Phosphoproteome Remodeling upon CDK1 Inhibition Restricts HSV-1 IE Gene Transcription and Replication

Abstract

Cyclin-dependent kinase 1 (CDK1) regulates multiple cellular processes that HSV-1 can exploit to promote its own replication, particularly during the early steps of lytic infection. We investigated whether CDK1 inhibition disrupts immediate-early (IE) gene expression and analyzed the host phosphoproteome early in infection to identify putative host factors and mechanisms that facilitate HSV-1 IE gene expression and are controlled by CDK1. Human foreskin fibroblasts (HFFs) were pre-treated with a CDK1 inhibitor and showed a 1000-fold reduction in HSV-1 replication and significant reductions in IE mRNAs and protein levels at 4 hpi. We characterized cells after CDK1 inhibition and HSV-1 infection at 3 hpi by tandem mass spectrometry and identified >5500 phosphopetides (~2600 proteins), analyzing differential phosphorylation and protein–protein interactions. We validated CDK1 inhibition by detecting phosphorylation-specific decreases in known CDK1 substrates, as well as Robust Kinase Activity Inference. Rank- and network-based analyses of our dataset highlighted several candidate proteins, linking their CDK-directed phosphorylation to HSV-1 IE gene expression. Notably, the C-terminal domain of the large subunit of RNA polymerase II (RNAPII), POLR2A, is extensively phosphorylated, and its phosphorylation is significantly reduced upon CDK1 inhibition during viral infection. Taken together, these data support a model in which CDK1 activity maintains a transcriptionally permissive cellular state required for efficient HSV-1 IE gene expression. Our data suggest that when CDK1 is pharmacologically inhibited, key transcriptional facilitators are dysregulated, impairing viral transcription and replication.

1. Introduction

Herpes simplex virus 1 (HSV-1) is a double-stranded DNA-containing, enveloped virus that belongs to the Herpesviridae family. HSV-1, as an obligate intracellular pathogen, is dependent on the cellular machinery, including all stages of its gene expression cascade. Viral gene expression follows a strict temporal order: immediate-early (IE) gene expression begins as early as 1 h post-infection (hpi), typically peaks at about 3–4 hpi, and does not require de novo viral protein synthesis; IE proteins activate early (E) genes, and together IE and E gene products induce late (L) genes. IE proteins have regulatory functions and modulate host immunity; E proteins are associated with viral DNA replication; and L genes encode structural proteins and factors for capsid assembly, genome packaging, and egress (reviewed in [1,2]).

Expression of HSV-1 IE proteins in cell culture is induced within the first few hours after infection. One of the key players in initiating this process is the HSV-1 tegument protein VP16 (also known as α-TIF) [3]. Following viral entry, VP16 is released into the cytoplasm of the infected cell, where it is recognized and shuttled into the nucleus by host cell factor 1 (HCF-1), as VP16 itself lacks a classical nuclear localization signal [4]. Inside the nucleus, the VP16-HCF-1 complex interacts with another cellular transcription factor, the octamer-binding protein 1 (Oct-1). Together, these three proteins form the enhancer core complex at viral IE promoters [5]. Oct-1 recognizes and binds a specific cis-regulatory element, the 5′-TAATGARAT-3′ motif (enhancer core element), located upstream of the IE transcription start site and nucleates the assembly of the HCF-1-VP16-Oct-1 enhancer core complex [6]. Unlike HCF-1, VP16 can recognize part of the TAATGARAT motif, though it interacts with DNA once it binds to Oct-1 [7]. When this enhancer core complex is assembled on the IE promoter, VP16 and HCF-1 recruit multiple host factors, including chromatin remodelers, histone modifiers, transcription factors, and coactivators, establishing a permissive chromatin for transcription [8,9]. These steps lead to the recruitment and assembly of the preinitiation complex, which is composed of host general transcription factors and RNA polymerase II (RNAPII), leading to the initiation, elongation, and termination of IE transcription [10].

Cyclin-dependent kinases (CDKs) belong to a class of serine/threonine kinases that are regulated by specific regulatory subunits, cyclins, which not only activate the kinases but also confer substrate specificity. The human genome encodes approximately 20 CDKs, which are classified into two groups: cell cycle-related CDKs and transcriptional CDKs. The first group comprises 11 kinases, including the canonical cell cycle regulators CDK1, CDK2, CDK4, and CDK6. The remaining nine enzymes are transcriptional CDKs, as their primary roles are in regulating transcription. Notable members of this group include CDK7 (a component of transcription initiation factor TFIIH), CDK9 (a subunit of the elongation factor P-TEFb), and CDK8 (a component of the Mediator complex) (reviewed in [11]).

Although cell cycle-related CDKs are not traditionally considered components of the transcriptional machinery, their role in the regulation of cell cycle processes also includes the modulation of gene expression by direct or indirect mechanisms. Thus, multiple evidence indicates that CDK1, CDK2, CDK4, and CDK6 target various transcription regulatory factors, including general transcription factors (GTFs) and the RNAPII C-terminal domain (CTD) [12,13,14,15,16,17,18,19,20,21,22].

The broad-spectrum CDK inhibitor, Roscovitine (Rosco), has been shown to significantly decrease HSV-1 replication in Vero cells and human embryonic lung fibroblasts (HEL) at concentrations that are sufficient to block the cell cycle but do not promote cell toxicity [23,24,25]. Notably, similar concentrations of Rosco and phosphonoacetic acid, a specific inhibitor of the virus-encoded DNA polymerase, were required in both cell lines to suppress HSV-1 replication. Importantly, the reduction in viral replication and IE transcription following Rosco treatment was not attributable to cell cycle arrest alone but instead to CDK inhibition [23]. In addition, the accumulation of IE mRNAs, such as ICP0 and ICP4, was reduced as early as 2 hpi in HSV-1-infected cells either pre-treated or treated with Rosco at 1 hpi [23]. Run-on transcription analysis indicated that Rosco specifically impairs the initiation of the transcription of the HSV-1 IE promoters ICP4 and ICP27 [26]. Similarly, treatment with the CDK1/2 inhibitor BMS-265246 resulted in reduced HSV-1 replication and a decrease in IE mRNA levels as early as 4 hpi, and this inhibitor did not impact viral attachment or entry [27].

While the requirement of CDKs on HSV-1 replication is well-documented, it reflects a broader viral strategy: many viruses exploit interactions with CDKs through multiple mechanisms to favor their replication. In some cases, these interactions accelerate or block the cell cycle, thereby creating a favorable environment for viral propagation by exploiting cell cycle phase-specific factors (reviewed in [28,29]). In other instances, viral proteins can serve as direct substrates for CDKs, thereby facilitating various aspects of a virus’ lytic cycle [30,31]. Furthermore, CDKs can also phosphorylate host proteins and appear to impact their activities or properties to enhance the life cycle of several viruses [32,33].

