2024; 22(2): 322-332  https://doi.org/10.9758/cpn.23.1124
Comparison of MicroRNA Levels of 18−60-month-old Autistic Children with Those of Their Siblings and Controls
Hülya Karagöz1, Ömer Faruk Akça2, Mahmut Selman Yıldırım3, Ayşe Gül Zamani3, Mehmet Burhan Oflaz4
1Department of Child and Adolescent Psychiatry, Binali Yıldırım University Mengücek Gazi Training and Research Hospital, Erzincan, Turkey
2Department of Child and Adolescent Psychiatry, Meram Faculty of Medicine, Necmettin Erbakan University, Konya, Turkey
3Department of Genetic, Meram Faculty of Medicine, Necmettin Erbakan University, Konya, Turkey
4Department of Pediatrics, Meram Faculty of Medicine, Necmettin Erbakan University, Konya, Turkey
Correspondence to: Hülya Karagöz
Department of Child and Adolescent Psychiatry, Binali Yıldırım University Mengücek Gazi Training and Research Hospital, Başbağlar Hacı Ali Akın cd.no:32, Erzincan/ Merkez 24100, Turkey
E-mail: akatashulya@gmail.com
ORCID: https://orcid.org/0000-0002-5294-1879
Received: August 18, 2023; Revised: January 30, 2024; Accepted: February 27, 2024; Published online: March 22, 2024.
© The Korean College of Neuropsychopharmacology. All rights reserved.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Objective: The present study aims to compare the levels of 7 microRNAs (mi-RNAs) (mi-RNA-125b, mi-RNA-23a-3p, mi-RNA-146a-5p, mi-RNA-106a, mi-RNA-151a-3p, mi-RNA-28, mi-RNA-125a) in the blood of the preschool children with autism and those of their siblings with healthy controls, and to investigate the association between these mi-RNAs and the severity of autism, behavioral problems, and siblings’ autistic traits.
Methods: A total of 35 children diagnosed with autism spectrum disorder (ASD) at the ages of 18−60 months (patient group), 35 non-affected siblings of the ASD group (sibling group), and 30 control subjects (control group) were involved in the study. The severity of ASD was measured using the Childhood Autism Rating Scale and the Autism Behavior Checklist (ABC). The behavioral problems of the children with ASD were assessed with the Aberrant Behavior Checklist, and the autistic traits of the siblings were assessed using the Autism spectrum screening scale for children.
Results: mi-RNA-106a-5p, mi-RNA-151a-3p, and mi-RNA-28-3p were found to be expressed significantly lower in the patient group compared to the control group. There was a significant positive correlation between mi-RNA-23a and the sensory subscale of the ABC. mi-RNA-151a was significantly associated with sound sensitivity and mi-RNA-28 with echolalia. After controlling for age and sex, the differences between groups were disappeared.
Conclusion: The present study examined mi-RNAs that have been reported as biomarkers in the literature. Although several symptom clusters are found to be related to certain mi-RNA expression levels, they were not found to be significant in discriminating the patient and healthy groups.
Keywords: Autism; Micro RNA; Siblings
INTRODUCTION

Autism spectrum disorder (ASD) is a neurodevelop-mental disorder characterized by communication and social disabilities [1]. Many family studies reported that ASD is an inherited disease [2,3] and genetic factors are responsible for an estimated 50−60% of the risk for ASD [4]. In addition to genetic factors, the role of environmental and prenatal factors was also identified [5]. Environmental factors are thought to act through epigenetic mechanisms such as histone modification, DNA methylation, and microRNAs (mi-RNA) [6]. Recent studies showed that mi-RNAs play a role in the pathogenesis of ASD [7].

