Abstract
Background/Aim: Altered glial function, and increased proinflammatory cytokines are associated with behavioral and cognitive problems seen in autism spectrum disorder (ASD). In this study, we aimed to investigate the neuroinflammatory process in ASD and the relationship between neuroinflammatory markers and the severity of autism symptoms.
Materials and Methods: We evaluated the gene expression levels of NLRP-3 and RANK-RANKL-OPG inflammasome pathways in 50 children with ASD (AC), 34 typically developing siblings (HSAC), and 16 healthy controls (HC) and the correlation between gene expression levels and severity of neuro-psychiatric dysfunctions in ASD. All children were aged 3-18 years. The severity of autism core symptoms and neurologic dysfunction was determined by the Childhood Autism Rating Scale (CARS), Turkish Communication Development Inventory (TCDI), Dunn’s Sensory Profile, Adolescents/Adults Sensory Profile and Clinical Observation of Neuromotor Performance, and the scores were correlated with gene expression levels.
Results: Up-regulation was observed in all genes (IL-1β, Casp1, NLRP3, NLRP1, TNFRSF11B, TNFRSF11A, and TNFSF11) in the ASD group, but only the difference between the AC and HC groups was statistically significant for TNFRSF11B, TNFRSF11A, and TNFSF11. There were significant correlations in the linguistic and cognitive skills, sensory profile, and neuromotor performance domains for genes associated with both inflammatory pathways.
Conclusion: This is the first study showing that the RANK-RANKL-OPG pathway is active in ASD cases. Our results emphasize the harmful influence of the RANK-RANKL-OPG and NLRP3 inflammasome complexes on neurologic development.
Introduction
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication, limited, stereotyped and repetitive patterns of behavior, interests and activities, and a delay or abnormal functioning in at least one of the language or symbolic/imaginative play skills used in social interaction and communication (1). ASD can include both psychiatric and nonpsychiatric findings such as increased proinflammatory cytokines (2). Cytokines are produced in neurons, astrocytes, and microglia, and abnormal levels have been associated with various neurodevelopmental diseases. In recent years, it has been suggested that increased proinflammatory cytokines may be the source of behavioral and cognitive problems seen in ASD. Many studies have shown that members of the cytokine family play an important role in early brain development and synaptic plasticity that develops as a result of damage (3-5).
Inflammation is a protective response aimed at eliminating dangerous stimuli such as microbial infection or tissue damage. Inflammation is regulated by inflammasomes. Inflammasomes are multimeric protein complexes and enzymatic systems that convert cytokines into active forms as a result of caspase activation in the cytoplasm and play a crucial role in the innate immune system. The nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) family plays a significant role in innate immune system-related inflammatory response as an inflammasome (6). The NLR protein 3 (NLRP3) subfamily is the most extensively researched member of the NLR family (7). NLRP3 is a cytosolic receptor protein that recognizes danger signals reaching immune system cells such as macrophages, dendritic cells, and microglia. NLRP3-mediated inflammasome activation and conversion of pro-caspase-1 to caspase-1 (Casp1) are required for the conversion of pro-interleukin-1β (pro-IL-1β) and pro-IL-18 to their active derivatives, IL-1β and IL-18 (8). The inflammatory response resulting from this pathway results in the destruction of invading pathogens. Studies have shown that NLRP3 is an important mediator in various autoimmune diseases and is activated in ASD (2, 9, 10).
The receptor activator of nuclear factor kappa B (RANK)-receptor activator of nuclear factor kappa b ligand (RANKL)-osteoprotegerin (OPG) is a complex signaling pathway that plays a role in bone metabolism, cancer, mammary epithelial cells, and the immune system (11, 12). RANK (TNFRSF11A) is a receptor mainly expressed on osteoclasts, dendritic cells, and T cells. RANKL (TNFSF11) is a member of the tumor necrosis factor-α family and binds and activates the RANK. RANKL stimulation promotes osteoclast differentiation, contributing to bone resorption, and increased dendritic cell-stimulated naive T cell proliferation and survival (13). OPG (TNFRSF11B) is another member of the tumor necrosis factor receptor (TNFR) superfamily and inhibits the activation of RANK signaling by interacting with RANKL. It is a potent inhibitor of bone resorption and reduces osteoclastogenesis (14). Many studies have shown the relationship between OPG and RANKL and the immune system. Binding of RANKL to RANK increases the survival of dendritic cells, their capacity to stimulate the immune system, and inhibition of apoptosis (15). Publications in the literature have reported that NLRP3 inflammasome pathways activate RANK-RANKL-OPG pathways and that these two inflammatory pathways work together. In the study by Yamaguchi et al. (16), OPG was not activated in NLRP-3 deficient mice, and accordingly, it was revealed that inflammasome pathways activate OPG.
