Graphical abstract

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Highlights

     * •
       Upon T cell activation, alternative splicing (AS) precedes gene
       expression changes
     * •
       AS impacts cellular processes while gene expression links to
       immune-related pathways
     * •
       Naive T cell splicing patterns in tumors predict the response to
       checkpoint inhibitors
     __________________________________________________________________

   Biological sciences; Immune response; Immunology; Molecular biology

Introduction

   T cells serve as integral constituents of the human immune system,
   wielding multifaceted functions, including pathogen clearance,[46]^1
   elimination of aberrant cells,[47]^2 regulation of inflammatory
   responses,[48]^3 and termination of immune reactions when no longer
   required.[49]^3 These cells exhibit a remarkable ability to adapt to
   environmental cues, transitioning between active and quiescent states
   as dictated by the body’s need for immune surveillance and regulation.
   Such phenotypic alterations are orchestrated by dynamic changes in the
   transcriptional landscape that encompass alterations in gene expression
   and splicing patterns.

   Although considerable attention has been devoted to elucidating gene
   expression dynamics in T cell biology, the regulatory role of splicing
   in T cell activation remains relatively underexplored. Splicing, a
   pivotal editing process of pre-mRNA molecules that encompasses 95% of
   genes, governs the inclusion or exclusion of specific exons, thereby
   influencing the sequence and functionality of the resultant mRNA
   transcript. This molecular rearrangement can yield diverse
   protein[50]^4^,[51]^5 products or modulate mRNA stability,[52]^6 thus
   impacting crucial cellular processes within T cells.

   Early investigations have illuminated the pivotal role of splicing in
   shaping T cell functionality through specific molecular examples.
   Notably, AS of CD45 has emerged as a paradigmatic example,
   orchestrating the balance of tyrosine kinase activity essential for T
   cell activation. This process, observed in activated and memory T
   cells, involves the exclusion of exons 4, 5, and 6, resulting in a
   modified protein product that expedites cellular activation.[53]^7
   Moreover, splicing variants have been implicated in autoimmune
   pathogenesis, as exemplified by the regulatory influence of CD44
   isoforms.[54]^8 In addition, modulation of critical signaling pathways
   such as the NF-κB pathway has been elucidated through alterations in
   the isoform expression of MALT1 following T cell receptor (TCR)
   activation.[55]^9 Similarly, the intricate regulation of apoptosis
   within activated T cells has been delineated, with splicing-mediated
   changes in the BCL2 gene family emerging as key regulatory
   mechanisms.[56]^10 Despite the discovery of these specific examples,
   the dynamic interplay between splicing alterations and gene expression
   during transitions between T cell states remains an underexplored
   field, warranting further investigation to unravel its full
   implications.

   In this study, we delved into the intricate interplay between gene
   expression and splicing dynamics in the T cell immune response,
   particularly during the transition from the naive to the activated
   state. By leveraging a comprehensive analysis utilizing multiple time
   points, we elucidated the temporal kinetics of splicing modulation and
   changes in gene expression during T cell activation. Our findings
   underscore the rapid responsiveness of splicing to activation cues,
   impacting protein segments that affect functionality and apoptotic
   regulation. Moreover, differential expression and splicing analyses
   revealed profound alterations in key genes, including IL2RA and CTLA4,
   implicating their isoform diversity in shaping T cell responses.

   Importantly, our study established a link between T cell dynamics and
   patient responses to immunotherapy in melanoma, demonstrating the
   clinical relevance of understanding T cell splicing patterns. By
   shedding light on the intricate regulatory mechanisms governing T cell
   function, our findings offer valuable insights into immune responses
   and hold promise in delineating a novel immune-therapeutic direction.

Results

Gene splicing alteration in activated CD4 T cell precedes expression dynamics
and affects different genes

   To decipher the multifaceted dynamic alterations prompted by T cell
   activation, we performed a longitudinal assessment of gene expression
   and RNA splicing in CD4 T cells, juxtaposing their splicing patterns
   and expression profiles. For synchronized activation, naive CD4 T cells
   were stimulated with anti-CD3 and anti-CD28 monoclonal antibodies
   (MoAb) ([57]Figure 1A). For dimensionality reduction, principal
   component analysis (PCA) was performed to depict the distinct
   trajectories of mRNA expression and splicing. PCA of the gene
   expression map demonstrated that disparities between the time points of
   analysis were predominantly observed at later times, specifically at
   12, 24, and 72 h post-activation. Conversely, in the spliced PCA
   clusters, the major distinctions between the samples were primarily
   evident at the early time points of 3 and 6 h, differentiating them
   from the naive state ([58]Figures 1B and 1C). These findings suggest
   that, upon activation, splicing alterations manifest at a faster rate
   and precede changes in gene expression.

Figure 1.

   [59]Figure 1
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   Gene expression and splicing dynamics of in vitro activated CD4^+ T
   cells

   (A) Experimental outline of primary naive human CD4 T cell activation
   using anti-CD3∖CD28 antibodies and longitudinal bulk RNA sequencing
   (reanalyzed from Hajaj et al.[61]^11).

   (B and C) Principal component analysis of (B) expression and (C)
   splicing of activated CD4 T cells at different time points.

   (D) Enriched pathways of differentially expressed and spliced genes
   that showed a significant pattern of change over time.

   (E) Venn diagram of the number of significantly overlapping genes at
   time point 72h in the expression analysis (DES) and the genes with at
   least one significant splicing analysis event (DSG) at time point 72h.

   (F and G) Heat maps showing the most differentially expressed genes (F)
   and the most altered splicing events (G).

