Integrated Systems Analysis Explores Dysfunctional Molecular Modules and Regulatory Factors in Children with Autism Spectrum Disorder

Immunogenetics of autism spectrum disorder: A systematic literature review

The aetiology of autism spectrum disorder (ASD) is complex and, partly, accounted by genetic factors. Nonetheless, the genetic underpinnings of ASD are poorly defined. The presence of immune dysregulations in autistic individuals, and their families, supports a role of the immune system and its genetic regulators. Albeit immune responses belong either to the innate or adaptive arms, the overall immune system genetics is broad, and encompasses a multitude of functionally heterogenous pathways which may have different influences on ASD. Hence, to gain insights on the immunogenetic underpinnings of ASD, we conducted a systematic literature review of previous immune genetic and transcription studies in ASD. We defined a list of immune genes relevant to ASD and explored their neuro-immune function. Our review confirms the presence of immunogenetic variability in ASD, accounted by inherited variations of innate and adaptive immune system genes and genetic expression changes in the blood and post-mortem brain of autistic individuals. Besides their immune function, the identified genes control neurodevelopment processes (neuronal and synaptic plasticity) and are highly expressed in pre/peri-natal periods. Hence, our synthesis bolsters the hypothesis that perturbation in immune genes may contribute to ASD by derailing the typical trajectory of neurodevelopment. Our review also helped identifying some of the limitations of prior immunogenetic research in ASD. Thus, alongside clarifying the neurodevelopment role of immune genes, we outline key considerations for future work into the aetiology of ASD and possible novel intervention targets.

A systematic review of non-coding RNA genes with differential expression profiles associated with autism spectrum disorders

AIMS

To identify differential expression of shorter non-coding RNA (ncRNA) genes associated with autism spectrum disorders (ASD).

BACKGROUND

ncRNA are functional molecules that derive from non-translated DNA sequence. The HUGO Gene Nomenclature Committee (HGNC) have approved ncRNA gene classes with alignment to the reference human genome. One subset is microRNA (miRNA), which are highly conserved, short RNA molecules that regulate gene expression by direct post-transcriptional repression of messenger RNA. Several miRNA genes are implicated in the development and regulation of the nervous system. Expression of miRNA genes in ASD cohorts have been examined by multiple research groups. Other shorter classes of ncRNA have been examined less. A comprehensive systematic review examining expression of shorter ncRNA gene classes in ASD is timely to inform the direction of research.

METHODS

We extracted data from studies examining ncRNA gene expression in ASD compared with non-ASD controls. We included studies on miRNA, piwi-interacting RNA (piRNA), small NF90 (ILF3) associated RNA (snaR), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), transfer RNA (tRNA), vault RNA (vtRNA) and Y RNA. The following electronic databases were searched: Cochrane Library, EMBASE, PubMed, Web of Science, PsycINFO, ERIC, AMED and CINAHL for papers published from January 2000 to May 2022. Studies were screened by two independent investigators with a third resolving discrepancies. Data was extracted from eligible papers.

RESULTS

Forty-eight eligible studies were included in our systematic review with the majority examining miRNA gene expression alone. Sixty-four miRNA genes had differential expression in ASD compared to controls as reported in two or more studies, but often in opposing directions. Four miRNA genes had differential expression in the same direction in the same tissue type in at least 3 separate studies. Increased expression was reported in miR-106b-5p, miR-155-5p and miR-146a-5p in blood, post-mortem brain, and across several tissue types, respectively. Decreased expression was reported in miR-328-3p in bloods samples. Seven studies examined differential expression from other classes of ncRNA, including piRNA, snRNA, snoRNA and Y RNA. No individual ncRNA genes were reported in more than one study. Six studies reported differentially expressed snoRNA genes in ASD. A meta-analysis was not possible because of inconsistent methodologies, disparate tissue types examined, and varying forms of data presented.