In this study, we aimed to investigate the neuroinflammatory processes in ASD and the relationship between neuroinflammatory markers and the severity of autistic symptoms. For this purpose, we examined the gene expression levels related to the NLRP-3 and RANK-RANKL-OPG pathways in children with ASD to investigate correlation between the expression levels of these genes and the severity of autistic core findings and neuro-psychiatric dysfunction using a prospective study design. The results of this study may provide new clues about neuroinflammatory mechanisms in ASD and guide treatment strategies targeting neuroinflammation.
Materials and Methods
Patients and data collection. Data collection was completed between 2021 and 2023. Gene expression studies and statistical analyses were performed in 2024. The study and control groups each consisted of 50 cases. We did not perform a power analysis.
The study group included 50 children aged ≥3-18 years who attended Kocaeli University Medical Faculty Hospital Child and Adolescent Psychiatry Clinic and were diagnosed with ASD (the AC group) based on the DSM-V clinical diagnostic criteria and the Childhood Autism Rating Scale (CARS).
Healthy siblings of children in the AC group (the HSAC group), also aged ≥3-18 years, were included as the first control group. When there was more than one sibling who met these criteria, only the sibling closest in age to the sibling with ASD was included in the control group. There were 34 children with these characteristics, two of them were twins of a sibling in the AC group. When children diagnosed with ASD did not have a sibling aged ≥3-18 years, an age- and sex-matched healthy control was included (n=16, the HC group). The children in this group were selected from those who attended the pediatric neurology clinic with non-specific complaints that were not associated with a neurological disease and who agreed to participate in the study.
The inclusion criteria for the study were that the diagnosis of ASD was supported by a child psychiatrist in the study group (AC) using DSM-V diagnostic criteria and at least one of the psychometric tests used in the diagnosis of ASD; that the diagnosis of ASD was clinically excluded in the control groups; and that the children and/or their parents approved that a blood sample would be separated during routine blood collection to be used in this study.
The exclusion criteria were as follows: comorbid genetic, metabolic, and chronic inflammatory disease diagnosis other than ASD in the study group; known acute or chronic inflammatory diseases (rheumatic, infectious, etc.) in the study and control groups, and acute infection, trauma, fever, etc. at the time of blood sampling.
Collection and storage of blood samples for gene expression analysis. Peripheral blood samples were collected from 50 patients in the AC group, 34 in the HSAC group, and 16 in the HC group at Kocaeli University Hospital’s Department of Paediatric Neurology between 2021 and 2023. These samples were taken to Kocaeli University Medical Genetics Laboratory within 4 h to obtain RNA. The isolated RNA samples were stored at −80°C. After all samples in the AC, HSAC, and HC groups were collected, gene expression analyses were performed in 2024.
Evaluation of the severity of autism core findings and neurologic dysfunction. The CARS. The CARS was developed to diagnose autism and to distinguish children with autism from children with other developmental disorders. The scale was developed by Schopler et al. (17) (1980) and translated into Turkish by Sucuoğlu et al. (18) (1996); validity and reliability studies were conducted by İncekaş et al. (19) (2009). The scale can be used to evaluate children >2 years of age. Relationships with people, imitation, emotional responses, use of body and objects, adaptation to change, visual response, listening response, tasting, smelling, touching response and use, fear or nervousness, verbal and nonverbal communication, activity level, level and consistency of mental responses, as well as general impressions are evaluated by the practitioner. Each item of the 15-item scale is scored by the clinician between 1 and 4 points, and half-point scores are also possible. A score of 1 indicates normal behavior according to the age group of the child; a score of 2 defines mild behavior, 3 indicates moderate behavior, and 4 indicates severe abnormal behavior. Those who score 15-29.5 are grouped as “no autism”; those who score 30-36.5 are classified as “mild-moderate autism” and those who score 37-60 are classified as “severe autism” (20).
