RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Purine signaling pathway dysfunction in autism spectrum disorders: Evidence from multiple omics data

Introduction

Previous studies have suggested that the dysregulation of purine metabolism may be associated with autism spectrum disorder (ASD). Here, we adopted metabolomics and transcriptomics to verify and explore the underlying molecular mechanism of purine metabolism dysfunction in ASD and identify potential biomarkers within the purine metabolism pathway.

Methods

Ultra-high-performance liquid chromatography-mass spectrometry was used to obtain the plasma metabolic profiles of 12 patients with ASD and 12 typically developing (TD) children. RNA sequencing was used to screen differentially expressed genes related to the purine metabolic pathway and purine receptor-coding genes in 24 children with ASD and 21 healthy controls. Finally, serum uric acid levels were compared in 80 patients with ASD and 174 TD children to validate the omics results.

Results

A total of 66 identified metabolites showed significant between-group differences. Network analysis showed that purine metabolism was the most strongly enriched. Uric acid was one of the most highlighted nodes within the network. The transcriptomic study revealed significant differential expression of three purine metabolism-related genes (adenosine deaminase, adenylosuccinate lyase, and bifunctional enzyme neoformans 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) transformylase/inosine monophosphate (IMP) cyclohydrolase) (p < 0.01) and five purinergic receptor genes (P2X7, P2Y2, P2Y6, P2Y8, and P2Y10) (p < 0.05). In the validation sample, there was a significant difference in serum uric acid levels between the two groups (p < 0.001), and the area under the curve for uric acid was 0.812 (sensitivity, 82.5%; specificity, 63.8%).

Discussion

Patients with ASD had dysfunctional purine metabolic pathways, and blood uric acid may be a potential biomarker for 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.

Catatonia in autism and other neurodevelopmental disabilities: a state-of-the-art review

Individuals with neurodevelopmental disabilities (NDDs) may be at increased risk for catatonia, which can be an especially challenging condition to diagnose and treat. There may be symptom overlap between catatonia and NDD-associated behaviors, such as stereotypies. The diagnosis of catatonia should perhaps be adjusted to address symptom overlap and to include extreme behaviors observed in patients with NDDs, such as severe self-injury. Risk factors for catatonia in individuals with NDDs may include trauma and certain genetic variants, such as those that disrupt SHANK3. Common etiologic features between neurodevelopmental disabilities and catatonia, such as excitatory/inhibitory imbalance and neuroimmune dysfunction, may partially account for comorbidity. New approaches leveraging genetic testing and neuroimmunologic evaluation may allow for more precise diagnoses and effective treatments.

Blood RNA Sequencing Indicates Upregulated BATF2 and LY6E and Downregulated ISG15 and MT2A Expression in Children with Autism Spectrum Disorder

Mutations in over 100 genes are implicated in autism spectrum disorder (ASD). DNA SNPs, CNVs, and epigenomic modifications also contribute to ASD. Transcriptomics analysis of blood samples may offer clues for pathways dysregulated in ASD. To expand and validate published findings of RNA-sequencing (RNA-seq) studies, we performed RNA-seq of whole blood samples from an Israeli discovery cohort of eight children with ASD compared with nine age- and sex-matched neurotypical children. This revealed 10 genes with differential expression. Using quantitative real-time PCR, we compared RNAs from whole blood samples of 73 Israeli and American children with ASD and 26 matched neurotypical children for the 10 dysregulated genes detected by RNA-seq. This revealed higher expression levels of the pro-inflammatory transcripts BATF2 and LY6E and lower expression levels of the anti-inflammatory transcripts ISG15 and MT2A in the ASD compared to neurotypical children. BATF2 was recently reported as upregulated in blood samples of Japanese adults with ASD. Our findings support an involvement of these genes in ASD phenotypes, independent of age and ethnicity. Upregulation of BATF2 and downregulation of ISG15 and MT2A were reported to reduce cancer risk. Implications of the dysregulated genes for pro-inflammatory phenotypes, immunity, and cancer risk in ASD are discussed.

