bioRxiv Science⌕ Search

bioRxiv · 10.1101/2020.05.14.096586

Expression changes in immune and epigenetic gene pathways associated with nutritional metabolites in maternal blood from pregnancies resulting in autism and atypical neurodevelopment

Abstract

BackgroundThe prenatal period is a critical window to study factors involved in the development of autism spectrum disorder (ASD). Environmental factors, especially in utero nutrition, can interact with genetic risk for ASD, but how specific prenatal nutrients in mothers of children later diagnosed with ASD or non-typical development (Non-TD) associate with gestational gene expression is poorly understood. Maternal blood collected prospectively during pregnancy provides a new opportunity to gain insights into nutrition, particularly one-carbon metabolites, on gene pathways and neurodevelopment. MethodsGenome-wide transcriptomes were measured using microarrays in 300 maternal blood samples from all three trimesters in the Markers of Autism Risk in Babies - Learning Early Signs (MARBLES) study. Sixteen different one-carbon metabolites, including folic acid, betaine, 5-methyltretrahydrofolate (5-MeTHF), and dimethylglycine (DMG) were measured. Differential expression analysis and weighted gene correlation network analysis (WGCNA) were used to compare gene expression between children later diagnosed as typical development (TD), Non-TD and ASD, and to nutrient metabolites. ResultsUsing differential gene expression analysis, six transcripts associated with four genes (TGR-AS1, SQSTM1, HLA-C and RFESD) showed genome-wide significance (FDR q < 0.05) with child outcomes. Genes nominally differentially expressed compared to TD specifically in ASD, but not Non-TD, significantly overlapped with seven high confidence ASD genes. 218 transcripts in common to ASD and Non-TD differential expression compared to TD were significantly enriched for functions in immune response to interferon-gamma, apoptosis, and metal ion transport. WGCNA identified co-expressed gene modules significantly correlated with 5-MeTHF, folic acid, DMG, and betaine. A module enriched in DNA methylation functions showed a protective association with folic acid/5-MeTHF concentrations and ASD risk. Independent of child outcome, maternal plasma betaine and DMG concentrations associated with a block of co-expressed genes enriched for adaptive immune, histone modification, and RNA processing functions. LimitationsBlood contains a heterogeneous mixture of cell types, and many WGCNA modules correlated with cell type and/or nutrient concentrations, but not child outcome. Gestational age correlated with some co-expressed gene modules in addition to nutrients. ConclusionsThese results support the premise that the prenatal maternal blood transcriptome is a sensitive indicator of gestational nutrition and childrens later neurodevelopmental outcomes.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhu, Y., Mordaunt, C. E., Durbin-Johnson, B. P., Caudill, M. A., Malysheva, O. V., Miller, J. W., Green, R., James, S. J., Melnyk, S. B., Fallin, M. D., Hertz-Picciotto, I., Schmidt, R. J., LaSalle, J. M.. 2020-05-16. Expression changes in immune and epigenetic gene pathways associated with nutritional metabolites in maternal blood from pregnancies resulting in autism and atypical neurodevelopment. https://doi.org/10.1101/2020.05.14.096586

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction

Predicting protein melting temperature (Tm) and organismal optimal growth temperature (OGT) is essential for enzyme engineering, biocatalyst design, and metagenomic bioprospecting, yet computational prediction faces several fundamental bottlenecks: sequence-only representations miss tertiary packing; dual-task coupling induces destructive gradient conflict; natural database skew (>87% mesophiles) drives mesophilic collapse; and existing tools produce uncalibrated point predictions. To address these challenges, we present StableProt, a structure-aware framework that couples amino acid sequences with Foldseek 3Di structural tokens via a frozen SaProt backbone to capture 3D contacts. Disjoint heads condition thermodynamic stability on ecological context without gradient conflict, while targeted 14% mesophilic subsampling expands thermophile representation to eliminate mesophilic collapse. Calibrated ensemble modeling reduces calibration error to 1.92%, providing dependable 95% confidence intervals. Evaluating models across 10{degrees}C temperature intervals reveals severe baseline collapse at thermal extremes, whereas StableProt maintains robust accuracy across regimes. On ProThermDB and zero-shot FireProtDB, StableProt achieves state-of-the-art probabilistic accuracy (CRPS = 4.52{degrees}C and 8.71{degrees}C) while recovering 92.0% of hyperthermophiles ([&ge;]80{degrees}C), and in prospective validation, its calibrated intervals capture both enzyme stability (75.3% coverage) and host environmental growth temperatures (78.1% coverage). StableProt is freely accessible as an interactive web server at https://stableprot.dixitlab.org/.

bioinformatics↗

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