Mi-RNAs are short (∼21 nucleotides) and non-protein-coding RNA molecules that regulate post-transcriptional gene expression [7]. Two-thirds of mi-RNAs were found to be expressed in the human central nervous system [1]. Mi-RNAs are considered to play an important role in the pathogenesis of psychiatric disorders by affecting many target proteins involved in axon development, neuron proliferation, dendrite development, and synapto-genesis. For example, the expression levels of mi-RNAs were found to be altered in several diseases, including psychiatric disorders such as schizophrenia and mood disorders [8]. Many mi-RNAs with different expressions were identified in the brain, saliva, blood, and olfactory cells of ASD patients [9]. Mi-RNA-125b-5p was originally identified as a tumor suppressor. Subsequent studies revealed the critical role of mi-RNA-125b-5p in the regulation of cell proliferation, apoptosis, and extracellular matrix [10]. In a study comparing mi-RNAs in the saliva of children with ASD with those in the saliva of normally developed children, miR-125b was found to be significantly less expressed [11]. Mi-RNA-125a-5p was reported to be expressed in microvascular endothelial cells of the blood-brain barrier [12], and play a role in decreasing permeability of the blood-brain barrier [13]. Besides, it is thought to play a role in the pathogenesis of ASD through the neuregulin 1 pathway which is estimated to be a target of miR125a-3p [14]. Mi-RNA-146a was shown to be expressed in regions, that are important for cognitive and social functions, such as the frontal cortex, amygdala, and hippocampus [15]. In different studies carried out on various tissues, the level of mi-RNA-146a was reported to have changed in subjects with ASD compared to the controls [16,17]. Mi-RNA-106a-363 cluster is known to be located on the chromosome X in humans [18]. In a previous candidate gene identification study carried out on ASD, it was determined that mi-RNA-106a-5p targeted the autism-related genes [19]. Mi-RNA-23a is known to be a non-coding RNA, which belongs to the mi-RNA-23a-27a-24-2 cluster and is located on the 19th chromosome of the human genome [20]. It was reported that mi-RNA-23a-3p modulates various disease processes such as cancer, inflammation, and cognitive disorders. In different studies carried out on various tissues, the level of mi-RNA-23a was shown to have changed in autistic subjects in comparison to the controls. Mi-RNA-28-3p is known to act on several cancer-related genes and regulate cell proliferation and migration [21]. In a recent study on a large sample of individuals with ASD, those with developmental delay, and normal control subjects, 14 mi-RNA levels in saliva were found to differ from those of control subjects, and the highest level of difference was found in mi-RNA 28-3p [22]. Mi-RNA-151a-3p is known to be coded in the region of chromosome 8q [23]. In studies carried out previously, it was reported that miRNA-151a regulates close homolog of L1 (CHL1) playing a role in neural cell proliferation, differentiation, and axon guidance [24,25]. Previous studies showed that the level of expression was different in autism samples [19,26]. Table 1 shows previous studies explored the mi-RNAs that are associated with ASD and several related neurodevelop-mental disorders.

In the literature review, it can be seen that, even though the relationships of various mi-RNAs with ASD were reported, it couldn’t be determined which symptoms they are associated with. Moreover, it is not known how the mi-RNA expression levels of the individuals with autism differ from their non-affected siblings and healthy con-trols. Furthermore, these mi-RNAs were not studied in terms of subthreshold autistic characteristics in non-autistic individuals. Since the genetical etiology of ASD is well established through the sibling and twin studies [27], revealing how the mi-RNA levels differ between ASD subjects and their siblings, and how the autistic traits do associate with the autistic features in siblings of the ASD very important in order to determine the risk ratio in this group. Similarly, a better understanding of the epigenetic difference between siblings with similar genetic and environmental factors might offer an advantage in understanding the pathogenesis of ASD. To the best of our knowledge, there is no study comparing the individuals diagnosed with ASD to their non-diagnosed siblings [28] In this study, mi-RNAs that were consistently shown to be associated with ASD in the previous research, –are thought to be closely associated with ASD pathogenesis– were investigated and it is assumed that they would be expressed at different levels in ASD subjects their non-affected siblings and healthy controls. Therefore, we have aimed to test the relationship between certain mi-RNA expression levels, ASD symptom severity and behavioral problems in children with ASD. In addition, we have aimed to test the relationship between the mentioned mi-RNA expression levels and specific symptom clusters of ASD (i.e. social-communication symptoms, restricted interest, stereotypical behaviors, hyper-hypo sensitivity) in subjects with ASD. Also, we have aimed to search the relationship between ASD traits and mi-RNA expression levels in non-affected siblings.