It is evident that many viruses rely on host CDK activities at various stages of their replication, through the direct phosphorylation of viral proteins or modulation of host proteins. For HSV-1, the induction of IE transcription requires not only host CDK activity, but also the involvement of multiple host transcription factors, chromatin remodelers, histone modifiers, and the general transcription machinery. While cell cycle-related CDKs, including CDK1, are shown to regulate these transcriptional components during cell cycle progression, viruses have evolved to take advantage of such mechanisms for their own benefit, including the expression of their genes. While the current study shows that a CDK1 inhibitor limits HSV-1 replication and IE transcription, the specific host proteins that CDK1 regulates are largely unknown. Thus, in this study we sought to identify potential CDK1 phosphorylation targets that influence the HSV-1 lytic infection, focusing on IE transcription.

2. Materials and Methods

2.1. Cells and Virus

Human foreskin fibroblasts (HFFs; mycoplasma-free) provided by Dr. Nicholas Wallace (Kansas State University) were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) containing 2 mM L-glutamine, 100 units/mL penicillin, and 100 μg/mL streptomycin. The HSV-1 strain KOS was routinely propagated and titered in Vero cells by plaque assays as described in [34,35]. Vero cells were obtained from the ATCC (catalog number CCL-81, Manassas, VA, USA). Infections were carried out at a multiplicity of infection (MOI) of 1 or 2 PFU/cell by adding inoculum in DMEM (+inhibitor or vehicle) for 1 h at 37 °C. Inoculum was removed, cells were washed once with PBS, and fresh complete medium (+inhibitor or vehicle) was added. At 24 hpi, supernatants and cells were collected, subjected to one freeze–thaw cycle, sonicated, clarified by centrifugation, and serially diluted in DMEM, and plaque assays were performed to quantitate virus.

2.2. CDK Inhibitor Cell Cycle Effect and Viability

Unsynchronized HFFs were plated on 6-well plates (170,000 cells per well) and exposed to a range of CDK1-inhibitor CGP74514A (CDK1i; dissolved in DMSO, Calbiochem, San Diego, CA, USA) [36] concentrations for 24 h. Cells were fixed in 70% ethanol, treated with RNase A, stained with propidium iodide, and analyzed on a flow cytometer BD Accuri C6+, 12,000 single-cell events per sample. The dose with the maximal G2/M fraction with preserved viability (3.5 μM) was selected for subsequent experiments (Figure S1A,B). Cell viability was assessed after 24 h exposure to a log2-diluted series of CDK1i using alamarBlue (BioRad, Hercules, CA, USA) according to the manufacturer’s protocol. Briefly, HFFs were seeded in 96-well plates (9000 cells per well), treated with the inhibitor, incubated with alamarBlue reagent (10% v/v in medium) for 4 h at 37 °C, and absorbance was measured at 570 and 600 nm wavelengths. Cell cycle distributions were evaluated using FlowJo v10.3. At 4 μM, viability was ~97% of control (Figure S1C).

2.3. RNA Isolation and RT-qPCR

For real-time qPCR experiments, HFF cells were seeded in 6-well plates (220,000 cells per well), pre-treated with CDK1 or CDK2 inhibitors, and, after 1 h incubation, infected with wild-type HSV-1 (MOI = 1). At 4 hpi, total RNA was extracted from cells with TRIzol/chloroform and ethanol-precipitated. Total RNA of ~0.7 ug for each sample was reverse transcribed using a High Capacity cDNA Reverse Transcription Kit (Ref 4368814, Applied Biosystems, Foster City, CA, USA) according to manufacturer’s protocol. qPCR was performed using Roche FastStart Universal Probe Master Mix (Rox) (MilliporeSigma, Burlington, MA, USA) on the Applied Biosystems real-time instrument, followed by melt-curve analysis. The following primers (IDT, Coralville, IA, USA) targeted HSV-1 IE genes ICP0: 5′-AGCGAGTACCCGCCGGCCTG-3′, 5′-CAGGTCTCGGTCGCAGGGAAAC-3; ICP4: 5′-TGATCACGCGGCTGCTGTA-3′, 5′-TGATGAAGGAGCTGCT GTTGCG-3′); ICP27: 5′-GTCGGGGTCGGAGAGAAGAT-3′, 5′-GCGGTGCGTGTCTAGGATTT-3′; and GAPDH: 5′-CTCTGCTCCTCCTGTTCGAC-3′, 5′-AGTTAAAAGCAGCCCTGGTGA-3′. GAPDH served as the endogenous control. Each reaction was run in technical replicates. Expression was calculated in absolute copy numbers using standard curves.

2.4. Immunoblotting

For immunoblotting, HFFs were treated the same way as for real-time qPCR. At 0 or 4 hpi, cells were washed with cold PBS and collected into a 1x Laemmli buffer supplemented with protease and phosphatase inhibitors. Samples were run in SDS-PAGE (8.8% acrylamide) and transferred to nitocellulose membranes by standard Western blotting methods. Membranes were blocked in 3% BSA in TBST and incubated with primary antibodies to ICP0 (Santa Cruz Biotechnology, Dallas, TX, USA, sc-53070; 1:333 dilution) or ICP4 (Santa Cruz, sc-69809; 1:600 dilution) overnight at 4 °C. β-actin (Santa Cruz, sc-47778; 1:1000 dilution) served as the loading control. Then membranes were incubated with HRP-conjugated secondary antibodies (HRP-conjugated AffiniPure Goat Anti-Mouse IgG, Jackson ImmunoResearch, #115-035-146; 1:5000 dilution) and visualized using chemiluminescence by the LI-COR digital imager. POLR2A hypophosphorylation was monitored with primary antibodies specific to total POLR2A (Invitrogen, Carlsbad, CA, USA, cat. #MA1-10882; 1:1500 dilution) and phosphorylation-specific antibodies: phospho-Ser2-POLR2A (Novus Biologicals, Centennial, CO, USA, NBP2-59215; 1:1500 dilution) and phospho-Ser5-POLR2A (Abcam, Waltham, MA, USA, ab5408; 1500 dilution).

2.5. Experimental Design and Statistics

All statistical analyses were performed using R. Data are presented as mean + SEM. Normality was assessed by Shapiro–Wilk and the homogeneity of variance by Levene’s test; two-sided unpaired Student’s t-tests (or Welch’s t-tests when variances were unequal) were used for pairwise comparisons; if non-normal, the Mann–Whitney test was applied. Benjamini–Hochberg FDR was used for multiple comparisons correction. Significance was set at p < 0.05.