   At the pathway level, the expression of genes related to immune
   processes exhibited the most significant increase upon activation; for
   example, genes encoding cytokines and pro-inflammatory molecules
   ([62]Figure 1D). In contrast, splicing alterations were most abundant
   in the pathways involved in fundamental cellular functions,
   particularly protein metabolism ([63]Figure 1D). Intriguingly, the
   prominence of genes involved in mRNA splicing as subjects of AS
   themselves indicated that the splicing landscape, in general, is
   affected by the subjection of splicing factors to the same mechanism
   they regulate. This phenomenon has been seen before in mice.[64]^12
   Shared pathways between expression and splicing analyses included NF-κB
   signaling and replication. Pathways related to cell replication
   exhibited significant splicing changes starting at 3 h post-activation,
   whereas the change in gene expression was first observed at 24 h. These
   findings suggest that changes in splicing patterns start immediately
   following activation, faster than changes in gene expression, which
   become apparent at later time points. These data contradict earlier
   reports that used RNA harvested at later time points.[65]^10
   Overlooking shorter time points may miss the immediate nature of RNA
   splicing, which constitutes a rapid-onset regulatory mechanism.

   Next, we examined the overlap between differentially expressed genes
   (DEGs) and differentially spliced genes. Remarkably, an average of only
   9% of the total genes showed overlapping changes in expression and
   splicing at each time point, as observed in previous
   studies[66]^13^,[67]^14^,[68]^15 ([69]Figures 1E and [70]S1A). This
   limited overlap suggests that gene expression and splicing undergo
   independent and parallel alterations, emphasizing the unique
   contributions of each process in the cellular response to activation.

   By clustering the genes based on expression levels, we identified four
   temporally discrete cohorts ([71]Figure 1F). The first group consisted
   of genes that were highly expressed in naive CD4 T cells and were
   downregulated after activation. This set included IL7R, a naive T cell
   marker, and JUN, a transcription factor that regulates other genes in T
   cells. Downregulation of these genes has been reported
   previously.[72]^16^,[73]^17 In the second group, we identified genes
   that were predominantly upregulated within the initial 3–6 h following
   activation, including FASLG and TNFSF14, which have previously been
   shown to appear soon after
   activation.[74]^18^,[75]^19^,[76]^20^,[77]^21 The third group of genes
   displaying high expression levels between 24 and 72 h included MCM2 and
   CDC25C, which are involved in cell replication.[78]^22^,[79]^23 The
   fourth group consisted of genes that exhibited relatively early
   expression, starting 6 h post-activation, and maintained stable
   expression thereafter. Notable genes in this group include IL2 and
   IL2RA, both of which are involved in the survival of activated T
   cells,[80]^24 the costimulatory receptor ICOS,[81]^25 and IRF4,[82]^26
   which regulate CD4 T cell differentiation.

   Among the differentially spliced events (DSE), we specifically observed
   a noteworthy splicing alteration in CD45 (PTPRC) which has been
   repeatedly described in the past following activation[83]^7^,[84]^27
   Exon 6 of the CD45 transcript was gradually excluded until it was
   almost completely skipped at 72 h ([85]Figure 1G).

   As anticipated, splicing factor genes also underwent differential
   splicing following activation. For example, SRSF7 encodes a splicing
   factor that triggers the inclusion of an exon near its binding
   site[86]^28 ([87]Figures 1G and [88]S1C). Post activation exon 4, which
   contains an early stop codon, is included in the SRSF7 transcript,
   leading to its degradation by a nonsense-mediated decay mechanism.
   Differential splicing was also observed in genes modulating RNA
   expression, including DICER, an endoribonuclease involved in RNA
   silencing, and NIPBL, which is responsible for DNA topology
   preservation and cohesion ([89]Figures 1G, [90]S1D, and S1E). Our data
   indicate that DSE at early time points, 3 and 6 h post activation, can
   be divided into two major groups: early transient DSE, which included
   splicing events with an early onset, and early sustained DSE, which
   included events that started early but remained significant at 6 h and
   beyond. Late onset DSE constituted a third cluster, which became
   significant at 12 h onward. Consistent with the PCA plots ([91]Figure
   1C), the early sustained group showed the highest number of DSE events
   ([92]Figure S1B). Events involved in expression modulation and splicing
   pathways were among the most significant early events, whereas late
   events affected genes responsible for protein metabolism. These
   findings shed light on the dynamic patterns of RNA expression and
   splicing in key genes, highlighting the intricate regulatory processes
   that occur during CD4 T cell activation.

The eventual structure of proteins derived from splicing isoforms can be
predicted from RNA sequencing datasets

   AS leads to changes in mRNA sequences, which in turn affect the
   function and localization of proteins. We focused on known annotation
   categories of the protein sequence, including characteristic
   three-dimensional structures (“domains”), binding motives,
   transmembrane segments, disulfide sites, and disordered protein
   segments, most of which belong to the larger “regions” category.
   Disordered regions are known to serve various functions, including
   peptide binding, regulation of protein function, modulation of protein
   half-life, and adaptation to different conformations in a
   partner-dependent manner.[93]^29

   By utilizing the curated protein data from Uniprot,[94]^30 we sought to
   identify specific structural configurations resulting from exon
   skipping and called them “unique annotations.” 41% of genes with
   significant AS events involving skipped exons had annotation data in
   any category. Among these splicing events, the most prevalent
   categories were regions (66%) and domains (48%) ([95]Figure 2A).
   Notably, these categories exhibited a relatively high ratio of unique
   annotations compared with the total number of events with annotation
   references ([96]Figure 2B). Interestingly, the transmembrane category