annotation_id	annotator	created_at	id	label	lead_time	text	updated_at
1	1	2025-09-18T09:14:33.602208Z	1	[{"start":16663,"end":16674,"text":"lymphocytes","labels":["CellType"]},{"start":6272,"end":6277,"text":"PBMCs","labels":["CellType"]},{"start":9860,"end":9865,"text":"PBMCs","labels":["CellType"]},{"start":10554,"end":10559,"text":"PBMCs","labels":["CellType"]},{"start":11028,"end":11033,"text":"PBMCs","labels":["CellType"]},{"start":15246,"end":15251,"text":"PBMCs","labels":["CellType"]},{"start":12193,"end":12218,"text":"J-Lat 5A8 model cell line","labels":["CellLine"]},{"start":13680,"end":13692,"text":"U1 cell line","labels":["CellLine"]},{"start":6081,"end":6101,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":7361,"end":7381,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":7747,"end":7767,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":9734,"end":9754,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":1529,"end":1542,"text":"immune system","labels":["Tissue"]},{"start":713,"end":727,"text":"infected cells","labels":["VagueCellCategory"]},{"start":1391,"end":1405,"text":"infected cells","labels":["VagueCellCategory"]},{"start":8544,"end":8559,"text":"J-Lat 5A8 cells","labels":["CellLine"]},{"start":10727,"end":10742,"text":"J-Lat 5A8 cells","labels":["CellLine"]},{"start":11925,"end":11933,"text":"U1 cells","labels":["CellLine"]},{"start":11953,"end":11964,"text":"ACH-2 cells","labels":["CellLine"]},{"start":16642,"end":16647,"text":"cells","labels":["VagueCellCategory"]},{"start":16761,"end":16766,"text":"Cells","labels":["VagueCellCategory"]},{"start":16984,"end":16989,"text":"cells","labels":["VagueCellCategory"]},{"start":19237,"end":19255,"text":"DMSO-treated cells","labels":["VagueCellCategory"]}]	1152.703	Introduction The persistence of replication-competent, but transcriptionally inhibited HIV-1 proviral DNA in long-lived, latent cellular reservoirs is a significant barrier to the development of a functional cure [1,2] Even after long-term, suppressive antiretroviral therapy (ART), spontaneous reactivation of proviral gene expression from the latent reservoir is sufficient to initiate viral rebound shortly after ART cessation, thus requiring life-long adherence [3–5]. Multifaceted and heterogenous blocks to viral gene expression establish and maintain HIV-1 latency at the epigenetic, transcriptional, and post-transcriptional levels [2]. Several strategies to either reactivate latent proviruses and clear infected cells (i.e., “shock and kill”) or reinforce latency to prevent spontaneous reactivation (i.e., “block and lock”) are currently under investigation [6,7]. While many small molecule latency reversing agents (LRAs) have been described that reactivate latent proviruses ex vivo, they have had little success in clinical trials [8,9]. This failure is partly due to the latent reservoir’s heterogenous nature, such that treatment by a single agent that acts to target a specific block may only ever reactivate a small fraction of proviruses in vivo [10]. Even if transcriptional reactivation is achieved, it is unlikely this is sufficient to result in the clearance of these infected cells without additional immune augmentation such as the administration of antibodies, vaccines, or immunotherapies to prime the immune system [6]. Given these limitations, much research is now focused on combinatorial approaches to trigger more widespread and robust reactivation [11]. For example, a recent study reported synergistic reactivation potential between an activator of non-canonical NF-kB signaling (AZD5582) and BET bromodomain inhibitors that act to lift blocks to transcriptional initiation and elongation, respectively [12]. Blocks to transcriptional elongation are major contributors to establishing and maintaining HIV-1 latency [13,14]. After integration of the proviral DNA, RNA Polymerase II (RNA Pol II) is recruited to the transcription start site by transcription factors that bind cis-elements in the HIV-1 promoter region. After transcriptional initiation, RNA Pol II synthesizes 20–60 nucleotides before stalling through a well-conserved process known as promoter-proximal pausing [15]. Pausing is enforced by several negative elongation factors, including negative elongation factor (NELF), DRB Sensitivity Inducing Factor (DSIF), and the RNA Polymerase II Associated Factor 1 (PAF1) complex [16–18]. Pause release is regulated by positive transcription elongation factor-b (P-TEFb), a heterodimeric protein complex composed of cyclin-dependent kinase 9 (CDK9) and cyclin T1 (CCNT1) [19–21]. P-TEFb phosphorylates the C-terminal tail of RNA Pol II and several negative elongation factors, which collectively license transcriptional elongation [22–24]. Recruitment of P-TEFb to sites of nascent transcription is a highly regulated process mediated by several cellular complexes. The majority of cellular P-TEFb is sequestered in an inactive state by the 7SK ribonucleoprotein (RNP) complex [25,26]. Diverse extracellular stimuli and intracellular signals can induce the release of P-TEFb from the 7SK complex [27–29] where it can be recruited to sites of nascent transcription by transcription factors (i.e., NF-kB and c-MYC) [30–32], epigenetic regulators (i.e., BRD4) [33,34], or super elongation complexes (SECs) composed of an ARF4/FMR2 (AFF) family scaffold protein in complex with AF9, ENL, an eleven-nineteen Lys-rich leukemia (ELL) family protein, and an ELL-associated factor (EAF) protein [35]. To circumvent this regulatory step, HIV-1 encodes a trans-activator protein (Tat) that binds to and recruits P-TEFb specifically to sites of nascent proviral transcription through recognition of a transactivation response (TAR) RNA stem loop produced at the immediate 3’ end of all viral RNA transcripts [36–38]. The distribution of and competition for P-TEFb binding among different complexes is an area of active investigation, with several strategies to enhance the biogenesis or availability of P-TEFb showing promise for HIV-1 latency reversal. For example, BET bromodomain inhibitors (such as JQ1) have been shown to be potent LRAs in ex vivo models by releasing P-TEFb from BRD4 [12]. Likewise, the release of P-TEFb from the 7SK RNP complex has been shown to reactivate latent proviruses in ex vivo models [39]. That being said, the release of P-TEFb from the 7SK RNP complex has been shown to directly correlate with increased BRD4 binding, suggesting that release from any one complex will not necessarily increase the amount of unbound P-TEFb or the amount recruited to specific sites of transcription [33]. Furthermore, post-translational modification of P-TEFb (i.e., through phosphorylation of CDK9 at Serine 175) has been shown to influence P-TEFb distribution in certain regulatory complexes, again highlighting the unique properties of release from each complex [40]. While the release of P-TEFb from the 7SK RNP complex and BET proteins such as BRD4 have been explored as strategies for HIV-1 latency reversal, the release of P-TEFb from SECs has not been explored. Previous work has demonstrated that HIV-1 Tat biochemically co-purifies with several SEC proteins [41,42], though the reason for this is unclear as they seemingly have functionally redundant purposes in P-TEFb recruitment. In this study, we test the hypothesis that the SEC is not necessary for HIV-1 viral transcription and that the release of P-TEFb from cellular SEC complexes can serve as a novel strategy to reactivate latent HIV-1 proviruses. Results Discussion In this study, we demonstrate a potential new strategy for enhancing HIV-1 latency reversal through the release of P-TEFb from the cellular pool of SECs. We show that KL-2, a small molecule inhibitor of the interaction between the SEC and P-TEFb, is sufficient to enhance viral transcription in primary CD4+ T cells and can synergistically enhance the activity of other LRAs in certain cell line models of latency. Finally, we demonstrate that KL-2 can increase HIV-1 gag expression in PBMCs from PLWH on suppressive ART, most notably in combination with the non-canonical NF-kB agonist, AZD5582. We propose a model in which KL-2 release of P-TEFb from the cellular pool of SECs enhances transcriptional elongation of integrated proviruses, akin to BET bromodomain inhibitors and 7SK RNP inhibitors (Fig 6D). These results have several implications for our understanding of viral transcription and future directions. Latency reversal through the release of P-TEFb from cellular SECs had not been previously explored, likely due to the perceived dependency of viral transcription on the SEC. This effect has been supported by biochemical purifications of HIV-1 Tat from human cell lines that revealed interactions with a larger SEC [41,42], as well as by genetic knock-down experiments in cell lines showing a decrease in Tat-dependent transcription upon SEC component depletion [64,65]. However, given that both the SEC and Tat recruit P-TEFb to sites of nascent transcription, they share some functional redundancy. We found that knock-out of SEC components from activated, primary CD4+ T cells from 12 independent donors did not inhibit viral replication, suggesting that the SEC is not required in this cellular context. This result was independently verified using KL-2, which inhibits the interaction between CCNT1 (P-TEFb) and AFF1/4 (of the larger SEC). Notably, disruption of the SEC using KL-2 resulted in significant increases in HIV-1 replication in primary CD4+ T cells whereas genetic knockout of most SEC members had minimal to no impact on replication. One potential explanation for this difference is that genetic knockout results in the ablation of SEC assembly and relocalization of P-TEFb into other complexes at steady-state whereas chemical perturbation by KL-2 results in the release of P-TEFb from SECs that are continually being formed. Understanding the dynamics of P-TEFb distribution and relocalization upon different types of perturbation is an ongoing area of investigation. While we expected KL-2 to enhance the transcriptional elongation of integrated proviruses due to the release of P-TEFb from cellular SECs, we also saw increases in transcriptional initiation as measured by qRT-PCR for TAR transcript levels. Likewise, in J-Lat 5A8 cells, we saw increases in transcriptional elongation, transcriptional initiation, and in RNA Pol II recruitment to the proviral promoter when KL-2 was added, even though KL-2 addition alone was not sufficient for reactivation as measured by GFP positivity in this model. This finding suggests that either KL-2 has secondary effects not mediated by P-TEFb or that the redistribution of P-TEFb away from SECs has secondary effects that could impact transcriptional initiation at proviral integration sites. BET bromodomain inhibitors have also been reported to increase HIV-1 transcriptional initiation [12,13], though these effects have been suggested to occur through modulation of the epigenetic regulatory functions of these proteins [66]. Other reports have indicated that P-TEFb mediated release of paused RNA Pol II can result in enhanced transcriptional initiation and even RNA Pol II recruitment simply by increasing the number of transcribing polymerases [67]. Still, this connection between transcriptional elongation and initiation has yet to be fully understood in the context of HIV-1 transcription. While KL-2 was sufficient to boost viral replication in activated, primary CD4+ T cells, in and of itself it displayed minimal reactivation potential in both cell line models of latency and in PBMCs from PLWH on suppressive ART. This finding is similar to our recent report of a novel inhibitor of the PAF1 complex (iPAF1C) that had minimal activity on its own, but greatly enhanced the reactivation potential of other LRAs [16]. Both cases highlight the multifaceted nature of the blocks to viral gene expression that underlie the latent state as well as the limitations to single agent drug screening to identify promising, next-generation LRAs. Combinatorial approaches to dissect the genetic underpinnings of HIV-1 latency and discover new, synergistic drug interactions should be prioritized. While KL-2 alone failed to significantly increase HIV-1 gag transcript levels in patient PBMCs, in combination with AZD5582 it resulted in a 16-fold increase over AZD5582 treatment alone and a 53-fold increase over the DMSO control. Crosswise dose titrations in J-Lat 5A8 cells showed a strong synergistic potential between AZD5582 and KL-2. This is consistent with reports of robust synergy between AZD5582 and P-TEFb release through BET bromodomain inhibition [12]. Notably, the BET bromodomain inhibitor JQ1 showed minimal reactivation activity in our patient PBMCs, even in the presence of KL-2, in contrast to our cell line data. This finding could reflect stochastic differences driven by variations in patient characteristics, integration site, chromatin state, transcription factor availability, etc. [13]. Future work will compare P-TEFb release from SECs to release from other cellular reservoirs, such as BRD4 or the 7SK RNP. Recent studies have shown that post-translational modifications of P-TEFb, most notably phosphorylation of CDK9 Serine 175 and Threonine 186, can drive inclusion into different complexes and may strongly influence bioavailability and activity [40, 68–70]. Therefore, it is possible that disruption of complexes housing ‘active’ P-TEFb is a more direct route to redirecting P-TEFb activity. This is not to say that the SEC is never required for viral transcription. In latency model cell lines that lacked a functional Tat (U1 cells) or TAR stem loop (ACH-2 cells), KL-2 inhibited the reactivation potential of several LRAs, suggesting that viral transcription may be more dependent on the SEC when Tat is either defective or not expressed. We attempted to test this by inhibiting Tat in the J-Lat 5A8 model cell line using two previously described Tat-dependent transcription inhibitors, Triptolide and Spironolactone. While both compounds reduced LRA efficacy, KL-2 still boosted the activity of JQ1 and PMA, but not AZD5582. This suggests that P-TEFb can be recruited to proviral integration sites in a Tat and SEC-independent manner upon PMA or JQ1 