Significant variations in alternative splicing patterns and expression profiles between human-mouse orthologs in early embryos

Abstract

Human and mouse orthologs are expected to have similar biological functions; however, many discrepancies have also been reported. We systematically compared human and mouse orthologs in terms of alternative splicing patterns and expression profiles. Human-mouse orthologs are divergent in alternative splicing, as human orthologs could generally encode more isoforms than their mouse orthologs. In early embryos, exon skipping is far more common with human orthologs, whereas constitutive exons are more prevalent with mouse orthologs. This may correlate with divergence in expression of splicing regulators. Orthologous expression similarities are different in distinct embryonic stages, with the highest in morula. Expression differences for orthologous transcription factor genes could play an important role in orthologous expression discordance. We further detected largely orthologous divergence in differential expression between distinct embryonic stages. Collectively, our study uncovers significant orthologous divergence from multiple aspects, which may result in functional differences and dynamics between human-mouse orthologs during embryonic development. ortholog, alternative splicing, RNA-seq, early embryo, gene expression Citation:

Genotype–Phenotype Association Analysis Reveals New Pathogenic Factors for Osteogenesis Imperfecta Disease

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Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

“…Five basic modes are generally recognized for AS, including exon-skipping (cassette exon), mutually exclusive exons, alternative donor site, alternative acceptor site, and intron retention. Lots of studies have shown that AS is very common in mammalians and over 90% of human genes could undergo AS based on bulk RNA-seq data (Wang et al, 2008; Chen et al, 2017a). Moreover, AS play crucial roles in a variety of biological processes and abnormal AS may be correlated with cancers (Sveen et al, 2016).…”

“…Gene regulatory network inference has been widely conducted in numerous bulk RNA-seq studies, while scRNA-seq also provides great potential for such analysis. For bulk RNA-seq data, networks are usually constructed from a number of samples using the tools like weighted gene co-expression network analysis (WGCNA) (Langfelder and Horvath, 2008; Chen et al, 2017a). A basic assumption is that the genes highly correlated in expression could be co-regulated.…”

Developmental processes regulate craniofacial variation in disease and evolution

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