METHODS

Participants

Thirty-five children aged between 18 and 60 months diagnosed with ASD according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) diagnostic criteria, and their non-ASD siblings were involved in this study. The participants who were admitted to the outpatient clinic of Necmettin Erbakan University Meram Faculty of Medicine, Department of Child and Adolescent Psychiatry with a diagnosis of ASD, and their siblings were assigned to the patient (ASD) and sibling groups respectively. Exclusion criteria for the patient group were chronic physical, metabolic, genetic, respiratory, or neurological disease (e.g., diabetes mellitus, hypertension, epilepsy, cerebral palsy, developmental disability etc.), severe head trauma, or organic brain damage. The exclusion criterion for the sibling group was defined as having an ASD diagnosis in addition to the above-mentioned criteria.

Children applied to Meram Faculty of Medicine’s Department of Child Cardiology for a routine check for being accepted to a sports team, or being followed up for an innocent heart murmur were recruited to the study to compose the control group. Children who have an ASD, mental or physical developmental retardation and an additional cardiological problem were excluded from the study. Twenty-one of the control group composed of the subjects applied for a routine check before attending a sports team, and 9 of them were being followed for a benign murmur. The ethics committee approval for this study was obtained from the Ethics Committee of Necmettin Erbakan University (approval number: 201518027). Parents of participating children gave written informed consent, in addition to their verbal consent.

Diagnosis and Symptom Assessment

An experienced child and adolescent psychiatrist (H.K.) carried out a diagnostic clinical evaluation based on DSM-5 to ensure the diagnoses of the children who have been admitted to inpatient unit of Child and Adolescent Psychiatry Department of Necmettin Erbakan University Medical School with a diagnosis of ASD. In the next step, same physician has applied the Autism Behavior Check-list (ABC) and Childhood Autism Rating Scale (CARS) to determine the severity of autistic symptoms. The mothers or fathers of the participants in the patient group filled out the Autistic Behaviors Checklist and Aberant Behavior Checklist (ABCL) for the children with ASD, and the Autism spectrum screening scale (ASSS) for their non-ASD children. Since there is no validated scale to assess the autistic features in preschool age children in our language, we had to rely on ASSS to measure the autistic features of the siblings. All participants were assessed through clinical evaluation conducted by H.K. and subjects who were diagnosed with a physical or mental retardation were excluded from the study. ABC, CARS, ASSS, and ABCL scales were tested for their validity and reliability in the Turkish population [29-32].

Blood Samples

Venous blood samples from patients, siblings and children in the control group were collected in ethylenediamine tetraacetic acid (EDTA) tubes, independent of variables such as time, hunger, and satiety. The tube, in which the blood was collected, was kept upright at room temperature for 30 minutes without mixing or touching. Samples were centrifuged to separate plasma fractions and stored at −80°C until analysis. The expression levels of 8 mi-RNAs, including mi-RNA-125b, mi-RNA-23a-3p, mi-RNA-146a-5p, mi-RNA-106a, mi-RNA-151a-3p, mi-RNA-125a, mi-RNA 28-3p, and control mi-RNA, were determined by using quantitative real-time polymerase chain reaction (qRT-PCR) method (Light Cycler 96; Roche Diagnostics).

Methods Used in Expression Analyses

Real-time quantitative polymerase chain reaction (qPCR, RT-qPCR)

PCR, with stages similar to replication, refers to the synthesis of DNA fragments by using primers. PCR method includes the stages of denaturation of double-strand DNA, binding the specific primers to the region, and chain prolongation. The resultant DNA sequences are amplified logarithmically. Real-time PCR refers to the collection of data throughout the PCR process; thus, this method combines amplification and detection in the same step. Reactions are characterized using the time point, when the target amplification is detected for the first time, (or PCR cycle) (cycle threshold, ct) [33]. This value is generally named cycle threshold. It is a fast, sensitive, easy-to-implement method. Therefore, this method is widely used in expression analyses.

Application Steps

Plasma collection

After obtaining a blood sample in a plain or EDTA tube, it was mixed by gently turning it upside down 5−10 times. Plasma separation was carried out within 2 hours of blood collection.

