Skip to content
naulon

No retraction, correction or concern found when last checked on Oct 22, 2026.

Alternative Splicing and Protein Diversity: Plants Versus Animals

Saurabh Chaudhary, Waqas Khokhar, Ibtissam Jabre and 4 more

Frontiers in Plant Science · 2019 · Review · CC BY

doi:10.3389/fpls.2019.00708

Abstract

Plants, unlike animals, exhibit a very high degree of plasticity in their growth and development and employ diverse strategies to cope with the variations during diurnal cycles and stressful conditions. Plants and animals, despite their remarkable morphological and physiological differences, share many basic cellular processes and regulatory mechanisms. Alternative splicing (AS) is one such gene regulatory mechanism that modulates gene expression in multiple ways. It is now well established that AS is prevalent in all multicellular eukaryotes including plants and humans. Emerging evidence indicates that in plants, as in animals, transcription and splicing are coupled. Here, we reviewed recent evidence in support of co-transcriptional splicing in plants and highlighted similarities and differences between plants and humans. An unsettled question in the field of AS is the extent to which splice isoforms contribute to protein diversity. To take a critical look at this question, we presented a comprehensive summary of the current status of research in this area in both plants and humans, discussed limitations with the currently used approaches and suggested improvements to current methods and alternative approaches. We end with a discussion on the potential role of epigenetic modifications and chromatin state in splicing memory in plants primed with stresses.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 22, 2026. Paragraph numbers are ours, so an answer can point at one.

Introduction

Plants have evolved various developmental and physiological strategies to control daily activities that respond to variable and extreme environmental conditions. To maximize efficiency under diverse conditions, the crosstalk between multiple layers of gene regulation including co-transcriptional, post-transcriptional, and post-translational regulation is crucial for plants. Alternative splicing (AS) is one such mechanism, which is widespread in plants and humans, generates two or more mRNAs from the same precursor-mRNA (pre-mRNA) and is thought to significantly contribute toward protein diversity. The basic mechanism of AS in higher eukaryotes is similar, however, some differences in gene architecture, splicing and transcription machinery between plants and animals suggest plant-specific regulation of AS.

The advances in next-generation sequencing (NGS) technology and omics approaches in plants have revealed that up to 70% of multi-exon genes undergo AS. Among all AS events, intron retention (IR) is the predominant mode of AS in plants, whereas exon-skipping (ES) is the major type in humans (Figure 1). Interestingly, IR generates mostly non-sense mRNAs harboring premature terminal codons (PTC+) and are either degraded by the non-sense-mediated mRNA decay (NMD) pathway, or escape NMD to produce truncated proteins, thereby regulating the function and abundance of their full-length counterparts. The NMD pathway is a post-transcriptional mRNA quality control mechanism which acts to degrade PTC+ mRNAs. Some studies suggest alternative roles for transcripts with IR, which are either sequestered in the nucleus and released on demand or function as protein-coding introns known as exitrons (Figure 1), a new class of retained introns with some features of exons.

Plants modulate their gene expression patterns via AS coupled to NMD during different developmental stages, abiotic and/or biotic stresses and the circadian clock function. Stressful conditions control not only the ratios but the timing of both sense and non-sense AS transcripts. However, it is unclear how environmental signals modulate splicing ratios and timing to help plants acclimate to such stresses in the short and long term. Furthermore, it is largely unknown to what extent AS transcripts are recruited for translation to be functionally significant at the proteomic level in plants.

Alternative splicing regulates essential functions in humans such as autophagy, apoptosis, protein localization, enzymatic activities and interaction with ligands, transcription factors activity and mRNA abundance, etc.. Hence, it is not surprising that any aberrant or dysregulation in AS can cause several human diseases including cancer, neurological disorders, heart, and skeletal muscle abnormalities, and multiple genetic disorders. Recent transcriptome (RNA-Seq), translatome (ribosomal foot-printing), and proteome data have shown a significant contribution of AS toward protein diversity in humans. On the other hand, some proteomic studies suggest that AS may not significantly contribute to protein diversity and only single dominant isoforms are represented at the protein level for most of the protein-coding genes. Apparently, these contradictions stem from the lower depth and limitations of mass spectrometry (MS) techniques to detect changes in protein domains as a result of AS. In this review, basic differences in the mechanism of AS and its contribution toward protein diversity in plants and humans are discussed. We also discuss some emerging aspects of IR, NMD pathway, chromatin structure, and splicing memory in plants.

Coupling of Transcription and Splicing in Plants and Humans

Plant spliceosome machinery is not well characterized due to the unavailability of in vitro systems. However, in a recent study, an attempt has been made to develop an in vitro pre-mRNA splicing assay using plant nuclear extracts, and it may help to delineate and characterize components of the plant spliceosome machinery. Sequence similarity based analyses suggest conserved regulation of AS in higher eukaryotes. Briefly, splicing is carried out by the spliceosome, which consists of five small nuclear ribonucleoprotein particles (snRNPs) designated as U1, U2, U4, U5, and U6 and additional spliceosome-associated non-snRNP proteins. The cis-acting elements present on pre-mRNA include 5′ splice sites (5′SS), 3′ splice sites (3′SS), polypyrimidine tracts (PPT) and branch point sequences, which are recognized by the trans-acting factors such as splicing factors (SFs) mainly SR proteins and hnRNPs. The trans-acting SFs and cis-regulatory elements guide and modulate the spliceosome to recognize differential splice sites present on pre-mRNA. The details on the assembly of the spliceosome and regulation of AS has been reviewed extensively and readers are referred to excellent articles on this topic.

Recent evidence from metazoans indicates that the process of splicing is largely co-transcriptional. Extensive studies in animals and emerging data in plants show that the splicing process for the majority of genes is predominantly co-transcriptional in nature (Figure 2). The co-transcriptional behavior of splicing means that the chromatin environment such as methylation status, histone modifications, nucleosome occupancy and RNA Polymerase II (RNAPII) processivity has a strong influence on splicing outcomes.

Interestingly, long non-coding RNAs (lncRNAs) can also influence the splicing dynamics of their target genes either directly and/or after processing into short interfering or micro RNAs. Non-coding RNAs can affect AS via modulating chromatin structure, splicing factor recruitment and altering the phosphorylation status of spliceosomal proteins. Circular RNAs which are generated by the so-called non-canonical “backsplicing” of pre-mRNAs are known to regulate AS in animals and examples from plants are beginning to emerge as well. CircRNAs could make DNA:RNA hybrids with the genomic DNA to generate the so-called R-loop. Indeed, a circRNA derived from exon 6 of the SEPALLATA3 (SEP3) gene forms an R-loop via direct interaction with the SEP3 locus. The R-loop formation around exon 6 of the SEP3 gene results in skipping of this exon and affects petal and stamen number in Arabidopsis.

Plant promoters are largely devoid of nucleosomes, as a result of lower GC content (high AT enrichment) as compared with humans. Therefore, the dynamics of transcription initiation are fundamentally different between humans and plants. Depending upon the chromatin context in animals and plants, RNAPII is recruited at a promoter to form the pre-initiation complex (PIC), however, its processivity is inherently dependent on the chromatin structure along gene bodies and influences RNA-processing during transcription. Techniques such as native elongation transcript sequencing (NET-Seq) in mammals (mNET-Seq) and plants (pNET-Seq) and global run-on sequencing (GRO-seq), have revealed some important aspects of RNAPII elongation and structural features during transcription and RNA-processing, in humans and plants, respectively. The carboxyl-terminal domain (CTD) of the largest subunit of RNAPII contains a heptad repeat “Tyr1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7.” The Ser2 and Ser5 of this heptad repeat undergoes phosphorylation and plays a key role in the coordination of transcription and other RNA processing activities. In mNET-Seq, phosphorylation-specific antibodies were used to study immunoprecipitated RNAPII transcripts in humans. The comparative analysis of un-phosphorylated (unph) or low-phosphorylated and phosphorylated CTD of RNAPII revealed the accumulation of different forms at differential positions on protein-coding genes. For instance, the RNAPII unph-CTD shows a peak at the transcription start site (TSS), whereas RNAPII Ser5P CTD accumulates at the 5′SS of exon–intron boundaries and its density reduces as the RNAPII elongation proceeds downstream toward the 3′ end of the intron (Figure 2A). Similarly, RNAPII Ser2P CTD spreads over gene bodies (GB) and shows accumulation at the transcription end site (TES) (Figure 2A). Moreover, genes that undergo co-transcriptional splicing, such as TARS in humans, show a major peak of RNAPII Ser5P CTD at 5′SS, suggesting pausing at the exon to allow time for the spliceosome to catalyze the first splicing reaction. Similar to humans, the dynamics of RNAPII in plants is also established during transcription. As shown in the proposed model of co-transcriptional splicing in Figure 2A, plants RNAPII CTD is phosphorylated as transcription proceeds. However, in both humans and plants, unph RNAPII is recruited at the promoter region to form the PIC. After initiation, phosphorylation of RNAPII Ser5 CTD and Ser2 CTD begins as transcription proceeds toward the 3′ end. The RNAPII Ser5P CTD pauses at 5′SS, whereas RNAPII Ser2P CTD shows accumulation immediately after polyadenylation site (PAS), suggesting their role in splicing and transcription termination, respectively.