treatment, potentially through a transcription factor such as NF-kB. The inability of AZD5582 to do so suggests that non-canonical NF-kB activation does not recruit the same milieu of transcription factors, making it uniquely Tat or SEC dependent. This is consistent with the complete lack of activity of AZD5582 in cell line models lacking functional Tat/TAR activity and may underlie the remarkable synergy between non-canonical NF-kB agonists and agents that release P-TEFb [12]. Additionally, triptolide has been characterized outside of HIV-1 transcription in its ability to prevent RNA Pol II reinitiation following pausing through inhibition of xeoderma pigmentosum group B-complementing protein (XPB) and is often used as a tool compound for measuring the fate of paused RNA Pol II at different time points [51,71]. With this in mind, it is possible that compounds JQ1 and PMA result in de novo recruitment of RNA Pol II thereby increasing transcriptional initiation whereas AZD5582 may be more reliant on RNA Pol II pause-release. To further explore the Tat dependency of KL-2, we tried to rescue Tat function in the U1 cell line using a Dox-inducible system. We hypothesized that by providing Tat, the SEC would no longer be required for viral gene expression such that SEC disruption by KL-2 would enhance reactivation as seen in the J-Lat models. While Tat induction itself was sufficient for reactivation, this reactivation was completely abolished by the addition of KL-2. Even when Tat was minimally induced and other LRAs were added, KL-2 still inhibited reactivation, suggesting that additional factors—such as steady-state levels of P-TEFb, integration site, epigenetic factors, or transcription factor availability—may drive SEC dependency besides just the presence or absence of Tat. Indeed, SEC disruption by KL-2 in the original report of the inhibitor demonstrated an outsized impact on Myc-dependent transcription [32], suggesting that additional factors driving the SEC dependency of proviral transcription have yet to be described. Regardless, the dual-acting nature of KL-2 in enhancing latency reactivation in some circumstances (i.e., if Tat is present) and inhibiting latency reactivation in others (i.e., when the cell state dictates SEC dependency) presents a unique opportunity to leverage the heterogenous nature of the latent reservoir to both reverse and promote latency. Taken together, our results indicate that release of P-TEFb from cellular SECs is a novel mechanism for promoting HIV-1 viral transcription during both active and latent infection. We demonstrated the enhancement of latency reversal in multiple latent cell line models and in primary PBMCs from PLWH on suppressive ART. This work demonstrates the importance of increasing the production or availability of free P-TEFb for recruitment to viral loci as a powerful strategy for bolstering current LRAs, most notably non-canonical NF-kB agonists. Due to the heterogeneity of blocks to viral replication in the latent reservoir, it is likely that combinatorial LRA treatments will be the best strategy for potent latency reversal moving forward. Further efforts are needed to understand the intracellular distribution of active P-TEFb to characterize the most critical reservoir to target to enhance transcription. Additionally, our work demonstrates that disruption of SECs could enhance latency reversal or promote the maintenance of latency depending on the cellular context. Understanding the mechanism that controls this molecular switch would aid in understanding whether SEC disruptors could be a viable dual-acting molecule to aid in finding a functional cure for HIV-1 infection. Methods Flow cytometry and analysis of viability/infection/reactivation data Flow cytometry analysis was performed on an Attune NxT acoustic focusing cytometer (Thermo Fisher Scientific), recording all events in a 40-μL sample volume after one 150 μL of mixing cycle. Data were exported as FCS3.0 files using Attune NxT Software v3.2.0 and analyzed with a consistent template on FlowJo. Briefly, cells were gated for lymphocytes by light scatter followed by doublet discrimination in both side and forward scatter. Cells with equal fluorescence in the BL-1 (GFP) channel and the VL-2 (AmCyan) channel were identified as autofluorescent and excluded from the analysis. A consistent gate was then used to quantify the fraction of remaining cells that expressed the target of interest. Analysis of hiv-specific rna transcripts Following cDNA synthesis, viral transcripts were assessed by qRT-PCR as described previously [13]. For HIV-1 TAR, the following primers were used: PF: 5′-GTCTCTCTGGTTAGACCAG-3′; PR: 5′-TGGGTTCCCTAGYTAGCC-3′; and probe: 5′-AGCCTGGGAGCTC-3′. HIV-1 Long LTR levels were assessed using the following primers: PF: 5′-GCCTCAATAAAGCTTGCCTTGA-3′; PR: 5′-GGGCGCCACTGCTAGAGA-3′; and probe: 5′-CCAGAGTCACACAACAGACGGGCACA-3. For expression level normalization, β-Actin was used (Thermo Fisher Scientific, catalog no. 4331182). Notably, β-Actin is an RNA Pol II controlled gene. To ensure that β-Actin levels were not being altered by KL-2 induced SEC disruption, we compared β-Actin expression to that of the 18S ribosomal subunit, an RNA Pol I controlled gene (Thermo Fisher Scientific, catalog no. 4331182). No statistically significant changes were noted between the two housekeeping genes upon KL-2 treatment, so β-Actin was used as the normalization gene for subsequent qPCR analyses. The reaction was performed using TaqMan Fast Advanced Master Mix (Thermo Fisher Scientific, catalog no. 4444553) according to manufacturer’s instructions. Briefly, 10 μL of reaction was mixed using 5 μL of Taqman Master Mix, 0.5 μL of 20x primer probe mix (18 μM of primers and 5 μM probe), 2.5 μL water, and 2 μL of template cDNA. The PCR cycles were as follows: 50 °C for 2 minutes, 95 °C for 20 seconds, followed by 40 cycles of 95 °C for 1 second and 60 °C for 20 seconds. Rna sequencing analysis Sequencing data was demultiplexed and trimmed using Trimmomatic v0.36 to remove adapters and low-quality reads. Trimmed reads were aligned to the Homo sapiens reference genome GRCh38 and transcripts quantified using the Hisat2-StringTie pipeline [76]. Differential gene expression analysis of the quantified gene transcripts was performed with DESeq2 v.1.42.0 R package using R v.4.3.2. After retaining genes with nonzero total read count and with more than 10 reads in total between all samples, we fitted a model that included all treatments to account for overall variability and identified differentially expressed genes (DEGs) within that model between all tested conditions against DMSO-treated cells (i.e. KL2 vs DMSO, AZD5582 vs DMSO, and AZD5582+KL2 vs DMSO). To define DEGs, we used as cut-offs an absolute log2 fold change > 1 and a false discovery rate (FDR) compareCluster function. For these analyses all genes whose gene symbols could be mapped to ENTREZ Ids using the org.Hs.eg.db v.3.18.0 Bioconductor annotation package were included. Statistical analysis All statistical analysis was performed using GraphPad Prism version 10.2.0 (392) for Windows 64-bit, GraphPad Software, Boston, Massachusetts, USA (www.graphpad.com). Source paper: PMC11419360	2025-09-19T10:27:00.943310Z
2	1	2025-09-18T09:14:33.602342Z	2	[{"start":449,"end":463,"text":"HeLa cell line","labels":["CellLine"]},{"start":5062,"end":5079,"text":"Jurkat 2D10 cells","labels":["CellLine"]},{"start":4027,"end":4047,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":5219,"end":5231,"text":"CD4+ T cells","labels":["CellType"]},{"start":418,"end":427,"text":"NH1 cells","labels":["CellLine"]},{"start":774,"end":783,"text":"NH1 cells","labels":["CellLine"]},{"start":1336,"end":1341,"text":"cells","labels":["VagueCellCategory"]},{"start":2646,"end":2655,"text":"NH1 cells","labels":["CellLine"]},{"start":2912,"end":2921,"text":"NH1 cells","labels":["CellLine"]},{"start":3222,"end":3227,"text":"cells","labels":["VagueCellCategory"]},{"start":4102,"end":4115,"text":"patient cells","labels":["VagueCellCategory"]},{"start":5246,"end":5274,"text":"HIV-1 infected patient cells","labels":["VagueCellCategory"]},{"start":6571,"end":6592,"text":"HIV-1 reservoir cells","labels":["VagueCellCategory"]},{"start":7531,"end":7541,"text":"host cells","labels":["VagueCellCategory"]},{"start":11478,"end":11492,"text":"PRMT3 KO cells","labels":["CellLine"]},{"start":12659,"end":12668,"text":"NH1 cells","labels":["CellLine"]},{"start":12774,"end":12779,"text":"Cells","labels":["VagueCellCategory"]},{"start":12957,"end":12962,"text":"cells","labels":["VagueCellCategory"]},{"start":12977,"end":12982,"text":"cells","labels":["VagueCellCategory"]},{"start":13102,"end":13107,"text":"cells","labels":["VagueCellCategory"]},{"start":13109,"end":13114,"text":"Cells","labels":["VagueCellCategory"]},{"start":13218,"end":13223,"text":"Cells","labels":["VagueCellCategory"]},{"start":16974,"end":16979,"text":"Cells","labels":["VagueCellCategory"]},{"start":17144,"end":17149,"text":"cells","labels":["VagueCellCategory"]}]	1278.895	Results Dcas9-targeted hiv-1 ltr-interactome analysis identified prmt3 as a ltr-binding factor Seeking host factors that specifically associate with the HIV-1 LTR, we conducted an LTR-interacted proteome analysis based on a refined version of a previously described nuclease-deficient Cas9 (dCas9)-targeted chromatin-based purification strategy (CLASP) (Cas9 locus-associated proteome) (Fig. 1a)33,34. The screen used NH1 cells, which is a modified HeLa cell line that harbors an integrated HIV-1 LTR-driven luciferase reporter gene in the host genome35 (Fig. 1b). To get the most effective sgRNAs that target LTR, we used two methods for screening. We individually inserted the sgRNAs into the Cas9 plasmid, which was then co-transfected with a Tat-expression plasmid into NH1 cells that harbors the LTR-driven luciferase reporter gene. If a sgRNA-Cas9 combination can target and disrupt the LTR sequence, a reduction in luciferase activity is observed. We designed seven sgRNAs targeting different regions of the HIV-1 LTR (Fig. 1b). An sgRNA targeting the yeast Gal4 gene was used as the negative control. Among the designed sgLTRs, the top three sgLTRs (# 5, 6, 7 in pink, Fig. 1b) exhibiting the capacity to attenuate the LTR driven-luciferase expression activated by HIV-1 Tat (Fig. 1c). Then, we performed anti-Flag ChIP-qPCR in cells transfected with the sgLTRs-dCas9-3 × Flag plasmids to further verify the abilities of the sgLTRs to target dCas9-3 × Flag to the LTR, and found that sgLTR-1 and -2 failed to recruit dCas9 to LTR efficiently (Supplementary Fig. 1a, b), sgLTR-5 and -6 displayed the most significant effect, whereas sgLTR-3, -4 and -7 all displayed a similar and partial effect (Supplementary Fig. 1c, d). Thus, sgLTR-5, -6, -7 were selected for subsequent ChIP-qPCR analysis. By utilizing the combination of the three sgRNAs, we found that in comparison to sgGal4, these sgRNAs can guide dCas9 to the LTR as revealed by ChIP-qPCR analysis (Fig. 1d). Next, we performed in vitro transcription reactions to generate these sgRNAs, which were then used for dCas9/sgRNAs complex formation. This complex was subsequently incubated with sheared chromatin that contained the LTR-bound proteins for dCas9-3 × Flag immunoprecipitation, following with mass spectrometry analysis.Fig. 1***dCas9-targeted HIV-1 LTR-interactome analysis identified PRMT3 as a LTR-binding factor.*** a Schematic of dCas9-targeted proteome analysis workflow. (1) sgRNAs targeting the LTR were complex formation with dCas9-3 × Flag; (2) Crosslinking of proteins bound to the LTR; (3) dCas9-3 × Flag-sgRNA complex was incubated with sheared fragments from NH1 cells that stably expressed LTR-luciferase reporter; (4) Immunoprecipitation was used to capture LTR bound proteins; (5) LTR-interacted proteins were identified by mass spectrometry. b Schematic of the position of 7 sgRNAs. c Luciferase activity was measured in NH1 cells transfected with a plasmid encoding each sgRNA and control sgRNA (sgGal4), which expresses the Cas9 in the presence of Tat (p = 0.0016, p = 0.0024, p = 0.0042). d ChIP-qPCR analyses were conducted to assess the occupancy of 3 × Flag-dCas9 at the Promoter (Left) or Nascent region (Right) of LTR when cells were transfected with control plasmid, a mixture of plasmids containing sgLTRs-5, 6, 7 or the sgGal4 plasmid (Promoter: p = 0.0009, p = 0.0011, Nascent: p = 0.000004, p = 0.000003). e Proteins showing over 1.5-fold enrichment relative to the control sample identified from 3 × Flag-dCas9 immunoprecipitation following by mass spectrometry analysis were listed. f ChIP-qPCR analyses were conducted to assess the occupancy of PRMT3 at LTR-luciferase region (p = 0.0001, p = 0.0012, p = 0.0005, p = 0.0008). The schematic display of the four primers was shown on the top of the panel. g ChIP-qPCR analyses were conducted to assess the occupancy of PRMT3 at LTR-luciferase regions in the absence or presence of Tat (p = 0.0022, p = 0.0012, p = 0.0045, p = 0.0017). h CUT&Tag sequencing was performed in primary CD4+ T cells isolated from virologically suppressed HIV-1-infected patient cells that were treated with PMA, by using IgG or PRMT3 antibody. The alignment results of sequencing with the HIV-1 genome in three patients were displayed. Error bars = mean +/− SD of three biological replicates. *p p p t test. Source data are provided as a Source Data file. Mass spectrometry detected a total of 28 proteins with at least 1.5-fold enrichment relative to the non-targeting negative control (Fig. 1e and Supplementary Data 1). PRMT3, which has been shown to function in transcriptional regulation and antiviral innate immunity15,30,36, was among the hits with the highest fold enrichment of sgLTR/sgGal4, and was therefore selected for subsequent investigation. We performed ChIP-qPCR analysis to examine the binding of PRMT3 to the LTR. PRMT3 was significantly enriched at LTR-luciferase reporter region (Fig. 1f), which can be further increased in the presence of Tat (Fig. 1g). The binding of PRMT3 at the LTR was also verified in Jurkat 2D10 cells (Supplementary Fig. 2). Notably, we performed Cleavage Under Targets & Tagmentation (CUT&Tag) sequencing by using IgG or PRMT3 antibody in CD4+ T cells isolated