CONCLUSION

There is limited but promising evidence associating the expression of certain miRNA genes and ASD, although the studies are of variable methodological quality and the results are largely inconsistent. There is emerging evidence associating differential expression of snoRNA genes in ASD. It is not currently possible to say whether the reports of differential expression in ncRNA may relate to ASD aetiology, a response to shared environmental factors linked to ASD such as sleep and nutrition, other molecular functions, human diversity, or chance findings. To improve our understanding of any potential association, we recommend improved and standardised methodologies and reporting of raw data. Further high-quality research is required to shine a light on possible associations, which may yet yield important information.

Using Machine Learning to Explore Shared Genetic Pathways and Possible Endophenotypes in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous condition, characterized by complex genetic architectures and intertwined genetic/environmental interactions. Novel analysis approaches to disentangle its pathophysiology by computing large amounts of data are needed. We present an advanced machine learning technique, based on a clustering analysis on genotypical/phenotypical embedding spaces, to identify biological processes that might act as pathophysiological substrates for ASD. This technique was applied to the VariCarta database, which contained 187,794 variant events retrieved from 15,189 individuals with ASD. Nine clusters of ASD-related genes were identified. The 3 largest clusters included 68.6% of all individuals, consisting of 1455 (38.0%), 841 (21.9%), and 336 (8.7%) persons, respectively. Enrichment analysis was applied to isolate clinically relevant ASD-associated biological processes. Two of the identified clusters were characterized by individuals with an increased presence of variants linked to biological processes and cellular components, such as axon growth and guidance, synaptic membrane components, or transmission. The study also suggested other clusters with possible genotype-phenotype associations. Innovative methodologies, including machine learning, can improve our understanding of the underlying biological processes and gene variant networks that undergo the etiology and pathogenic mechanisms of ASD. Future work to ascertain the reproducibility of the presented methodology is warranted.

Novel insights into the immune cell landscape and gene signatures in autism spectrum disorder by bioinformatics and clinical analysis

The pathogenesis of autism spectrum disorder (ASD) is not well understood, especially in terms of immunity and inflammation, and there are currently no early diagnostic or treatment methods. In this study, we obtained six existing Gene Expression Omnibus transcriptome datasets from the blood of ASD patients. We performed functional enrichment analysis, PPI analysis, CIBERSORT algorithm, and Spearman correlation analysis, with a focus on expression profiling in hub genes and immune cells. We validated that monocytes and nonclassical monocytes were upregulated in the ASD group using peripheral blood (30 children with ASD and 30 age and sex-matched typically developing children) using flow cytometry. The receiver operating characteristic curves (PSMC4 and ALAS2) and analysis stratified by ASD severity (LIlRB1 and CD69) showed that they had predictive value using the "training" and verification groups. Three immune cell types - monocytes, M2 macrophages, and activated dendritic cells - had different degrees of correlation with 15 identified hub genes. In addition, we analyzed the miRNA-mRNA network and agents-gene interactions using miRNA databases (starBase and miRDB) and the DSigDB database. Two miRNAs (miR-342-3p and miR-1321) and 23 agents were linked with ASD. These findings suggest that dysregulation of the immune system may contribute to ASD development, especially dysregulation of monocytes and monocyte-derived cells. ASD-related hub genes may serve as potential predictors for ASD, and the potential ASD-related miRNAs and agents identified here may open up new strategies for the prevention and treatment of ASD.

Biomarker prediction in autism spectrum disorder using a network-based approach

BACKGROUND

Autism is a neurodevelopmental disorder that is usually diagnosed in early childhood. Timely diagnosis and early initiation of treatments such as behavioral therapy are important in autistic people. Discovering critical genes and regulators in this disorder can lead to early diagnosis. Since the contribution of miRNAs along their targets can lead us to a better understanding of autism, we propose a framework containing two steps for gene and miRNA discovery.