Biological implications of genetic variations in autism spectrum disorders from genomics studies

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition characterized by atypical social interaction and communication together with repetitive behaviors and restricted interests. The prevalence of ASD has been increased these years. Compelling evidence has shown that genetic factors contribute largely to the development of ASD. However, knowledge about its genetic etiology and pathogenesis is limited. Broad applications of genomics studies have revealed the importance of gene mutations at protein-coding regions as well as the interrupted non-coding regions in the development of ASD. In this review, we summarize the current evidence for the known molecular genetic basis and possible pathological mechanisms as well as the risk genes and loci of ASD. Functional studies for the underlying mechanisms are also implicated. The understanding of the genetics and genomics of ASD is important for the genetic diagnosis and intervention for this condition.

Peripheral blood transcriptome heterogeneity and prognostic potential in lung cancer revealed by RNA-Seq

Understanding of the complex interaction between the peripheral immune system and lung cancer (LC) remains incomplete, limiting patient benefit. Here, we aimed to characterize the host peripheral immune response to LC and investigate its potential prognostic value. Bulk RNA-sequencing data of peripheral blood leucocytes (PBLs) from healthy volunteers and LC patients (n = 142) were analysed for characterization of host systemic immunity in LC. We observed broad blood transcriptome perturbations in LC patients that were heterogeneous, as two new subtypes were established independent of histology. Functionally, the heterogeneity between the two subtypes included dysregulation of diverse biological processes, such as the cell cycle, blood coagulation and inflammatory signalling pathways, together with the abundance and activity of blood cells, particularly lymphocytes and neutrophils, ultimately manifesting as differences in antitumour immune status. Based on these findings, a prognostic model composed of ten genes dysregulated in one LC subtype with relatively poor immune status was developed and validated in a Gene Expression Omnibus (GEO) data set (n = 108), helping to generate a prognostic nomogram. Collectively, our study provides novel and comprehensive insight into the heterogeneity of the host peripheral immune response to LC. The expression heterogeneity-based predictive model may help guide prognostic management for LC patients.

Identification of aberrant innate and adaptive immunity based on changes in global gene expression in the blood of adults with autism spectrum disorder

Background

Autism spectrum disorder (ASD) is characterized as a neurodevelopmental disorder, and one of the main hypotheses regarding its cause is genetic factors. A previous meta-analysis of seven microarray studies and one RNA sequencing (RNA-seq) study using the blood of children with ASD identified dysregulation of gene expressions relevant to the immune system. In this study, we explored changes in global gene expression as the phenotype of ASD in the blood of adults with ASD.

Methods

We recruited an RNA-seq cohort (ASD vs. control; n = 6 each) and a replication cohort (ASD vs. control; n = 19 each) and conducted RNA-seq to explore changes in global gene expression. We then subjected the significantly up- and downregulated genes to gene ontology (GO) and core analyses. Weighted gene correlation network analysis (WGCNA) was performed with all 11,617 genes detected in RNA-seq to identify the ASD-specific gene network.

Results

In total, 117 significantly up- and 83 significantly downregulated genes were detected in the ASD compared with the control group, respectively (p < 0.05 and q < 0.05). GO analysis revealed that the aberrant innate and adaptive immunity were more obvious in the 117 upregulated than in the 83 downregulated genes. WGCNA with core analysis revealed that one module including many immune-related genes was associated with the natural killer cell signaling pathway. In the results for the replication cohort, significant changes with same trend found in RNA-seq data were confirmed for MAFB (p = 0.046), RPSAP58 (p = 0.030), and G2MK (p = 0.004).

Limitations

The sample size was relatively small in both the RNA-seq and replication cohorts. This study examined the mRNA expression level, so the interaction between mRNA and protein remains unclear. The expression changes between children and adults with ASD were not compared because only adults with ASD were targeted.

Conclusions

The dysregulated gene expressions confirmed in the blood of adults with ASD were relevant to the dysfunction of innate and adaptive immunity. These findings may aid in understanding the pathogenesis of ASD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-021-02154-7.

