Using a nested case-cohort design within an Australian population-derived prebirth cohort study, placental transcriptomic signature of impaired mitochondrial function and gene transcription regulation among infants subsequently diagnosed with autism has profound implications for understanding both risk factors and prediction.
Abstract
Autism development involves multiple genetic and early-life environmental factors. Studying the placenta's gene expression profile may reveal key mechanistic pathways in autism development. Here, using a nested case-cohort design within an Australian population-derived prebirth cohort study (n=1074), we identified 1,644 differentially expressed genes (DEGs; FDR<0.05) in the placenta of children with autism diagnosis (n=43), compared to those without (n=120). The top enriched pathways related to mitochondrial translation, oxidative stress, RNA processing and transcription regulation. CYP1A1, the most important xenobiotic-metabolising enzyme of the placenta, was the top downregulated DEG in the placenta of children with autism, while immuno-regulatory human leukocyte antigen (HLA)-related genes were among the top upregulated DEGs. A machine learning-based approach predicted autism from the transcriptomic data with a median sensitivity of 0.57 (2.5th-97.5th centiles: 0.29, 0.76) and median specificity of 0.92 (2.5th-97.5th centiles: 0.78, 0.98). Weighted Gene Correlation Network Analysis identified eight affected placental gene modules, with the largest five modules being enriched primarily for mitochondrial bioenergetics, oxidative phosphorylation and RNA processing pathways. This placental transcriptomic signature of impaired mitochondrial function and gene transcription regulation among infants subsequently diagnosed with autism has profound implications for understanding both risk factors and prediction, suggesting the possibility of identifying modifiable prenatal pathways to improve autism outcomes.
Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and molecular mechanism. Identifying reliable molecular biomarkers remains a critical challenge. In this study, we integrated mRNA expression profiles from five post-mortem brain tissue GEO datasets to identify ASD-associated genes. Following batch effect correction, differentially expressed genes (DEGs) were analysed and Weighted Gene Co-expression Network Analysis (WGCNA) was performed to screen genes correlated with ASD. Then, five machine learning algorithms - Random Forest, LASSO, Boruta, CatBoost, and LightGBM - were applied to screen hub genes. Lastly, alterations of the hub gene(s) were investigated with a maternal immune activation (MIA) rat model using poly I:C by measuring mRNA expression of the hub genes in the rat nucleus accumbens (NAc) and caudate putamen (CPu). A total of 30 DEGs and 54 WGCNA module genes were identified, yielding 29 key candidates by intersecting these two gene sets. EIF4A1 (Eukaryotic Translation Initiation Factor 4A1) was the sole gene consistently ranked among the top five by all five machine learning algorithms. Analysis of the integrated dataset confirmed that EIF4A1 mRNA expression was significantly elevated in ASD subjects. Finally, using the MIA rat model of ASD, we found that EIF4A1 mRNA expression was significantly down-regulated in the NAc and CPu, and this deficit was rescued by treatment with the antipsychotics olanzapine or risperidone. In conclusion, the present study positions EIF4A1 as a promising candidate molecular indicator with potential implications for understanding disease mechanisms and developing targeted interventions of ASD.
Background Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders whose underlying molecular mechanisms and biological processes remain incompletely understood. In this study, we used a multi-layered systems biology approach to prioritize candidate genes and regulatory factors associated with ASD. Method Gene expression data from peripheral blood samples were obtained from the Gene Expression Omnibus (GEO) database (GSE18123). Using analyses performed in R software, differentially expressed genes (DEGs) in patients with ASD were identified (p-value < 0.05 and |log2FC| > 0.5). These DEGs were used to perform weighted gene co-expression network analysis (WGCNA) and construct a protein–protein interaction (PPI) network. By integrating the results of these network analyses with feature selection techniques (LASSO and random forest feature importance), candidate genes associated with ASD were prioritized and evaluated using qRT-PCR in the valproic acid (VPA)-induced rat model of autism. Furthermore, a gene regulatory network (GRN) was constructed to identify the regulatory factors associated with DEGs. Result TLR8 and CASP4 were prioritized as candidate genes that may be associated with ASD, because they were located within the co-expression module that showed the strongest correlation with ASD, were identified as key nodes of the PPI network, and were selected by feature selection algorithms. Our experimental validation showed increased expression of TLR8 and CASP4 in the autism model compared with controls; TLR8 was upregulated in both the hippocampus and peripheral blood, whereas CASP4 was upregulated only in the hippocampus. Furthermore, GRN analysis identified miR-891b and miR-627-3p as potential regulators of TLR8, and miR-26b-5p as associated with CASP4. Conclusion These findings indicate that CASP4 and TLR8, together with their associated regulatory miRNAs, may represent promising biomarkers and potential therapeutic targets for future ASD research and contribute to a better understanding of the pathophysiological mechanisms underlying ASD.
Sara Hosseinpoor, H. Zali, Hassan Zohrevand et al.· PLoS ONE· 0 citations
RNA-sequencing, 3-dimensional protein-centric chromatin conformation, and whole genome DNA methylation sequencing approaches are used to investigate hippocampal tissue from an ASD mouse model to determine if multi-omic data integration improves the resolution of key molecular pathways contributing to the complex ASD phenotype.