Despite similarities in the dynamics of RNAPII during transcription and co-transcriptional splicing among plants and humans, significant differences have also been reported, suggesting species-specific regulation of transcription and splicing. For instance, the engaged RNAPII profiles suggest, promoter-proximal pausing and divergent transcripts in Arabidopsis and maize are absent, whereas, these are prominent features of the human transcription. In plants, the lack of promoter-proximal-pausing and a high correlation between transcription and steady-state RNA suggests initiation level regulation of transcription as compared to humans. In contrast to GRO-Seq analysis, the combination of GRO-Seq and pNET-Seq data in Arabidopsis show that RNAPII pauses or slows down in some genes after initiation of transcription. However, unlike humans, which show RNAPII pausing in narrow regions (20–25 nt), plant RNAPII pausing in the promoter-proximal-regions is much broader (Figure 2C). Additionally, a strong positive correlation has been observed between RNAPII pausing at PAS, CpG methylation and longer genes in plants than in humans, which further suggests plant-specific regulation of transcription and splicing regulation.

Many features of transcription are conserved between humans and plants, however, some important differences exist between them. For example, there is a higher RNAPII elongation rate and AS in the presence of light than dark, demonstrating coupling between AS transcription and growth conditions, which is an important mechanism for plants to respond to different environmental conditions. Thus, the role of RNAP II processivity and its impact on AS needs to be analyzed in a tissue- and condition-dependent manner in plants. In the last decade, significant progress has been made to understand the co-transcriptional behavior of splicing/AS in animals, and yeast systems. However, this area is relatively new in plants and more studies are required to illuminate the co-transcriptional dynamics and its impact on RNA processing in tissue- and condition-specific manner.

Aspects of IR and NMD in Plants and Humans

Intron retention is the most prevalent AS event in plants with observed frequencies between 28% to as high as 64% (Figure 1) depending upon growth condition, tissue type and the coverage of transcriptome data. In comparison with plants, only 5% of IR events were observed in humans (Figure 1), owing to the large size of animal introns, sequencing depth and bioinformatics challenges to detect them. As a consequence, IR had received limited interest in humans until recently, whereas in plants IR has been found to be an important regulator in growth, development, physiology, and stress responses. However, recent research is unveiling various menace regulatory functions of IR in humans. For example, in addition to physiologically regulated events, any mutation in the splice site or splicing regulatory sequences cause aberrant IR, which further results in perturbed splicing patterns and potentially cause diseases.

In humans, possible causes of IR and its abundance in response to cell differentiation and stresses have been studied recently. For instance, to predict the prevalence of IR, and their regulation and biological significance, a deep quantitative survey using Poly(A+) RNA-Seq data from 40 human and mouse tissue samples was conducted. This study involved the quantitative measurement and comparison of reads across unspliced (exon–intron) and spliced (exon–exon) junctions, as well as, reads within introns in terms of “percent intron retention” (PIR). These findings suggest a large number of multiexonic genes are affected by the variable frequency of IR events processed in different tissues, which is much higher in comparison with previously estimated values. Comparative analysis across various species revealed tissue-specific IR events in neurons and immune cells. Furthermore, IR in neurons is highly conserved as compared with other AS events. In contrast with previous studies, IR was prevalent and mainly enriched in untranslated regions (UTRs), non-coding RNAs, depleted protein coding regions, and/or at the 3′ end of RNAs among different tissues in humans. Moreover, the frequency of IR in the nucleus was observed to be higher than the cytoplasm, suggesting nuclear sequestration or coupling with the NMD pathway.

In comparison with humans, the prevalence and significance of IR in plants and its role in development, stress and tissue-specific physiology are well documented. The observed frequency of IR in plants is as high as 64%, and potentially fine-tunes the transcriptome functionality. However, the mechanisms behind the high occurrence of IR in plants are still not very clear, yet many studies emphasize its significance in plants under normal, stress and various development and growth conditions. For example, the expression of INTERMINATE DOMAIN 14 (IDD 14) isoforms controlled via IR mediate starch accumulation and utilization under cold stress in Arabidopsis. Similarly, cold-dependent IR in clock genes such as CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), LATE ELONGATED HYPOCOTYL (LHY) and PSEUDO-RESPONSE REGULATOR7 (PRR7), modulate their transcript and protein abundance for CCA1. In wheat, the PECTIN METHYL ESTERASE INHIBITOR (PMEI), which secretes pectin for the cell wall, is also regulated by IR. Although, PMEI IR isoforms are found in almost all tissues but only anthers contained mature transcripts without IR, suggesting possible tissue-specific functionality of these transcripts. Similarly, studies in a Marsilea vestita and Arabidopsis provide a useful model to explain unproductive AS via IR. It has been demonstrated in M. vestita that some NMD insensitive IR transcripts remain in the nucleus as un-spliced mRNAs. Subsequently, these IR transcripts could be spliced and their translation results in a specific function, such as gamete development.

Interestingly, many of IR PTC+ transcripts are not subjected to NMD in plants, suggesting regulatory functions. Components of the NMD machinery are highly conserved between plants and humans and its efficiency is strongly influenced by the pioneer round of translation (activity of ribosomes). However, it is intriguing that NMD responses are much less pronounced under stressful conditions in humans and plants, affecting the expression and translation of stress-responsive genes and splice variants. For example, inhibition of NMD mediates plant defense response during pathogen attack in Arabidopsis NMD mutants as they constitutively make more salicylic acid (SA) and show a heightened response after infection with Pseudomonas syringae. However, mechanistic details of AS and its role via protein diversity in subverting a pathogen attack is not clear. Since the NMD pathway is translation dependent, slow engagement of different non-canonical transcripts with the ribosomal machinery may be the cause of their degradation. Intriguingly, in several model species including Arabidopsis, PTCs in the first and last intron appear earlier in their sequence than expected by chance alone, to keep the metabolic cost of producing truncated proteins and their subsequent degradation. This data supports the notion that the appearance of earlier PTCs in introns seems to be favored by selection. Presence of PTCs in the first and last introns also points toward multiple features favoring degradation of non-sense transcripts.

Interestingly, introns in plants UTRs also play a crucial role by affecting translation efficiency via a process called intron-mediated enhancement (IME). IME was proposed as a conserved phenomenon enhancing the translation efficiency of IR transcripts. For example, analysis of 5′ UTR introns identified an intron element in transcripts of the Mg2+/H+ ion exchange (MHX) gene in Arabidopsis, which further show an increase in translation efficiency. In summary, differences in the frequencies of IR events suggest a varied mode of downstream processing and fates of IR transcripts in plants and humans. However, further work is needed to illuminate the mechanistic details of the IME mechanism.