2.6. CME bHPLC phosphoTMT Methods—Orbitrap Eclipse

All proteins from each sample were reduced, alkylated, and purified by chloroform/methanol extraction prior to digestion with a sequencing-grade modified porcine trypsin (Promega, Madison, WI, USA). The resulting tryptic peptides were labeled with TMT isobaric reagents (Thermo Fisher Scientific, Waltham, MA, USA) and phosphopeptides were enriched sequentially using High-Select TiO₂ and Fe-NTA enrichment kits (Thermo Fisher Scientific) according to the manufacturer’s protocols. Both phospho-enriched and un-enriched labeled peptides were fractionated into 46 fractions on a 100 × 1.0 mm Acquity BEH C18 column (Waters Corporation, Milford, MA, USA) using an UltiMate 3000 UHPLC system (Thermo Fisher Scientific) under basic pH conditions with a 50 min gradient from 99:1 to 60:40 buffer A:B, and then combined into 18 super-fractions. Each super-fraction was further separated by reverse-phase chromatography on an in-line 150 × 0.075 mm column packed with XSelect CSH C18 2.5 µm resin (Waters) using an UltiMate 3000 RSLCnano system (Thermo Fisher Scientific). Peptides were eluted with a 75 min gradient from 97:3 to 60:40 buffer A:B, ionized by electrospray (2.4 kV), and analyzed on an Orbitrap Eclipse Tribrid mass spectrometer (Thermo Fisher Scientific) operated in multi-notch MS3 mode. Full MS scans were acquired in the Orbitrap (FTMS) in top-speed profile mode at 120,000 resolution over an m/z range of 375–1500. Following CID fragmentation (normalized collision energy, 31.0), MS/MS spectra were acquired in the ion trap in centroid mode. Using synchronous precursor selection, up to 10 MS/MS precursors were selected for HCD fragmentation (normalized collision energy, 55.0), and MS3 reporter ions were acquired in the Orbitrap (FTMS) in profile mode at 50,000 resolution over an m/z range of 100–500. Buffer A consisted of 0.1% formic acid and 0.5% acetonitrile, and buffer B consisted of 0.1% formic acid and 99.9% acetonitrile; for offline basic pH fractionation, both buffers were adjusted to pH 10 with ammonium hydroxide.

2.7. Data Analysis—ProteoViz (phosphoTMT)

Proteins were identified and reporter-ions quantified by searching a composite FASTA consisting of the Homo sapiens UniProtKB proteome and HSV-1 strain KOS protein sequences derived from GenBank accession JQ673480.1 (translated CDS features) [37] using MaxQuant (Max Planck Institute, Munich, Germany; v2.2.0.0). Searches were conducted with a parent-ion tolerance of 3 ppm, fragment-ion tolerance of 0.5 Da, reporter-ion tolerance of 0.001 Da, and trypsin/P specificity allowing up to two missed cleavages. Variable modifications included methionine oxidation, protein N-terminal acetylation, and phosphorylation on S/T/Y, with carbamidomethylation of cysteine specified as a fixed modification. Protein identifications were accepted at a false discovery rate (FDR) of <1.0%, and identifications supported only by modified peptides were excluded. Protein probabilities were assigned using the ProteinProphet algorithm [38]. MS3 reporter-ion intensities from the un-enriched lysate were used to quantify changes in total protein abundance, whereas phosphorylated (S/T/Y) sites were identified and quantified from the phosphopeptide-enriched samples. Enriched and un-enriched peptide pools were analyzed in separate TMT multiplexes (one TMT batch per pool). Comparisons of normalization approaches and quality-control diagnostics for both the proteome and phosphoproteome datasets (including ProteiNorm-based normalization selection and QC reports) are provided in Figure S2. Following database searching, MS3 reporter-ion intensities were normalized using ProteiNorm [39]; variance-stabilizing normalization (VSN) was applied [40], and differential abundance testing was performed with proteoDA using limma linear modeling with empirical Bayes moderation of standard errors [41,42]. Phosphopeptide differential analysis followed the same framework with additional filtering steps: phosphosites were retained only when localization probability exceeded 0.75, peptides containing zero intensity values were removed, and intensities were log2-transformed prior to limma-based testing. Proteins and phosphopeptides with an FDR-adjusted p-value < 0.05 and an absolute fold change > 2 were considered significant.

2.8. Overrepresentation Analysis of Known CDK1 Phosphorylation Targets

To assess the representation of known CDK1 phosphorylation targets among significantly hypophosphorylated peptides, we first compiled a reference set of CDK1 substrate motifs by uniting data from both the PhosphoSitePlus (v6.7.9) [43,44] and SIGNOR 3.0 [45] databases. The 15-amino acid motif sequences with the phosphorylated residue in its center were extracted from each database. Duplicate motifs were removed, resulting in a unified set of unique CDK1 substrate motifs. Significantly hypophosphorylated peptide 15-amino acid sequences with the phosphorylated residue in its center identified in the pairwise comparison between mock-infected/CDK1 inhibitor-treated and mock-infected/DMSO-treated samples were compared to the reference set, and exact matches were identified using R. To evaluate the statistical significance of the overlap between hypophosphorylated peptides and known CDK1 targets, we performed Fisher’s exact test, implemented in R. This approach was also applied to the set of significantly hyperphosphorylated peptides for completeness.

2.9. Kinase Activity Inference Using RoKAI

Kinase activity inference from phosphoproteomic data was performed using the Robust Kinase Activity Inference (RoKAI) [46] computational tool (https://rokai.io/), version 2.3.0. Phosphoproteomic data were formatted according to RoKAI’s input requirements (UniProt protein identifiers, phosphosite annotations, and log2 fold-change values calculated by Limma from the pairwise comparison of Mock-infected/CDK1-inhibitor-treated vs. Mock-infected/DMSO-treated samples). Default RoKAI parameters were applied, selecting the human UniProt reference proteome and the PhosphoSitePlus + Signor kinase–substrate dataset, and the combined RoKAI network consisted of kinase–substrate associations, protein-protein interaction, structural distance and coevolution evidence (KS + PPI + SD + CoEv). Kinase activities and statistical significance (Z-scores) were inferred and visualized using RoKAI’s online tool. Only kinases with a minimum of three identified substrates and a p-value < 0.05 (after multiple comparison correction by FDR) were considered for subsequent interpretation.

2.10. Heatmap and Hierarchical Clustering

Each phosphopeptide normalized intensities were z-transformed across all replicates and experimental conditions and ranked by the standard deviation of their z-scores, and the heatmap was generated using the top 1000 most variable phosphopeptides. Hierarchical clustering was performed using the agglomerative approach with Euclidean distance as the distance metric and complete linkage as the clustering method, implemented via the R package “pheatmap” (v1.0.12). The dendrogram was visualized with the same R package. The cophenetic correlation coefficient was used to evaluate the accuracy of hierarchical clustering quantitatively.

2.11. Principal Component Analysis

Principal Component Analysis (PCA) was performed using z-score standardized phosphopeptide intensities of the top 1000 most variable phosphopeptides via the prcomp function in R. K-means clustering was subsequently applied to the PCA scores to classify sample replicates into groups.