Tubes were centrifuged at 2,000 g for 10 minutes. After the centrifugation process, tubes were carefully removed without shaking and lids were gently opened. Plasma was pipetted five times using 200 μl pipettes (using nucleases-free, filtered pipette tips) from the supernatant part of the plasma. A total of 1,000 μl of the sample was collected in clean Eppendorf tubes. This 1,000 μl plasma sample collected was centrifuged at 2,000 g for 10 minutes, and 250 μl from the supernatant part of the plasma (excluding a slightly visible lipid layer at the very top) was transferred to a sterile Eppendorf tube. Separated plasma samples were stored at −80°C until the day of analysis.

Mi-RNA isolation

Samples were incubated at room temperature for 5−10 minutes with mi-RNA extractor. Chloroform (0.2 ml) was added and vortexed for 30 seconds. It was then centrifuged at 12,000 g for 10 minutes at 4°C. Supernatant (540 μl) was transferred to a 1.5 ml ribonuclease (RNase)-free centrifuge tube, and 180 μl of 100% ethanol was added. The solution was mixed by pipetting up and down several times. The solution was transferred to a spin column centrifuge and centrifuged at 12,000 g for 2 minutes. It was then transferred to a new 1.5 ml RNase-free centrifuge tube. Ethanol 100% (460 μl) was added and mixed by pipetting. The solution was transferred to the spin column and centrifuged at 12,000 g for 2 minutes. RPE solution (0.5 ml) was added to the spin column, and it was centrifuged at 12,000 g for 30 seconds. They were centrifuged at 12,000 g again for 30 seconds. The spin column was placed in a new 1.5 ml centrifuge tube, and 30−50 μl of nuclease-free liquid was added. It was left for 2 minutes and then centrifuged at 12,000 g for 30 seconds. The eluted RNA solution was stored at −80°C.

cDNA Synthesis from mi-RNAs

cDNA was obtained from the isolated mi-RNAs using the cDNA isolation kit (Applied Biological Materials). All components included in the kit were prepared in the quantities specified in Supplementary Table 1 (available online) for each sample. The procedural protocol of the kit was followed to achieve a final volume of 20 μl. Following these steps, the heating protocol specified in Supplementary Table 2 (available online) was applied by using the Light Cycler device.

Real-time PCR

Bright Green Master Mix and U6 PCR Primer Mixes (Applied Biological Materials) were prepared according to the manufacture’s recommendations in order to amplify cDNAs in terms of the reference gene and to mark the relevant regions. The volumes specified in Supplementary Table 3 (available online) were followed. Reference gene real-time PCR mixes and target gene real-time PCR mixes, along with suitable cDNAs, were combined on 96-well plates specific to the Light Cycler 96 system. Then, the real-time PCR process was initiated on the Light Cycler 96 system using the temperature protocol outlined in Supplementary Table 4 (available online). Ct values were determined using fluorescent signals exceeding the threshold for mi-RNAs. ∆CT, ∆∆CT, and 2-∆∆CT values were calculated. Cycle threshold 2-∆∆CT values were used for statistical analysis [33].

Statistical Analysis

Demographic findings were evaluated using descriptive analysis methods with frequency (n) and percentage (%) values. The distribution of numerical data was analyzed by using Kolmogorov-Smirnov test. Since the data did not exhibit a normal distribution, the difference in mi-RNA expression levels between groups was analyzed using the Kruskal-Wallis test. Mi-RNAs with significant differences were compared between the paired groups by using post-hoc analysis. The relationship between mi-RNAs and the relationship between mi-RNA levels and scales were tested using Spearman’s rho correlation analysis. Multinomial logistic regression analysis was performed to predict the effect of related mi-RNAs in disease diagnosis. Data less than 0.05 were considered statistically signifi-cant for the pvalue.

RESULTS

The participants were assigned to the patient (ASD), sibling and control groups. The patient group consisted of 35 subjects, whereas the sibling group consisted of 35 subjects and the control group consisted of 30 subjects. The age of the participants ranged between 18 months and 60 months and the groups were similar in terms of their ages (p > 0.05), however, sex distribution of the groups was statistically different (p < 0.05). Age, sex and clinical scale distributions of the subjects were given in Table 2.