As and Protein Diversity in Humans: Supporting Evidence

Higher eukaryotes are diverse with varying degrees of biological complexity, nonetheless, the number of protein-coding genes is comparable between different species. Comparative sequencing and evolutionary studies between different eukaryotic species (including complex avian and mammals to species with fewer cell types) suggest a strong correlation between AS and organism complexity. AS plays a crucial role to enrich the expression of many genes and mediates various biological functions, pathways, and processes. In humans, despite significant advancements in the field of transcriptome and proteome analysis techniques, the extent to which AS transcripts contribute to protein diversity remains unclear. However, renewed interest in humans has led to concerted efforts to illuminate this phenomenon in the recent past (Table 1). For example, isolation and sequencing of ribosome-bound transcripts have enabled researchers to delineate how the variety and abundance of mRNAs correlate with ribosomal recruitment (potentially translating mRNA). In a recent study, the ribosomal-engaged landscape of AS transcripts was surveyed using ribosomal-profiling in humans. The ribosomal profiling data suggest transcripts with exon skipping events are present in medium to high abundance and thus likely to be translated. On the contrary, transcripts present in low abundance at the transcriptome level were not engaged with the ribosomes. This might be due to either the presence of introns in the low abundance transcripts, which remain in the nucleus or incomplete RNA processing, preventing ribosomal engagement. Similarly, other studies using Frac-Seq (subcellular fractionation and RNA-sequencing) and TrIP-Seq (transcript isoforms in polysomes sequencing), also detected a large proportion of splice variants in the polyribosome fractions suggesting spliced isoforms play a significant role in controlling protein output in human cells. However, the degree to which ribosomal bound AS transcripts are translated and represented at the protein level is unclear. For example, pre-mRNA processing in the nucleus influences an isoform’s association with polyribosomes. Approximately 30% of mRNA processing events are differentially partitioned between cytoplasmic and polyribosome fractions. Moreover, differences in the polyribosome association are the result of a change in the cis-regulatory landscapes such as inclusion or exclusion of uORFs and Alu-elements in the 5′UTR, and microRNA target sites in the 3′UTR by AS. Similarly, TrIP-Seq analysis revealed that each transcript isoform harbors special regulatory features controlling ribosome occupancy and translation. found robust translational control by 5′ UTRs between cell lines, whereas 3′ UTRs impact cell type-specific expression. This work also suggested that transcript isoform diversity must be considered when associating RNA and protein levels.

Some proteomic studies contradict ribosome profiling data and argue that only a small fraction of splice variants are represented at the protein level. Furthermore, the shotgun MS techniques used in many proteomic studies have their own limitations of coverage and sensitivity to detect low abundance splice variants at the protein level. To improve isoforms detection efficiency, alternative approaches need to be developed to overcome the limitations of the techniques used at present. Toward this goal, full-length ORFs of AS isoforms from a large number of human genes were cloned and protein–protein interaction (PPI) profiling was performed to demonstrate the functionality of hundreds of protein isoforms. This study demonstrated vastly different interaction profiles among isoforms as a result of AS. Strikingly, the isoforms encoded by the same genes exhibit widespread functional differences in the PPI network analysis. Since differences between protein isoforms are as high as observed between different genes, isoforms-specific partners could have different expression and functional characteristics. proposed that a vast diversity of “functional alloforms” are generated that contribute to different physiological and developmental processes.

In humans, a number of studies have been conducted to identify protein isoforms that result from AS by comparing transcriptome and proteome data. However, most of these studies were carried out in a steady state manner and do not explain the consequences of perturbation in splicing to protein diversity. To overcome these limitations, an integrated approach was developed to illuminate how variation in mRNA splicing patterns could subsequently change the proteome composition in a systematic manner. Selectively depleted spliceosome U5 component PRPF8 orchestrated changes at the transcriptome and proteome level that were determined using RNA-Seq and Sequential Window Acquisition of all Theoretical Spectra-Mass Spectrometry (SWATH-MS), respectively. After PRPF8 depletion, quantification of splice variants and a large fraction of proteome identified 1,542 proteins that displayed at least one peptide with altered expression. Functional annotation revealed that transcripts with altered splicing patterns possess similar cellular functions and processes (such as RNA splicing, the mitotic cell cycle and ubiquitination) as those found in proteins with altered levels. Thus, splicing variants at the transcriptomic level were found to be functionally represented at the protein level. Furthermore, to identify the differentially spliced event at the transcriptome level, the authors used a transcript-centric approach, in which a transcript is considered as a whole unit. Firstly, transcript expression is estimated, followed by identification of differentially used transcripts and expressed genes. The correlation analysis between fold changes in the expression level after PRPF8 depletion suggests protein expression levels are exclusively associated with the alternatively spliced transcripts involving differential transcripts usage (DTU). Interestingly, IR events, which are considered as one of the major regulatory events for gene expression, had reduced representation at the protein level. Although, around 75% of multi-exon genes are affected by IR and help in regulating transcript levels, its impact on protein expression is inverse because an increase in the level of IR transcripts, throughout the genome, is associated with PRPF8 depletion. The peptide expression of 270 genes with retained introns showed downregulation of protein expression coded by genes with IR. Moreover, the relative abundance of transcripts also plays a significant role in protein expression as the low abundance transcripts with IR do not affect the protein expression until they are present in high abundance. These observations suggest IR reduces the protein diversity but fine-tunes the human proteome functionality. However, this finding may not be strictly applicable to plants as IR is the predominant mode of AS and may fine-tune the proteome function via modulating its abundance, especially in stressful conditions.

Collectively, various studies in the recent past such as ribosomal profiling, PPI interaction analysis of spliced isoforms, and integrative analysis using perturbed systems suggest a strong correlation between AS and protein diversity in humans. Moreover, these studies provide an alternative to MS techniques, which have limitations of coverage and sensitivity to detect low level splice isoforms at the protein level and could be useful to study plant systems in the future.

As and Protein Diversity in Humans: Opposing Evidence

The contribution of AS toward protein diversity in humans is well documented. However, recent data from some proteomic studies in humans supports the opposing view and suggest that AS may not be the key contributor to protein diversity. Substantial amount of AS data has been generated in various RNA-Seq experiments in humans, however, most of the alternative isoforms in proteomic experiments are undetectable even in large-scale MS-based analyses. Moreover, some studies suggest that AS is the result of noise in the splicing machinery and does not contribute to protein diversity as expected. For example, proposed a stochastic noise model of splicing machinery, which explained that AS events arise as a result of noise in the splicing machinery. The idea of noise in the splicing machinery has also been supported by other studies as well, suggesting a large proportion of alternative isoforms are non-functional. Further, it was recently demonstrated that the majority of expressed genes have a single major isoform represented at the protein level. This was supported by monitoring peptide evidence from eight large-scale MS experiments and observing that only one main protein isoform was dominant at the protein level from almost all coding genes. On the other hand, several reports have supported the presence of a small number of alternative protein isoforms in humans, drosophila, and mouse in large-scale proteomic studies. However, AS events such as ES detected in RNA-Seq studies have revealed subtle effects on the structure and function of proteins. Tress et al. argue that it is the gene expression that is conserved across species, have strong tissue dependence, and are translated to detectable proteins but not the alternatively spliced isoforms. Clearly, more work and evidence is needed to illuminate the relationship between AS and protein diversity in tissue- and condition-dependent manner.

The efficiency of the MS also needs to be enhanced because current MS techniques cannot reliably detect changes in protein domains as a result of AS. For example, lysine and arginine coding triplets are the most abundant amino acids at the end of exons or exon–exon junctions, and are the preferential sites for trypsin, which is the most common enzyme used in MS analyses. Since trypsin digests exon–exon junctions, it hinders with the detection of novel AS derived peptides in MS-based proteome analysis. To improve efficiency, enzymes such as chymotrypsin can be used as an alternative to improve the detection of AS-derived peptides in proteome studies.

The Contribution of as Toward Protein Diversity in Plants

The role of AS in the expansion of functional protein diversity is less clear in plants as compared to humans. However, in the absence of in-depth proteomic studies to elucidate the role of AS toward protein diversity is tenuous. Recently, some studies have evaluated the influence of AS on protein diversity in plants. For example, hypoxia in Arabidopsis mediates an increase in the number of IR events in many mRNA isoforms, and show ribosomal engagement and potentially influence protein variety and abundance. Interestingly, transcriptome and translatome profiling among shoot apical meristem (SAM) and leaf domains, suggest 751 genes isoforms show domain-specific enrichment in the translatome data. Another study in Arabidopsis has shown that 35% of AS events are represented among the polysome-bound mRNAs and expected to undergo translation. Among all transcripts, IR is the least representative among translated transcripts, compared with untranslated transcripts, suggesting a variable role of IR in regulating transcript level via NMD machinery or sequestration in the nucleus and further processing on demand. In contrast, other splicing events such as ES, 5′AD, and 3′AA have higher proportions among transcripts that may be translated. Sequence analysis of translated transcripts suggests that any alteration in the CDS by AS could lead to a change in protein sequences. Interestingly, a large proportion of a new class of exon-like introns called exitrons (Figure 1) was found at the transcriptome as well as translatome level, suggesting these unique events of AS may contribute to protein diversity. A recent report in Physcomitrella patens suggests that AS shapes the transcriptome rather than the proteome, because only 85 isoform-specific peptides, representing only 25 differentially AS genes, were found in moss cells. Among all, only five genes unambiguously showed two or more protein isoforms from the same locus. The number of AS genes identified in this study was substantially large (approximately 66 times) as compared to proteomic datasets, nonetheless, only support a small contribution of AS on protein diversity. Collectively, these data support the view that AS increases protein complexity, however, its contribution is found to be lower as compared with humans. Further, supporting as well as the opposing evidence presented above for the notion, “AS contributes toward protein diversity,” suggests that the exact number of splice isoforms represented at the proteome level in humans as well as in plants is still elusive. On the other hand, IR events are the predominant AS type in plants and may not be translated due to nuclear sequestration or degradation by the NMD pathway and thus remain poorly represented in MS experiments. Since limited information is available at the proteome level, we envisage that strategies like cloning of spliced isoforms and PPI profiling (like in humans ), could be beneficial and may uncover different aspects of AS contribution toward protein diversity in plants.