2.12. Gene Ontology and Pathway Enrichment Analyses

Gene Ontology (GO) enrichment analysis was performed using the enrichR package (v.3.4) in R [47,48]. Lists of gene symbols were compiled from the set of differentially phosphorylated proteins and used as input. To facilitate interpretation, GO term redundancy was reduced by semantic similarity clustering. GO term similarity was calculated using the Wang method as implemented in package GOSemSim (v2.34.0; Bioconductor 3.21) [49]. Pathway enrichment analysis was similarly performed using enrichR, with the KEGG 2021 Human database. Adjusted p-values < 0.05 were considered significant. Scripts are available upon request.

2.13. Δlog2FC Calculation and Selection for Enrichment

To quantify how infection modulates the CDK1-inhibition response, we computed for each protein a difference in contrasts: Δlog2FC = log2FC(HSV-1/CDK1i vs. HSV-1/DMSO) − log2FC(Mock/CDK1i vs. Mock/DMSO). To ensure robustness, proteins were retained for downstream “Δ” analyses only if both underlying contrasts were individually significant (Benjamini–Hochberg-adjusted p < 0.05) and |Δlog2FC| ≥ 1. This conservative Δ set was used as input for GO enrichment.

2.14. Protein–Protein Interaction Network

Differentially phosphorylated proteins from HSV-1/CDK1i vs. HSV-1/DMSO comparison (n = 570) were mapped to H. sapiens STRING web resource (https://string-db.org/) identifiers and interactions were retrieved at highest confidence (STRING combined score ≥ 0.9). The resulting network was imported into Cytoscape (v3.10.3) as an undirected graph. Node centralities were computed in Cytoscape plugin cytoHubba [50]. We defined the PPIN backbone as the union of nodes appearing in the top decile of at least one of the three metrics (MCC, Degree, Betweenness). PPIN was visualized using R programming language.

3. Results

3.1. CDK1 Inhibition Reduces HSV-1 Progeny and IE Gene Expression

To determine the optimal concentration of the CDK1 inhibitor (CDK1i), CGP74514A, unsynchronized human foreskin fibroblast (HFF) cells were incubated with increasing doses for 24 h. The highest percentage of cells arrested at the G2/M phase was observed at 3.5 µM, which was therefore used in all subsequent experiments (Figure S1A,B). An alamarBlue assay performed across a log2 dilution series of the inhibitor showed that 4 µM did not affect cell viability after 24 h, with viability remaining at 97% compared to untreated controls (Figure S1C).

To assess the impact of CDK1 inhibition on the HSV-1 life cycle, HFF cells were pre-treated with either vehicle (DMSO) or CGP74514 (CDK1i) and infected with HSV-1 KOS (MOI = 1) one hour later. Samples were collected at 24 hpi. Viral yield assays showed an approximate 3-log reduction in progeny virus following inhibitor treatment compared to the control (Figure 1a). To further investigate the effect on viral gene expression, we collected samples at 4 hpi—a time point corresponding to peak IE gene expression. RT-qPCR analysis revealed a 2- to 10-fold reduction in mRNA levels of the IE genes ICP0, ICP4, and ICP27 in inhibitor-treated cells (Figure 1b). With these reductions in transcript levels, Western blot analyses demonstrated an almost complete loss of ICP0 and ICP4 protein expression under these conditions (Figure 1c).

3.2. Phosphoproteomics: Experimental Design and Analysis Workflow

Because we observed the greatest decrease in HSV-1 replication with CDK1 inhibitor, we were interested in identifying potential CDK1 cellular targets linked to IE gene expression and designed a phosphoproteomic experiment to capture early changes during HSV-1 early life cycle events encompassing IE gene expression. As outlined in Figure 2, HFF cells were pre-treated with CDK1i (3.5 µM) or DMSO for 1 h, infected at MOI = 2, synchronized by a 1 h adsorption, and collected at 3 hpi. Samples that included four conditions (Mock + CDK1i, Mock + DMSO, HSV-1 + CDK1i, HSV-1 + DMSO) were processed by TMT-based LC-MS/MS. Primary processed data were subjected to functional analyses (GO, KEGG, PPI) to highlight pathways and candidate CDK1 substrates; normalized intensity tables with differential analyses are provided (Tables S1 and S2). Overall, we identified over 5500 phosphorylated peptides corresponding to approximately 2600 proteins.

3.3. Phosphoproteomic Analysis: Inhibitor Efficiency Assessment

To test the efficiency of the CDK1i, we assessed the abundance of differentially phosphorylated peptides (adj. p < 0.05) from the pairwise comparison between Mock/CDK1i and Mock/DMSO samples, in known phosphorylated targets of CDK1 from the PhosphoSitePlus (v6.7.9) and SIGNOR 3.0 databases. Fisher’s exact test revealed that the established human CDK1 targets are significantly overrepresented (p-value < 0.05) among hypophosphorylated peptides (log2FC < −1), 47 out of 627 phosphopeptides (Figure 3a). To confirm the targeted activity of the CDK1 inhibitor used, kinase enrichment analysis was performed using Robust Kinase Activity Inference (RoKAI) [46]. This method is not only based on kinase–substrate relationships from curated databases (PhosphoSitePlus and SIGNOR) but also integrates multiple sources of evidence, such as protein-protein interactions (PPI), structural domain interactions, and evolutionary co-conservation data, to infer kinase activities from phosphoproteomic datasets. As indicated in Figure 3b, CDK1 exhibited the greatest decrease in activity, with 115 substrates identified (FDR < 0.05). Interestingly, the only other kinases that showed statistically significant downregulation with at least three identified substrates were CDK2 (78 substrates) and CDK5 (26 substrates). The only kinase that demonstrated increased activity in response to CDK1 inhibition was MAPKAPK2 (MAPK-activated protein kinase 2). The complete results of the RoKAI kinase activity inference, which includes all identified kinases, substrate counts, Z-scores, p-values, and FDR-adjusted significance, are provided in Table S3.

3.4. Phosphoproteomic Analysis: Heatmap and Principal Component Analysis (PCA)

Heatmap and PCA plots were generated to summarize the global phosphoproteomic changes (Figure 4). Hierarchical clustering (Figure 4a) clearly separated samples primarily based on CDK1 inhibitor treatment, suggesting that CDK1 activity is the strongest factor that modulates the cellular phosphoproteomic landscape. Replicates are tightly clustered together, which reflects experimental reproducibility. PCA (Figure 4b) confirmed these observations, showing clear separation along PC1, with CDK1 inhibitor-treated samples clustering distinctly apart from DMSO controls indicating a strong negative correlation in the direction of phosphorylation changes between these two differently treated clusters, and minor separation by HSV-1 infection status along PC2. These findings demonstrate that CDK1 activity is a key driver of phosphorylation state alterations in infected and uninfected cells.

3.5. Phosphoproteomic Analysis: Differentially Phosphorylated Peptides/Proteins upon CDK1 Inhibition with HSV-1 Infection

To identify potential CDK1 phosphorylation targets that facilitate HSV-1 IE transcription or even earlier events we analyzed differentially phosphorylated peptides from the pairwise comparison of HSV-1/CDK1i vs. HSV-1/DMSO. The volcano plot visualizes that 642 peptides (or 494 unique proteins) were hypophosphorylated (adj. p < 0.05 and log2FC ≤ −1) and 117 peptides (corresponds to 103 unique proteins) were hyperphosphorylated (adj. p < 0.05 and log2FC ≥ 1) (Figure 5a).

