Gene expression or gene regulation studies often assume one gene expresses one mRNA. However, contrary to the conventional idea, a single gene in mammalian cells can express multiple transcript variants translated into several different proteins. The transcript variants are generated through transcription from alternative start sites and alternative post-transcriptional processing of the precursor mRNA (pre-mRNA). In addition, gene mutations and RNA editing further enhance the diversity of the transcript variants. The transcript variants can encode proteins with various domains, expanding the functional repertoire of a single gene. Some transcript variants may not encode proteins but function as non-coding RNAs and regulate gene expression. The expression level of the transcript variants may vary between cell types or within the same cells under different biological conditions. Transcript variants are characteristic of cell differentiation in a particular tissue, and the variants may play a key role in normal development and aging. Studies also reported that some transcript variants may have roles in disease pathogenesis. The biological significances urge studying the complexity of gene expression at the transcript level. This article updates the molecular basis of transcript variants in mammalian cells, including the formation mechanisms and potential roles in host biology. Gaining insight into the transcript variants will not only identify novel mechanisms of gene regulation but also unravel the role of the variants in health and disease.

Most eukaryotic genes express more than one mature mRNA, defined as transcript variants. This complex phenomenon arises from various mechanisms, such as using alternative transcription start sites and alternative post-transcriptional processing events. The resulting transcript variants can lead to synthesizing proteins that possess distinct functional domains or may even generate noncoding RNAs, each with unique roles in cellular processes. The generation of these transcript variants is not merely a random occurrence; it is cell-type specific and varies with developmental stages, aging processes, or pathogenesis of diseases. This highlights the biological significance of transcript variants in regulating gene expression and their potential impact on cellular functionality. Despite the biological importance, investigating transcript variants has been hampered by challenges associated with detecting their expression. This review article addresses the advancements in molecular techniques in detecting transcript variants. Traditional methods such as RT-PCR and RT-qPCR can easily detect known transcript variants using primers that target unique exons associated with the variants. Other techniques like RACE-PCR and hybridization-based methods, including Northern blotting, RNase protection assays, and microarrays, have also been utilized to detect transcript variants. Nevertheless, RNA sequencing (RNA-Seq) has emerged as a powerful technique for identifying transcript variants, especially those with previously unknown sequences. The effectiveness of RNA sequencing in transcript variant detection depends on the specific sequencing approach and the precision of data analysis. By understanding the strengths and weaknesses of each laboratory technique, researchers can develop more effective strategies for detecting mRNA transcript variants. This ability will be crucial for our comprehensive understanding of gene regulation and the implications of transcript diversity in various biological contexts.

Cellular gene expression varies in different physiological or pathological conditions. Conventional gene expression analysis assumes that each gene produces a single mRNA, which is not accurate. On average, genes express more than three mRNA isoforms. A particular cell type expresses different mRNA isoforms from a specific gene depending on its developmental or differentiation state. Different isoforms encode distinct proteins or noncoding regulatory RNAs, each with its own unique functions. Pathological states also alter the expression of transcript variants, which can either cause a disease or facilitate recovery. Still, the detection of mRNA isoforms or variants is not preferred to avoid complex analyses. As a result, gene expression studies often fail to identify the actual mRNA isoforms or variants associated with pathophysiology. In this article, we summarize the data on mRNA isoforms and disease-associated variants identified in various physiological and pathological conditions. These findings emphasize the importance of detecting mRNA isoforms for a better understanding of physiological or pathological conditions.

Cellular gene expression varies in different physiological and pathological conditions. Analysis of differential gene expression enables researchers to understand the cellular changes associated with physiological or pathological conditions. During gene expression analyses, researchers calculate the transcripts expressed from a gene under that gene's name, assuming only a single mRNA is expressed by that gene. However, this assumption is biologically inaccurate, but it is often preferred to avoid the complex analyses of isoforms. Most mammalian genes express more than three mRNAs, which encode various proteins or act as noncoding RNAs. We have previously addressed the molecular basis mRNA isoform formation and detection strategies. In this review article, we have discussed the physiological and pathological roles of mRNA isoforms. Any specific cell may express different isoforms of mRNA from a specific gene depending on its differentiation state. Different isoforms can be expressed from a single gene at various stages of development and during aging. The same cell may also express mRNA isoforms related to pathological conditions. Summarized findings highlight the importance of detecting mRNA isoforms over conventional gene identification.

During gonadotropin-induced ovarian follicle development, an antral cavity forms within the layers of granulosa cells (GCs). Gonadotropin stimulation also differentiates the GCs into two distinct lineages: mural GCs (mGCs), which surround the antral cavity, and cumulus GCs (cGCs), which stay in contact with the oocyte. We examined the transcriptomes of mouse mural and cumulus cells to understand the mechanism of differentiation. In addition to analyzing a single transcript expression per gene, we also considered multiple isoform expressions to explore differential transcriptomics. GC-specific core transcription factors Foxl2, Nr5a1, Nr5a2, Runx1 , and Runx2 were expressed at high levels in mGCs but downregulated in cGCs, indicating that cGCs acquire a more differentiated state. Both single-transcript and multiple-isoform analyses revealed differential expression of about 70% of transcripts between mGCs and cGCs. Although the counts were similar, the differentially expressed genes (DEGs) at the single transcript level did not correlate well with the respective differentially expressed transcript isoforms (DETI). We identified DETIs originating from key epigenetic and transcriptional regulator genes, such as Chd1, Ezh2, Kdm5a, Kdm5b, Gata4, Esr2, Fos, Myc , and Ybx1 , that were not differentially expressed at the single-transcript analysis. Further analysis revealed a transcript switch in one-third of the DETIs. Most of the transcript isoforms were protein-coding, followed by non-coding regulatory RNAs. A total of 1,302 transcript isoforms were silenced in cGCs, including those of Adar, Cebpa, Dnmt3a, Foxo4, Pgr, Rest, Runx1, Satb2, Sirt1, Sirt2 , and Tead1 . Conversely, 529 transcript isoforms were activated in cGCs, including transcripts for Brd7, Crem, Chd1, Med21, Med27, Nfkbia, Rbm39, Rbmx, Suv39h2, Tcf12, Xist , and Ybx3 . Additionally, 57 genes exhibited DETIs, with at least one isoform turned off and another turned on in cGCs, including Csde1, Dab2, Ezh2, Gata4, Gnas, Gtf2i, Macf1, Klf10, Setdb1 , and Sp3 . Finally, we explored the mechanisms underlying transcript switching during the differentiation of mGCs and cGCs. Our findings suggest that gonadotropin-induced transcript switching in GCs is crucial for mural and cumulus granulosa differentiation, a key insight that would be missed without mRNA isoform analysis.