Splicing Memory and Plant Stress Tolerance

Successful attempts have been made in plant systems to understand the impact of stress, its tolerance and the development of genetically engineered stress tolerant crops. However, the majority of studies are restricted to acute and single stress only. Since stresses are usually multiple, recurring and chronic, plants have evolved sophisticated defense mechanisms to deal with a variety of stresses. Plants have the ability to acquire tolerance to chronic stress through establishing “molecular stress memory” to confer tolerance through a phenomenon referred to as priming or acclimation, in response to previous exposure to a mild stress. Priming establishes a new cellular state in plants, which is different from the naïve or unexposed plants. In recent years, it has become increasingly apparent that various epigenetic features, such as chromatin modifications, nucleosome positioning, and DNA methylation, are important components of adaptation and play a role in stress memory. Since the splicing process is largely co-transcriptional in nature, the chromatin structure has a strong influence on the transcriptional as well as the splicing processes. Recent DNase I-Seq data suggest enrichment of IR in DNase I hypersensitivity sites (DHSs) in both Arabidopsis and rice. Since RNAPII elongation speed is high in regions with open chromatin, the spliceosome machinery has less time to recognize introns, resulting in more IR during co-transcriptional splicing. Furthermore, condition-dependent variation in the chromatin environment under different stresses and environmental cues plays an additional regulatory and fine-tuning role. Moreover, along with the positioning and spacing of the nucleosome, posttranslational modifications and DNA methylation also affect the transcriptional and splicing dynamics. Hence, various epigenetic modifications may provide a basic regulatory mechanism to orchestrate stress and splicing memory (Figure 3) in the same or future generations to respond to recurring stress more efficiently.

Not surprisingly, a recent study uncovered splicing memory response to heat stress priming in Arabidopsis as revealed by genome-wide differentially expressed genes (DEGs) and AS patterns. DEGs in response to heat stress were identified for different stages of priming, and genes responsible for potentially controlling heat stress memory were selected. With the help of gene networking analysis, heat and abiotic responsive genes were found to be involved in stress memory. Importantly, IR was found to be the most prevalent event under heat stress and contributed significantly toward establishing the splicing memory in response to heat. The primed plants produced comparable splicing patterns and efficiency compared with control plants, which were not exposed to heat stress before. In contrast, non-primed plants showed a significant increase in IR and produced splicing variants in heat conditions. Therefore, the primed plants, after relief from the second exposure to heat stress, maintain the splicing memory and perform in a similar manner to the control plants under non-stressful conditions. suggested that heat stress priming might be established at the post-transcriptional level and maintains splicing memory, which is crucial for plant survival and adaptation under stress. It is tempting to speculate that exposure to multiple stresses and coordination of gene expression and splicing patterns mediated by the chromatin environment may influence predictable responses and adaptive solutions in the long term. However, further research is needed to explore splicing memory and the underlying molecular mechanisms in response to different stresses in plants. We envisage that in addition to its contribution to protein diversity, AS may also play regulatory roles, and after repeated episodes of stress, splicing memory may also fine-tune stress-specific protein diversity to enhance plants networking capability to cope with given stress.

Conclusion

Emerging evidence indicates that the splicing process is also predominantly co-transcriptional in plants as in humans. In plants, environmental fluctuations modulate chromatin structure, which in turn, could influence the co-transcriptional splicing process. Intriguingly, recent work indicates that plants can establish splicing memory in response to higher temperature conditions and thus may “remember” a particular stress, likely through specific epigenetic signatures. This strategy may allow plants to engender an appropriate and reproducible response to a given stress. Further, IR transcripts are prevalent in plants and a majority of these are “trapped” in the nucleus. In addition, IR and many other AS transcripts are NMD sensitive and potentially degraded by the NMD pathway. It is clear that AS modulates transcriptome composition and splicing ratios, however, its role in diversifying proteome complexity is far from being understood.

It was a surprising discovery to find that the human genome codes for only ∼20,000 to 21,000 protein-coding genes, which is comparable with a weed (Arabidopsis, which has over 27,000 protein-coding genes) with a much smaller genome. Since 95% of human genes and over 70% of genes in some plants are alternatively spliced, they can potentially make multiple proteins from each gene and considerably increase their proteome complexity. Although it is clear that AS does increase proteome complexity, the extent to which it enhances proteome diversity is far from clear. Multiple proteomic studies do not support a linear relationship between splicing and proteome complexity in humans. Therefore, in-depth proteome analyses in multiple tissues and conditions, in conjunction with the variable expression of corresponding genes, need to be performed to illuminate the relationship between AS and proteome complexity in plants.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References 153

  1. 1.

    Abascal F. Ezkurdia I. Rodriguez-Rivas J. Rodriguez J. M. del Pozo A. Vázquez J. (2015 ). Alternatively spliced homologous exons have ancient origins and are highly expressed at the protein level. PLoS Comput. Biol. 11:e1004325.

    Not in our library10.1371/journal.pcbi.1004325

  2. 2.

    Akua T. Shaul O. (2013 ). The Arabidopsis thaliana MHX gene includes an intronic element that boosts translation when localized in a 5′ UTR intron. J. Exp. Bot. 64 4255–4270.

    Not in our library10.1093/jxb/ert235

  3. 3.
  4. 4.

    Barbosa-Morais N. L. Irimia M. Pan Q. Xiong H. Y. Gueroussov S. Lee L. J. (2012 ). The evolutionary landscape of alternative splicing in vertebrate species. Science 338 1587–1593.

    Not in our library10.1126/science.1230612

  5. 5.

    Becklin K. M. Anderson J. T. Gerhart L. M. Wadgymar S. M. Wessinger C. A. Ward J. K. (2016 ). Examining plant physiological responses to climate change through an evolutionary lens. Plant Physiol. 172 635–649.

    Not in our library10.1104/pp.16.00793

  6. 6.

    Behringer M. G. Hall D. W. (2016 ). Selection on position of nonsense codons in Introns. Genetics 204 1239–1248.

    Not in our library10.1534/genetics.116.189894

  7. 7.

    Bensimon A. Heck A. J. R. Aebersold R. (2012 ). Mass spectrometry-based proteomics and network biology. Annu. Rev. Biochem. 81 379–405.

    Not in our library10.1146/annurev-biochem-072909-100424

  8. 8.

    Bicknell A. A. Cenik C. Chua H. N. Roth F. P. Moore M. J. (2012 ). Introns in UTRs: why we should stop ignoring them. BioEssays 34 1025–1034.

    Not in our library10.1002/bies.201200073

  9. 9.

    Blencowe B. J. (2017 ). The relationship between alternative splicing and proteomic complexity. Trends Biochem. Sci. 42 407–408.

    Not in our library10.1016/j.tibs.2017.04.001

  10. 10.

    Boothby T. C. Zipper R. S. Van der Weele C. M. Wolniak S. M. (2013 ). Removal of retained introns regulates translation in the rapidly developing gametophyte of Marsilea vestita. Dev. Cell 24 517–529.

    Not in our library10.1016/j.devcel.2013.01.015

  11. 11.

    Boutz P. L. Bhutkar A. Sharp P. A. (2015 ). Detained introns are a novel, widespread class of post-transcriptionally spliced introns. Genes Dev. 29 63–80.

    Not in our library10.1101/gad.247361.114

  12. 12.
  13. 13.