from HIV-1 infected patient cells, and demonstrated that while IgG displayed undetectable signal at the LTR, PRMT3 showed significant binding signal around LTR (Fig. 1h). Thus, these results demonstrated that PRMT3 is an HIV-1 LTR binding factor, which is further enhanced in the presence of Tat. Discussion The elusive nature of latent HIV-1 reservoirs presents a formidable challenge to eradicating the virus from infected individuals49–51. Despite the various strategies employing either the latency reversal agents (LRAs) or latency promoting agents (LPAs), which are aimed at either purging or deeply silencing the latent viral reservoirs, these agents have yet to be made into effective drugs for curing HIV/AIDS and targets are still needed for the development of therapies. Furthermore, the premise of using the current and future versions of LRAs or LPAs for treating HIV-1 hinges on the potential to epigenetically modulate the HIV-1 promoter activity, aiming for a sustained activation or suppression of viral transcription, respectively52–54. The significance of targeting epigenetic control of viral transcription was highlighted recently in a human clinical trial, where the combination of panobinostat, a potent pan-histone deacetylase inhibitor, with interferon-α2a resulted in an enhanced vulnerability of latent HIV-1 reservoir cells, underscoring the pivotal role of epigenetic manipulation in combating HIV-1 persistence2. In the current study, by performing the dCas9-targeted LTR interactome screening, we have identified PRMT3 as a positive regulator to promote HIV-1 latency reversal. Mechanistically, PRMT3 activates HIV-1 transcription by interaction with TEAD4 and the two proteins co-localize at the LTR by using specific TEAD4-binding motifs, thereby regulating chromatin accessibility and facilitating P-TEFb/Tat recruitment. Thus, our findings propose a promising epigenetic strategy to combat latent HIV-1 infection, underscoring PRMT3 and its partners as promising therapeutic targets for anti-HIV-1 drug development. Our study demonstrates TEAD4 as a positive regulator of HIV-1 transcription, acting alongside PRMT3 through a specific interaction with the LTR’s GGAAT motif. This discovery expands our current understanding of TEAD4’s regulatory roles in host cells and suggests its potential regulation of HIV-1 viral expression. The specificity of TEAD4’s interaction with the motif containing the core GGAAT sequence within the HIV-1 LTR and its synergy with PRMT3 in promoting Tat-dependent HIV-1 transcription and a specific subset of host gene transcription is particularly intriguing given TEAD4’s established roles in cell survival, proliferation, tissue regeneration, stem cell maintenance, embryonic trophoblast and organ development and tumorigenesis9,11,13,14,55. Specifically, we selected three host genes, PRDM1, SLITRK5, and TGFB2, for further validation of our proposed PRMT3-TEAD4 regulation model. Our data demonstrate that in addition to the HIV-1 LTR, PRMT3 and TEAD4 indeed associated with these cellular gene promoters, where the binding of TEAD4 significantly decreases after the depletion of PRMT3, suggesting a common regulatory mechanism involving the PRMT3-TEAD4 complex that exists at the HIV-1 LTR, as well as the promoters of selected host genes. At the HIV-1 LTR, the interaction of the PRMT3-TEAD4 complex with Tat and the P-TEFb provides further regulation of HIV-1 transcription. On cellular gene promoters such as the three genes mentioned above, it is conceivable that transcription can also be modulated through controlling the function and/or binding of the PRMT3-TEAD4 complex to the GGAAT motif in response to changes in physiological or pathological conditions. This co-regulation of viral and host gene transcription by PRMT3-TEAD4 underscores a finely tuned regulatory mechanism that integrates the cellular and viral transcriptional control. Furthermore, TEAD4 has also been demonstrated to interact with P-TEFb, which is the core transcriptional component of the Super Elongation Complex (SEC) essential for Tat-activated HIV-1 transcription. Given the fact that TEAD4 is a DNA sequence-specific transcription factor, it remains to be investigated whether it plays a key role in recruiting SEC to both host and HIV-1 gene promoters that contain the TEAD4-binding motif. While previous studies have indicated PRMT3’s involvement as a key factor in mediating host responses to viral infection17,30, its specific role in HIV-1 infection and latency remains largely unexplored. For instance, in zebrafish, PRMT3 has been shown to negatively regulate antiviral responses17. Similarly, recent studies utilizing the PRMT3 inhibitor, SGC707 treatment, or PRMT3 knockout mice have revealed PRMT3’s facilitation of HSV-1 infection30. In conjunction with our current discovery demonstrating PRMT3’s promotion of HIV-1 transcription to reverse latency, these findings suggest that PRMT3 may serve as a potential target for broad-spectrum antiviral drugs. However, future comprehensive research is imperative to fully comprehend the extent of PRMT3’s involvement in mediating viral-host interactions. It is noteworthy that besides PRMT3, other PRMTs have also been implicated in regulating anti-HIV activity. For instance, PRMT6 has been shown to inhibit HIV-1 replication in vitro by directly methylating several HIV-1 proteins, including Tat, Rev, and nucleocapsid protein, thereby interfering with their functions56,57. Similarly, PRMT2 was recently discovered to suppress HIV-1 transcription by methylating Tat and promoting the phase separation of P-TEFb and Tat58. Thus, in addition to PRMT3, other members of the PRMT family should be explored as potential targets for developing effective antiviral drugs. In addition to its effects on viral gene expression, PRMT3’s regulation of host gene transcription has been implicated in various cellular processes, including tumorigenesis, oxaliplatin resistance in liver cancer, retinoic acid signaling, and hepatic lipogenesis18,20,28,29,59,60. However, the precise mechanism(s) underlying these transcriptional effects of PRMT3 remain unclear. In this study, the comparison of ATAC-Seq and RNA-Seq data between WT and PRMT3 KO cells reveals that PRMT3 selectively regulates chromatin accessibility and transcription of a small subgroup of human genes, including some well-studied genes such as TGFB2, which is relevant to the potential pathogenic effects of PRMT3 in tumorigenesis. The selectivity of PRMT3’s action is apparently achieved through forming a complex with TEAD4, which then co-localizes at specific gene promoter regions via binding to the TEAD4-recognition motif. Together, these findings have revealed the mechanistic basis for PRMT3’s control of the transcription of HIV-1 and a selected group of host genes. Although the PRMT3-created transcription hub containing TEAD4 and P-TEFb is demonstrated in the current study as important for HIV-1 to escape latency, it is presently unknown whether it also plays a role during the establishment of latency. It is conceivable that the loss of expression/function of any component of the hub could be responsible in this latter process. Further studies are thus necessary to test this hypothesis and explore the possibility of targeting the PRMT3 transcription hub to cure HIV/AIDS. Methods Dcas9-targeted ltr proteome analysis One million NH1 cells with an integrated with HIV-1 5’ LTR-driven luciferase reporter construct, which contains 424 bp of LTR. Cells were fixed with 1% formaldehyde for 15 min at room temperature and 0.125 M Glycine for 5 min to quench unreacted formaldehyde at room temperature. Use 20 ml of cold PBS to wash cells twice. Scrape cells with 2 ml cold PBS containing protease inhibitor and dithiothreitol (DTT). Spin at 700 × g at 4 °C for 3 min to pellet cells. Cells were lysed with lysis buffer [1% SDS, 10 mM EDTA, 50 mM Tris, pH 8.1] and incubated on ice for 20 min. Cells were sheared with a Covaris sonicator M220 until genomic DNA was visualized to be at 200–1000 bp. The DNA was cleared with a high-speed spin (15,000 g for 10 min) at 4 °C. The DNA was diluted by ChIP Dilution Buffer [0.01% SDS, 1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris-HCl, pH 8.1, 167 mM NaCl]. dCas9-3 × Flag/sgRNAs complex was added to the sheared DNA and incubated at 4 °C overnight. The anti-Flag M2 agarose resin (Sigma) was added, followed by a 3 h incubation at 4 °C. The resin was spun down at 1500 × g at 4 °C for 1 min and washed with high salt wash buffer [0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl, pH 8.1, 300 mM NaCl] for once, low salt wash buffer [0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl, pH 8.1, 150 mM NaCl] for three times, and TE buffer [10 mM Tris-HCl, 1 mM EDTA, pH 8.0] for twice, with each wash for 5 min rotated at 4 °C. The products concentrated by beads were used for subsequent experiments. Mass spectrometry (ms) and analysis The sample of dCas9-3 × Flag-sgLTRs and dCas9-3 × Flag-sgGal4 (control) immnuoprecipitated proteins were prepared and analyzed by MS. In general, the eluted LTR binding proteins were reduced in 20 mM DTT at 95 °C for 5 min, and subsequently alkylated in 50 mM iodoacetamide for 30 min in the dark at room temperature. After alkylation, the samples were transferred to a 10 kD centrifugal spin filter (Millipore) and sequentially washed with 200 μl of 8 M urea for three times and 200 μl of 50 mM ammonium bicarbonate for two times by centrifugation at 14,000 × g. Next, tryptic digestion was performed by adding trypsinat 1:50 (enzyme/substrate, m/m) in 200 μl of 50 mM ammonium bicarbonate at 37 °C for 16 hours. Peptides were recovered by transferring the filter to a collection tube and spinning at 14,000 × g. To increase the yield of peptides, the filter was washed twice with 100 μl of 50 mM ammonium bicarbonate. Peptides were desalted by StageTips. MS experiments were performed on a nanoscale EASY-nLC 1200 UHPLC system (Thermo Fisher Scientific) connected to an Orbitrap Fusion Lumos equipped with a nanoelectrospray source (Thermo Fisher Scientific). Mobile phase A contained 0.1% formic acid (v/v) in water; mobile phase B contained 0.1% formic acid in 80% acetonitrile (ACN). The peptides were dissolved in 0.1% formic acid (FA) with 2% acetonitrile and separated on an RP-HPLC analytical column (75 μm × 25 cm) packed with 2 μm C18 beads (Thermo Fisher Scientific) using a linear gradient ranging from 5% to 22% ACN in 90 min and followed by a linear increase to 35% B in 20 min at a flow rate of 300 nl/min. The Orbitrap Fusion Lumos acquired data in a data-dependent manner, alternating between full-scan MS and MS2 scans. The spray voltage was set at 2.2 kV, and the temperature of the ion transfer capillary was 300 °C. The MS spectra (350–1500 m/z) were collected with 120,000 resolutions, AGC of 4 × 105, and 50 ms maximal injection time. Selected ions were sequentially fragmented by HCD with 30% normalized collision energy, specified isolated windows 1.6 m/z, and 15,000 resolutions. AGC of 5 × 104 and 40 ms maximal injection time were used. Dynamic exclusion was set to 40 s. Unassigned ions or those with a charge of 2 + and >7 + were rejected for MS/MS. Raw data was processed using Proteome Discoverer (PD, version 2.4), and MS/MS spectra were searched against the reviewed SwissProt human proteome database. Only peptides with at least six amino acids in length were considered. The peptide and protein identifications were filtered by PD to control the false discovery rate (FDR) ***Generation of stable knockout cell line*** sgRNA sequence was ligate into plasmid pSpCas9 (BB)−2A-Puro (PX459), which was purchased from Addgene (#48139). Cells were transfected with the plasmid expressing sgPRMT3 by using Lipofectamine 3000 (Invitrogen) according to the manufacturer’s protocol. After 48 h of transfection, cells that did not contain the plasmid were killed with puromycin for 2 days, then changed into DMEM with no antibiotics and plated into 96-well plate for single clone selection. After verification with western blot, the genome was extracted and sent for sequencing. Source paper: PMC12081701	2025-09-19T10:27:44.193443Z