Turkish Communication Development Inventory (TCDI). The TCDI is used to measure the communication and language skills of children between the ages of 8 and 36 months. They consist of three separate areas: (i) gestures and movements, (ii) vocabulary, and (iii) grammar. The items in the inventory were asked in interviews with the primary caregiver who spends the most time with the child and knows the child best. TCDI consists of two main sections: (i) early words and (ii) movements and gestures. The sections are also divided into scales: (i) Early words: (A) first signs of understanding, 3 questions; (B) expressions, 28 items; (C) beginning to speak, 2 questions; and (D) vocabulary, 418 words from 20 semantic categories. (ii) Movements and gestures: (A) early gestures, 24 items; (B) late gestures, 45 items; and (C) total gestures, 69 items (21).
Dunn’s sensory profile. The Sensory Profile tool was developed by Winnie Dunn (22) in 1999 to assess the sensory behavior of children between the ages of 3 and 10 years. The questionnaire, which is completed by parents, consists of 125 questions divided into three sections: sensory processing (related to different sensory systems), modulation, and behavioral and emotional responses. Once the questionnaire is completed, nine factor scores and three part scores are generated. Each question is rated on a scale from 1 to 5 (1, always; 5, never). Kayıhan et al. (23) (2015) checked the validity and reliability of the test in Turkish. The results are interpreted as “typical performance”, “probable difference” or “definite difference” under the headings “less than others” or “more than others”. A lower score indicates poorer performance (22). The assessment tool also classifies children’s sensory responses into the 4 quadrants suggested by the Dunn Sensory Processing Model: Registration, Seeking, Sensitivity, and Avoidance. In this study, the four quadrant scores were calculated to provide an understanding of children’s sensory responses (22, 23).
Adolescents/Adults sensory profile. The Adolescent/Adult Sensory Profile, consisting of 60 items, evaluates six sensory models and the response to different sensory stimuli with Turkish validity and reliability (24, 25). It evaluates the taste/smell process, which evaluates the response of individuals to odor and taste stimuli; the movement process, which evaluates the response of individuals to sensory and vestibular stimuli; the visual process, which evaluates the response of individuals to visual stimuli; the tactile process, which evaluates the response to tongue and skin stimuli; the auditory process, which examines the response to auditory stimuli; and the activity level, which evaluates participation and desire in daily life activities. It is used in adolescents and adults aged ≥11 years (24). It consists of four quadrants based on Dunn’s Sensory Processing Theory. At the end of the test, individuals are rated according to quartiles as “much less than most people”, “less than most people”, “similar to most people”, “more than most people”, and “much more than most people” (26, 27). According to Dunn’s sensory processing model, the first quartile of the classification consists of four quartiles: (i) low recording (refers to weak or slower than expected responses to sensory stimuli); a high low recording score indicates a high neurologic threshold and rapid habituation. If the low recording score is low, the individual responds faster than normal to sensory stimuli, but this does not necessarily mean that they are sensitive. (ii) Sensory seeking (refers to wanting to receive sensory stimuli and being in a sensory search). Individuals with a high sensory seeking score quickly get bored in low-stimulus environments. A low sensory seeking score does not mean that the individual avoids the stimulus. (iii) Sensory sensitivity (refers to having a low threshold for sensory stimuli and responding to stimuli more than normal). (iv) Sensory avoidance (refers to deliberately avoiding sensory stimuli) (24).
Clinical observation of neuromotor performance. Clinical Observation of Neuromotor Performance is used to assess an individual’s performance in specific activities requiring neuromotor skills (28). Neuromotor performance skills include assessment of subparameters such as postural difficulties, poor bilateral integration and sequencing, somatodyspraxia, visually controlled eye movements, and other clinical observations common to individuals with central nervous system developmental delay or sensory integration dysfunction. It is assessed with minus and plus signs. The minus sign indicates no difficulty or functional impairment. The plus sign indicates evidence of difficulty or functional impairment (29). This assessment tool was used to assess the neuromotor performance of participants.