Since the data were not normally distributed, the difference in mi-RNA expression levels among the groups was analyzed by using the Kruskal-Wallis test. Post-hoc analyses were performed for mi-RNAs that showed significant differences among the groups. Accordingly, the Kruskal-Wallis analyses revealed that there were statistically significant differences between the groups in terms of mi-RNA-106a-5p 2-∆∆Ct (p = 0.030), mi-RNA-151a 2-∆∆Ct (p = 0.036) and miRNA-28 2-∆∆Ct (p = 0.005) values (Table 3). The post-hoc analyses results showed that mi-RNA-106a-5p was significantly less expressed in the patient (ASD) group compared to the control group. Mi-RNA-151a was expressed less in the patient group in comparison to the control group. Mi-RNA-28 was significantly less expressed in the patient group compared to the control group, and it was also expressed significantly less in the patient group compared to the sibling group (Supplementary Tables 5−7; available online).

The relationship between the mi-RNA levels and the scales was examined using correlation analyses. As a result of the Spearman correlation analyses, there was no correlation between mi-RNA levels and autism spectrum screening scale (Supplementary Table 8; available online). There was a positive correlation between the mi-RNA-28 2-∆∆Ct value and item 11 of CARS “Verbal Com-munication” (p = 0.035, r = 0.357) (Fig. 1, Supplementary Table 9; available online). Moreover, a positive correlation was found between the 32nd item of the ABC “Repeats sentences many times” (p = 0.023, r = 0.384), and the 48th item of the ABC “Repeats other people’s sentences or questions” (p = 0.03, r = 0.368) (Supplementary Table 10; available online). There was a positive correlation between mi-RNA-23a 2-∆∆Ct value and the sensory subscale of the Autism Behavior Checklist (p = 0.018, r = 0.396) (Fig. 1, Supplementary Table 11; available online) The mi-RNA-151 2-∆∆Ct value was positively correlated with item 8 of the CARS, “Listening response” (p = 0.011, r = 0.426), and item 39 of the ABC scale, “He/she closes his/her ears to many sounds” (p = 0.026, r = 0.375) (Fig. 1, Supplementary Tables 9, 10; available online).

Logistic regression analyses were conducted to explore whether the expressions of the mi-RNA levels predict the group of the participants while controlling for age and sex of the subjects. Results of the logistic regression analyses revealed no significant results connoting that any of the mi-RNA predicted the group belonging of the participants (p > 0.05) (Supplementary Tables 12−18; available online).

DISCUSSION

ASD is a phenotypically heterogeneous clinical spectrum, and affected individuals are influenced at different levels. The present study aims to compare the levels of 7 mi-RNAs (mi-RNA-125b, mi-RNA-23a-3p, mi-RNA-146a-5p, mi-RNA-106a, mi-RNA-151a-3p, mi-RNA-125a, mi-RNA-28-3p) –that were previously shown to be associated with ASD– in the blood samples of autistic preschool children with ASD symptoms at different severity levels, and their non-affected siblings to compare with healthy controls. In addition, this study aims to investigate the relationship between the mentioned mi-RNA levels and specific autistic symptoms and behavioral problems in subjects with ASD. There is still no biological marker for ASD and the etiology is not fully understood. This study could introduce new perspectives for understanding the epigenetic processes observed in affected individuals. We found that levels of mi-RNA-106a-5p, mi-RNA-151a-3p, and mi-RNA-28-3p are significantly different in patients with ASD compared to the controls. However, the statistical significance in the multinomial logistic regression analysis disappeared when controlled for age and sex.

Mi-RNA-106a-5p was found to be located on the X ch-romosome in humans and has effects on cell proliferation and apoptosis [34]. Mi-RNA-106a-5p was shown to play a role in the neural differentiation stage of mesenchymal stem cells by regulating the expression of Neurogenin-2 [35]. A previous postmortem brain study revealed that mi-RNA-106a-5p is lower in the brain tissue of individuals with autism compared to those without autism [36]. In a candidate gene study carried out in 2018, it was reported that mi-RNA-106a-5p primarily targets the genes associated with autism [19]. In a study performed with saliva samples of the children with ASD, those with developmental delay, and those with normal development, no significant difference was found in mi-RNA-106a-5p but it was positively correlated with the Autism Diagnostic Observation Schedule (ADOS) restricted/re-petitive behaviors subscale [22]. In the present study, it was concluded that the mi-RNA-106a-5p 2-∆∆Ct value was significantly lower in the patient group when compared to the control group. However, there was no statistical significance in the multinomial logistic regression analysis. This could be because mi-RNAs encoded on the X chromosome are differentially expressed in males and females and the study groups in this study differ from this aspect.