3	1	2025-09-18T09:14:33.602565Z	3	[{"start":180,"end":190,"text":"HeLa cells","labels":["CellLine"]},{"start":609,"end":619,"text":"HeLa cells","labels":["CellLine"]},{"start":2189,"end":2199,"text":"HeLa cells","labels":["CellLine"]},{"start":3080,"end":3090,"text":"HeLa cells","labels":["CellLine"]},{"start":4396,"end":4406,"text":"HeLa cells","labels":["CellLine"]},{"start":4778,"end":4788,"text":"HeLa cells","labels":["CellLine"]},{"start":4881,"end":4891,"text":"HeLa cells","labels":["CellLine"]},{"start":4933,"end":4943,"text":"HeLa cells","labels":["CellLine"]},{"start":5138,"end":5148,"text":"HeLa cells","labels":["CellLine"]},{"start":5154,"end":5164,"text":"HeLa cells","labels":["CellLine"]},{"start":5864,"end":5868,"text":"HeLa","labels":["CellLine"]},{"start":6008,"end":6018,"text":"HeLa cells","labels":["CellLine"]},{"start":6182,"end":6192,"text":"HeLa cells","labels":["CellLine"]},{"start":6644,"end":6654,"text":"HeLa cells","labels":["CellLine"]},{"start":6857,"end":6867,"text":"HeLa cells","labels":["CellLine"]},{"start":7119,"end":7128,"text":"HeLa cell","labels":["CellLine"]},{"start":7656,"end":7666,"text":"HeLa cells","labels":["CellLine"]},{"start":9154,"end":9164,"text":"HeLa cells","labels":["CellLine"]},{"start":12469,"end":12479,"text":"HeLa cells","labels":["CellLine"]},{"start":13545,"end":13555,"text":"HeLa cells","labels":["CellLine"]},{"start":14024,"end":14028,"text":"HeLa","labels":["CellLine"]},{"start":14795,"end":14805,"text":"HeLa cells","labels":["CellLine"]},{"start":5849,"end":5854,"text":"HepG2","labels":["CellLine"]},{"start":704,"end":716,"text":"normal cells","labels":["VagueCellCategory"]},{"start":909,"end":935,"text":"apoptotic cell populations","labels":["VagueCellCategory"]},{"start":2209,"end":2214,"text":"cells","labels":["VagueCellCategory"]},{"start":2349,"end":2354,"text":"cells","labels":["VagueCellCategory"]},{"start":2917,"end":2932,"text":"apoptotic cells","labels":["VagueCellCategory"]},{"start":2993,"end":3007,"text":"necrotic cells","labels":["VagueCellCategory"]},{"start":3100,"end":3105,"text":"cells","labels":["VagueCellCategory"]},{"start":3247,"end":3252,"text":"cells","labels":["VagueCellCategory"]},{"start":3484,"end":3489,"text":"Cells","labels":["VagueCellCategory"]},{"start":3761,"end":3771,"text":"live cells","labels":["VagueCellCategory"]},{"start":3804,"end":3819,"text":"apoptotic cells","labels":["VagueCellCategory"]},{"start":3851,"end":3875,"text":"apoptotic\/necrotic cells","labels":["VagueCellCategory"]},{"start":3906,"end":3925,"text":"dead\/necrotic cells","labels":["VagueCellCategory"]},{"start":3970,"end":3975,"text":"cells","labels":["VagueCellCategory"]},{"start":6238,"end":6250,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":6256,"end":6268,"text":"normal cells","labels":["VagueCellCategory"]},{"start":7719,"end":7724,"text":"cells","labels":["VagueCellCategory"]},{"start":8139,"end":8144,"text":"cells","labels":["VagueCellCategory"]},{"start":8717,"end":8729,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":11377,"end":11382,"text":"cells","labels":["VagueCellCategory"]},{"start":15082,"end":15094,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":6706,"end":6727,"text":"cervical cancer cells","labels":["CellType"]},{"start":5856,"end":5862,"text":"HCT116","labels":["CellLine"]},{"start":5874,"end":5879,"text":"MCF-7","labels":["CellLine"]}]	1428.181	Methods The cytotoxic effects of the A. incana DCM fraction were evaluated in a dose-dependent manner using the MTT assay on several cancer cell lines, with particular emphasis on HeLa cells. Flow cytometry was used to assess cell cycle arrest and apoptosis, while RT-qPCR quantified changes in the expression of apoptotic markers (Bax, Bcl-2, and p53). Chemical composition analysis was conducted using gas chromatography-mass spectrometry/flame ionization detection (GC-MS/FID) to identify the major bioactive compounds within the fraction. Results The DCM fraction exhibited dose-dependent cytotoxicity in HeLa cells, with an IC50 value of 135.6 µg/mL and a selectivity index (SI) of 2.72 relative to normal cells. Flow cytometry analysis revealed G0/G1 cell cycle arrest, significantly hindering progression through the S and G2/M phases. Moreover, there was a significant increase in both early and late apoptotic cell populations, correlating with the upregulation of pro-apoptotic genes (Bax and p53) and the downregulation of the anti-apoptotic gene Bcl-2. The chemical analysis identified 22 compounds in the unsaponifiable fraction, chiefly terpenoids such as phytol (65.74%). The saponifiable fraction presented a balanced composition of saturated (48.69%) and unsaturated (51.29%) fatty acids, with palmitic acid, linolenic acid, and linoleic acid as the predominant compounds. Conclusion While the DCM fraction’s relatively high IC50 value may limit its utility as a standalone treatment, its ability to induce cell cycle arrest and apoptosis demonstrates its promise as a co-therapeutic agent with conventional anticancer drugs. Further research is essential to elucidate its precise mechanisms of action and to evaluate its efficacy in combination therapies, potentially advancing its role in cervical cancer treatment. Methods Cell cycle analysis To assess the impact of A. incana DCM fraction on cell cycle progression, propidium iodide (PI) staining followed by flow cytometry was employed as outlined previously [17]. PI (Company) is a fluorescent dye that binds to DNA allowing differentiation of cell cycle phases (G0/G1, S, G2/M) based on their DNA content. Briefly, HeLa cells (2 × 105 cells/mL) were cultured overnight and then treated with the DCM fraction at its IC50 concentration (135.6 µg/mL) for 24 h. After treatment, cells were fixed, stained with a PI/RNase solution, and analyzed using a FACSCalibur Scan flow cytometer (BD Biosciences) to determine cell cycle distribution. Apoptosis analysis using annexin v-fitc/pi assay To determine the mode of cell death induced by A. incana DCM fraction, the Annexin V-FITC apoptosis detection kit (BioVision Inc., CA, USA, K101-25) was used following the manufacturer’s protocol. This assay detects phosphatidylserine translocation, a hallmark of early apoptosis, by Annexin V-FITC staining. PI, a viability dye excluded by healthy and early apoptotic cells with intact membranes, stains the DNA of late apoptotic and necrotic cells with compromised membranes, allowing for their identification. Briefly, HeLa cells (2 × 105 cells/mL) were cultured for 24 h and treated with the extract at its IC50 concentration (135.6 µg/mL) for an additional 48 h. Following treatment, cells were stained with Annexin V-FITC and PI. Apoptosis was quantified by flow cytometry using a BD FACSCalibur Scan system (BD Biosciences). For accurate quantification of apoptotic populations, a specific gating strategy was applied. Cells were initially gated based on forward scatter (FSC) versus side scatter (SSC) to exclude debris, followed by gating on FSC-Height (FSC-H) versus FSC-Area (FSC-A) to exclude doublets. Apoptotic populations were classified by gating on Annexin V-FITC versus PI as follows: live cells (Q4) were Annexin V-/PI-, early apoptotic cells (Q3) were Annexin V+/PI-, late apoptotic/necrotic cells (Q2) were Annexin V+/PI+, and dead/necrotic cells (Q1) were Annexin V-/PI+. The percentage of cells in each quadrant was calculated to assess the extent of apoptosis and necrosis. Statistical analysis Statistical analysis was performed using SPSS version 24.0. Differences between the means of two independent groups were analyzed using an independent t-test, with p ***Results*** Effect of a. incana dcm fraction on cell cycle analysis To investigate the effect of the A. incana DCM fraction on cell cycle progression, HeLa cells were treated with the IC50 concentration (135.6 µg/mL) of the fraction for 48 h. Cell cycle distribution was assessed by flow cytometry using Propidium Iodide (PI) staining, which binds to DNA and allows differentiation of cell cycle phases (G0/G1, S, G2/M) based on DNA content. As illustrated in Fig. 2, a significant increase (p A. incana exerts cytostatic effects on HeLa cells by inducing cell cycle arrest. Fig. 2Flow cytometric analysis of cell cycle distribution in HeLa cells treated with the A. incana DCM fraction. HeLa cells were treated with the IC50 concentration (135.6 µg/mL) of the A. incana DCM fraction for 48 h, and cell cycle phase distribution was analyzed via flow cytometry. Panels: (a) Control (untreated) HeLa cells, (b) HeLa cells treated with the DCM fraction, and (c) A graphical comparison of cell percentages across each cell cycle phase between treated and untreated groups Discussion This study investigated the cytotoxic potential of the DCM fraction from the leaves of the medicinal plant A. incana. Given the documented cytotoxic activity of the DCM extract from the related subspecies A. rugosa [8], we hypothesized that A. incana might exhibit similar effects. Although previous studies confirmed the cytotoxic properties of its methanol extract [10], the effects of the DCM fraction remain unexamined. To address this gap, the fraction’s cytotoxicity was evaluated against four human cancer cell lines: HepG2, HCT116, HeLa, and MCF-7, representing diverse cancer types. The A. incana DCM fraction demonstrated IC50 values ranging from 135.6 to 273.1 µg/mL, with HeLa cells (a cervical cancer model) showing the highest sensitivity (IC50 = 135.6 µg/mL). Significantly, the DCM fraction exhibited a selectivity index (SI) of 2.72 against HeLa cells, indicating preferential cytotoxicity toward cancer cells over normal cells and highlighting its therapeutic potential. Although the IC50 values for A. incana were higher than those reported for the DCM extract of the related subspecies A. rugosa [8], this difference may be attributed to variations in the concentration or composition of active compounds in each extract. Despite this, both species showed their most potent cytotoxic effects against HeLa cells, suggesting a potential shared mechanism targeting cervical cancer cells specifically. Additionally, prior studies have reported that the methanol extract of A. incana leaves has a pronounced effect on HeLa cells (IC50 of 68.5 µg/mL) [10]. The selective cytotoxicity observed in both A. incana and A. rugosa DCM extracts, alongside the methanol extract’s activity, indicates the presence of bioactive compounds within these species that may preferentially disrupt HeLa cell function. This selective activity underscores the potential of Alnus species as promising candidates for developing targeted therapies against gynecological cancers, particularly cervical cancer. Given the substantial cytotoxic response observed, further investigation into the active compounds and mechanisms of action of the DCM fraction of A. incana leaves is warranted. To explore the mechanisms underlying this cytotoxicity, we investigated the effects of A. incana DCM fraction on cell cycle progression and apoptosis in HeLa cells. The results revealed a significant accumulation of cells in the G0/G1 phase, indicating effective cell cycle arrest. This is a crucial finding science cell cycle arrest at key checkpoints is a well-established therapeutic strategy for inhibiting cancer cell proliferation. The mammalian cell cycle comprises four distinct phases: G1, S, G2, and M, where RNA and protein synthesis occur in G1 and G2, DNA replication in S, and chromosome segregation in M [18]. By halting cells in the G0/G1 phase, the A. incana DCM fraction appears to block progression to DNA synthesis, which could lead to senescence or cell death. Several plant-derived anticancer agents, such as taxanes and vinca alkaloids, function by disrupting key cell cycle regulators [19]. In addition to inducing cell cycle arrest, the DCM fraction significantly promoted apoptosis, a fundamental mechanism in cancer treatment. Apoptosis, or programmed cell death, is triggered by many natural cytotoxic agents, both clinically approved and experimental, and is essential for eliminating cancer cells [20]. Flow cytometric analysis using Annexin V and propidium iodide staining revealed a marked increase in apoptotic cell death (40.67-fold increase, 18.71% vs. 0.46% in the control) induced by the A. incana DCM fraction. Early apoptosis rose to 14.32% from 0.37%, and late apoptosis increased to 4.39% from 0.09%. Necrosis also increased by 2.5-fold (2.84% vs. 1.15% in the control), indicating a broad cytotoxic effect on HeLa cells. These findings reinforce the potential of A. incana DCM fraction as an effective anticancer agent by simultaneously inducing cell cycle arrest and apoptosis. To elucidate the molecular mechanisms behind these effects, we evaluated the expression of apoptotic regulators, including p53, Bcl-2, and Bax, through RT-PCR. The intrinsic apoptotic pathway is regulated by the balance between pro-apoptotic Bax and anti-apoptotic Bcl-2, where Bcl-2 stabilizes the mitochondrial membrane and prevents cytochrome c release, whereas Bax promotes apoptosis by facilitating cytochrome c release [21]. p53, a critical tumor suppressor, enhances Bax expression, thus its stimulation tipping the balance towards apoptosis [22]. In our study, the A. incana DCM fraction significantly upregulated Bax and p53 while downregulating Bcl-2, resulting in an elevated Bax/Bcl-2 ratio, a key indicator of apoptosis activation. Previous research reported that elevated p53 expression and a higher Bax/Bcl-2 ratio correlate with increased tumor sensitivity to anticancer therapies [23], further supporting the potential of the A. incana DCM fraction as a therapeutic agent. Given these findings, it is essential to contextualize the anticancer activity of the A. incana DCM fraction by comparing it to established cervical cancer treatments, such as cisplatin and paclitaxel. Cisplatin exerts its cytotoxic effects primarily through DNA cross-linking, causing extensive DNA damage that leads predominantly to G2/M cell cycle arrest and subsequent apoptosis induction mainly via p53-dependent pathways [24, 25]. Despite its efficacy, cisplatin is often limited by severe adverse effects, including nephrotoxicity, neurotoxicity, and drug resistance [25]. Paclitaxel, another cornerstone treatment, acts by stabilizing microtubules, causing prolonged G2/M mitotic arrest and promoting apoptosis through a p53-independent pathway involving cyclin B1/CDC2 activation and Bcl-2 phosphorylation [24, 26]. However, paclitaxel similarly has clinical limitations, including dose-dependent toxicities such as peripheral neuropathy and myelosuppression [27]. In contrast, our findings demonstrate that the A. incana DCM fraction distinctly arrests cells at the G0/G1 phase and promotes apoptosis via a p53-dependent pathway. These unique molecular characteristics suggest a complementary role alongside traditional chemotherapeutics, potentially reducing toxicities and overcoming drug resistance, despite its relatively high IC50. The chemical profile of the A. incana DCM fraction, analyzed via GC-MS/FID detection, revealed a high content of phytol (65.74%) in unsaponifiable matter, a compound known for its broad-spectrum biological activities, particularly in cancer therapy. Phytol, a diterpenoid derived from chlorophyll, has been widely recognized for its anti-inflammatory and anticancer properties, including the modulation of cell cycle and apoptosis pathways [28]. Previous studies have demonstrated its ability to induce apoptosis across various cancer cell lines, including lung, colon, and breast cancers, primarily through the disruption of mitochondrial membrane potential and subsequent activation of the intrinsic apoptotic pathway [28, 29]. Our findings, which show significant G0/G1 cell cycle arrest and apoptosis in HeLa cells, align with these established mechanisms of action, further validating phytol’s role as a potent anticancer agent. In addition to phytol, the saponifiable matter of the DCM fraction contained palmitic acid (31.27%) and linolenic acid (26.98%) as the major components. Both fatty acids are well-documented for their apoptosis-inducing capabilities. Palmitic acid, for instance, has been reported to upregulate pro-apoptotic proteins such as Bax and p53, while downregulating anti-apoptotic Bcl-2, leading to enhanced apoptosis via caspase