    Braunschweig U. Barbosa-Morais N. L. Pan Q. Nachman E. N. Alipanahi B. Gonatopoulos-Pournatzis T. (2014 ). Widespread intron retention in mammals functionally tunes transcriptomes. Genome Res. 24 1774–1786.

    Not in our library10.1101/gr.177790.114

  14. 14.

    Brosch M. Saunders G. I. Frankish A. Collins M. O. Yu L. Wright J. (2011 ). Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and “resurrected” pseudogenes in the mouse genome. Genome Res. 21 756–767.

    Not in our library10.1101/gr.114272.110

  15. 15.

    Brugiolo M. Herzel L. Neugebauer K. M. (2013 ). Counting on co-transcriptional splicing. F1000Prime Rep. 5:9.

    Not in our library10.12703/p5-9

  16. 16.
  17. 17.

    Chaudhary S. Jabre I. Reddy A. S. N. Staiger D. Syed N. H. (2019 ). Perspective on alternative splicing and proteome complexity in plants. Trends Plant Sci. 24 496–506.

    Not in our library10.1016/j.tplants.2019.02.006

  18. 18.
  19. 19.

    Chen W. Moore M. J. (2015 ). Spliceosomes. Curr. Biol. 25 R181–R183.

    Not in our library10.1016/j.cub.2014.11.059

  20. 20.

    Churchman L. S. Weissman J. S. (2011 ). Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469 368–373.

    Not in our library10.1038/nature09652

  21. 21.

    Conn V. M. Hugouvieux V. Nayak A. Conos S. A. Capovilla G. Cildir G. (2017 ). A circRNA from SEPALLATA3 regulates splicing of its cognate mRNA through R-loop formation. Nat. Plants 3:17053.

    Not in our library10.1038/nplants.2017.53

  22. 22.

    Conrath U. Beckers G. J. M. Langenbach C. J. G. Jaskiewicz M. R. (2015 ). Priming for enhanced defense. Annu. Rev. Phytopathol. 53 97–119.

    Not in our library10.1146/annurev-phyto-080614-120132

  23. 23.

    Core L. J. Waterfall J. J. Lis J. T. (2008 ). Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322 1845–1848.

    Not in our library10.1126/science.1162228

  24. 24.

    Ding Y. Fromm M. Avramova Z. (2012 ). Multiple exposures to drought “train” transcriptional responses in Arabidopsis. Nat. Commun. 3:740.

    Not in our library10.1038/ncomms1732

  25. 25.

    Drechsel G. Kahles A. Kesarwani A. K. Stauffer E. Behr J. Drewe P. (2013 ). Nonsense-mediated decay of alternative precursor mrna splicing variants is a major determinant of the Arabidopsis steady state transcriptome. Plant Cell 25 3726–3742.

    Not in our library10.1105/tpc.113.115485

  26. 26.

    Edwards C. R. Ritchie W. Wong J. J.-L. Schmitz U. Middleton R. An X. (2016 ). A dynamic intron retention program in the mammalian megakaryocyte and erythrocyte lineages. Blood 127 e24–e34.

    Not in our library10.1182/blood-2016-01-692764

  27. 27.

    Erhard K. F. Talbot J. E. R. B. Deans N. C. McClish A. E. Hollick J. B. (2015 ). Nascent transcription affected by RNA polymerase IV in Zea mays. Genetics 199 1107–1125.

    Not in our library10.1534/genetics.115.174714

  28. 28.

    Ezkurdia I. Del Pozo A. Frankish A. Rodriguez J. M. Harrow J. Ashman K. (2012 ). Comparative proteomics reveals a significant bias toward alternative protein isoforms with conserved structure and function. Mol. Biol. Evol. 29 2265–2283.

    Not in our library10.1093/molbev/mss100

  29. 29.

    Ezkurdia I. Rodriguez J. M. Carrillo-De Santa Pau E. Vázquez J. Valencia A. Tress M. L. (2015 ). Most highly expressed protein-coding genes have a single dominant isoform. J. Proteome Res. 14 1880–1887.

    Not in our library10.1021/pr501286b

  30. 30.

    Fesenko I. Khazigaleeva R. Kirov I. Kniazev A. Glushenko O. Babalyan K. (2017 ). Alternative splicing shapes transcriptome but not proteome diversity in Physcomitrella patens. Sci. Rep. 7 2698.

    Not in our library10.1038/s41598-017-02970-z

  31. 31.

    Filichkin S. A. Priest H. D. Givan S. A. Shen R. Bryant D. W. Fox S. E. (2010 ). Genome-wide mapping of alternative splicing in Arabidopsis thaliana. Genome Res. 20 45–58.

    Not in our library10.1101/gr.093302.109

  32. 32.
  33. 33.

    Filichkin S. A. Cumbie J. S. Dharmawardhana P. Jaiswal P. Chang J. H. Palusa S. G. (2015 ). Environmental stresses modulate abundance and timing of alternatively spliced circadian transcripts in Arabidopsis. Mol. Plant 8 207–227.

    Not in our library10.1016/j.molp.2014.10.011

  34. 34.

    Filichkin S. Priest H. D. Megraw M. Mockler T. C. (2015 ). Alternative splicing in plants: directing traffic at the crossroads of adaptation and environmental stress. Curr. Opin. Plant Biol. 24 125–135.

    Not in our library10.1016/j.pbi.2015.02.008

  35. 35.

    Filichkin S. A. Hamilton M. Dharmawardhana P. D. Singh S. K. Sullivan C. Ben-Hur A. (2018 ). Abiotic stresses modulate landscape of poplar transcriptome via alternative splicing, differential intron retention, and isoform ratio switching. Front. Plant Sci. 9:5.

    Not in our library10.3389/fpls.2018.00005

  36. 36.
  37. 37.

    Friedrich T. Faivre L. Bäurle I. Schubert D. (2018 ). Chromatin-based mechanisms of temperature memory in plants. Plant. Cell Environ. 42 762–770.

    Not in our library10.1111/pce.13373

  38. 38.

    Gallego-Paez L. M. Bordone M. C. Leote A. C. Saraiva-Agostinho N. Ascensão-Ferreira M. Barbosa-Morais N. L. (2017 ). Alternative splicing: the pledge, the turn, and the prestige: the key role of alternative splicing in human biological systems. Hum. Genet. 136 1015–1042.

    Not in our library10.1007/s00439-017-1790-y

  39. 39.

    Gallegos J. E. Rose A. B. (2015 ). The enduring mystery of intron-mediated enhancement. Plant Sci. 237 8–15.

    Not in our library10.1016/j.plantsci.2015.04.017

  40. 40.

    Godoy Herz M. A. Kubaczka M. G. BrzyŻek G. Servi L. Krzyszton M. Simpson C. (2019 ). Light regulates plant alternative splicing through the control of transcriptional elongation. Mol. Cell 73:1066-1074.e3.

    Not in our library10.1016/j.molcel.2018.12.005

  41. 41.

    Gohring J. Jacak J. Barta A. (2014 ). Imaging of endogenous messenger RNA splice variants in living cells reveals nuclear retention of transcripts inaccessible to nonsense-mediated decay in Arabidopsis. Plant Cell 26 754–764.

    Not in our library10.1105/tpc.113.118075

  42. 42.

    Grasser M. Grasser K. D. (2018 ). The plant RNA polymerase II elongation complex: a hub coordinating transcript elongation and mRNA processing. Transcription 9 117–122.

    Not in our library10.1080/21541264.2017.1356902

  43. 43.

    Gratani L. (2014 ). Plant phenotypic plasticity in response to environmental factors. Adv. Bot. 2014 1–17.

    Not in our library10.1155/2014/208747

  44. 44.
  45. 45.

    Guo J. Price D. H. (2013 ). RNA polymerase II transcription elongation control. Chem. Rev. 113 8583–8603.

    Not in our library10.1021/cr400105n

  46. 46.

    Harlen K. M. Churchman L. S. (2017 ). The code and beyond: transcription regulation by the RNA polymerase II carboxy-terminal domain. Nat. Rev. Mol. Cell Biol. 18 263–273.

    Not in our library10.1038/nrm.2017.10

  47. 47.

    Hartmann L. Wießner T. Wachter A. (2018 ). Subcellular compartmentation of alternatively-spliced transcripts defines SERINE/ARGININE-RICH PROTEIN 30 Expression. Plant Physiol. 176 2886–2903.