It is known that mi-RNA-151a-3p regulates the encoding of CHL1, which plays a central role in neural cell proliferation, migration, differentiation, and thalamocortical axon guidance [24,37]. Furthermore, miRNA-151a-3p was shown to play a role in cochlear development and it controls the NOTCH signaling pathway, which has a function in the differentiation of the auditory sensory epithelium and is a novel therapeutic target for hearing disorders [38-40]. In the first study on mi-RNA and autism, which was carried out in 2014, mi-RNA-151a-3p was found to be significantly underexpressed when compared to healthy controls [26]. In a study comparing salivary mi-RNAs obtained from children with ASD and normally developing children, mi-RNA-151a-3p was found to be expressed significantly less in the ASD group and was correlated with total ADOS scores. In the present study, mi-RNA-151a-3p was significantly lower in the patient (ASD) group when compared to the control group in the Kruskal-Wallis analysis, but there was no statistically significant difference in the multinomial logistic regression analysis, which also includes age and sex. No significant difference was found between the sibling and control groups in terms of mi-RNA-151a-3p. This could be due to insufficient sample size. Moreover, a positive correlation was found between item 8 of CARS, “Listening response” (p = 0.011) and mi-RNA-151a-3p, and item 39 of the ABC scale “He/she closes his/her ears to many sounds” was also positively correlated with mi-RNA-151a-3p. This might suggest that mi-RNA-151a, which plays a role in cochlear development, has a role in the pathogenesis of hypo/hypersensitivity to certain sounds in autism; it could also represent a new treatment option in treating symptoms such as hyperacusis, which significantly impair the quality of life in autism.

It was reported that mi-RNA-125b inhibits multiple target genes and promotes neuronal differentiation of human cells [41]. In a study comparing salivary mi-RNAs obtained from children with ASD, mi-RNA-125b was significantly less expressed in the ASD group compared to controls and correlated with scores on the social subscale of ADOS [42]. A longitudinal study of mi-RNAs in the saliva of children with ASD estimated that mi-RNA-125b levels at baseline and levels were significantly different after a special education intervention [11]. In the present study, there was no statistically significant difference between the patient and control groups in terms of mi-RNA-125b values.

Mi-RNA-28-3p was shown to target various cancer-related genes and is a mi-RNA potentially involved in cell proliferation and migration [21]. In a recent study using a large sample of individuals with ASD, those with developmental delay, and normal controls, salivary levels of 14 mi-RNAs were found to differ from controls, and the highest level of difference was found in mi-RNA 28-3p [22]. In the present study, there was a statistically significant difference in mi-RNA-28 2-∆∆Ct values between the patient and control groups and a significant difference between the patient and sibling groups. However, no statistically significant difference was found in the logistic regression analysis performed with demographic data. It was aimed to match study groups in terms of the relevant factors such as age, sex, and ethnicity. However, this was not optimal due to the current coronavirus disease 2019 (COVID-19) pandemic and the fact that one of the groups was a sibling group. These factors might have influenced our results.

At the same time, there was a positive correlation between mi-RNA-28 2-∆∆Ct value and item 11 of CARS “Verbal Communication” in the ASD group (p = 0.035). Also, a positive correlation of mi-RNA-28 2-∆∆Ct value was also found with the 32nd item of the ABC “Repeats sentences many times” (p = 0.023) and the 48th item “Repeats other people’s sentences or questions”. Given these data, it can be suggested that mi-RNA-28 is associated with the echolalia pattern in autism. It was previously reported that echolalia in autism is related to mirror neuron system dysfunction [43]. Mi-RNA-28 may play a regulatory role in the mirror neuron system. In autism, there often is a general speech delay. In verbal utterances, the echolalia pattern may appear first, which may explain the low mi-RNA-28 level in ASD. Future studies should investigate this subject.

The mi-RNA-125a family was shown to play a fundamental role in cell differentiation/development [44]. Dif-ferential expression of mi-RNA-125a was reported in male and female frontal lobe regions during normal development [45]. A recent study comparing mi-RNAs between individuals with autism, those with developmental delay, and neurotypical individuals revealed that mi-RNA-125a expression was significantly increased [22]. In the present study, there was no significant difference between the groups. This might be because mi-RNA-125a is expressed differentially between sexes and this couldn’t be controlled during the study because of heterogeneous distri-bution.

