. C., Peter, I., and Loos, R. J. F. (2017). The prenatal gut microbiome: are we colonized with bacteria in utero? Pediatr Obes 12 Suppl 1, 3–17.
Wang, Y. D., Chen, W. D., Yu, D., Forman, B. M., and Huang, W. (2011). The G-protein-coupled bile acid receptor, Gpbar1 (TGR5), negatively regulates hepatic inflammatory response through antagonizing nuclear factor κ light-chain enhancer of activated B cells (NF-κB) in mice. Hepatology 54, 1421–1432. doi: 10.1002/hep.24525
Wang, G., Fan, Y., Zhang, G., Cai, S., Ma, Y., Yang, L., et al. (2024). Microbiota-derived indoles alleviate intestinal inflammation and modulate microbiome by microbial cross-feeding. Microbiome. 12:59. doi: 10.1186/s40168-024-01750-y
Wang, L. S., Kuo, C. T., Cho, S. J., Seguin, C., Siddiqui, J., Stoner, K., et al. (2013). Black raspberry-derived anthocyanins demethylate tumor suppressor genes through the inhibition of DNMT1 and DNMT3B in colon cancer cells. Nutr. Cancer 65, 118–125. doi: 10.1080/01635581.2013.741759
Wang, H., Lee, I. S., Braun, C., and Enck, P. (2016). Effect of probiotics on central nervous system functions in animals and humans: a systematic review. J. Neurogastroenterol. Motil. 22, 589–605. doi: 10.5056/jnm16018
Wang, X., Xu, Q., Bey, A. L., Lee, Y., and Jiang, Y. H. (2014). Transcriptional and functional complexity of Shank3 provides a molecular framework to understand the phenotypic heterogeneity of SHANK3 causing autism and Shank3 mutant mice. Mol. Autism. 5:30. doi: 10.1186/2040-2392-5-30
Wang, Y., Xu, H., Zhou, X., Chen, W., and Zhou, H. (2024). Dysregulated bile acid homeostasis: unveiling its role in metabolic diseases. Med. Rev. 4, 262–283.
Wang, Q., Yang, Q., and Liu, X. (2023). The microbiota–gut–brain axis and neurodevelopmental disorders. Protein Cell 14, 762–775. doi: 10.1093/procel/pwad026
Wang, X. Q., Zhang, A. H., Miao, J. H., Sun, H., Yan, G. L., Wu, F. F., et al. (2018). Gut microbiota as important modulator of metabolism in health and disease. RSC Adv. 8, 42380–42389. doi: 10.1039/c8ra08094a
Wang, W., Zhou, J., Chen, M., Huang, X., Xie, X., Li, W., et al. (2018). Exposure to concentrated ambient PM2.5 alters the composition of gut microbiota in a murine model. Part. Fibre Toxicol. 1:17. doi: 10.1111/ijpo.12217
West, K. A., Yin, X., Rutherford, E. M., Wee, B., Choi, J., Chrisman, B. S., et al. (2022). Multi-angle meta-analysis of the gut microbiome in autism spectrum disorder: a step toward understanding patient subgroups. Sci. Rep. 12:17034. doi: 10.1038/s41598-022-21327-9
Wild, C. P. (2012). The exposome: from concept to utility. Int. J. Epidemiol. 41, 24–32. doi: 10.1093/ije/dyr236
Winglee, K., Howard, A. G., Sha, W., Gharaibeh, R. Z., Liu, J., Jin, D., et al. (2017). Recent urbanization in China is correlated with a westernized microbiome encoding increased virulence and antibiotic resistance genes. Microbiome 5:121. doi: 10.1186/s40168-017-0338-7
Wrønding, T., Vomstein, K., Bosma, E. F., Mortensen, B., Westh, H., Heintz, J. E., et al. (2023). Antibiotic-free vaginal microbiota transplant with donor engraftment, dysbiosis resolution and live birth after recurrent pregnancy loss: a proof of concept case study. Eclinicalmedicine. 61:102070. doi: 10.1016/j.eclinm.2023.102070
Wu, W. L., Hsiao, E. Y., Yan, Z., Mazmanian, S. K., and Patterson, P. H. (2017). The placental interleukin-6 signaling controls fetal brain development and behavior. Brain Behav. Immun. 62, 11–23. doi: 10.1016/j.bbi.2016.11.007
Wu, H., Van Der Pol, W. J., Dubois, L. G., and Morrow, C. D.Tollefsbol, TO (2023). Dietary supplementation of inulin contributes to the prevention of estrogen receptor-negative mammary cancer by alteration of gut microbial communities and epigenetic regulations. Int. J. Mol. Sci. 24:9015. doi: 10.3390/ijms24109015
Xu, Y., Wang, Y., Xu, J., Song, Y., Liu, B., and Xiong, Z. (2022). Leveraging existing 16SrRNA microbial data to define a composite biomarker for autism spectrum disorder. Microbiol. Spectr. 10, e00331–e00322.