In assessing whether selected proteins are potentially valid CDK1 targets, several approaches were used to prioritize specific ones. First, we applied a rank-sum (Borda-style) score, that is used in different omics analyses [51,52], combining the rank of statistical significance (−log10adj.p.Val.) and the rank of biological effect size (|log2FC|) (Figure 5b). The second ranking method was similar: we applied the π-score which also combines the statistical significance and the effect size into one ranking metric [53] (Figure 5c). In addition, comparing the exact match of obtained significantly hypophosphorylated peptides with amino acid sequences of known CDK1 phosphorylation targets from PhosphoSitePlus and SIGNOR databases, we found 44 exact matches (Table S4); these include those prioritized by rank-sum score and π-score were the proteins phosphoribosylaminoimidazole carboxylase and phosphoribosylaminoimidazolesuccinacarboxamide synthase (PAICS) and its differentially phosphorylated residue Serine-27 (S27), ribonucleotide reductase M2 (RRM2, S20), high mobility group AT-hook 1 (HMGA1, T53), and histone H1.4 (T18).

We evaluated the top 15 candidates from a ranking approach (22 unique phosphopeptides) with kinase-specific predictors, structural context, and cyclin-docking motifs. Several patterns were consistent with potential direct CDK1 regulation. First, many candidates scored positively or were supported for CDK1 phosphorylation target by GPS 6.0, NetPhos 3.1, and KinasePhos 3.0 software [54,55,56]. Second, a substantial fraction of sites was predicted by NetSurf3.0 software [57] to be surface exposed and embedded in disordered regions—a sequence context that favors phosphorylation. Third, well-established cyclin-docking motifs (RxL/KxL or LxF) were present in the surrounding protein window for a notable subset, providing a plausible recruitment mechanism for the cyclin-CDK1 complex. The complete per-site predictor outputs, motif calls (for 22 top phosphopeptides), and integrated ranks for all significantly hypophosphorylated peptides (adj. p < 0.05 and log2FC ≤ −1) are summarized in Table S4. In addition, applying the similar ranking system, we ranked hyperphosphorylated peptides when comparing HSV-1/CGP vs. HSV-1/DMSO (Table S5). The top candidates include the protein and phosphorylation sites, La-related protein 1 (LARP1, S766), nucleoporin 50 (NUP50, S221), ribosomal protein S6 kinase alpha-4 (RPS6KA4, S678), and serine- and arginine-rich splicing factor 10 (SRSF10, S131).

3.6. HSV-1 Reshapes the CDK1-Inhibited Phosphoproteome

To isolate the infection-dependent component of the CDK1-inhibition response, we compared differentially phosphorylated phosphopeptides (adj. p < 0.05, |log2FC| ≥ 1) between the HSV-1/CDK1i vs. HSV-1/DMSO and Mock/CDK1i vs. Mock/DMSO contrasts. A total of 589 peptides (mapping to 465 proteins) were shared between contrasts, whereas 159 (146 proteins) were unique to the infected condition under CDK1 inhibition, and 135 (126 proteins) were unique to the mock condition (Figure 6a). To quantify how infection modulates CDK1i-driven phosphorylation, we plotted log2FC (HSV-1/CDK1i vs. HSV-1/DMSO) against log2FC (Mock/CDK1i vs. Mock/DMSO) and computed Δlog2FC = log2FC (HSV-1/CDK1i vs. HSV-1/DMSO) — log2FC (Mock/CDK1i vs. Mock/DMSO) for all identified phosphopeptides. Points falling near the y = x diagonal represent phosphosites that respond similarly to CDK1i treatment regardless of infection status. In contrast, points that deviate further from this diagonal reflect phosphosites whose CDK1i sensitivity is either enhanced or diminished by infection. The dotted lines positioned at y = x ± 1 demarcate peptides exhibiting at least a 2-fold difference between the two experimental contrasts (Figure 6b). We highlighted a small subset of the top 5 proteins showing log2FC values above 1 or below −1 that also satisfied the criterion of adjusted p-value < 0.05 in both contrasts. Complete lists of unique/overlapping peptides and ranked Δlog2FC values are provided in Table S6. GO enrichment of the interaction phosphoprotein set—defined as differentially phosphorylated proteins (adj. p < 0.05, |log2FC| ≥ 1) unique for HSV-1/CDK1i vs. HSV-1/DMSO or Mock/CDK1i vs. Mock/DMSO, together with proteins significant in both contrasts with |Δlog2FC| ≥ 1—revealed a nucleus-centric signature (Figure 6c).

3.7. Phosphoproteomic Analysis: Protein-Protein Interaction Network (PPIN) Analysis of Differentially Phosphorylated Proteins upon CDK1 Inhibition and HSV-1 Infection

To examine phosphoproteome changes at the level of protein connectivity, we built a STRING PPI network from the differentially phosphorylated proteins from HSV-1/CDK1i vs. HSV-1/DMSO pair and analyzed its giant component (GC). Table S7 contains node and edge characteristics extracted from STRING. Using STRING “highest confidence” (score ≥ 0.9) as our primary network, the GC contained 194 nodes linked by 393 edges. Short average distances (characteristic path length, CPL = 4.989) together with high local clustering (ACC = 0.38), which we observed in our PPIN, is the hallmark of small-world organization (high clustering with short paths), as originally formalized by Watts & Strogatz and widely observed in biological networks [58,59]. To make sure that these patterns are not due to chance, we compared the GC against randomized null models. Accordingly, STRING’s own enrichment analysis detected significantly more protein-protein interactions than would be expected by chance for a randomly selected set of proteins of equivalent size (p-value ≤ 10⁻¹⁶), thus confirming that these differentially phosphorylated proteins indeed form a highly interconnected network at the specified confidence threshold.

To find the most essential hubs in our PPIN, we quantified node centralities on the 570-protein network and selected the top decile (10%) by Maximal Clique Centrality (MCC), Degree, and Betweenness and delineated a structural “backbone” that is composed of dense core hubs and inter-module connectors (Figure 7). For each metric, 57 proteins were retained. This procedure highlights proteins most likely to organize and propagate network flow under CDK1 inhibition during HSV-1 infection. Backbone nodes were concentrated in the GC and combined high local neighborhood density—consistent with the clique-rich structure captured by MCC—showing high Degree (many direct partners) and high Betweenness (placement on paths that link modules), a topology expected for regulators that coordinate transcription, chromatin state, and nucleocytoplasmic transport. To facilitate reproducibility, all centrality values for the 570 nodes are provided (Table S8). Within the backbone, POLR2A (the largest RNAPII subunit bearing the CTD) was the most extensively remodeled phosphoprotein, having the highest number of differentially phosphorylated sites overall, all of which were hypophosphorylated.