It was reported that mi-RNA-23a is highly conserved in different species and plays regulatory roles in various disease processes such as cancer, inflammation, and cognitive disorders. It is suggested that mi-RNA-23a has a potential modulatory function in CNS diseases [46] In a study comparing mi-RNA-23a levels in lymphoblasts obtained from patients with autism and control subjects, it was shown that there was a higher level of expression in patients with ASD [47]. In a previous postmortem brain study, mi-RNA-23a was found to be at a lower level in brain tissue taken from individuals with autism compared to individuals without autism [36]. In postmortem brain mi-RNA research, mi-RNA-23a expression levels were reported to increase in individuals with ASD [48]. In a study including only male subjects and comparing siblings with and without ASD conducted in lymphoblastoid cell cultures revealed that mi-RNA-23a is significantly higher [28]. In a study that examined saliva material from preschool children in Bosnia and Herzegovina and compared individuals with ASD to control subjects, miR-23a was found to be significantly underexpressed [49]. In the present study, the lowest expression levels were found in the patient (ASD) group and the highest expression levels in the control group and a difference close to statistical significance was observed (p = 0.058). The data achieved in the present study are in contrast to a previous study comparing male patients with male siblings but consistent with other data that underestimated mi-RNA-23a. At the same time, mi-RNA-23a was positively correlated with the Autism Behavior Checklist’s Sensory subscale. Similar to mi-RNA-28, a specific relationship between mi-RNA-23a and sensorial symptoms of ASD may exist. Future studies should examine this subject.

It was shown that mi-RNA-146a is expressed in regions that are important for higher cognitive and social functions, such as the frontal cortex, amygdala, and hippocampus at the postnatal stage, and that overexpression of mi-RNA-146a stimulates neuronal differentiation and alters dendritic branching [15]. In a study examining olfactory mucosal stem cells in a small sample and comparing individuals with ASD to control subjects, two-fold increase in mi-RNA-146a expression levels was found [16]. In another study comparing mi-RNA-146a levels in postmortem brain tissue of autism and control groups, mi-RNA-146a expression was reported to be higher in ASD subjects [15]. However, the present study revealed no significant difference among groups in terms of mi-RNA-146a expression levels of subjects. Future studies are needed in this issue.

The limitations of this study include the small sample size, cross-sectional study design, the groups that could not be matched accurately in terms of sex because of COVID-19 pandemics, the specific educational treatments received by the patients that were not controlled, no development test administered to the participants, and the diagnosis of autism made by clinical interview. In addition, despite we have conducted multiple testing in correlation analyses, we did not conduct a correlation method. We used ASSS to assess the autistic traits in the sibling group despite their ages were lower than the age group in the validation study. Since this scale is the single validated scale to assess autistic traits in the child age group in our language, we had to rely on this scale. However, the present study also has strengths. One of the strengths is that it is one of the rare studies in the literature that compares siblings of patients with ASD with the normal group and examines the association of mi-RNAs investigated in ASD with additional problems besides the core autism symptoms, and to the best of our knowledge, it is the first study to examine the association of mi-RNAs with autistic traits.

In summary, the authors of this study believe that mi-RNAs play a role in several symptom clusters in ASD and that this topic should be investigated in future studies. In addition, future studies should involve the groups with a broader disorder severity and mi-RNA levels should also be examined in groups with only autistic traits. New studies taking into account environmental factors can shed light on which mi-RNA acts through which epigenetic mechanism.

Funding

Funding for this study was provided by a grant from the Necmettin Erbakan University Scientific Research Projects Unit within the scope of project number 201518027.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Author Contributions

Conceptualization: Hülya Karagöz, Ömer Faruk Akça. Data acquisition: Hülya Karagöz, Mehmet Burhan Oflaz. Formal analisis: Mahmut Selman Yıldırım, Ayşe Gül Zamani. Statistical analysis: Ömer Faruk Akça, Hülya Karagöz. Supervision: Ömer Faruk Akça. Writing—original draft: Hülya Karagöz.