Genetic analysis. Total RNA was extracted from leukocytes using the QIAamp RNA Blood Mini Kit (QIAGEN, Valencia, CA, USA) followed by DNase I treatment, in accordance with the manufacturer’s protocol. The quantity and purity of the RNA were assessed using a NanoDrop 2000 UV-vis spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). Complementary DNA (cDNA) was synthesized from 1 μg of total RNA using the Transcript or First Strand cDNA Synthesis Kit (Roche Diagnostics, Mannheim, Germany). Gene expression levels were measured via quantitative reverse transcription-polymerase chain reaction (qRT-PCR) using the LightCycler 480 SYBR Green I Master Kit (Roche Diagnostics). Standard curves were generated through serial dilutions of the beta-globulin gene (DNA Control Kit; Roche, Penzberg, Germany) and gene-specific primers (Table I) were provided by Oligomer Biotechnology (Ankara, Turkey). The resulting gene expression values were normalized to the housekeeping gene beta-2 microglobulin, and the gene expression ratios between the patient and control groups were analyzed using the Relative Expression Software Tool (REST) (Qiagen, Hilden, Germany) (30).
Primer sequences of the studied genes.
Ethical approval. The study was approved by the Kocaeli University Faculty of Medicine Non-Interventional Medical Research Ethics Committee (no. 2019/221).
Statistical analysis. Statistical calculations were performed using IBM SPSS Statistics 27 (IBM, Armonk, NY, USA). Following the examinations of gene expression ratios were performed based on ΔΔCt method and independent Student’s t-tests and Mann-Whitney U tests were used to identify significant differences between the AC group and the control groups (either HSAC or HC groups). The ΔΔCt method was also used for graph generation. The results were considered statistically significant at p≤0.05. Effect sizes were calculated as Cohen’s d for independent Student’s t-tests and as rank-biserial correlation (r) for Mann-Whitney U tests where comparisons were statistically significant.
Correlation analysis. The correlation of up/down changes in gene expression levels with the results of the CARS test, TCDI, Dunn’s Sensory Profile, Adolescents/Adults Sensory Profile, and Clinical Observation of Neuromotor Performance tests were analyzed. Nonparametric tests were preferred because the groups did not comply with normal distribution. The Spearman test was applied to perform correlation analysis between scored groups. Linear regression analysis was performed in significant comparisons. Differences between groups were examined for categorical variables; the Mann-Whitney U test was used for double-group scoring and the Kruskal-Wallis test was used for multi-group scoring. Effect size was calculated in significant comparisons. Dunn’s test was applied for detailed analysis of significant comparisons in multiple groups.
Results
Comparison of gene expression levels in the study groups. IL-1β, Casp1, NLRP3, NLRP1, TNFRSF11B, TNFRSF11A, and TNFSF11 gene expression levels were studied in 50 children in the AC group, 34 children in the HSAC group, and 16 children in the HC group. Primer sequences of seven genes are shown in Table I. When gene expression levels of 50 children in the AC group and 50 children in the HSAC and HC groups were compared, up-regulation was observed in all genes in the AC group, but this difference was not statistically significant (Figure 1). Similarly, when 34 children with healthy siblings aged >3 years in the AC group were compared with their 34 healthy siblings in the HSAC group, all gene expression levels were increased in the AC group compared with the HSAC group, but no statistically significant differences were found. When the gene expression levels of 16 children in the AC group who did not have a healthy sibling aged >3 years were compared with 16 children in the HC group, the NLRP1 gene expression level was down-regulated and the other genes were up-regulated in the AC group; these changes were not statistically significant, but were statistically significant for TNFRSF11B (p=0.004), TNFRSF11A (p=0.009), and TNFSF11 (p=0.006). The effect size for TNFRSF11B was large (Cohen’s d=0.86), while the effect sizes for TNFRSF11A and TNFSF11 were medium (r=0.33 and r=0.34, respectively).