METHODS

The first step, called the FA_gene algorithm, finds a small set of genes involved in autism. This algorithm uses the WGCNA package to construct a co-expression network for control samples and seek modules of genes that are not reproducible in the corresponding co-expression network for autistic samples. Then, the protein-protein interaction network is constructed for genes in the non-reproducible modules and a small set of genes that may have potential roles in autism is selected based on this network. The second step, named the DMN_miRNA algorithm, detects the minimum number of miRNAs related to autism. To do this, DMN_miRNA defines an extended Set Cover algorithm over the mRNA-miRNA network, consisting of the selected genes and corresponding miRNA regulators.

RESULTS

In the first step of the framework, the FA_gene algorithm finds a set of important genes; TP53, TNF, MAPK3, ACTB, TLR7, LCK, RAC2, EEF2, CAT, ZAP70, CD19, RPLP0, CDKN1A, CCL2, CDK4, CCL5, CTSD, CD4, RACK1, CD74; using co-expression and protein-protein interaction networks. In the second step, the DMN_miRNA algorithm extracts critical miRNAs, hsa-mir-155-5p, hsa-mir-17-5p, hsa-mir-181a-5p, hsa-mir-18a-5p, and hsa-mir-92a-1-5p, as signature regulators for autism using important genes and mRNA-miRNA network. The importance of these key genes and miRNAs is confirmed by previous studies and enrichment analysis.

CONCLUSION

This study suggests FA_gene and DMN_miRNA algorithms for biomarker discovery, which lead us to a list of important players in ASD with potential roles in the nervous system or neurological disorders that can be experimentally investigated as candidates for ASD diagnostic tests.

Altered gut microbiota correlates with behavioral problems but not gastrointestinal symptoms in individuals with autism

BACKGROUND

Despite inconsistent results across studies, emerging evidence suggests that the microbial micro-environment may be associated with autism spectrum disorder (ASD). Geographical and cultural factors highly impact microbial profiles, and there is a shortage of data from East Asian populations. This study aimed to comprehensively characterize microbial profiles in an East Asian sample and explore whether gut microbiota contributes to clinical symptoms, emotional/behavioral problems, and GI symptoms in ASD.

METHODS

We assessed 82 boys and young men with ASD and 31 typically developing controls (TDC), aged 6-25 years. We analyzed the stool sample of all participants with 16S V3-V4 rRNA sequencing and correlated its profile with GI symptoms, autistic symptoms, and emotional/behavioral problems.

RESULTS

Autistic individuals, compared to TDC, had worse GI symptoms. There were no group differences in alpha diversity of species richness estimates (Shannon-wiener and Simpson diversity indices). Participants with ASD had an increased relative abundance of Fusobacterium, Ruminococcus torques group (at the genus level), and Bacteroides plebeius DSM 17135 (at the species level), while a decreased relative abundance of Ruminococcaceae UCG 013, Ervsipelotrichaceae UCG 003, Parasutterella, Clostridium sensu stricto 1, Turicibacter (at the genus level), and Clostridium spiroforme DSM 1552 and Intestinimonas butyriciproducens (at the species level). Altered taxonomic diversity in ASD significantly correlated with autistic symptoms, thought problems, delinquent behaviors, self dysregulation, and somatic complaints. We did not find an association between gut symptoms and gut microbial dysbiosis.

CONCLUSIONS

Our findings suggest that altered microbiota are associated with behavioral phenotypes but not GI symptoms in ASD. The function of the identified microbial profiles mainly involves the immune pathway, supporting the hypothesis of a complex relationship between altered microbiome, immune dysregulation, and ASD that may advance the discovery of molecular biomarkers for ASD.