activation in colorectal and breast cancer models [30]. Similarly, linolenic acid exerts anticancer effects by modulating the Bcl-2 family proteins, increasing pro-apoptotic Bax expression, and inducing endoplasmic reticulum (ER) stress, which contributes to cancer cell death. Linolenic acid has also been shown to inhibit the PI3K/Akt signaling pathway and suppress fatty acid synthase (FASN), an enzyme overexpressed in many cancers [31]. Our study’s findings of increased Bax expression and a heightened Bax/Bcl-2 ratio in HeLa cells are consistent with these mechanisms, suggesting that palmitic and linolenic acids likely contribute to the cytotoxic effects observed. Despite these promising results, the study possesses limitations typical of in vitro investigations. Notably, the biological complexity of tumor microenvironments, such as gene expression variability, signaling heterogeneity, immune interactions, and stromal influences, cannot be adequately mimicked using isolated cell lines like HeLa. Additionally, the relatively high IC50 necessitates exploration of advanced drug delivery systems or combination therapies. Future research should incorporate additional cancer cell lines, normal cell toxicity assessments, patient-derived xenografts (PDX), and innovative delivery strategies, such as nanoparticle encapsulation, to enhance efficacy, reduce toxicity, and facilitate clinical translation. These steps are crucial for validating the preclinical promise and advancing the clinical applicability of the Alnus incana DCM fraction. Conclusion This study demonstrates that the A. incana DCM fraction, rich in bioactive compounds such as phytol, palmitic acid, and linolenic acid, exerts notable cytotoxic effects on various cancer cell lines, particularly HeLa cells. These effects are mediated through inducing G0/G1 cell cycle arrest and apoptosis via modulation of Bax, Bcl-2, and p53 pathways. Although the relatively high IC50 value (135.6 µg/mL) limits its potential as a standalone therapy, the fraction’s selective cytotoxicity toward cancer cells suggests a potentially favorable safety profile. Its unique ability to activate apoptotic and cell-cycle arrest pathways supports its promise as an adjunctive therapy, particularly in combination with conventional chemotherapeutic agents, which may improve treatment outcomes by reducing associated toxicities or overcoming resistance. Further in-depth mechanistic studies, along with rigorous in vivo evaluations using clinically relevant models such as patient-derived xenografts, are essential to validate these promising findings and fully realize the clinical potential of this natural fraction. Source paper: PMC12105127	2025-09-19T10:28:32.559073Z
4	1	2025-09-18T09:14:33.602726Z	4	[{"start":3403,"end":3442,"text":"HIV-infected Jurkat-derived T cell line","labels":["CellLine"]},{"start":415,"end":442,"text":"memory-resting CD4+ T cells","labels":["CellType"]},{"start":1058,"end":1086,"text":"dormant HIV-infected T cells","labels":["CellType"]},{"start":1959,"end":1974,"text":"infected T cell","labels":["CellType"]},{"start":2146,"end":2162,"text":"infected T cells","labels":["CellType"]},{"start":4311,"end":4328,"text":"J-Lat 6.3 T cells","labels":["CellLine"]},{"start":4728,"end":4748,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":5214,"end":5234,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":5390,"end":5410,"text":"resting CD4+ T cells","labels":["CellType"]},{"start":6755,"end":6762,"text":"T cells","labels":["CellType"]},{"start":6880,"end":6887,"text":"T cells","labels":["CellType"]},{"start":9286,"end":9304,"text":"stimulated T cells","labels":["CellType"]},{"start":10798,"end":10814,"text":"Fetal Calf Serum","labels":["Tissue"]},{"start":455,"end":480,"text":"cells of myeloid lineages","labels":["CellType"]},{"start":3016,"end":3021,"text":"cells","labels":["VagueCellCategory"]},{"start":3592,"end":3607,"text":"J-Lat 6.3 cells","labels":["CellLine"]},{"start":3636,"end":3641,"text":"cells","labels":["VagueCellCategory"]},{"start":3762,"end":3767,"text":"cells","labels":["VagueCellCategory"]},{"start":4365,"end":4370,"text":"cells","labels":["VagueCellCategory"]},{"start":4489,"end":4494,"text":"cells","labels":["VagueCellCategory"]},{"start":4525,"end":4530,"text":"cells","labels":["VagueCellCategory"]},{"start":5601,"end":5616,"text":"cell population","labels":["VagueCellCategory"]},{"start":5990,"end":6010,"text":"CYTOR-depleted cells","labels":["VagueCellCategory"]},{"start":9493,"end":9513,"text":"CYTOR-depleted cells","labels":["VagueCellCategory"]},{"start":10509,"end":10514,"text":"cells","labels":["VagueCellCategory"]},{"start":10647,"end":10652,"text":"Cells","labels":["VagueCellCategory"]},{"start":11219,"end":11236,"text":"transfected cells","labels":["VagueCellCategory"]},{"start":11297,"end":11310,"text":"Control cells","labels":["VagueCellCategory"]},{"start":11340,"end":11345,"text":"cells","labels":["VagueCellCategory"]},{"start":11524,"end":11529,"text":"Cells","labels":["VagueCellCategory"]},{"start":5286,"end":5304,"text":"CD4+ primary cells","labels":["InVitroPrimaryCell"]},{"start":3444,"end":3453,"text":"J-Lat 6.3","labels":["CellLine"]}]	2644.033	Introduction The introduction of antiretroviral therapy (ART) has successfully limited the spread of Human Immunodeficiency Virus (HIV) and improved patient clinical outcomes. However, a complete cure for HIV infection remains out of reach, as the transcriptionally silent but replication-competent provirus that is integrated into the host genome persists in long-lived cellular reservoirs, which are comprised of memory-resting CD4+ T cells, as well as cells of myeloid lineages [1,2]. These reservoirs are highly stable and are resistant to both ART and the effects of the host immune surveillance, thus posing a significant obstacle to eradicating the HIV reservoirs. Consequently, in most people living with HIV, interrupting ART leads to rapid viral load rebound, usually within weeks after treatment cessation [3–6]. As T cell stimulation triggers activation of proviral transcription, one strategy that has been proposed to eliminate the HIV reservoirs is a “Shock-and-Kill” approach, which utilizes latency-reversing agents (LRAs) to first activate dormant HIV-infected T cells and facilitate cell death by viral cytopathic effects or immune-mediated killing. This step is done in the presence of ART, so there are no further rounds of HIV replication. [7–9]. Alternatively, a “Block and Lock” approach frees infected individuals from ART by silencing HIV transcription and inducing a deep state of latency. Nevertheless, despite promising therapeutic options, these strategies and others have regretfully failed to achieve significant clinical efficacy. These failures highlight our lack of knowledge of the molecular mechanisms that govern latency establishment and reversal and the need for alternative therapies capable of eliminating the viral reservoirs [10–15]. Epigenetic constraints that suppress proviral gene transcription are essential for establishing HIV latency [16,17]. Low levels of basal and elongating transcription factors in the infected T cell, together with the absence of the viral trans-activator of transcription (Tat), ensure that proviral transcription remains below detectable thresholds [18,19]. Within the infected T cells, gene transcription of the integrated provirus and the host genome are synchronized [20,21]. Both display key steps of gene transcription, which include initiation, promoter arrest, and elongation. HIV-Tat orchestrates transcription elongation of the provirus by binding to TAR RNA and recruiting P-TEFb and Super Elongation Complex (SEC) to the viral promoter [22–26]. However, despite extensive efforts to elucidate the mechanisms of metazoan transcriptional control and its role in the regulation of HIV gene transcription, the knowledge of how HIV latency is established is still incomplete [27]. Long non-coding RNAs (lncRNAs) are transcripts with longer than 200 nucleotides that lack protein-coding capacity. To date, over 200,000 cell type-specific lncRNAs have been identified and display critical regulatory functions of many processes within cells [28–31]. However, the functions of most of these transcripts remain poorly understood. In the context of HIV, roles for several cellular lncRNAs have been documented [32–40]. Moreover, significant gaps still remain in our knowledge about the mechanistic roles that lncRNAs play in CD4 T cell activation and HIV latency. In this study, we monitored changes in gene expression in an HIV-infected Jurkat-derived T cell line (J-Lat 6.3) upon response to T cell stimulation with Phorbol 12-myristate 13-acetate—PMA/Ionomycin (P/I). We documented RNA expression in stimulated J-Lat 6.3 cells that carry either active or cells latent HIV, and among identified ncRNA, Cytoskeleton Regulator RNA (CYTOR) exhibited a profound change in expression in cells that expressed active HIV following T cell stimulation. CYTOR directly binds the HIV promoter and activates viral gene transcription and latency reversal by recruiting P-TEFb to the viral promoter. CYTOR also exerts its effects indirectly by controlling global gene expression along with actin dynamic pathways, thereby affecting T cell activation and HIV infection. Results Discussion In search of regulators of HIV latency, we profiled changes in the expression of ncRNAs by employing RNA-Seq analysis in resting and stimulated HIV-infected J-Lat 6.3 T cells, comparing RNA expression levels in cells that carry active HIV (GFP+) or latent HIV (GFP-). Our analysis show that different transcriptional profiles exist in cells where HIV is activated versus cells where it remains latent. CYTOR lncRNA was identified as one of these RNAs, and its expression is elevated upon T cell stimulation, where HIV is active. These observations were further confirmed in primary CD4+ T cells (Fig 1). Functional analyses show that following T cell stimulation, over-expression of CYTOR activates HIV gene expression, while its depletion inhibits viral gene expression. Significantly, upon T cell stimulation, depletion of CYTOR promoted entry of HIV into a latent state, while its over-expression delayed entry into latency and enhanced latency reversal (Fig 2). Effects of CYTOR on HIV infection and latency establishment were also confirmed in stimulated primary CD4+ T cells (Fig 3). We are aware that the model of stimulated CD4+ primary cells does not recapitulate the actual state of the reservoir, which is mainly comprise of resting CD4+ T cells that do not support HIV infection. As this is a limitation of the current study, we are trying to adopt a recently developed gene editing approach to lncRNAs to deplete CYTOR in this unique cell population and monitor the effects of latency kinetics without altering its activation [56]. Mechanistically, our observations show that CYTOR directly binds to the HIV promoter and enhances the phosphorylation of the Ser2 CTD of RNAPII through association with P-TEFb to activate viral gene expression (Figs 4 and 5). Changes in histone activation marks around the viral promoter in CYTOR-depleted cells also imply that CYTOR activates the proviral gene expression (Fig 4). In addition to the direct effects of CYTOR on HIV gene expression, we also demonstrate that CYTOR controls global gene expression. CYTOR is recruited to other gene promoters that are regulated by P-TEFb, like myc, NF-κB, and IL2Ra (S3 Fig). Among the identified enriched pathways that potentially are regulated by CYTOR are those that are involved in actin dynamics. Consistently, reduced levels of CYTOR expression are associated with reduced polymerization of cortical actin in response to TCR engagement (Fig 6). In turn, elevated levels of CYTOR do not further increase actin polymerization in response to T cell stimulation and cannot induce morphological responses of T cells in the absence of stimulation (S5 Fig). Thus, CYTOR is an important regulator of TCR-induced actin polymerization in T cells. However, its normal endogenous expression levels are sufficient for a proper response. To test a mechanistic link between actin remodeling, CYTOR levels, and HIV gene expression, we inhibited actin dynamics with specific inhibitors (Fig 6I). Effects of inhibition of actin polymerization phenocopied the effect of CYTOR depletion on HIV gene expression, suggesting that CYTOR may affect HIV gene expression by the regulation of genes that control cellular actin dynamics (Fig 6I). Accordingly, we propose a model where CYTOR exerts its effects on global gene expression and promotes HIV gene expression by both direct and indirect effects (Fig 7). CYTOR directly binds the HIV promoter and recruits the elongation transcription machinery to enhance RNAPII CTD phosphorylation and deposition of active histone markers around the HIV promoter, ultimately activating HIV gene expression. Indirectly, CYTOR controls gene targets that regulate actin dynamics in the nucleus and at the plasma membrane to optimize the response to T cell activation, presumably via the regulation of cellular gene expression. 10.1371/journal.ppat.1012172.g007Fig 7***A working model for CYTOR functions.