Relative gene expression values (2−ΔΔCt) for autistic children (AC), their healthy biological siblings (HSAC) and healthy control (HC) group. (a) ILB, b) Casp1, (c) NLRP3, (d) NLRP1, (e) TNFRSF11B, (f), TNFRSF11A, (g) TNFSF11. Boxes represent the interquartile range with the median indicated by the central line; whiskers indicate the range. Extreme values and mean of the groups are not shown. AC group differed significantly from HC group at **p<0.01. IL-1β: Interleukin-1β; Casp1: caspase 1; NLRP3: NLR protein 3; NLRP1: NLR protein 1; TNFRSF11B: tumor necrosis factor receptor superfamily member 11b; TNFRSF11A: tumor necrosis factor receptor superfamily member 11a; TNFSF11: tumor necrosis factor superfamily member 11.
Correlation between gene expression levels and CARS test scores. CARS test was applied to all children in the AC group. No significant correlation was found between CARS test scores and gene expression levels (Table II).
Correlation values between Childhood Autism Rating Scale (CARS) scores and gene expression measurements.
Correlation between gene expression levels and TCDI scores. The TCDI test was applied to 36 children in the AC group. The correlations between TCDI scores and gene expression levels are shown in Table III. These results show that as the up-regulation in Casp1 gene expression increases, the rate of Response to Name may decrease; as the up-regulation in NLRP3 gene expression increases, the rate of Mother Here Response and Speech Initiation as Imitation may decrease; as the up-regulation in NLRP1 gene expression increase, the rate of Reaction to Name and Reaction to No may decrease; and as the up-regulation in TNFRSF11B gene expression increase, the rate of Late Gestures may decrease.
Correlation values between TCDI scores and gene expression measurements.
Correlation between gene expression levels and Dunn’s Sensory Profile scores. Dunn’s Sensory Profile test was applied to 42 children in the AC group (Table IV). The IL-1β gene expression level in the group showing “More Low Registration than most people” was significantly lower than in the group “Similar to most people”. This finding suggests that decreased IL-1β gene expression is associated with increased Low Registration sensory processing. The effect size of this difference was large, indicating that the magnitude of the observed group difference was substantial. In the analysis evaluating differences in TNFRSF11A gene expression across Sensory Seeking categories, a statistically significant overall group difference was detected. However, post-hoc pairwise comparisons between individual groups did not remain statistically significant after multiple comparison testing, suggesting that the overall significance may reflect a general trend across categories rather than a specific difference between two individual groups (Table IV).
Evaluation of difference between Dunn’s Sensory Profile scores and gene expression measurements.
Correlation between gene expression levels and clinical observation of neuromotor performance scores. The Clinical Observation of Neuromotor Performance test was applied to 42 children in the AC group (Table V). IL-1β gene expression differed significantly between the groups that could or could not Jump and could or could not perform Compound Movement functions. The group that did not have difficulty in jumping and compound movement functions had significantly lower IL-1β gene expression levels with a medium effect size. Casp1 gene expression differed significantly between the groups that had or did not have difficulty in Extension Action in Prone Position, had or did not have difficulty in Extensor Muscle Tone Action, and had or did not have difficulty in Midline Crossing Action. Casp1 gene expression was lower in the group having difficulty in Extension Action in Prone Position and Extensor Muscle Tone Action in the prone position, whereas Casp1 gene expression was higher in the group having difficulty in Midline Crossing Action, with a medium effect size. NLRP1 gene expression showed a significant difference in the groups having difficulty and not having difficulty in Neck Flexion Action in the Supine Position. NLRP1 gene expression was found to be higher in the group having difficulty in Neck Flexion Action in the Supine Position with a medium effect size (Table V).
Evaluation of difference between Clinical Observation of Neuromotor Performance scores and gene expression measurements.
Discussion
Although the pathogenesis of ASD has not been fully elucidated, numerous studies supporting the role of neuroinflammation have attracted attention. In a significant number of animal, human and postmortem studies, findings of microglia and astrocyte activation in the brain and changes in cytokine and chemokine profiles have been described, however, there are also studies in which these changes have not been demonstrated (5, 30-34). It is accepted that neuroinflammation triggers, such as maternal immune activation and gut dysbiosis, which are among the most important environmental factors, initiate the neuroinflammatory process in the intrauterine and postnatal periods and cause neurodevelopmental impairment in developing brain structures (34-38). Although evidence on the role of neuroinflammation in ASD pathogenesis is increasing, it is not sufficiently known when the neuroinflammation process begins, how long it remains active, and which brain structures are more prominently affected. With the recent emergence of treatments that can suppress neuroinflammation, the onset and duration of the effects of neuroinflammation on the developing brain will become even more important.