    Not in our library10.1104/pp.17.01260

  48. 48.

    Hetzel J. Duttke S. H. Benner C. Chory J. (2016 ). Nascent RNA sequencing reveals distinct features in plant transcription. Proc. Natl. Acad. Sci. U.S.A. 113 12316–12321.

    Not in our library10.1073/pnas.1603217113

  49. 49.

    Hilker M. Schwachtje J. Baier M. Balazadeh S. Bäurle I. Geiselhardt S. (2016 ). Priming and memory of stress responses in organisms lacking a nervous system. Biol. Rev. 91 1118–1133.

    Not in our library10.1111/brv.12215

  50. 50.

    Irimia M. Roy S. W. (2014 ). Origin of spliceosomal introns and alternative splicing. Cold Spring Harb. Perspect. Biol. 6 a016071.

    Not in our library10.1101/cshperspect.a016071

  51. 51.

    Irimia M. Weatheritt R. J. Ellis J. D. Parikshak N. N. Gonatopoulos-Pournatzis T. Babor M. (2014 ). A highly conserved program of neuronal microexons is misregulated in autistic brains. Cell 159 1511–1523.

    Not in our library10.1016/j.cell.2014.11.035

  52. 52.

    Jabre I. Reddy A. S. N. Kalyna M. Chaudhary S. Khokhar W. Byrne L. J. (2019 ). Does co-transcriptional regulation of alternative splicing mediate plant stress responses? Nucleic Acids Res. 47 2716–2726.

    Not in our library10.1093/nar/gkz121

  53. 53.
  54. 54.

    James A. B. Syed N. H. Bordage S. Marshall J. Nimmo G. A. Jenkins G. I. (2012 ). Alternative splicing mediates responses of the Arabidopsis circadian clock to temperature changes. Plant Cell 24 961–981.

    Not in our library10.1105/tpc.111.093948

  55. 55.

    Jung H. Lee D. Lee J. Park D. Kim Y. J. Park W. Y. (2015 ). Intron retention is a widespread mechanism of tumor-suppressor inactivation. Nat. Genet. 47 1242–1248.

    Not in our library10.1038/ng.3414

  56. 56.

    Juntawong P. Girke T. Bazin J. Bailey-Serres J. (2014 ). Translational dynamics revealed by genome-wide profiling of ribosome footprints in Arabidopsis. Proc. Natl. Acad. Sci. U.S.A. 111 E203–E212.

    Not in our library10.1073/pnas.1317811111

  57. 57.

    Kalyna M. Simpson C. G. Syed N. H. Lewandowska D. Marquez Y. Kusenda B. (2012 ). Alternative splicing and nonsense-mediated decay modulate expression of important regulatory genes in Arabidopsis. Nucleic Acids Res. 40 2454–2469.

    Not in our library10.1093/nar/gkr932

  58. 58.

    Kan Z. States D. Gish W. (2002 ). Selecting for functional alternative splices in ESTs. Genome Res. 12 1837–1845.

    Not in our library10.1101/gr.764102

  59. 59.

    Kelemen O. Convertini P. Zhang Z. Wen Y. Shen M. Falaleeva M. (2013 ). Function of alternative splicing. Gene 514 1–30.

    Not in our library10.1016/j.gene.2012.07.083

  60. 60.

    Keren H. Lev-Maor G. Ast G. (2010 ). Alternative splicing and evolution: diversification, exon definition and function. Nat. Rev. Genet. 11 345–355.

    Not in our library10.1038/nrg2776

  61. 61.

    Khodor Y. L. Rodriguez J. Abruzzi K. C. Tang C. H. A. Marr M. T. Rosbash M. (2011 ). Nascent-seq indicates widespread cotranscriptional pre-mRNA splicing in Drosophila. Genes Dev. 25 2502–2512.

    Not in our library10.1101/gad.178962.111

  62. 62.

    Kim E. Magen A. Ast G. (2007 ). Different levels of alternative splicing among eukaryotes. Nucleic Acids Res. 35 125–131.

    Not in our library10.1093/nar/gkl924

  63. 63.

    Koncz C. deJong F. Villacorta N. Szakonyi D. Koncz Z. (2012 ). The spliceosome-activating complex: molecular mechanisms underlying the function of a pleiotropic regulator. Front. Plant Sci. 3:9.

    Not in our library10.3389/fpls.2012.00009

  64. 64.

    Kornblihtt A. R. Schor I. E. Alló M. Dujardin G. Petrillo E. Muñoz M. J. (2013 ). Alternative splicing: a pivotal step between eukaryotic transcription and translation. Nat. Rev. Mol. Cell Biol. 14 153–165.

    Not in our library10.1038/nrm3525

  65. 65.

    Kwon Y.-J. Park M.-J. Kim S.-G. Baldwin I. T. Park C.-M. (2014 ). Alternative splicing and nonsense-mediated decay of circadian clock genes under environmental stress conditions in Arabidopsis. BMC Plant Biol. 14:136.

    Not in our library10.1186/1471-2229-14-136

  66. 66.
  67. 67.
  68. 68.

    Listerman I. Sapra A. K. Neugebauer K. M. (2006 ). Cotranscriptional coupling of splicing factor recruitment and precursor messenger RNA splicing in mammalian cells. Nat. Struct. Mol. Biol. 13 815–822.

    Not in our library10.1038/nsmb1135

  69. 69.
  70. 70.

    Lopez-Casado G. Covey P. A. Bedinger P. A. Mueller L. A. Thannhauser T. W. Zhang S. (2012 ). Enabling proteomic studies with RNA-Seq: the proteome of tomato pollen as a test case. Proteomics 12 761–774.

    Not in our library10.1002/pmic.201100164

  71. 71.

    Lu T. Lu G. Fan D. Zhu C. Li W. Zhao Q. (2010 ). Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. Genome Res. 20 1238–1249.

    Not in our library10.1101/gr.106120.110

  72. 72.

    Luco R. F. Allo M. Schor I. E. Kornblihtt A. R. Misteli T. (2011 ). Epigenetics in alternative pre-mRNA splicing. Cell 144 16–26.

    Not in our library10.1016/j.cell.2010.11.056

  73. 73.

    Mandadi K. K. Scholthof K.-B. G. (2015 ). Genome-wide analysis of alternative splicing landscapes modulated during plant-virus interactions in Brachypodium distachyon. Plant Cell 27 71–85.

    Not in our library10.1105/tpc.114.133991

  74. 74.

    Marquez Y. Brown J. W. S. Simpson C. Barta A. Kalyna M. (2012 ). Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Res. 22 1184–1195.

    Not in our library10.1101/gr.134106.111

  75. 75.
  76. 76.

    Matera A. G. Wang Z. (2014 ). A day in the life of the spliceosome. Nat. Rev. Mol. Cell Biol. 15 108–121.

    Not in our library10.1038/nrm3742

  77. 77.

    Matlin A. J. Clark F. Smith C. W. J. (2005 ). Understanding alternative splicing: towards a cellular code. Nat. Rev. Mol. Cell Biol. 6 386–398.

    Not in our library10.1038/nrm1645

  78. 78.

    Melamud E. Moult J. (2009 ). Stochastic noise in splicing machinery. Nucleic Acids Res. 37 4873–4886.

    Not in our library10.1093/nar/gkp471

  79. 79.

    Merkhofer E. C. Hu P. Johnson T. L. (2014 ). Introduction to cotranscriptional RNA splicing. Methods Mol. Biol. 1126 83–96.

    Not in our library10.1007/978-1-62703-980-2_6

  80. 80.

    Merkin J. Russell C. Chen P. Burge C. B. (2012 ). Evolutionary dynamics of gene and isoform regulation in mammalian tissues. Science 338 1593–1599.

    Not in our library10.1126/science.1228186

  81. 81.

    Misteli T. Cáceres J. F. Clement J. Q. Krainer A. R. Wilkinson M. F. Spector D. L. (1998 ). Serine phosphorylation of SR proteins is required for their recruitment to sites of transcription in vivo. J. Cell Biol. 143 297–307.

    Not in our library10.1083/jcb.143.2.297

  82. 82.

    Modrek B. Resch A. Grasso C. Lee C. (2001 ). Genome-wide detection of alternative splicing in expressed sequences of human genes. Nucleic Acids Res. 29 2850–2859.

    Not in our library10.1093/nar/29.13.2850

  83. 83.