*** Following T cell activation, levels of CYTOR are elevated in the nucleus. CYTOR is recruited to the HIV promoter and binds to P-TEFb, leading to the activation of viral gene expression. Cellular genes regulated by CYTOR include actin remodeling genes that promote actin polymerization and the indirect activation of HIV gene expression. Like CYTOR, other lncRNAs have been reported to occupy the HIV promoter and modulate its activity at either transcriptional or posttranscriptional levels [57]. Most act as scaffolds that associate with other transcriptional activators or repressors to control HIV gene expression [35–39,58–60]. In the case of CYTOR, its effects on gene expression occur by recruiting the transcription elongation machinery to activate gene expression, either from the viral promoter or other cellular promoters. It will be essential to identify other partners that are associated with CYTOR lncRNA and control HIV promoter activity. As we also aim to dissect the role of CYTOR in gene expression control, specifically for HIV gene regulation, it will be essential to define how events within the nucleus are regulated by CYTOR and translated to the control of downstream effector functions of stimulated T cells. Future studies will further identify the downstream targets of CYTOR that control actin dynamics upon T-cell activation. As additional pathways were identified by our RNA-seq analysis in CYTOR-depleted cells, we visualize that future work will identify novel downstream targets of CYTOR and elucidate their mechanisms of function in regulating HIV gene expression and latency. These may open new ways for developing novel therapeutic tools that will be integrated or substitute current strategies to successfully eliminate the HIV reservoir. Materials and methods Analysis of actin dynamics in response to t cell activation Actin remodeling in response to T cell receptor (TCR) engagement was monitored by forming circumferential F-actin rings as previously described [61,62]. In brief, stimulatory coverslips were prepared by coating with a 0.01% poly-L-lysine (PLL; Sigma) solution for 10 minutes at room temperature, followed by wet-chamber incubation for 3 hours at 37°C with 7 μg/ml anti-CD3 antibody (50 μl per coverslip, clone HIT3a against CD3E; BD Biosciences) in phosphate-buffered saline (PBS). Stimulatory coverslips were subsequently washed in PBS and stored at 4°C in PBS until use. 5x105 cells per anti-CD3-coated coverslip, respectively) were used to seed coverslips for 4 minutes to allow TCR-mediated actin ring formation. Cells were subsequently fixed in 3% paraformaldehyde for 15 minutes, permeabilized for 2 minutes in 0.1% TritonX-100, and blocked for 30 minutes in 1% Fetal Calf Serum (FCS) in PBS. F-actin was visualized with tetramethyl rhodamine isothiocyanate (TRITC)-conjugated phalloidin (1:1,000, 1 hour, room temperature; Sigma). Samples were mounted on glass slides and analyzed by epifluorescence (Olympus IX81 S1F-3, cellM software) and confocal (spinning-disc PerkinElmer UltraView VoX, Velocity software) microscopes. For quantification of phenotype frequencies, at least 100 transfected cells were counted. Chromatin immunoprecipitation (chip) analysis Control cells expressing scramble shRNA or cells where CYTOR expression was depleted (KD) were cross-linked with 1% formaldehyde for 10 minutes and then washed with PBS and reverse cross-linked with glycine (125mM; 5 minutes). Cells were then lysed for 10 minutes on ice in 130μl sonication buffer (20 mM Tris pH-7.8, 2 mM EDTA, 0,5% SDS, 0.5 mM phenylmethylsulfonyl fluoride (PMSF), and 1% protease inhibitor cocktail), and the nuclear pellets were collected. DNA was fragmented by sonication at the following settings: amplitude 20% for 30 cycles at 10 seconds on/10 seconds off. Samples were centrifuged (15 minutes, 14,000 rpm, 4°C). The soluble chromatin fraction (25 μg) was collected and immunoprecipitated (IP) overnight at 4°C on a rotating wheel in IP buffer (0.5% Triton X-100, 2 mM EDTA, 20 mM Tris pH-7.8, 150 mM NaCl and 10% glycerol) with 2.5 μg of one of the indicated antibodies. The next day, the IP material was incubated with 25 μl dynabeads protein G for two hours to ensure the binding of the antibody to the magnetic beads. DNA was eluted with freshly prepared elution solution (1% SDS and 0.1 M NaHCO3) and heated at 65°C overnight to reverse-crosslink the samples. Precipitated DNA fragments were then extracted using a ChIP DNA clean and concentrator kit (ZYMO Research), and HIV DNA levels were quantified by qPCR with the primers specifically located on the NFκB region at the HIV-LTR promoter. All signals were normalized relative to input DNA. ChIP assays were also performed with an anti-rabbit or mouse IgG as negative control. Primers used for qpcr analysis Primers on the HIV promoter: NFκB forward: 5’ - AGGTTTGACAGCCGCCTA -3’ NFκB Reverse: 5’ - AGAGACCCAGTACAGGCAAAA -3’ gapdh Forward: 5’ - AGCCACATCGCTCAGACAC -3’ gapdh Reverse: 5’ - GCCCAAACGACCAAATCC -3’ Primers for CYTOR: Forward: 5’- AACTTGCCAGCCTCCATC; Reverse: 5’- GAGCTTCCTGTTTCATCTCCC Primers for 7SK: Forward; 5‘- GAGGGCGATCTGGCTGCGACAT Reverse: 5‘- ACATGGAGCGGTGAGGGAGGAA Source paper: PMC11075828	2025-09-23T11:41:58.629944Z
5	1	2025-09-18T09:14:33.602884Z	5	[{"start":10297,"end":10308,"text":"splenocytes","labels":["CellType"]},{"start":10836,"end":10847,"text":"splenocytes","labels":["CellType"]},{"start":6151,"end":6191,"text":"CD4+ T cell line models of HIV-1 latency","labels":["CellLine"]},{"start":6624,"end":6636,"text":"T cell lines","labels":["CellLine"]},{"start":13310,"end":13321,"text":"whole blood","labels":["Tissue"]},{"start":13387,"end":13392,"text":"blood","labels":["Tissue"]},{"start":3779,"end":3785,"text":"plasma","labels":["Tissue"]},{"start":13223,"end":13229,"text":"Plasma","labels":["Tissue"]},{"start":13593,"end":13599,"text":"plasma","labels":["Tissue"]},{"start":10681,"end":10687,"text":"spleen","labels":["Tissue"]},{"start":4779,"end":4784,"text":"liver","labels":["Tissue"]},{"start":4785,"end":4791,"text":"thymus","labels":["Tissue"]},{"start":4767,"end":4778,"text":"bone marrow","labels":["Tissue"]},{"start":355,"end":367,"text":"CD4+ T cells","labels":["CellType"]},{"start":1859,"end":1873,"text":"infected cells","labels":["VagueCellCategory"]},{"start":3554,"end":3584,"text":"latently infected CD4+ T cells","labels":["CellType"]},{"start":4406,"end":4426,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":4500,"end":4512,"text":"latent cells","labels":["VagueCellCategory"]},{"start":5578,"end":5588,"text":"host cells","labels":["VagueCellCategory"]},{"start":6643,"end":6650,"text":"T cells","labels":["CellType"]},{"start":6888,"end":6903,"text":"cancerous cells","labels":["VagueCellCategory"]},{"start":7007,"end":7022,"text":"cancerous cells","labels":["VagueCellCategory"]},{"start":7307,"end":7335,"text":"HIV latently infected CD4+ T","labels":["CellType"]},{"start":7546,"end":7570,"text":"T cell models of latency","labels":["CellLine"]},{"start":8110,"end":8143,"text":"CD4+ T cell models of HIV latency","labels":["CellLine"]},{"start":8226,"end":8246,"text":"primary CD4+ T cells","labels":["InVitroPrimaryCell"]},{"start":8442,"end":8447,"text":"cells","labels":["VagueCellCategory"]},{"start":8513,"end":8525,"text":"latent cells","labels":["VagueCellCategory"]},{"start":8782,"end":8799,"text":"reactivated cells","labels":["VagueCellCategory"]},{"start":9143,"end":9148,"text":"cells","labels":["VagueCellCategory"]},{"start":10027,"end":10046,"text":"CD4+ T cell subsets","labels":["CellType"]},{"start":10082,"end":10105,"text":"splenic Th17-like cells","labels":["CellType"]},{"start":10110,"end":10139,"text":"FOXP3-expressing CD4+ T cells","labels":["CellType"]},{"start":10182,"end":10191,"text":"Th17-cell","labels":["CellType"]},{"start":10365,"end":10375,"text":"Th17 cells","labels":["CellType"]},{"start":10477,"end":10487,"text":"Th17 cells","labels":["CellType"]},{"start":10530,"end":10564,"text":"integrated HIV DNA-harboring cells","labels":["VagueCellCategory"]},{"start":10644,"end":10673,"text":"FOXP3-expressing CD4+ T cells","labels":["CellType"]},{"start":10929,"end":10963,"text":"p24-expressing FOXP3+ CD4+ T cells","labels":["CellType"]},{"start":11093,"end":11119,"text":"memory CD4+ T cell subsets","labels":["CellType"]},{"start":12207,"end":12218,"text":"CD45+ cells","labels":["CellType"]},{"start":13271,"end":13288,"text":"white blood cells","labels":["CellType"]},{"start":13327,"end":13341,"text":"red blood cell","labels":["CellType"]},{"start":13376,"end":13381,"text":"Cells","labels":["VagueCellCategory"]},{"start":317,"end":340,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":728,"end":751,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":4368,"end":4391,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":6416,"end":6439,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":6595,"end":6618,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":8363,"end":8386,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":8808,"end":8831,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":9273,"end":9296,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":11621,"end":11644,"text":"latently infected cells","labels":["VagueCellCategory"]},{"start":4010,"end":4030,"text":"non-lymphoid tissues","labels":["VagueTissue"]},{"start":372,"end":385,"text":"myeloid cells","labels":["CellType"]},{"start":7340,"end":7353,"text":"myeloid cells","labels":["CellType"]},{"start":3997,"end":4005,"text":"lymphoid","labels":["Tissue"]}]	3095.487	Introduction Antiretroviral therapy (ART) has been highly effective at suppressing human immunodeficiency virus (HIV) replication, thus significantly reducing disease progression and mortality in infected individuals. However, ART is not curative and does not meaningfully impact persistent viral reservoirs (VRs) of latently infected cells that comprise CD4+ T cells and myeloid cells.1,2,3 Indeed, upon antiviral treatment interruption (ATI), the virus quickly rebounds after several weeks, highlighting how the presence of VRs obviates an HIV cure.4,5,6 Therefore, eliminating or controlling VRs remains an unwavering priority for HIV cure research. In this context, the aim of the “shock-and-kill” strategy is to reactivate latently infected cells with latency reversal agents (LRAs) and render them vulnerable to cell death or clearance by the immune system.7,8 Previously reported approaches to HIV reactivation, including protein kinase C (PRKC) agonists, histone deacetylase (HDAC) inhibitors (HDACi), and toll-like receptor (TLR) agonists,9 have been highly effective in in vitro models of latency, but their efficacy is only moderate when tested in vivo.10,11,12,13 Moreover, by broadly activating cellular pathways, these compounds elicit significant proinflammatory effects or alter the function and fate of specific immune effector cells,14,15 hence limiting their use in clinical settings.16,17,18,19 Similarly, promising candidates such as bryostatin-1 and analogs, phorbol esters, and phosphatidylethanolamine binding protein 1 (PEBP1) agonists reactivate HIV via activation of the canonical nuclear factor kappa B (NFKB) pathway,13,20 inadvertently leading to uncontrolled cytokine release and overt T cell activation.21,22 Consequently, there is a need to develop alternative approach(es) that can reactivate latent reservoirs and eliminate infected cells without triggering systemic activation, inflammation, or impairment of immune clearance mechanisms. In this regard, small molecules known as mimetics of the second mitochondrial activator of caspases (SMAC mimetics [SMs]) were originally developed as cancer therapeutics. They have received increasing recognition because they specifically activate the non-canonical NFKB pathway,23,24,25 which naturally exhibits higher functional selectivity and more confined proinflammatory effects compared to the canonical NFKB pathway.24,26 Engagement of the non-canonical NFKB pathway by SMAC leads to degradation of cellular inhibitor of apoptosis (cIAP), accumulation of NFKB-inducing kinase (NIK), and activation of component of inhibitor of NFKB kinase complex (CHUK) homodimer, culminating in cleavage of inactive NFKB2 p100 to active p52.26 An association between RELB and p52 induces expression of target genes27 and, in the context of HIV, the pathway reactivates latently infected VRs.20 The non-canonical NFKB pathway can also be triggered by signaling intermediates of the apoptosis cascade. In fact, cleavage of SMAC/DIABLO exposes the N-terminal motif Ala-Val-Pro-Ile, which binds specifically to baculovirus intermediate repeat domains 2 and 3 of IAP proteins. These proteins in turn trigger downstream events that ultimately lead to degradation of baculoviral IAP repeat containing 2 (BIRC2, also known as cIAP1) and baculoviral IAP repeat containing 3 (BIRC3, also known as cIAP2)28 and potentiation of apoptosis.29 The unique ability of SMAC to degrade IAPs and activate apoptosis pathway(s) makes SMs interesting candidates in the field of HIV cure research30,31 because latently infected CD4+ T cells display aberrant expression of cell survival factors, including XIAP, BIRC2 and BCL2.8,32 Pharmacological activation of the non-canonical NFKB pathway by SMs was recently found to induce on-ART plasma viremia in animal models of HIV latency,24,25 underscoring the potential of this class of molecules as LRAs. However, it remains unclear whether induction of HIV expression by SMs leads to a reduction of VRs in lymphoid and non-lymphoid tissues of animal models. In this study, we show that bivalent APG-1387, currently in clinical development in the oncology field, activates the non-canonical NFKB pathway and hence, is a potent LRA. By degrading IAPs, this compound also induces the expression of active caspase 3 (CASP3), a key component of the execution phase of apoptosis, in latently infected cells. Likewise, in primary CD4+ T cells infected with a dual reporter-encoded HIV, APG-1387 reduces the level of latent cells without notably affecting the productively infected pool. Accordingly, in vivo treatment with APG-1387 could reactivate expression of latent viruses and was found to meaningfully reduce the integrated HIV-DNA level in tissues of ART-suppressed humanized bone marrow liver thymus (BLT) mice, without assessable immunotoxicity. Upon ART interruption, APG-1387-treated mice rebounded more slowly and to a lower set point. Overall, the study demonstrates that APG-1387 has the capacity to not only reverse HIV latency but also potentiate cell apoptosis, thus supporting the notion that bivalent SMs could be harnessed to reduce VRs without causing generalized T cell activation. Results Discussion Several LRAs have shown their biological activity both in vitro and in vivo but are clinically unsafe for further evaluations.46,47 The most potent LRAs, including HDACi and protein kinase C (PRKC) agonist bryostatin-1, activate the canonical NFKB pathway,46 causing explicit T cell activation and broad cytotoxicity, and hence eliciting significant collateral damage on host cells. Reactivation of latent HIV by PRKC agonists has recently been demonstrated to induce resistance to apoptosis, a phenomenon often associated with phosphorylation and activation of the antiapoptotic