Although the RANK-RANKL-OPG system is known primarily for its role in bone remodeling, it has also been shown to modulate several neurologic functions, including neuroinflammation and neuronal survival. RANK-RANKL-OPG signaling is intricately involved in various neurologic disorders, such as Alzheimer disease, multiple sclerosis, and brain injuries (39). Most importantly, NLRP3-inflammasome system-induced IL-1β activates the RANK-RANKL signaling pathway, which in turn modulates microglial activity and cerebral T cell infiltration, thereby influencing the immune response within the brain in several neurologic diseases. Furthermore, OPG contributes to modulation of neuroinflammation by preventing RANKL from binding to RANK and thus attenuation of the proinflammatory microglial response (39-41). Alterations in RANK-RANKL-OPG levels have been identified in the blood and cerebrospinal fluid of patients with several disorders, indicating the potential diagnostic and prognostic biomarker potential of this pathway (39). Since early initiation of treatment efforts has a critical role in the management of autism, molecular biomarkers are urgently required for reliable and prompt prognosis of this disease.
Given the recently established role of neuroinflammation in ASDs, we hypothesized that an imbalance in the inflammasome-RANK-RANKL-OPG axis could exacerbate inflammation and cell death and potentially promote the progression of disease in children with autism. Autistic children showed trends towards exhibiting elevated TNFRSF11 and TNFSF11 levels and this trend attained significance in children without siblings. Healthy siblings of children with autism also showed trends for elevated TNFRSF11 and TNFSF11 levels suggesting a genetic predisposition to autoinflammation in families with autistic children. Furthermore, we managed to disclose several moderate associations between specific cognitive tasks and mediators of inflammation, lending further support to the contribution of neuroinflammation in the induction of ASDs.
In typically developing infants, the emergence of self-recognition in mirrors, self-referential behavior, and the ability to distinguish oneself from others manifests between 14 and 18 months of age. By 25 months, infants have the capacity to label themselves by name (42). In healthy infants, the ability to respond to one’s own name typically occurs between 4 and 5 months (43). The absence of this response in infants at risk for autism is considered a clinical indicator specific to ASDs, and the capacity to respond to one’s name is incorporated as a therapeutic objective in early intervention strategies (44). This study observed that the up-regulation of Casp1 gene expression correlates with a decrease in the behavior of responding to one’s name. Furthermore, an examination of the literature regarding the effects of Casp1 inhibition on cognitive functions reveals that inhibition of NLRP-3 and Caspase-1 enhances episodic and spatial memory in animal models of Alzheimer disease, thereby strengthening cognitive functions (45, 46). In addition, in another animal model, it has been posited that the NLRP3-Caspase-1 pathway and the inhibition of the NLRP3 inflammasome contribute to the attenuation of age-related cognitive dysfunction (47).
The expressive language ability and social-communicative behavior of the autistic children included in this study are inherently limited due to the nature of autism. Consequently, an in-depth analysis of receptive language comprehension signals was conducted to evaluate their existing language skills. Another significant finding from this research indicates that the up-regulation of NLRP-3 gene expression diminishes the response to statements such as “Mom is here”. The behavior of searching for one’s mother or father on hearing utterances like “Mom/Dad is here” suggests that the infant comprehends the lexical-semantic context created when the terms Mom and Dad are integrated into a syntactic structure. Comprehension of syntactic units and sentences in auditory stimuli necessitates a bi-hemispheric processing framework, predominantly involving the posterior superior and medial temporal gyri, the anterior temporal lobe, and associated subcortical structures and axonal pathways, with a significant emphasis on left hemisphere activation (48). Thus, it can be inferred that the up-regulation of NLRP-3 gene expression may have an adverse effect on auditory comprehension in children with autism.