    Naftelberg S. Schor I. E. Ast G. Kornblihtt A. R. (2015 ). Regulation of alternative splicing through coupling with transcription and chromatin structure. Annu. Rev. Biochem. 84 165–198.

    Not in our library10.1146/annurev-biochem-060614-034242

  84. 84.

    Narang V. Sung W. K. Mittal A. (2005 ). Computational modeling of oligonucleotide positional densities for human promoter prediction. Artif. Intell. Med. 35 107–119.

    Not in our library10.1016/j.artmed.2005.02.005

  85. 85.
  86. 86.

    Nilsen T. W. Graveley B. R. (2010 ). Expansion of the eukaryotic proteome by alternative splicing. Nature 463 457–463.

    Not in our library10.1038/nature08909

  87. 87.

    Ning K. Nesvizhskii A. I. (2010 ). The utility of mass spectrometry-based proteomic data for validation of novel alternative splice forms reconstructed from RNA-Seq data: a preliminary assessment. BMC Bioinformatics 11(Suppl. 11):S14.

    Not in our library10.1186/1471-2105-11-s11-s14

  88. 88.

    Nojima T. Gomes T. Grosso A. R. F. Kimura H. Dye M. J. Dhir S. (2015 ). Mammalian NET-seq reveals genome-wide nascent transcription coupled to RNA processing. Cell 161 526–540.

    Not in our library10.1016/j.cell.2015.03.027

  89. 89.
  90. 90.

    Olsen J. V. Ong S.-E. Mann M. (2004 ). Trypsin cleaves exclusively C-terminal to arginine and lysine residues. Mol. Cell. Proteomics 3 608–614.

    Not in our library10.1074/mcp.t400003-mcp200

  91. 91.
  92. 92.

    Pan Q. Shai O. Lee L. J. Frey B. J. Blencowe B. J. (2008 ). Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40 1413–1415.

    Not in our library10.1038/ng.259

  93. 93.

    Paronetto M. P. Passacantilli I. Sette C. (2016 ). Alternative splicing and cell survival: from tissue homeostasis to disease. Cell Death Differ. 23 1919–1929.

    Not in our library10.1038/cdd.2016.91

  94. 94.

    Parra G. Bradnam K. Rose A. B. Korf I. (2011 ). Comparative and functional analysis of intron-mediated enhancement signals reveals conserved features among plants. Nucleic Acids Res. 39 5328–5337.

    Not in our library10.1093/nar/gkr043

  95. 95.
  96. 96.

    Petrillo E. Godoy Herz M. A. Fuchs A. Reifer D. Fuller J. Yanovsky M. J. (2014 ). A chloroplast retrograde signal regulates nuclear alternative splicing. Science 344 427–430.

    Not in our library10.1126/science.1250322

  97. 97.

    Poulos M. G. Batra R. Charizanis K. Swanson M. S. (2011 ). Developments in RNA splicing and disease. Cold Spring Harb. Perspect. Biol. 3 a000778.

    Not in our library10.1101/cshperspect.a000778

  98. 98.

    Preker P. Nielsen J. Kammler S. Lykke-Andersen S. Christensen M. S. Mapendano C. K. (2008 ). RNA exosome depletion reveals transcription upstream of active human promoters. Science 322 1851–1854.

    Not in our library10.1126/science.1164096

  99. 99.
  100. 100.

    Reddy A. S. N. Marquez Y. Kalyna M. Barta A. (2013 ). Complexity of the alternative splicing landscape in plants. Plant Cell 25 3657–3683.

    Not in our library10.1105/tpc.113.117523

  101. 101.

    Reddy A. S. N. Rogers M. F. Richardson D. N. Hamilton M. Ben-Hur A. (2012 ). Deciphering the plant splicing code: experimental and computational approaches for predicting alternative splicing and splicing regulatory elements. Front. Plant Sci. 3:18.

    Not in our library10.3389/fpls.2012.00018

  102. 102.

    Rocchi V. Janni M. Bellincampi D. Giardina T. D’Ovidio R. (2012 ). Intron retention regulates the expression of pectin methyl esterase inhibitor (Pmei) genes during wheat growth and development. Plant Biol. 14 365–373.

    Not in our library10.1111/j.1438-8677.2011.00508.x

  103. 103.

    Romero-Barrios N. Legascue M. F. Benhamed M. Ariel F. Crespi M. (2018 ). Splicing regulation by long noncoding RNAs. Nucleic Acids Res. 46 2169–2184.

    Not in our library10.1093/nar/gky095

  104. 104.

    Rost H. L. Malmstrom L. Aebersold R. (2015 ). Reproducible quantitative proteotype data matrices for systems biology. Mol. Biol. Cell 26 3926–3931.

    Not in our library10.1091/mbc.e15-07-0507

  105. 105.

    Saldi T. Cortazar M. A. Sheridan R. M. Bentley D. L. (2016 ). Coupling of RNA polymerase II transcription elongation with Pre-mRNA splicing. J. Mol. Biol. 428 2623–2635.

    Not in our library10.1016/j.jmb.2016.04.017

  106. 106.
  107. 107.
  108. 108.

    Sanyal R. P. Misra H. S. Saini A. (2018 ). Heat-stress priming and alternative splicing-linked memory. J. Exp. Bot. 69 2431–2434.

    Not in our library10.1093/jxb/ery111

  109. 109.

    Seo P. J. Ryu J. Kang S. K. Park C. M. (2011 ). Modulation of sugar metabolism by an indeterminate domain transcription factor contributes to photoperiodic flowering in Arabidopsis. Plant J. 65 418–429.

    Not in our library10.1111/j.1365-313x.2010.04432.x

  110. 110.

    Severing E. I. van Dijk A. D. J. Stiekema W. J. van Ham R. C. H. J. (2009 ). Comparative analysis indicates that alternative splicing in plants has a limited role in functional expansion of the proteome. BMC Genomics 10:154.

    Not in our library10.1186/1471-2164-10-154

  111. 111.

    Shaul O. (2015 ). Unique aspects of plant nonsense-mediated mrna decay. Trends Plant Sci. 20 767–779.

    Not in our library10.1016/j.tplants.2015.08.011

  112. 112.

    Shen Y. Zhou Z. Wang Z. Li W. Fang C. Wu M. (2014 ). Global dissection of alternative splicing in paleopolyploid soybean. Plant Cell 26 996–1008.

    Not in our library10.1105/tpc.114.122739

  113. 113.

    Sheynkman G. M. Shortreed M. R. Frey B. L. Smith L. M. (2013 ). Discovery and mass spectrometric analysis of novel splice-junction peptides using RNA-Seq. Mol. Cell. Proteomics 12 2341–2353.

    Not in our library10.1074/mcp.o113.028142

  114. 114.

    Shukla S. Oberdoerffer S. (2012 ). Co-transcriptional regulation of alternative pre-mRNA splicing. Biochim. Biophys. Acta 1819 673–683.

    Not in our library10.1016/j.bbagrm.2012.01.014

  115. 115.

    Skelly M. J. Frungillo L. Spoel S. H. (2016 ). Transcriptional regulation by complex interplay between post-translational modifications. Curr. Opin. Plant Biol. 33 126–132.

    Not in our library10.1016/j.pbi.2016.07.004

  116. 116.

    Staiger D. Simpson G. G. (2015 ). Enter exitrons. Genome Biol. 16:136.

    Not in our library10.1186/s13059-015-0704-3

  117. 117.

    Sterne-Weiler T. Martinez-Nunez R. T. Howard J. M. Cvitovik I. Katzman S. Tariq M. A. (2013 ). Frac-seq reveals isoform-specific recruitment to polyribosomes. Genome Res. 23 1615–1623.

    Not in our library10.1101/gr.148585.112

  118. 118.

    Struhl K. Segal E. (2013 ). Determinants of nucleosome positioning. Nat. Struct. Mol. Biol. 20 267–273.

    Not in our library10.1038/nsmb.2506

  119. 119.

    Sureshkumar S. Dent C. Seleznev A. Tasset C. Balasubramanian S. (2016 ). Nonsense-mediated mRNA decay modulates FLM-dependent thermosensory flowering response in Arabidopsis. Nat. Plants 2:16055.

    Not in our library10.1038/nplants.2016.55

  120. 120.

    Sveen A. Kilpinen S. Ruusulehto A. Lothe R. A. Skotheim R. I. (2016 ). Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes. Oncogene 35 2413–2427.