protein BCL2.48 Thus, the failure of common LRAs such as PRKC agonists and HDACi to safely purge HIV and effectively reduce VRs in people living with HIV necessitates the development of clinical approaches to achieve an HIV cure. In this study, we demonstrate that APG-1387, a bivalent SM initially developed as a cancer therapy, can efficiently reactivate HIV in CD4+ T cell line models of HIV-1 latency via a process that involves activation of the noncanonical NFKB pathway. Although this IAP antagonist modestly increases HIV RNA detection in virally suppressed hu-mice, it has the capability to reduce both the frequency of latently infected cells and the level of viral rebound upon ART treatment interruption. Indeed, APG-1387 treatment enhances the expression of caspase-3, a marker of apoptosis, in latently infected cells from T cell lines or in T cells from certain tissues of virally suppressed hu-mice. In the context of productive infection, in vitro stimulation with APG-1387 also enhanced cleavage of CASP3. IAPs are overly expressed in various cancers, enabling prolonged survival of cancerous cells [as reviewed by49]. Consequently, their antagonists are thought to either directly induce or sensitize cancerous cells to death by triggering proapoptotic pathways.50,51 Interestingly, IAPs such as BIRC2 have recently been shown to be negative regulators of HIV transcription, and their expression is found to be correlated with viral latency.23,31,32 Indeed, both BIRC2/3 and XIAP are overexpressed in HIV latently infected CD4+ T and myeloid cells, and the SMs which induce the degradation of these IAPs can reactivate HIV. We demonstrate herein that various monovalent and bivalent SMs can induce efficient viral reactivation in different T cell models of latency and that some are more potent than others. Bivalent SMs are more effective than their monovalent counterparts, probably because of the presence of dimers that may contribute to a more stable and enhanced activation via interactions with the two adjacent binding domains of IAPs.52 Among the bivalent SMs we examined, APG-1387 is most effective at degrading IAPs, facilitating a conversion of NFKB p100 to p52, and reactivating HIV expression through an NIK-dependent process, a hallmark of an activated non-canonical NFKB pathway.23,24 In CD4+ T cell models of HIV latency, APG-1387 treatment is associated with remarkable viral reactivation. However, in primary CD4+ T cells infected with HI.fate.E dual reporter virus, exposure to APG-1387 is accompanied by a reduction in the frequency of latently infected cells (Figure 2) without a detectable change in the level of cells supporting LTR-directed transcription. The data suggest that the latent cells might be preferentially targeted for elimination without reactivation in contrast to PMA and ionomycin stimulation. This said, given the intrinsic properties of SMs, we cannot completely exclude the possibility that there was no change in the frequency of reactivated cells because latently infected cells rapidly die after reactivation. Consistent with previous works with other SMs including AZD5582 24 and ciapavir,25 APG-1387 induces detection of viremia, albeit modestly, in ART-suppressed hu-mice as early as 48 h after treatment. Importantly, in mice treated with multiple doses of APG-1387, the proportion of cells carrying the integrated HIV DNA was meaningfully reduced, suggesting that the bivalent APG-1387 might preferentially target latently infected cells directly or indirectly for death (Figure 4). In addition, in APG-1387-treated mice the fact that the viral rebound was consistently lower throughout the ATI and plateaued at a level below pre-ART further strengthens the notion that APG-1387 impacts negatively the pool of VRs (Figure 6). It is conceivable that such an impact is significant considering the modest effect of APG-1387 on latency reversal in this experimental condition and the limited functional immune clearance mechanisms present in hu-BLT mice. This said, further experimentation with a larger group of animals analyzed at endpoint is needed to confirm the modulatory role of APG-1387 on VRs. A more detailed analysis gauging the effect of APG-1387 on different CD4+ T cell subsets revealed a potential modulation of splenic Th17-like cells and FOXP3-expressing CD4+ T cells. We observe a trend toward a reduction in Th17-cell frequency in both ART-suppressed mice treated in vivo with APG-1387 and in ART-naïve infected mice whose splenocytes were stimulated ex vivo with APG-1387 (Figure 5). Since Th17 cells have been proposed to be an important source of HIV latent reservoirs [reviewed in53], a decrease in Th17 cells might suggest a reduction in the level of integrated HIV DNA-harboring cells, an observation that was made with APG-1387 treated mice (Figure 4). Regarding FOXP3-expressing CD4+ T cells in the spleen, there was no significant difference between ART-suppressed mice treated with APG-1387 or with the vehicle control. However, ex vivo stimulation of splenocytes from ART-naïve, HIV-infected mice with APG-1387 modestly decreased the number of p24-expressing FOXP3+ CD4+ T cells, although the difference was not statistically significant. Whether the non-canonical NFKB pathway is more functional in certain memory CD4+ T cell subsets remains to be fully elucidated. The fact that the central memory subset has been shown to require strong TCR-mediated signaling for maintenance suggests a more important role of the canonical NFKB pathway in this context [reviewed in26,54,55]. Taken together, these results highlight the importance of evaluating the potential effects of SMs on various immune subsets known to be susceptible to HIV. Our study shows that APG-1387 has the capability to reactivate HIV, activate markers of apoptosis in latently infected cells, and reduce VRs without causing global T cell activation. However, the findings also underscore the need to combine bivalent SMs with other therapeutics to improve the reactivation and elimination of VRs. Indeed, recent findings have shown that combined panobinostat and pegylated interferon alpha 2 can transform the VR landscape through latency reversal and innate immune activation.56 Star★methods Experimental model and study participant details Experimental design No randomization and/or stratification was performed. Hu-mice with variable levels of human CD45+ cells were equally distributed among experimental groups. All efforts were made to have equal or comparable number of mice or samples (i.e., number of n) per experimental group or condition. The exact number of n was provided in relevant Figure legends. Experimenters were blinded during processing biological samples, conducting experiments and analyzing samples in different assays. No inclusion or exclusion criteria were applied to the study. No statistical methods were used to pre-determine strategies for randomization and/or stratification, population size, inclusion and exclusion of any data or subjects, or whether the data met assumptions of the statistical approach. Pharmacological and toxicity analysis Hu-BLT mice were left untreated or treated via intraperitoneal route (IP) with either vehicle- (10% sterile Cremophor dissolved in 5% polyethylene glycol-400 and 85% PBS) or 20 mg/kg (100 APG-1387 (Ascentage Pharma, China) every third day (maximum 100 μL injection volume) for up to 4 weeks. Plasma was collected at different intervals and white blood cells isolated by treating whole blood with red blood cell lysis buffer (Invitrogen, U.S.A). Cells from blood and tissues were analyzed by flow cytometry as described in Section ‘flow cytometry’ below for the effect of APG-1387 on cell proliferation and activation. Proinflammatory cytokines were evaluated in plasma of healthy untreated, APG-1387- and vehicle-treated hu-BLT mice using Legend Max enzyme-linked immunosorbent assay (ELISA) kits for human TNF and IL6 (both from BioLegend, U.S.A) as per the manufacturer’s protocol. Data analysis was performed using GraphPad Prism software (Version 8.0). Quantification and statistical analysis Flow cytometry data were analyzed using FlowJo (Versions 9.9.3 and 10.1). Quantification of Western blots was performed using ImageJ software. Data analysis and presentation was done using GraphPad Prism (Version 8.0). Experimenters were blinded during data analysis of samples. Descriptive measures (mean, median, minimum/maximum range, 95% confidence intervals, and percent) were used to summarize the data and illustrate in graphical presentations. All statistical analysis was done using GraphPad Prism (Version 8.0). Nonparametric (unpaired) Mann-Whitney’s U-test (two-tailed) was conducted to compare ranks between two experimental groups (e.g., treated with vehicle or with APG-1387). Nonparametric (paired) Wilcoxon test was performed to compare the ranks of two matched samples (e.g., before and after treatment with APG-1387). When comparing multiple groups, non-parametric tests Kruskal-Wallis or Friedman were performed and followed by Dunn’s multiple comparison test (e.g., vehicle-treated group compared to those treated with different concentrations of APG-1387 or combined PMA and ionomycin; effect of APG-1387 on viral rebound at different time points post treatment interruption). A p value of less than 0.05 was considered statistically significant. ns, ∗, ∗∗, ∗∗∗, signify not significant, Figure 2, the exact p values were ∗p = 0.026, ∗∗p = 0.007, ∗∗∗p = 0.0004. In the two-tailed Mann-Whitney unpaired rank test shown in Figure 3, the ∗p value was 0.0286 while in Figure 4, the p values were as follows: ∗p = 0.0317 and ∗∗p = 0.0043. For Figure S6, the ∗p value was No statistical methods were used to pre-determine strategies for randomization and/or stratification, population size, inclusion and exclusion of any data or subjects, or whether the data met assumptions of the statistical approach. All software used in data analysis along with statistical parameters were mentioned in the appropriate Figure legends. Details about the statistical tests, exact values of n and definitions of the n were as indicated in the legend for each relevant Figure. When applicable, definition of asterisks and descriptive measures were indicated in the Figure legends or Results. Drawing of the chemical structure of APG-1387 shown in Figure 1 was done using the ChemDraw software. Source paper: PMC11699618	2025-09-23T11:46:44.468234Z
6	1	2025-09-18T09:14:33.603081Z	6	[{"start":1396,"end":1405,"text":"NCI-H1299","labels":["CellLine"]},{"start":1602,"end":1617,"text":"NCI-H1299 cells","labels":["CellLine"]},{"start":3264,"end":3273,"text":"NCI-H1299","labels":["CellLine"]},{"start":5137,"end":5146,"text":"NCI-H1299","labels":["CellLine"]},{"start":5591,"end":5606,"text":"NCI-H1299 cells","labels":["CellLine"]},{"start":5838,"end":5853,"text":"NCI-H1299 cells","labels":["CellLine"]},{"start":6713,"end":6722,"text":"NCI-H1299","labels":["CellLine"]},{"start":7018,"end":7027,"text":"NCI-H1299","labels":["CellLine"]},{"start":7108,"end":7123,"text":"NCI-H1299 cells","labels":["CellLine"]},{"start":8307,"end":8316,"text":"NCI-H1299","labels":["CellLine"]},{"start":9760,"end":9769,"text":"NCI-H1299","labels":["CellLine"]},{"start":3327,"end":3348,"text":"effectory lymphocytes","labels":["CellType"]},{"start":1387,"end":1391,"text":"A549","labels":["CellLine"]},{"start":1593,"end":1597,"text":"A549","labels":["CellLine"]},{"start":3255,"end":3259,"text":"A549","labels":["CellLine"]},{"start":4438,"end":4448,"text":"A549 cells","labels":["CellLine"]},{"start":5128,"end":5132,"text":"A549","labels":["CellLine"]},{"start":5576,"end":5586,"text":"A549 cells","labels":["CellLine"]},{"start":5823,"end":5833,"text":"A549 cells","labels":["CellLine"]},{"start":6704,"end":6708,"text":"A549","labels":["CellLine"]},{"start":7009,"end":7013,"text":"A549","labels":["CellLine"]},{"start":7099,"end":7103,"text":"A549","labels":["CellLine"]},{"start":8298,"end":8302,"text":"A549","labels":["CellLine"]},{"start":9359,"end":9363,"text":"A549","labels":["CellLine"]},{"start":9751,"end":9755,"text":"A549","labels":["CellLine"]},{"start":2344,"end":2357,"text":"blood vessels","labels":["Tissue"]},{"start":2372,"end":2385,"text":"blood vessels","labels":["Tissue"]},{"start":3789,"end":3802,"text":"blood vessels","labels":["Tissue"]},{"start":325,"end":338,"text":"immune system","labels":["Tissue"]},{"start":723,"end":736,"text":"immune system","labels":["Tissue"]},{"start":7685,"end":7719,"text":"innate and adaptive immune systems","labels":["Tissue"]},{"start":643,"end":655,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":814,"end":826,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":1067,"end":1079,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":2704,"end":2716,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":2836,"end":2848,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":3750,"end":3762,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":4756,"end":4768,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":4882,"end":4894,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":5368,"end":5395,"text":"control and untreated cells","labels":["VagueCellCategory"]},{"start":5468,"end":5495,"text":"control and untreated cells","labels":["VagueCellCategory"]},{"start":7536,"end":7548,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":7663,"end":7674,"text":"tumor cells","labels":["VagueCellCategory"]},{"start":7927,"end":7939,"text":"target cells","labels":["VagueCellCategory"]},{"start":8035,"end":8047,"text":"target cells","labels":["VagueCellCategory"]},{"start":8132,"end":8144,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":6324,"end":6333,"text":"Leukocyte","labels":["CellType"]},{"start":1089,"end":1106,"text":"lung cancer cells","labels":["CellType"]},{"start":1338,"end":1355,"text":"lung cancer cells","labels":["CellType"]},{"start":1406,"end":1423,"text":"lung cancer cells","labels":["CellType"]},{"start":3274,"end":3291,"text":"lung cancer cells","labels":["CellType"]},{"start":5147,"end":5164,"text":"lung cancer cells","labels":["CellType"]},{"start":6723,"end":6740,"text":"lung cancer cells","labels":["CellType"]},{"start":6816,"end":6833,"text":"lung cancer