Carolina D Alberca, Kwangmoon Park, L. Papale et al.· Molecular Psychiatry· 0 citations
Background The placenta has a unique transcriptomic profile, including microRNAs that are secreted into maternal circulation throughout pregnancy. MicroRNAs are small, non-coding RNA that post-transcriptionally regulate gene expression. Spontaneous preterm birth (sPTB) is associated with substantial differences in both placental pathophysiology and placental gene expression compared to term birth. We aimed to generate microRNA signatures of sPTB and map them to target genes using a microRNA–mRNA network. Methods This study was conducted within the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Placental samples were collected at delivery, and RNA was isolated for mRNA and microRNA sequencing. To investigate sPTB, this study excluded placental samples of participants with iatrogenic indications for PTB or induced labor. We examined differences in microRNA expression in participants who delivered before 37 weeks (N=35) compared to term participants (N=404) in a series of covariate-adjusted linear regression models. We used paired placental microRNA and mRNA expression data from this cohort to validate associations between computationally predicted microRNA–mRNA pairs and establish a microRNA–mRNA network. Results Expression of 7 microRNAs were increased in sPTB (FDR<0.05) and were inversely correlated with sPTB-associated genes involved in immune signaling. Expression of 12 microRNAs were decreased in sPTB, including 4 members of the maternally expressed chromosome 14 microRNA cluster (miR-376a-3p, miR-376c-3p, miR-377-3p, and miR-381-3p). These microRNAs were predicted to negatively regulate oxidative phosphorylation genes that were increased in sPTB. The associations between miR-376c-3p and miR-377-3p and oxidative phosphorylation were confirmed in microRNA knockdown experiments. Conclusions This study highlights potential biological mechanisms by which placental microRNA dysfunction might contribute to sPTB and highlights putative sPTB biomarkers that may be detectable in maternal circulation.
Mariana Parenti, Elizabeth M. Kennedy, Evan J. Firsick et al.· bioRxiv· 0 citations
The Developmental Origins of Health and Disease (DOHaD) hypothesis proposes that the perinatal environment shapes susceptibility to complex traits across life [1]. The placenta, a transient organ mediating maternal-fetal exchange, plays a central role in this process and has emerged as a key molecular archive in utero [2-4]. Placental DNA methylation (DNAm) is a unique mediator between prenatal exposures, fetal genetics and later-life outcomes [5-9]. DNAm quantitative trait loci (mQTL) have helped disentangling causal mechanisms underlying GWAS loci for complex diseases [10-15]. Despite growing evidence that placental genomic regulation has broad and profound effects on the developmental programming of early- and later-life health outcomes [17], existing placental studies remain limited in scale and largely focused on growth- and neuro-related traits [12-16]. Here, we construct a high-resolution placental mQTL resource and systematically investigate how placental DNAm relates to early- and later-life traits, and to shared vulnerability and complex interactions among them.
A. Cilleros-Portet, Itziar González-Moro, Hachem Sadikki et al.· medRxiv· 0 citations
Postpartum depression (PPD) is among the most common complications of childbirth, and identifying novel treatments is vital. We aimed to identify potential drug targets for PPD by integrating the plasma proteome, transcriptome and epigenome. We designed a comprehensive analysis pipeline involving two-sample Mendelian randomisation (MR) (for proteins), colocalisation (for coding genes), and summary-based MR (SMR) (for mRNA and DNA methylation) to identify potential therapeutic targets for PPD. Genetic data on the plasma proteome were obtained from 4907 aptamers in 35,559 Icelanders and 7596 proteins in 828 FinnGen participants. The PPD genome-wide association study data were sourced from the Psychiatric Genomics Consortium (PGC) (Ncase = 17,339, Ncontrol = 53,426). A two-step MR approach was used to assess whether brain imaging-derived phenotypes (IDPs) and metabolites from blood, brain and cerebrospinal fluid mediated the observed effects. Across the two proteome datasets, the genetically predicted levels of 18 plasma proteins were nominally significantly associated with PPD, and the expression of steroid receptor RNA activator 1 (SRA1), a regulator of steroid hormone signalling, was significantly associated with PPD. SRA1, angiotensinogen (AGT, a key mediator of the renin-angiotensin and stress-response system), and glycerol-3-phosphate phosphatase (PGP, involved in lipid metabolism and cellular stress) showed increased colocalisation. The methylation of SRA1 at cg02434007 in the brain was associated with increased expression of SRA1 and a high risk of PPD, which aligns with the positive effect of SRA1 gene expression on PPD risk. Isoleucine (mediation proportion: 5.8%, p = 0.042) from blood metabolites and the IDP ICA100 edge 442 (mediation proportion: 7.6%, p = 0.044) may play mediating roles. This study reveals that SRA1 is a novel therapeutic target for PPD, which enhances the understanding of its molecular aetiology and the development of therapeutic strategies.
Ming Chen, Qinlin Wei, Haowen Li et al.· npj Mental Health Research· 0 citations