    Not in our library10.1038/onc.2015.318

  121. 121.

    Swarbreck D. Wilks C. Lamesch P. Berardini T. Z. Garcia-Hernandez M. Foerster H. (2008 ). The Arabidopsis information resource (TAIR): gene structure and function annotation. Nucleic Acids Res. 36 1009–1014.

    Not in our library10.1093/nar/gkm965

  122. 122.

    Syed N. H. Kalyna M. Marquez Y. Barta A. Brown J. W. S. (2012 ). Alternative splicing in plants - coming of age. Trends Plant Sci. 17 616–623.

    Not in our library10.1016/j.tplants.2012.06.001

  123. 123.

    Tanner S. Shen Z. Ng J. Florea L. Guigó R. Briggs S. P. (2007 ). Improving gene annotation using peptide mass spectrometry. Genome Res. 17 231–239.

    Not in our library10.1101/gr.5646507

  124. 124.

    Thatcher S. R. Zhou W. Leonard A. Wang B.-B. Beatty M. Zastrow-Hayes G. (2014 ). Genome-wide analysis of alternative splicing in Zea mays: landscape and genetic regulation. Plant Cell Online 26 3472–3487.

    Not in our library10.1105/tpc.114.130773

  125. 125.
  126. 126.

    Trcek T. Sato H. Singer R. H. Maquat L. E. (2013 ). Temporal and spatial characterization of nonsense-mediated mRNA decay. Genes Dev. 27 541–551.

    Not in our library10.1101/gad.209635.112

  127. 127.

    Tress M. L. Bodenmiller B. Aebersold R. Valencia A. (2008 ). Proteomics studies confirm the presence of alternative protein isoforms on a large scale. Genome Biol. 9 R162.

    Not in our library10.1186/gb-2008-9-11-r162

  128. 128.

    Tress M. L. Abascal F. Valencia A. (2017a ). Alternative Splicing may not be the key to proteome complexity. Trends Biochem. Sci. 42 98–110.

    Not in our library10.1016/j.tibs.2016.08.008

  129. 129.

    Tress M. L. Abascal F. Valencia A. (2017b ). Most alternative isoforms are not functionally important. Trends Biochem. Sci. 42 408–410.

    Not in our library10.1016/j.tibs.2017.04.002

  130. 130.

    Ullah F. Hamilton M. Reddy A. S. N. Ben-Hur A. (2018 ). Exploring the relationship between intron retention and chromatin accessibility in plants. BMC Genomics 19:21.

    Not in our library10.1186/s12864-017-4393-z

  131. 131.

    Vinocur B. Altman A. (2005 ). Recent advances in engineering plant tolerance to abiotic stress: achievements and limitations. Curr. Opin. Biotechnol. 16 123–132.

    Not in our library10.1016/j.copbio.2005.02.001

  132. 132.

    Wang E. T. Sandberg R. Luo S. Khrebtukova I. Zhang L. Mayr C. (2008 ). Alternative isoform regulation in human tissue transcriptomes. Nature 456 470–476.

    Not in our library10.1038/nature07509

  133. 133.

    Wang X. Codreanu S. G. Wen B. Li K. Chambers M. C. Liebler D. C. (2018 ). Detection of proteome diversity resulted from alternative splicing is limited by trypsin cleavage specificity. Mol. Cell. Proteomics 17 422–430.

    Not in our library10.1074/mcp.ra117.000155

  134. 134.

    Wang X. Hu L. Wang X. Li N. Xu C. Gong L. (2016 ). DNA methylation affects gene alternative splicing in plants: an example from rice. Mol. Plant 9 305–307.

    Not in our library10.1016/j.molp.2015.09.016

  135. 135.

    Wang X. Zhang B. Wren J. (2013 ). customProDB: an R package to generate customized protein databases from RNA-Seq data for proteomics search. Bioinformatics 29 3235–3237.

    Not in our library10.1093/bioinformatics/btt543

  136. 136.

    Wang Y. Liu J. Huang B. Xu Y. Li J. Huang L. (2014 ). Mechanism of alternative splicing and its regulation (Review). Biomed. Reports. 3 152–158.

    Not in our library10.3892/br.2014.407

  137. 137.

    Weatheritt R. J. Davey N. E. Gibson T. J. (2012 ). Linear motifs confer functional diversity onto splice variants. Nucleic Acids Res. 40 7123–7131.

    Not in our library10.1093/nar/gks442

  138. 138.

    Weatheritt R. J. Sterne-Weiler T. Blencowe B. J. (2016 ). The ribosome-engaged landscape of alternative splicing. Nat. Struct. Mol. Biol. 23 1117–1123.

    Not in our library10.1038/nsmb.3317

  139. 139.

    Wickramasinghe V. O. Gonzàlez-Porta M. Perera D. Bartolozzi A. R. Sibley C. R. Hallegger M. (2015 ). Regulation of constitutive and alternative mRNA splicing across the human transcriptome by PRPF8 is determined by 5’ splice site strength. Genome Biol. 16:201.

    Not in our library10.1186/s13059-015-0749-3

  140. 140.

    Will C. L. Lührmann R. (2011 ). Spliceosome structure and function. Cold Spring Harb. Perspect. Biol. 3 1–2.

    Not in our library10.1101/cshperspect.a003707

  141. 141.

    Willyard C. (2018 ). New human gene tally reignites debate. Nature 558 354–355.

    Not in our library10.1038/d41586-018-05462-w

  142. 142.

    Wong J. J.-L. L. Au A. Y. M. Ritchie W. Rasko J. E. J. (2015 ). Intron retention in mRNA: no longer nonsense. BioEssays 38 41–49.

    Not in our library10.1002/bies.201500117

  143. 143.

    Wong J. J. L. Ritchie W. Ebner O. A. Selbach M. Wong J. W. H. Huang Y. (2013 ). Orchestrated intron retention regulates normal granulocyte differentiation. Cell 154 583–595.

    Not in our library10.1016/j.cell.2013.06.052

  144. 144.

    Wong M. M. Chong G. L. Verslues P. E. (2017 ). Epigenetics and RNA processing: connections to drought, salt, and ABA? Methods Mol. Biol. 1631 3–21.

    Not in our library10.1007/978-1-4939-7136-7_1

  145. 145.

    Yang C. Bolotin E. Jiang T. Sladek F. M. Martinez E. (2007 ). Prevalence of the initiator over the TATA box in human and yeast genes and identification of DNA motifs enriched in human TATA-less core promoters. Gene 389 52–65.

    Not in our library10.1016/j.gene.2006.09.029

  146. 146.

    Yang X. Coulombe-Huntington J. Kang S. Sheynkman G. M. Hao T. Richardson A. (2016 ). Widespread expansion of protein interaction capabilities by alternative splicing. Cell 164 805–817.

    Not in our library10.1016/j.cell.2016.01.029

  147. 147.

    Yu H. Tian C. Yu Y. Jiao Y. (2016 ). Transcriptome survey of the contribution of alternative splicing to proteome diversity in Arabidopsis thaliana. Mol. Plant 9 749–752.

    Not in our library10.1016/j.molp.2015.12.018

  148. 148.

    Zentner G. E. Henikoff S. (2013 ). Regulation of nucleosome dynamics by histone modifications. Nat. Struct. Mol. Biol. 20 259–266.

    Not in our library10.1038/nsmb.2470

  149. 149.

    Zhang H. Lang Z. Zhu J. K. (2018 ). Dynamics and function of DNA methylation in plants. Nat. Rev. Mol. Cell Biol. 19 489–506.

    Not in our library10.1038/s41580-018-0016-z

  150. 150.
  151. 151.

    Zhu B. Zhang W. Zhang T. Liu B. Jiang J. (2015 ). Genome-wide prediction and validation of intergenic enhancers in Arabidopsis using open chromatin signatures. Plant Cell 27 2415–2426.

    Not in our library10.1105/tpc.15.00537

  152. 152.

    Zhu J.-K. (2016 ). Abiotic stress signaling and responses in plants. Cell 167 313–324.

    Not in our library10.1016/j.cell.2016.08.029

  153. 153.

    Zhu J. Liu M. Liu X. Dong Z. (2018 ). RNA polymerase II activity revealed by GRO-seq and pNET-seq in Arabidopsis. Nat. Plants 4 1112–1123.

    Not in our library10.1038/s41477-018-0280-0

Cited by papers we hold 3

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.