An Epigenetically Distinct Subset of Children With Autism Spectrum Disorder Resulting From Differences in Blood Cell Composition

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that often involves impaired cognition, communication difficulties and restrictive, repetitive behaviors. ASD is extremely heterogeneous both clinically and etiologically, which represents one of the greatest challenges in studying the molecular underpinnings of ASD. While hundreds of ASD-associated genes have been identified that confer varying degrees of risk, no single gene variant accounts for >1% of ASD cases. Notably, a large number of ASD-risk genes function as epigenetic regulators, indicating potential epigenetic dysregulation in ASD. As such, we compared genome-wide DNA methylation (DNAm) in the blood of children with ASD (n = 265) to samples from age- and sex-matched, neurotypical controls (n = 122) using the Illumina Infinium HumanMethylation450 arrays. Results: While DNAm patterns did not distinctly separate ASD cases from controls, our analysis identified an epigenetically unique subset of ASD cases (n = 32); these individuals exhibited significant differential methylation from both controls than the remaining ASD cases. The CpG sites at which this subset was differentially methylated mapped to known ASD risk genes that encode proteins of the nervous and immune systems. Moreover, the observed DNAm differences were attributable to altered blood cell composition, i.e., lower granulocyte proportion and granulocyte-to-lymphocyte ratio in the ASD subset, as compared to the remaining ASD cases and controls. This ASD subset did not differ from the rest of the ASD cases in the frequency or type of high-risk genomic variants. Conclusion: Within our ASD cohort, we identified a subset of individuals that exhibit differential methylation from both controls and the remaining ASD group tightly associated with shifts in immune cell type proportions. This is an important feature that should be assessed in all epigenetic studies of blood cells in ASD. This finding also builds on past reports of changes in the immune systems of children with ASD, supporting the potential role of altered immunological mechanisms in the complex pathophysiology of ASD. The discovery of significant molecular and immunological features in subgroups of individuals with ASD may allow clinicians to better stratify patients, facilitating personalized interventions and improved outcomes.

Bridging the Gap Between Physical Health and Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a highly complex and heterogeneous developmental disorder that affects how individuals communicate with other people and relate to the world around them. Research and clinical focus on the behavioural and cognitive manifestations of ASD, whilst important, have obscured the recognition that ASD is also commonly associated with a range of physical and mental health conditions. Many physical conditions appear with greater frequency in individuals with ASD compared to non-ASD populations. These can contribute to a worsening of social communication and behaviour, lower quality of life, higher morbidity and premature mortality. We highlight some of the key physical comorbidities affecting the immune and the gastrointestinal systems, metabolism and brain function in ASD. We discuss how healthcare professionals working with individuals with ASD and parents/carers have a duty to recognise their needs in order to improve their overall health and wellbeing, deliver equality in their healthcare experiences and reduce the likelihood of morbidity and early mortality associated with the condition.

Patient-specific multi-omics models and the application in personalized combination therapy

The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.

Twin study designs as a tool to identify new candidate genes for depression: A systematic review of DNA methylation studies

Monozygotic (MZ) twin studies constitute a key resource for the dissection of environmental and biological risk factors for human complex disorders. Given that epigenetic differences accumulate throughout the lifespan, the assessment of MZ twin pairs discordant for depression offers a genetically informative design to explore DNA methylation while accounting for the typical confounders of the field, shared by co-twins of a pair. In this review, we systematically evaluate all twin studies published to date assessing DNA methylation in association with depressive phenotypes. However, difficulty to recruit large numbers of MZ twin pairs fails to provide enough sample size to develop genome-wide approaches. Alternatively, region and pathway analysis revealed an enrichment for nervous system related functions; likewise, evidence supports an accumulation of methylation variability in affected subjects when compared to their co-twins. Nevertheless, longitudinal studies incorporating known risk factors for depression such as childhood trauma are required for understanding the role that DNA methylation plays in the etiology of depression.