Previous research on autism risk (ASD), developmental regulatory (DevReg), and central nervous system (CNS) genes suggests they tend to be large in size, enriched in nested repeats, and mutation intolerant. The relevance of these genomic features is intriguing yet poorly understood. In this study, we investigated the feature landscape of these gene groups to discover structural themes useful in interpreting their function, developmental patterns, and evolutionary history. ASD, DevReg, CNS, housekeeping, and whole genome control (WGC) groups were compiled using various resources. Multiple gene features of interest were extracted from NCBI/UCSC Bioinformatics. Residual variation intolerance scores, Exome Aggregation Consortium pLI scores, and copy number variation data from Decipher were used to estimate variation intolerance. Gene age and protein-protein interactions (PPI) were estimated using Ensembl and EBI Intact databases, respectively. Compared to WGC: ASD, DevReg, and CNS genes are longer, produce larger proteins, maintain greater numbers/density of conserved noncoding elements and transposable elements, produce more transcript variants, and are comparatively variation intolerant. After controlling for gene size, mutation tolerance, and clinical association, ASD genes still retain many of these same features. In addition, we also found that ASD genes that are extremely mutation intolerant have larger PPI networks. These data support many of the recent findings within the field of autism genetics but also expand our understanding of the evolution of these broad gene groups, their potential regulatory complexity, and the extent to which they interact with the cellular network.Lay Summary: Autism risk genes are more ancient compared to other genes in the genome. As such, they exhibit physical features related to their age, including long gene and protein size and regulatory sequences that help to control gene expression. They share many of these same features with other genes that are expressed in the brain and/or are associated with prenatal development.
The maintenance of gene expression patterns during metazoan development is achieved by the actions of Polycomb group (PcG) complexes. An essential modification marking silenced genes is monoubiquitination of histone H2A lysine 119 (H2AK119Ub) deposited by the E3 ubiquitin ligase activity of the non-canonical Polycomb Repressive Complex 1. The Polycomb Repressive Deubiquitinase (PR-DUB) complex cleaves monoubiquitin from histone H2A lysine 119 (H2AK119Ub) to restrict focal H2AK119Ub at Polycomb target sites and to protect active genes from aberrant silencing. BAP1 and ASXL1, subunits that form active PR-DUB, are among the most frequently mutated epigenetic factors in human cancers, underscoring their biological importance. How PR-DUB achieves specificity for H2AK119Ub to regulate Polycomb silencing is unknown, and the mechanisms of most of the mutations in BAP1 and ASXL1 found in cancer have not been established. Here we determine a cryo-EM structure of human BAP1 bound to the ASXL1 DEUBAD domain in complex with a H2AK119Ub nucleosome. Our structural, biochemical, and cellular data reveal the molecular interactions of BAP1 and ASXL1 with histones and DNA that are critical for remodeling the nucleosome and thus establishing specificity for H2AK119Ub. These results further provide a molecular explanation for how >50 mutations in BAP1 and ASXL1 found in cancer can dysregulate H2AK119Ub deubiquitination, providing new insight into understanding cancer etiology.
The human brain is a complex organ that consists of several regions each with a unique gene expression pattern. Our intent in this study was to construct a gene co-expression network (GCN) for the normal brain using RNA expression profiles from the Genotype-Tissue Expression (GTEx) project. The brain GCN contains gene correlation relationships that are broadly present in the brain or specific to thirteen brain regions, which we later combined into six overarching brain mini-GCNs based on the brain’s structure. Using the expression profiles of brain region-specific GCN edges, we determined how well the brain region samples could be discriminated from each other, visually with t-SNE plots or quantitatively with the Gene Oracle deep learning classifier. Next, we tested these gene sets on their relevance to human tumors of brain and non-brain origin. Interestingly, we found that genes in the six brain mini-GCNs showed markedly higher mutation rates in tumors relative to matched sets of random genes. Further, we found that cortex genes subdivided Head and Neck Squamous Cell Carcinoma (HNSC) tumors and Pheochromocytoma and Paraganglioma (PCPG) tumors into distinct groups. The brain GCN and mini-GCNs are useful resources for the classification of brain regions and identification of biomarker genes for brain related phenotypes.
Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.
Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.
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