Figures
Fig. 1. Correlation graphs of Mi-RNAs with the sub-items of CARS and ABC.
CARS, Childhood Autism Rating Scale; ABC, Autism Behavior Checklist.
Tables

Summary of literature on Autism spectrum disorder and microRNA (mi-RNA)

mi-RNA Study group Sample location Finding Authors
mi-RNA-125b ASD Saliva ↓↓ Levitsky et al. [42]
mi-RNA-125a ASD Saliva ↑↑ Hicks et al. [22]
mi-RNA-146a ASD Olfactory stem cells ↑↑ Nguyen et al. [16]
mi-RNA-146a ASD Postmortem brain ↑↑ Nguyen et al. [15]
mi-RNA-146a ASD Lymphoblastoid cell line ↑↑ Talebizadeh et al. [17]
mi-RNA-106a ASD Postmortem brain ↓↓ Abu-Elneel et al. [36]
mi-RNA-23a ASD Lymphoblastoid cell line ↑↑ Atwan et al. [47]
mi-RNA-23a ASD Postmortem brain ↓↓ Abu-Elneel et al. [36]
mi-RNA-23a ASD Postmortem brain ↑↑ Wu et al. [48]
mi-RNA-23a ASD Lymphoblastoid cell line ↑↑ Sarachana et al. [28]
mi-RNA-23a ASD Saliva ↓↓ Hicks and Middleton [9]
mi-RNA-23a ASD Saliva ↓↓ Sehovic et al. [49]
mi-RNA-28 ASD Saliva ↓↓ Hicks et al. [22]
mi-RNA-151a ASD Serum ↓↓ Mundalil Vasu et al. [26]
mi-RNA-151a ASD Saliva ↓↓ Hicks et al. [19]

Demographic characteristics and scale scores of the groups

Mean Standard deviation Kruskal Wallis- H test/χ2 pvalue
Control (n = 30)
Age 36.6 9.6 2.3 0.309
Sex
Male (n = 23) 9.3 0.009
Female (n = 7)
CARS N/A N/A
ABCL N/A N/A
ASSS N/A N/A
ABC N/A N/A
Sibling (n = 35)
Age (mo) 37.0 15.4 2.3 0.309
Sex
Male (n = 17) 9.3 0.009
Female (n = 18)
CARS N/A N/A
ABCL N/A N/A
ASSS 5.1 5.2
ABC N/A N/A
Autism (n = 35)
Age 40.9 12.5 2.3 0.309
Sex
Male (n = 28) 9.3 0.009
Female (n = 7)
CARS 31.2 4.6
ABCL 34.9 26.3
ASSS N/A N/A
ABC-SS 5.5 5.3
ABC-RS 8.9 7.7
ABC-BOU 9.1 7.6
ABC-LS 8.8 5.9
ABC-SSH 9.0 4.6

CARS, Childhood Autism Rating Scale; ABCL, Aberrant Behaviour Checlist; ASSS, Autism spectrum screening scale; ABC, Autism Behavior Checklist; ABC-SS, Autism Behavior Checklist Sensory Subscale; ABC-RS, Autism Behavior Checklist Relating Subscale; ABC-BOU, Autism Behavior Checklist Body-Object Use Subscale; ABC-LS, Autism Behavior Checklist Language Subscale; ABC-SSH, Autism Behavior Checklist social and self help.

Comparison of mi-RNA levels of 3 groups using Kruskall-Wallis tests and post-hoc analyses (p < 0.05)

mi-RNA Autism Sibling Control Z pvalue Post-hoc
miR106a 2-∆∆Ct 18.30 11.33 45.32 7.04 0.030 1>3
miR151 2-∆∆Ct 1.56 37.21 37.21 6.62 0.036 1>3
miR125b 2-∆∆Ct 4.30 16.59 122.01 3.78 0.151
miR28 2-∆∆Ct 6.74 12.69 31.04 10.46 0.005 1>3, 2>3
miR125a 2-∆∆Ct 1.64 18.00 25.82 0.77 0.679
miR23a 2-∆∆Ct 0.09 0.53 15.29 5.70 0.058
miR146a 2-∆∆Ct 4.21 6.60 27.17 3.25 0.197

miR, mi-RNA.

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