3.8. Validation of the Large Subunit of RNAPII Hypophosphorylation Under CDK1 Inhibition and HSV-1 Infection

Phosphoproteomics revealed a significant >2-fold phosphorylation decrease across all nine identified POLR2A residues (S1849, T1863, S1864, S1875, S1882, T1905, S1910, S1913, and T1919) upon CDK1 inhibition during HSV-1 infection (Figure 8a). Notably, all of these residues are located at the CTD regulatory positions S2/T4/S4/S5 of the YSPTSPS heptad. Given the importance of POLR2A CTD phosphorylation in transcription [60], we probed infected HFF lysates with phospho-specific antibodies. CDK1 inhibition reduced both p-S2 and p-S5 signals, while total POLR2A remained similar between conditions (Figure 8b). Densitometry normalized to total POLR2A confirmed the decrease (Figure 8c). Similar results in CDK1-inhibited cells without infection indicate that CDK1 inhibition is the primary factor that drives RNAPII CTD hypophosphorylation (Figure S3).

3.9. Phosphoproteomic Analysis: Gene Ontology (GO) and Pathway Enrichment Analyses of Differentially Phosphorylated Proteins Under CDK1 Inhibition with or Without HSV-1 Infection

To gain a broader understanding of how CDK1 inhibition impacts the cellular environment, we compared differentially phosphorylated proteins (adj. p < 0.05, |log2FC| ≥ 1) in both infected and uninfected CDK1-inhibited samples relative to untreated controls. Since CDK1 inhibition was the primary driver of phosphoproteomic changes, GO and pathway analyses yielded similar results regardless of infection (Figure 9). GO terms were grouped by their biological similarity into higher-level clusters and summarized across the three ontologies. Within Biological Processes, the most populated clusters comprised macromolecule biosynthesis/cellular metabolism, cell stress responses, chromatin organization and transcriptional regulation, cell migration/differentiation, cell cycle and mitotic regulation, RNA processing/splicing/transport, cytoskeleton organization and cell junctions/adhesion, nuclear transport/intracellular trafficking, and viral processes. For Molecular Functions, enriched clusters included RNA binding/regulation; cytoskeleton and cell-adhesion binding; protein-kinase binding/enzyme activity; DNA binding/regulation; GTPase binding/regulatory activity; chromatin-modifier binding/activity; and receptor binding/regulation. For Cellular Components, terms mapped broadly to intracellular organelles and nuclear compartments, cytoskeleton and mitotic structures, cell junctions, membrane structures, ribosomes/assembly intermediates, spliceosome and RNP complexes, chromatin/epigenetic complexes, and axonal structures. Both contrasts contributed phosphoproteins to nearly all clusters (Figure 9a). Pathway analysis (Figure 9b) highlighted overlapping processes in both contrasts, such as adherens/focal junctions, MAPK signaling, spliceosome, and cell cycle.

4. Discussion

Treatment of HFFs with the CDK1i CGP74514A reduced viral yields by approximately three logs (Figure 1a) and markedly decreased mRNAs and/or protein levels of ICP0, ICP4, and ICP27 at 4 hpi (Figure 1b,c). Because these effects are observed at the IE stage, upstream of viral DNA replication, they indicate an early block that prevents efficient initiation of the lytic program rather than a late defect in assembly or egress. Viability was unaffected at the working concentration, arguing against non-specific cytotoxicity as the basis of the antiviral phenotype.

Mechanistically, the coordinate failure of multiple IE transcripts implies impairment of a step common to their synthesis. Two non-mutually exclusive models fit our data and the literature. First, CDK1 may facilitate RNAPII-dependent transcription at viral IE promoters through direct phosphorylation of the large subunit of RNAPII CTD that favors transcription initiation, promoter clearance, and elongation. Second, it can indirectly modify host transcriptional co-factors required for IE transactivation. Specifically, the VP16-HCF1-Oct1 complex drives IE expression, and CDK1-dependent phosphorylation of one or more components of this complex could be required for efficient co-activator function or promoter recruitment. Notably, CDK1i blocks cells at G2/M; HSV-1 can replicate across all phases of the cell cycle [61], but G2/M features (chromatin condensation, altered nuclear pore dynamics, etc.) might render IE activation less permissive.

Given that CDK1 is a kinase and the antiviral phenotype of its inhibition occurs before the viral DNA replication, we hypothesized that altered phosphorylation of host proteins underlies the block in viral gene expression. To test this hypothesis, we performed an early time point phosphoproteomic analysis to delineate CDK1-dependent phosphorylation changes (Figure 2). Phosphoproteomic sample collection at 3 hpi endpoint targets prominent levels of IE gene expression before cytopathic effects or wide-spread remodeling of host signaling, and a 1 h-CDK1i pretreatment was done to ensure adequate CDK-inhibition prior to infection. An MOI of two affords robust infection (86% of cells are infected at least with one viral particle according to Poisson distribution) while limiting excessive cytotoxicity. Including mock ± inhibitor controls separates inhibitor-driven effects from virus-specific responses, and the HSV-1/DMSO vs. HSV-1/CDK1i comparison focuses o CDK1-dependent events during infection.

Our phosphoproteomic inhibitor-only contrast Mock/CDK1i vs. Mock/DMSO provides orthogonal evidence that CDK1i limits phosphorylation at intended targets in cells (Figure 3a). First, peptides established to be human CDK1 substrates were significantly enriched among hypophosphorylated sites, whereas only a negligible fraction of known CDK1 targets increased in phosphorylation. The fact that 47 hypophosphorylated peptides overlapped with the known CDK1 targets (vs. only two hyperphosphorylated) argues that the predominant effect of treatment is the loss of CDK1-dependent phosphorylation rather than a non-specific disruption of global phosphorylation homeostasis. Although most database-listed CDK1 targets were not detected as changing in our dataset, this is expected given peptide detectability constraints, site- and cell-state specificity of CDK1 signaling, and the single and early time point sampled. Second, network-based kinase activity inference (RoKAI) showed the strongest negative Z-score for CDK1, with a large substrate count passing FDR control, providing an additional unbiased readout consistent with direct inhibition (Figure 3b). CDK2 and CDK5 also demonstrated significant decreases, which may reflect partial cross-inhibition at the dose used. Thus, it is important to understand that, when interpreting downstream, phenotypes may not be able to fully exclude other CDKs or other potential off-target effects; nonetheless, our analyses indicate that CDK1 (or its inhibition) is the major driver of the phosphorylation changes. Interestingly, MAPKAPK2 appeared to be the only kinase with increased activity. MAPKAPK2 activation may be linked to stress-responsive p38 signaling and checkpoint adaptation [62,63]—a frequent compensatory response to CDK inhibition and G2/M arrest. Such compensation could stabilize or alter some processes and pathways that we discuss later. Thus, the enrichment of hypophosphorylated known CDK1 targets and the concordant RoKAI inference provide strong, orthogonal validation that our inhibitor treatment reduced the CDK1 activity in HFF cells. Although outside the scope of this study, future experiments will include CDK1 depletion, allowing us to compare two different approaches (i.e., genetic and pharmacological) regarding CDK1 function.