Re-emerging concepts of immune dysregulation in autism spectrum disorders

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by communication and social interaction deficits, and by restricted interests and stereotyped, repetitive behavior patterns. ASD has a strong genetic component and a complex architecture characterized by the interplay of rare and common genetic variants. Recently, increasing evidence suggest a significant contribution of immune system dysregulation in ASD. The present paper reviews the latest updates regarding the altered immune landscape of this complex disorder highlighting areas with potential for biomarkers discovery as well as personalization of therapeutic approaches. Cross-talk between the central nervous system and immune system has long been envisaged and recent evidence brings insights into the pathways connecting the brain to the immune system. Disturbance of cytokine levels plays an important role in the establishment of a neuroinflammatory milieu in ASD. Several other immune molecules involved in antigen presentation and inflammatory cellular phenotypes are also at play in ASD. Maternal immune activation, the presence of brain-reactive antibodies and autoimmunity are other potential prenatal and postnatal contributors to ASD pathophysiology. The molecular players involved in oxidative-stress response and mitochondrial system function, are discussed as contributors to the pro-inflammatory pattern. The gastrointestinal inflammation pathways proposed to play a role in ASD are also discussed. Moreover, the body of evidence regarding some of the genetic factors linked to the immune system dysregulation is reviewed and discussed. Last, but not least, the epigenetic traits and their interactions with the immune system are reviewed as an expanding field in ASD research. Understanding the immune-mediated pathways that influence brain development and function, metabolism, and intestinal homeostasis, may lead to the identification of robust diagnostic or predictive biomarkers for ASD individuals. Thus, novel therapeutic approaches could be developed, ultimately aiming to improve their quality of life.

Evaluation of circulating miRNAs and mRNAs expression patterns in autism spectrum disorder

Background

Autism spectrum disorder is a condition related to brain development that affects a person’s perception and socialization, resulting in problems in social interaction and communication. It has no single known cause, yet several different genes appear to be involved in autism. As a genetically complex disease, dysregulation of miRNA expression and miRNA–mRNA interactions might be a feature of autism spectrum disorder. The aim of the current study was to investigate the expression profile of circulating miRNA-128, miRNA-7 and SHANK gene family in ASD patients and to assess the possible influence of miRNA-128 and miRNA-7 on SHANK genes, which might provide an insight into the pathogenic mechanisms of ASD and introduce noninvasive molecular biomarkers for the disease diagnosis and prognosis. Quantitative real-time PCR technique was employed to determine expression levels of miRNA-128, miRNA-7 and SHANK gene family in blood samples of 40 autistic cases along with 30 age- and sex-matched normal volunteer subjects.

Results

Our study revealed a statistical significant upregulation of miRNA-128 expression levels in ASD cases compared to controls (p value < 0.001). A statistical significant difference in SHANK-3 expression was encountered on comparing cases to controls (p value < 0.001). However, miRNA-7 expression showed no significant difference between the studied groups.

Conclusions

MiRNA-128 and SHANK-3 gene are emerging players in the field of ASD. They are promising candidates as noninvasive biomarkers in autism. Future studies are needed to emphasize their pivotal role.

Convergent Canonical Pathways in Autism Spectrum Disorder from Proteomic, Transcriptomic and DNA Methylation Data

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with extensive genetic and aetiological heterogeneity. While the underlying molecular mechanisms involved remain unclear, significant progress has been facilitated by recent advances in high-throughput transcriptomic, epigenomic and proteomic technologies. Here, we review recently published ASD proteomic data and compare proteomic functional enrichment signatures with those of transcriptomic and epigenomic data. We identify canonical pathways that are consistently implicated in ASD molecular data and find an enrichment of pathways involved in mitochondrial metabolism and neurogenesis. We identify a subset of differentially expressed proteins that are supported by ASD transcriptomic and DNA methylation data. Furthermore, these differentially expressed proteins are enriched for disease phenotype pathways associated with ASD aetiology. These proteins converge on protein–protein interaction networks that regulate cell proliferation and differentiation, metabolism, and inflammation, which demonstrates a link between canonical pathways, biological processes and the ASD phenotype. This review highlights how proteomics can uncover potential molecular mechanisms to explain a link between mitochondrial dysfunction and neurodevelopmental pathology.