Early articulatory skills in infants commence in the initial weeks postnatally, characterized by the imitation of adult speech patterns and lip protrusion, subsequently evolving into babbling between four and seven months as the vocal apparatus undergoes sensorimotor maturation. Infants demonstrate a propensity to imitate vocalizations they encounter in their environment. Consequently, vocal imitation necessitates an awareness of the relationship between their own articulatory movements and the sounds of speech. Although the first words are typically observed around the 12th month, the maturation of articulation occurs significantly earlier through the process of speech imitation (49). Another noteworthy finding of this study indicates that the up-regulation of NLRP3 gene expression has an adverse impact on the imitation skill, which is integral to the initiation of speech. These results suggest that neuroinflammation may exert a detrimental influence on articulatory-acoustic capabilities.
The Sensory Profile results showed that low IL-1βgene expression was associated with an increase in the Low Registration state (e.g., unresponsiveness to sensory stimuli, low awareness). This finding is noteworthy because sensory registration processes are closely associated with subcortical functioning. Sensory integration is the process by which the brain organizes sensory inputs to produce meaningful perceptions, emotions, and behaviors. Sensory integration consists of three basic but interrelated processes: sensory registration, sensory modulation, and sensory discrimination. It is known that these processes, especially the sensory registration stage, are associated with subcortical functioning (50). IL-1β is known to modulate microglial activation and synaptic plasticity in the developing brain. Therefore, alterations in IL-1β expression may influence sensory registration processes through neuroimmune mechanisms affecting subcortical sensory processing networks. The fact that low IL-1β gene expression is associated with low registration difficulties suggests the existence of different inflammatory responses at the subcortical level.
When the bilateral integration and sequencing results of neuromotor performance were examined, IL-1β gene expression was higher in children who had difficulty in Jumping and Compound Movements, and Casp1 gene expression was higher in children who had difficulty in Crossing the Midline. These findings indicate that increased expression of the relevant genes may be effective in children who have difficulty in bilateral integration and sequencing. Bilateral integration is carried out through the corpus callosum, which provides information exchange between the two hemispheres of the brain. The somatosensory cortex, especially the primary somatosensory cortex (S1), has a fundamental role in processing bilateral sensory information. It has been shown that neurons in S1 integrate information from both hemispheres to create a holistic sensory experience (51). Thus, it is thought that functional disorders at the cortical level may be associated with increased expression of the IL-1β and Casp1 genes.
Posture is the ability of the body to maintain a stable position and is regulated by the integration of sensory information from the vestibular, proprioceptive, and visual systems (52). The postural results of neuromotor performance showed that the increase in NLRP1 gene expression was associated with the difficulty in Supine Neck Flexion. This suggests that inflammatory processes may play a role in the development of head and trunk control. However, the fact that low Casp1 gene expression was associated with difficulties in Extensor Muscle Tone and Prone Extension suggests that the inflammatory response may be lower in children who have difficulty in extension movements. These findings suggest that there may be different inflammatory responses in flexion and extension postural mechanisms.
Endophenotypes refer to the presence of certain biological aspects of a disease at a higher frequency in unaffected relatives than in the general population (53). In the autism endophenotype, clinical observations showed that siblings of autistic children may present mild neuro-psychiatric impairments, such as delay in verbal, cognitive, and motor functions, compared with typically developing children, and it has been hypothesized by the existence of a relationship between particular human leukocyte antigen alleles and ASD. Saresella et al. (54) showed changes in the T lymphocyte subpopulation, proinflammatory cytokine-producing monocyte-macrophages, and post-thymic differentiation in children with autism and reported that the immune profiles of children with autism were more similar to their siblings compared with healthy controls. In a later study by the same group, up-regulation of the NLRP3 inflammasome, increase in proinflammatory IL-1β and IL-8 cytokines, and decrease in anti-inflammatory IL-33 cytokine were shown in autistic children, but it was determined that these parameters did not show significant differences in siblings of autistic children and healthy controls (2). Similarly, although we showed up-regulation of the NLRP3 inflammasome in autistic children, this increase was not statistically significantly different from the healthy sibling and healthy control groups. However, we showed a significant difference in RANK-RANKL-OPG pathway-associated gene expression levels (TNFRSF11B, TNFRSF11A, and TNFSF11) in autistic children and healthy siblings from healthy controls that would support the autism endophenotype.