Several important limitations should be considered when interpreting these results. Databases of enzyme targets aggregate heterogeneous evidence (in vitro and context-specific in vivo data), and the absence of overlap does not imply absence of regulation. Phosphorylation site stoichiometry and directionality are influenced by multiple factors, including counteracting phosphatases, substrate abundance, etc. Thus, some true CDK1 sites may appear unchanged in our analysis if their basal phosphorylation levels are low or if parallel kinases maintain phosphorylation at these sites despite CDK1 inhibition.

CDK1 inhibition rewires a broad host phosphorylation program. Clear sample separation by the CDK1 inhibition on the heatmap (Figure 4a) or PCA plot (Figure 4b), together with GO and pathway enrichment that appeared remarkably similar in infected and uninfected conditions (Figure 9), indicates that CDK1 activity—rather than infection per se—is the principal determinant of the phosphoproteome at this time point. These findings support a model in which HSV-1 exploits an existing cellular state influenced by CDK1 to facilitate viral gene expression. When CDK1 is inhibited, the virus loses access to multiple host processes required for HSV-1 life cycle, including efficient IE transcription. CDK1 inhibition produces many more hypo- than hyper-phosphorylated peptides at 3 hpi—the time point corresponding to early HSV-1 infection events (Figure 5a). During this global CDK1-dependent phosphorylation shift, we observed reduced IE transcription suggesting that CDK1 activity normally helps establish a pro-transcriptional state in infected fibroblasts.

Our experiment is compatible with other global phosphoproteomics studies involving herpes viruses. One report using mass spectrometry showed that CDK1/2 and ERK1/2 kinases facilitate lytic infection of murine gammaherpesvirus-68 (MHV-68) in fibroblasts [64]. Another phosphoproteomic study in HFF cells identified host kinases, especially ATM, are pro-viral during HSV-1 replication [65], similarly to CDK1 in our research. Kulej et al., in their proteomic experiments, found that HSV-1 infection in HFF cells coincides with major chromatin remodeling signals [66]; therefore, in our model, CDK1 inhibition might block IE transcription by preventing the establishment of a permissive chromatin state.

To enhance the robustness of candidate prioritization, we employed two orthogonal computational strategies that integrate both effect size and statistical significance. The Borda-rank-sum and the π-score independently converged on a 22-protein shortlist, with DDX21 S71 repeatedly emerging at the top (Figure 5b,c). Assessing candidates across multiple parameters further increased confidence: 44 significantly hypophosphorylated peptides exactly matched known CDK1 targets in curated databases, and many of the shortlisted sites carried CDK-compatible sequence features, scored positively in kinase-specific predictors, and localized to solvent exposed or intrinsically disordered regions that favor phosphorylation (Table S4). The presence of cyclin-docking motifs near several sites provides a plausible recruitment mechanism for the cyclin-CDK1 complex. The biological functions of the leading candidates point to processes that HSV-1 may exploit at the beginning of infection. The majority of top-ranked proteins participate in DNA binding, chromatin dynamics, and transcriptional regulation (i.e., DDX21, HMGA1, Histone H1-3, Histone H1-4, MBD2, SIN3A, NUCKS, NPM1, SNAI2) [67,68,69,70,71,72,73]. Future experiments will examine the role of these factors play in CDK1 regulation of IE gene expression.

Further supporting the idea that host factors associated with transcription are regulated by CDK1, the analysis of phosphoproteome shift modulated by infection reveals how HSV-1 reshapes cellular processes beyond the baseline response to CDK1 inhibition (Figure 6). Multiple various host processes were affected, some of the top enrichments were associated with gene expression and DNA binding functions. Notably, GO terms associated with RNA polymerase II-mediated transcription were significantly enriched, suggesting that HSV-1 can manipulate transcriptional machinery even under CDK1 inhibition.

Rank-based approaches, such as the Borda rank-sum and π-score, prioritize proteins, showing large, statistically robust phosphorylation changes, but they are not sensitive to network context. As a consequence, proteins with dramatic yet isolated shifts may rank near the top, whereas functionally central players (signaling bottlenecks, hubs, inter-module connectors) can be under-ranked if their changes are moderate, even if statistically significant. To capture these context-critical regulators, we complemented rank-based prioritization by building a PPIN from the differentially phosphorylated proteins (|log2FC| ≥ 1, adj.p < 0.05) upon CDK1 inhibition and HSV-1 infection. The resulting PPIN revealed a small-world architecture—high local clustering with a short characteristic path length (densely connected modules bridged by a limited number of hubs and inter-module connectors), a topology typical of cellular networks. By assessing topological parameters, such as Degree, Betweenness, and Maximal Clique Centrality, we identified a structural backbone enriched for hubs expected to drive major cellular processes upon CDK1 inhibition and infection (Figure 7). Functionally, the backbone was resolved into interpretable modules corresponding to cell cycle regulation and mitosis, rRNA processing and ribosome biogenesis/ribosomal structural proteins, nuclear pore complex, mRNA splicing, cytoskeleton, adhesion and membrane remodeling, signaling, DNA replication/repair, and genome maintenance—consistent with processes enriched in GO analysis (Figure 9). Transcription-related cluster aggregated transcription factors, co-activators, co-repressors, mediator components, chromatin regulators, and general transcription machinery. Notably, the top-ranked protein by the Borda rank-sum and π-score, DDX21 (Figure 5b,c), as well as HCF-1, a component of the IE enhancer core complex, fell within the backbone. POLR2A, the largest RNAPII subunit with a highly phosphorylated CTD, showed the highest number of differentially phosphorylated residues among all backbone proteins; all identified nine sites were significantly (>2-fold) hypophosphorylated at regulatory positions S2/T4/S4/S5 of the CTD heptades (Figure 8a).

Immunoblot validation experiments with phospho-S2- and phospho-S5-specific antibodies confirmed POLR2A phosphorylation decrease upon CDK1 inhibition and HSV-1 infection at 3 hpi (Figure 8b,c). S2 and S5 positions of the YSPTSPS CTD heptads are canonical regulators of transcription initiation and promoter escape (S5) as well as transcription elongation (S2) [60] with hypophosphorylation expected to impair these steps and reduce transcription. Given that HSV-1 relies on RNAPII for IE transcription, CTD phosphorylation dynamics is thought to be crucial for the virus replication. Supporting this point, it has been shown that IE protein ICP22 alters the abundance of phosphorylated S2-CTD, and CTD phosphorylation is a regulated control point during the IE transcription [74]. Mechanistically, the effect on POLR2A may be indirect with CDK1 modulating the activity or recruitment of CTD kinases (such as CDK7/TFIIH and CDK9/P-TEFb), and CTD phosphatases (FCP1) [16,17,75,76,77]. Given this possibility, there are other studies that support CDK1 in directly altering the RNAPII CTD. In vitro assays and mammalian cells experiments have demonstrated that CDK1-cyclin B can phosphorylate the RNAPII CTD [18,19,20,21,22]. In yeast, where CDK1-mediated transcriptional regulation has been examined in greater detail, there is evidence for phosphorylation of all three RNA polymerases (I, II, and III), often resulting in positive transcriptional regulation [78]. Furthermore, post-translational modification databases such as PhosphoSitePlus and SIGNOR annotate POLR2A as a direct CDK1 phosphorylation target [43,45].