Yan, M. X., Ren, H. B., Kou, Y., Meng, M., and Li, Y. Q. (2012). Involvement of nuclear factor kappa B in high-fat diet-related pancreatic fibrosis in rats. Gut Liver. 6, 381–387. doi: 10.5009/gnl.2012.6.3.381
Yano, J. M., Yu, K., Donaldson, G. P., Shastri, G. G., Ann, P., Ma, L., et al. (2015). Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276. doi: 10.1016/j.cell.2015.02.047
Yanowitz, T. D., Jordan, J. A., Gilmour, C. H., Towbin, R., Bowen, A., Roberts, J. M., et al. (2002). Hemodynamic disturbances in premature infants born after chorioamnionitis: association with cord blood cytokine concentrations. Pediatr. Res. 51, 310–316. doi: 10.1203/00006450-200203000-00008
Yap, C. X., Henders, A. K., Alvares, G. A., Wood, D. L. A., Krause, L., Tyson, G. W., et al. (2021). Autism-related dietary preferences mediate autism-gut microbiome associations. Cell 184, 5916–5931.e17. doi: 10.1016/j.cell.2021.10.015
Younge, N. E., Newgard, C. B., Cotten, C. M., Goldberg, R. N., Muehlbauer, M. J., Bain, J. R., et al. (2019). Disrupted maturation of the microbiota and metabolome among extremely preterm infants with postnatal growth failure. Sci. Rep. 9:8167. doi: 10.1038/s41598-019-44547-y
Zha, Y., Liu, H., Lin, X., Yu, L., Gao, P., Li, Y., et al. (2022). Immune deviation in the decidua during term and preterm labor. Front. Immunol. 13:877314. doi: 10.3389/fimmu.2022.877314
Zhang, Y., Guo, M., Zhang, H., Wang, Y., Li, R., Liu, Z., et al. (2022). Lactiplantibacillus plantarum ST-III-fermented milk improves autistic-like behaviors in valproic acid-induced autism spectrum disorder mice by altering gut microbiota. Front. Nutr. 9:1005308. doi: 10.3389/fnut.2022.1005308
Zhang, Z., Xue, C., Ju, M., Guo, J., Wang, M., Yi, S., et al. (2021). Maternal gut dysbiosis alters offspring microbiota and social interactions. Microorganisms 9:1742. doi: 10.3390/microorganisms9081742
Zhang, J., Zhao, J., Jiang, W. j., Shan, X. w., Yang, X. m., and Gao, J. g. (2012). Conditional gene manipulation: Creating a new biological era. J Zhejiang Univ Sci B 13, 511–524. doi: 10.1631/jzus.B1200042
Zhao, C., Hu, X., Bao, L., Wu, K., Feng, L., Qiu, M., et al. (2021). Aryl hydrocarbon receptor activation by lactobacillus reuteri tryptophan metabolism alleviates escherichia coli-induced mastitis in mice. PLoS Pathog. 17:e1009774. doi: 10.1371/journal.ppat.1009774
Zheng, Y., Verhoeff, T. A., Perez Pardo, P., Garssen, J., and Kraneveld, A. D. (2020). The gut-brain axis in autism spectrum disorder: a focus on the metalloproteases ADAM10 and ADAM17. Int. J. Mol. Sci. 22:118. doi: 10.3390/ijms22010118
Zheng, X., Xia, C., Liu, M., Wu, H., Yan, J., Zhang, Z., et al. (2024). Role of folic acid in regulating gut microbiota and short-chain fatty acids based on an in vitro fermentation model. Appl. Microbiol. Biotechnol. 108:40. doi: 10.1007/s00253-023-12825-5
Zhu, S., Jiang, Y., Xu, K., Cui, M., Ye, W., Zhao, G., et al. (2020). The progress of gut microbiome research related to brain disorders. J. Neuroinflammation 17:25. doi: 10.1186/s12974-020-1705-z
Zwaigenbaum, L., Bauman, M. L., Choueiri, R., Kasari, C., Carter, A., Granpeesheh, D., et al. (2015). Early intervention for children with autism spectrum disorder under 3 years of age: recommendations for practice and research. Pediatrics 136 Suppl 1, S60–S81. doi: 10.1542/peds.2014-3667E
Keywords: maternal gut microbiota, autism spectrum disorder, gut-brain axis, microbial metabolites, perinatal dysbiosis, fetal neurodevelopment
Citation: Zhang D, Zhou M, Qiu Y, Xu H, Liu H, Liu Y and Xie L (2025) Cross-generational mechanisms of maternal gut microbiota in modulating offspring autism spectrum disorder risk: from the gut-brain axis to translational challenges in precision interventions. Front. Aging Neurosci. 17:1642240. doi: 10.3389/fnagi.2025.1642240
Edited by:
R. M. Damian Holsinger, The University of Sydney, AustraliaReviewed by:
Jose Manuel Salum Tome, Universidad Autonoma de Chile, ChileJose Velázquez-Aragón, Instituto Nacional de Pediatría, Mexico
Copyright © 2025 Zhang, Zhou, Qiu, Xu, Liu, Liu and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hanmin Liu, bGl1aG1Ac2N1LmVkdS5jbg==; Yang Liu, bGl1eWFuZ2x5QHNjdS5lZHUuY24=; Liang Xie, eGVyb254aWVAMTYzLmNvbQ==
†These authors have contributed equally to this work