Neuroinflammation may play a primary role in the cause of ASD or may be involved in secondary common pathogenetic pathways related to genetic, epigenetic, and environmental factors (55). Therefore, treatment approaches aimed at suppressing the neuroinflammatory process in the treatment of ASD are currently controversial. However, because different neuroinflammatory mechanisms may play a role in each case of ASD, it is not possible to recommend a standard anti-inflammatory treatment. As an ideal treatment approach, neuroinflammation indicators should be investigated in children diagnosed with ASD, the target mechanism related to neuroinflammation should be demonstrated, and targeted anti-inflammatory treatment should be applied. It does not seem appropriate to use drugs with serious short- and long-term side effects and broad-spectrum mechanisms such as corticosteroids. Today, the discovery of targeted anti-inflammatory drugs such as NLRP3 inhibitors and RANKL neutralizing antibodies (denosumab) has given hope for targeted treatments aimed at suppressing neuroinflammation in autism (56, 57). In our study, we found preliminary evidence of the putative involvement of the RANK-RANKL-OPG pathway and the NLRP3 inflammasome complex in ASD suggesting that these molecules can be targeted for therapeutic purposes in patients with ASD. New studies with different age groups, using samples of cerebrospinal fluid in addition to blood samples and with higher case numbers can reveal the potential role of this group of drugs in well-selected cases.
Limitations. As a limitation of our study, our assessments focused solely on peripheral blood and did not include evaluation of the intrathecal expression of NLRP3-RANK-RANKL-OPG mediators, which might be more representative of neuroinflammation in AC. In addition, these inflammation mediators might be more intricately involved in AC pathogenesis at an earlier stage of development. Thus, measurement of NLRP3-RANK-RANKL-OPG expression levels in children <3 years of age might provide further insight into the pathogenesis of autism.
Conclusion
This study supports other studies demonstrating the role of neuroinflammation in ASD, and it is the first study to show that the RANK-RANKL-OPG pathway is active in cases of ASD. Although we did not demonstrate a significant increase in the expression of genes associated with the NLRP3 inflammasome pathway, our results emphasize the harmful influence of the NLRP3 inflammasome complex on the development of linguistic and cognitive skills, sensory profile, neuromotor performance and flexion, and extension posture in children and endorse NLRP3, NLRP1, caspase-1, and IL-1β as potential biomarkers in autism. Thus, our findings prompt further assessment of alternative inflammation pathways in the peripheral blood and cerebrospinal fluid of children with ASD.
Footnotes
Authors’ Contributions
B.K.: conceptualization, data curation, funding acquisition, investigation, methodology, project administration, supervision, writing – review & editing; M.S.: conceptualization, data curation, methodology, project administration, supervision, writing – original draft; T.Ö.: data curation, formal analysis, investigation, methodology, software, validation, writing – original draft; Ş.G.Ş.: conceptualization, data curation, investigation, methodology, supervision, writing–original draft; R.A.: conceptualization, data curation, formal analysis, investigation, methodology, writing – original draft; S.Y.Ö.: data curation, formal analysis, investigation; Ş.A.; data curation, formal analysis, investigation; A.D.: data curation, formal analysis, investigation; A.S.G.; conceptualization, data curation, formal analysis, investigation; F.D.: formal analysis, investigation, methodology; D.S.A.: conceptualization, investigation, methodology, writing – original draft; N.Ç.: conceptualization, investigation, methodology, project administration, software, supervision, writing – original draft; E.T.: conceptualization, investigation, methodology, project administration, resources, software, supervision, writing – review & editing.
Conflicts of Interest
The Authors have no conflict of interest to declare in relation to this study.
Funding
This study was supported by Kocaeli University Scientific Research Projects (BAP) Unit (Project code: TSA-2020-1674).
Artificial Intelligence (AI) Disclosure
No artificial intelligence (AI) tools, including large language models or machine learning software, were used in the preparation, analysis, or presentation of this manuscript.
- Received January 30, 2026.
- Revision received March 8, 2026.
- Accepted March 11, 2026.
- Copyright © 2026 The Author(s). Published by the International Institute of Anticancer Research.
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