Nuclear run-on experiments demonstrated that it is the IE transcription that is affected by Rosco [26]. When Rosco was present in cell culture, run-on signals for IE, E, and L genes did not show up even though the in vitro run-on reaction itself contained no drug. On the contrary, when Rosco was added only to the run-on step it did not inhibit elongation. Hybridizations against cellular DNA fragments, together with α-amanitin controls, showed that Rosco does not have a significant impact on the host RNAPII transcription under these conditions. Interestingly, when an HSV-1 promoter was stably integrated in host chromatin, Rosco had minimal effect on reporter expression, whereas the same promoter on transient plasmid or the HSV-1 genome was strongly suppressed—yet in both contexts the expression remained α-amanitin sensitive, confirming RNAPII dependence. Thus, Rosco blocks initiation when promoters reside on HSV-1 or extrachromosomal DNA but does not largely impact the same promoter when embedded in host chromatin [26].

Any “omics” analysis conducted at a single time point provides a snapshot of ongoing events, making it exceedingly difficult to investigate chains of cause and effect unless the same samples are collected sequentially to capture a dynamic picture of processes in progress. Our samples were collected at 3 hpi, so the data describe the cellular state at that time. Temporal inference would require matched samples across multiple time points. Additionally, unlike classic RNA-seq experiments, where downregulation of mRNA often implies protein depletion and, in many cases, disruption of its function, for phosphoproteomics, changes in site occupancy do not map directly onto protein abundance or activity. The functional consequence of phosphorylation is site-specific and context-dependent: the same protein can carry activating and inhibitory sites. For instance, phosphorylation of the S259 residue on Raf-1 kinase impairs its function and disrupts its interactions with upstream activators such as Ras, as well as downstream partners such as MEK [79]. In contrast, autophosphorylation of Ca²⁺/calmodulin-dependent protein kinase II (CaMKII) at T286 activates the enzyme and increases its affinity for Ca²⁺/CaM, whereas phosphorylation at T305/306 is inhibitory [80]. Glycogen synthase kinase-3 (GSK3) is inhibited via a conformational change induced by AKT1 (PKB)-mediated phosphorylation at S9 [81]. Thus, an observed hypo- or hyper-phosphorylation at a given site always requires subsequent functional exploration of the identified phosphorylated substrate before drawing conclusions from GO or pathway enrichment and still relies largely on manual literature review; however, many sites still remain incompletely annotated.

To gain a broader understanding of how CDK1 inhibition impacts the cellular environment, we conducted GO and KEGG pathway enrichment analyses of differentially phosphorylated proteins (|logFC| ≥ 1, adj. p < 0.05) in the HSV-1/CDK1i vs. HSV-1/DMSO and Mock/CDK1i vs. Mock/DMSO comparisons (Figure 9). A substantial proportion of enriched GO terms and pathways were associated with such processes like cellular metabolism, cell cycle regulation and mitosis, nucleocytoplasmic transport, and cellular stress response. These findings align with the expected outcome of CDK1 inhibition at the G2/M boundary. To validate it, we conducted a functional exploration of selected differentially phosphorylated peptides, focusing on sites with known functional outcome for protein activity and cell cycle progression. In both comparisons, CDK1 exhibited significant hyperphosphorylation at T14 (log2FC > 1.6), a modification known to inhibit its kinase activity by preventing the activation of the CDK1-cyclin B complex [82]. Functionally, site-level changes support this: CDK1 was hyperphosphorylated at T14 (log2FC > 1.6), consistent with inhibition of the CDK1-cyclin B complex [82]; key proliferative effectors were hypophosphorylated, including RRM2 S20 (log2FC < −3.6), CDC6 S54 (log2FC < −1.2), and ECT2 T359 (log2FC < −1.4), aligning with suppressed DNA replication and cytokinesis [83,84,85]. Nucleocytoplasmic transport-related terms indicate NPC remodeling: NUP153 (S529, S334), NUP98 (S1447), NUP50 (S221), and NUP93 (T51) were hyperphosphorylated, while NUP35 (S73) was hypophosphorylated; KPNA2 S62 and SUN1 S48 were strongly hypophosphorylated (log2FC < −2.1 and −4.4, respectively). Because CDK1/PLK1-driven phosphorylation promotes mitotic NPC disassembly [86,87,88,89,90], inhibition would typically preserve interphase-like transport, yet the mixed-direction remodeling we observe, for instance, NUP50 S221 weakening importin-β/transportin binding [88], together with KPNA2 S62 and SUN1 S48 hypophosphorylation, suggests selective changes in import rather than a global shut-off. Stress response modulation is revealed with the following players involved: TP53 S315 hyperphosphorylation (log2FC > 1.7) with reported effects on turnover and transcription [91,92]; TP53BP1 S366 hyperphosphorylation (log2FC > 1.2) creating a TOPBP1 docking site and ultimately enhancing checkpoint enforcement [93]; ATRIP S224 hypophosphorylation (log2FC < −1.1) consistent with weaker checkpoint maintenance [94,95]; and marked NPM1 dephosphorylation at S254 and T219 (log2FC < −4.5 and −5.4, respectively) biasing nucleolar retention and potentially limiting TP53 stabilization and early damage-site engagement [96,97,98]. Overall, CDK1 inhibition, regardless of HSV-1 infection, represses mitotic and replicative effectors, selectively regulates nucleocytoplasmic transport, and manipulates stress signaling to reinforce G2/M arrest.

5. Conclusions

Pharmacological CDK1 inhibition suppresses HSV-1 IE transcription and viral replication and reshapes the host phosphoproteome. Mass spectrometry followed by dataset analysis allowed us to delineate and evaluate potential CDK1 targets that may influence viral gene transcription, and we identified key cellular processes impacted by HSV-1 infection in CDK1i- or vehicle-treated cultures. Multi-site RNAPII CTD hypophosphorylation at regulatory residues (S2/T4/S4/S5) detected by this approach (and confirmed biochemically) may provide a direct route to impaired initiation, promoter escape, or elongation at viral IE promoters. We conclude that CDK1 supports an early pro-transcriptional host state through phosphorylation of the direct facilitating proteins, which is exploited by HSV-1; its inhibition attenuates this state, diminishing IE activation.