cells","labels":["CellType"]},{"start":7028,"end":7045,"text":"lung cancer cells","labels":["CellType"]},{"start":8317,"end":8334,"text":"lung cancer cells","labels":["CellType"]},{"start":9364,"end":9381,"text":"lung cancer cells","labels":["CellType"]},{"start":9770,"end":9787,"text":"lung cancer cells","labels":["CellType"]},{"start":7987,"end":7995,"text":"CD8+ CTL","labels":["CellType"]},{"start":6602,"end":6609,"text":"T cells","labels":["CellType"]}]	6390.476	Materials and methods Statistical analysis The data were described as mean ± standard deviation (SD) and the statistical significance was determined by one-way ANOVA before LSD test for multiple comparisons and independent-sample t-test for comparison between the two groups. The level of p ***Results*** Discussion The host immune system exerts immunosurveillance to control cancer development. By inducing the expression of immunosuppressive factors, however, tumor HIF-1 signaling subdues both the innate and adaptive immune responses, thereby shielding the tumor from immune attacks. In addition, insufficient expression of MHC class I on cancer cells is one of the important strategies employed by cancer to evade the immune system. Therefore, inhibition of HIF-1 and improvement of MHC class I expression on cancer cells is important to induce effective responses of cell-mediated immunity against cancer such as lung cancer for immunotherapies, including PD-1/PD-L1 inhibitor. The effect of Endostar in inhibiting HIF-1 and promoting MHC class I expression on cancer cells, such as lung cancer cells, may counteract cancer immune evasion and thereby benefit cancer immunotherapy. However, this remains unclear. This study was designed to determine the effect of Endostar in inhibiting HIF-1 and promoting MHC class I expression on lung cancer cells. Endostar was administrated to A549 and NCI-H1299 lung cancer cells and the protein of HIF-1 and MHC class I and their mRNAs was detected by western blot and RT-qPCR. The lower HIF-1 and higher MHC class I were found in Endostar treated A549 and NCI-H1299 cells. These findings demonstrated inhibtion on HIF-1 and promotion on MHC class I by Endostar, suggesting the potential of Endostar to benefit lung cancer immunotherapy. Endostar, an N-terminal modified recombinant human endostatin, is a human recombinant endostatin, an attractive anti-angiogenesis protein. Therefore, Endostar has anti-angiogenesis effects. Endostar was developed as a specific drug permitted by the State Food and Drug Administration in China in 2005 for its use in NSCLC therapy.25,26 Endostatin is a natural protein, first isolated and extracted from mouse tumors by Judah Folkman, with a wide antitumor spectrum and strong antiangiogenic capacity.27,28 Angiogenesis is a physiological process of forming new blood vessels from existing blood vessels and circulating endothelial precursors. It is essential to the occurrence and development of tumors, which grow rapidly and metastasize eventually.24 Physiologically, angiogenesis is essential to physiological processes like embryogenesis, tissue growth, and regeneration.29,30 Oncologically, it is also important for cancer cells that grow rapidly and metastasize eventually because angiogenesis supplies oxygen and nutrients which are deficient in cancer cells for their rapid growth and eventual metastasis.31 Therefore, anti-angiogenesis is one of the most important cancer therapies.32 Endostar as a recombinant human endostatin with nine added amino acids (MGGSHHHHH)33 to maintain stability and a long half-life has effective antiangiogenic effect. Besides, it was shown in this study that Endostar inhibited HIF-1 with upregulation of MHC class I expression in A549 and NCI-H1299 lung cancer cells, benefiting cancer cell killing by effectory lymphocytes. HIF-1, a heterodimer consisting of a constitutive β-subunit and an oxygen-sensitive α-subunit,34 is a main transcriptional regulator responsible for metabolic adaptation to alterations in the oxygen environment. It involves in many physiological and pathological processes in the body and is closely associated with the pathogenesis of many diseases.13 In solid tumors, uncontrolled proliferation of cancer cells vs disorganized growth of blood vessels results in limited supply of nutrients and oxygen resulting in low oxygen tension; therefore, regions with hypoxic microenvironments are created. In these regions, the highly overexpressed HIF-1 is important to drive tumor growing, invasion, and metastasis in different human cancers.35,36 The association of a poor survival with the high expression of HIF-1α has been indicated by survival analysis in patients with lung cancer, and different SNPs in HIF-1α may have different effects on overall cancer risk in an ethnicity- and type-specific manner.37 Reduction of HIF-1α by Simvastatin enhanced Anti-tumor Effects of Bevacizumab in A549 cells.38 Targeting ATM/HIF-1α signaling by solanidine induced anti-angiogenesis and anti-cancer effect in lung cancer.39 Besides its involvement in various aspects of tumor development, such as tumor growth, invasion, metastasis, and angiogenesis, HIF-1 is also involved in tumor immune evasion which facilitates cancer cells to proliferate and metastasize, and contributes to failure in immunotherapy.40 Inhibition of HIF-1 expression in cancer cells contributes to cancer control and induction of effective anti-cancer immunity in cancer immunotherapy. Endostar has the effect to down-regulate HIF-1.41 It was shown in this study that 25 μg/ml Endostar inhibited HIF-1 expression in A549 and NCI-H1299 lung cancer cells. HIF-1 is induced by hypoxia in cancer cells,42 and in normoxia, it is not usually observed or only basal expression can be observed.43,44 In this study, experiments showed a high expression of HIF-1 in control and untreated cells. However, this did not necessarily mean that the expression of HIF-1 in control and untreated cells was really high because it was detected with the expression in Endostar treated A549 cells and NCI-H1299 cells as the backgroud. The expression detected by Western blot and Immunocytochemistry is relative quantification. The assays had been optimized for enough sensitivity to detect the reduced expression in Endostar treated A549 cells and NCI-H1299 cells. An effective adaptive response can be achieved through a multi-step antigen processing and pathway, namely the cellular antigen processing machinery (APM). Antigens must be processed into antigenic peptides by APM. These peptides are loaded onto an MHC class I molecule. MHC class I molecules are glycoproteins of heterodimers with a polymorphic heavy chain (α-chain) and an invariable β2 microglobulin (β2 m) light chain (β-chain). The α-chain is encoded by the Human Leukocyte Antigen-HLA A, B, and C genes in humans. There is a groove in the MHC class I molecules to preferentially bind 8-11mer peptides. The antigenic epitope binds to the exposed surface of this groove as a part of the MHC class I complex. It is recognized by the TCR on the T cells.45 This study showed that the expression of MHC class I α-heavy chain and β2 m light chain in A549 and NCI-H1299 lung cancer cells was improved by Endostar, which benefited cancer immunotherapy against the lung cancer cells. In the context of HIF-1 down-regulation by Endostar, which was shown in this study, to demonstrate the role of HIF-1 on the MHC class I α-heavy chain and β2 m light chain in A549 and NCI-H1299 lung cancer cells, the over-expressing HIF-1 gene was transfected into A549 and NCI-H1299 cells resulting in decrease of relative levels of MHC class I α-heavy chain and β2 m light chain. This results were in line with the decrease of HIF-1 by Endostar treatment accompanied with the enhancement of MHC class I α-heavy chain and β2 m light chain. In addition to the important role in metabolic adaptation to hypoxia stress caused by deficient supply of nutrients and oxygen because of uncontrolled growth of cancer cells and disorganized neoangiogenesis, the signaling pathways of HIF-1, a heterodimer highly expressed in a variety of tumor cells, suppress innate and adaptive immune systems to escape immune attack. That HIF-1 downregulates the antigen presenting MHC class I molecules is an important strategy for cancer to evade immune attack, because only in combination with MHC class I on the target cells, can tumor antigenic peptides be recognized by CD8+ CTL with the subsequent destruction of the target cells. Theoretically, inhibition of HIF-1 may upregulate the expression of MHC class I on cancer cells. This study demonstrated that 25 μg/ml Endostar inhibited expression of HIF-1 with the upregulation of MHC class I α-heavy chain and β2 m light chain in A549 and NCI-H1299 lung cancer cells, suggesting the potential for Endostar to facilitate cancer immunotherapy. Experimental evidence, however, is required and further research with experimental evidence remains to be performed in the future to confirm the possible suppression of immune evasion tactic with endostar treatment. Other limitations such as lack of an in vivo tumor model and further validation using flow cytometry remain to be supplementarily performed in the future. The mechanism of MHC class I regulation involves the innate immune molecule NLR family CARD domain containing 5 (NLRC5), which is a member of recently discovered NLRs-like receptor family of the highly conserved one.46 NLRC5 is an MHC class I gene transactivation factor which induces the MHC class I gene transcription and subsequently activates antigen presentation process.47,48 NLRC5 is transcriptionally regulated in JAK2/STAT3 signaling-dependent pathway.49 This study demonstrated that some concentrations of Endostar decreased the relative levels of STAT3 and pSTAT3 in A549 lung cancer cells with statistical significance, which is in line with the role of JAK2/STAT3 pathway in regulation of MHC class I via NLRC5, suggesting the underlying mechanism of MHC class I upregulation involving JAK2/STAT3 signaling pathway. Conclusions Endostar (25 μg/ml) inhibited the expression of HIF-1 with the upregulation of MHC class I α-heavy chain and β2 m light chain in A549 and NCI-H1299 lung cancer cells, which showed the potential for Endostar to facilitate cancer immunotherapy. It is needed for future studies that are warranted to confirm the role of Endostar treatment. Source paper: PMC12101583	2025-09-19T12:14:00.590887Z
7	1	2025-09-18T09:14:33.603373Z	7	[{"start":1092,"end":1099,"text":"neurons","labels":["CellType"]},{"start":8719,"end":8730,"text":"neutrophils","labels":["CellType"]},{"start":1793,"end":1805,"text":"HEK293 cells","labels":["CellLine"]},{"start":2471,"end":2477,"text":"HEK293","labels":["CellLine"]},{"start":3266,"end":3272,"text":"HEK293","labels":["CellLine"]},{"start":3573,"end":3579,"text":"HEK293","labels":["CellLine"]},{"start":3868,"end":3880,"text":"HEK293 cells","labels":["CellLine"]},{"start":4160,"end":4166,"text":"HEK293","labels":["CellLine"]},{"start":4811,"end":4817,"text":"HEK293","labels":["CellLine"]},{"start":5955,"end":5967,"text":"HEK293 cells","labels":["CellLine"]},{"start":9342,"end":9348,"text":"HEK293","labels":["CellLine"]},{"start":9644,"end":9650,"text":"HEK293","labels":["CellLine"]},{"start":9799,"end":9805,"text":"HEK293","labels":["CellLine"]},{"start":12278,"end":12295,"text":"SH-SY5Y cell 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cells","labels":["CellType"]},{"start":13222,"end":13279,"text":"CXCR1\/2-expressing human myeloid-derived suppressor cells","labels":["CellType"]},{"start":13388,"end":13405,"text":"cancer stem cells","labels":["CellType"]},{"start":13422,"end":13445,"text":"glioblastoma stem cells","labels":["CellType"]},{"start":13462,"end":13474,"text":"myeloid cell","labels":["CellType"]},{"start":15825,"end":15851,"text":"MYT1L-overexpressing cells","labels":["CellType"]},{"start":16068,"end":16080,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":16166,"end":16194,"text":"BRCA2-deficient cancer cells","labels":["CellType"]},{"start":16330,"end":16342,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":16787,"end":16813,"text":"ATM-defective cancer cells","labels":["CellType"]},{"start":17886,"end":17891,"text":"cells","labels":["VagueCellCategory"]},{"start":2322,"end":2357,"text":"DNA-PK-deficient glioblastoma cells","labels":["CellType"]},{"start":10504,"end":10522,"text":"glioblastoma cells","labels":["CellType"]},{"start":11878,"end":11896,"text":"glioblastoma cells","labels":["CellType"]},{"start":16960,"end":16999,"text":"MYT1L-overexpressing glioblastoma cells","labels":["CellType"]},{"start":20820,"end":20846,"text":"DNA-PK+ glioblastoma cells","labels":["CellType"]},{"start":21162,"end":21180,"text":"glioblastoma cells","labels":["CellType"]},{"start":8270,"end":8280,"text":"leukocytes","labels":["CellType"]},{"start":1070,"end":1088,"text":"non-neuronal cells","labels":["VagueCellCategory"]},{"start":11458,"end":11481,"text":"glioblastoma stem cells","labels":["CellType"]},{"start":5456,"end":5463,"text":"HEK-293","labels":["CellLine"]},{"start":5906,"end":5911,"text":"M059K","labels":["CellLine"]},{"start":15770,"end":15775,"text":"M059K","labels":["CellLine"]}]	261568.989	2. results 3. discussion Cell surface receptors and their coupled intracellular signaling pathways have been intensively studied in many physiological and pathological processes over five decades. Due to the critical feature of these receptor-mediated signaling in promoting malignant proliferation, invasion, and metastasis, designing/seeking small molecules and monoclonal antibodies that target receptors and downstream pathways has become a pivotal strategy in anti-cancer drug discovery. Therefore, a better understanding of the key players in the signaling pathways may offer opportunities for therapeutic intervention. This study reveals a positive feedback DNA-PK/MYT1L-CXCR1 proliferative signaling loop that contributes to the progression of glioblastoma via the ERK1/2 pathway and provides a novel insight into the role of DNA-PK in the MYT1L-mediated transactivation of CXCR1. MYT1L is predominantly expressed in human brains, in particular fetal brains [32]. Several key studies have demonstrated the pivotal role of MYT1L in the direct conversion of human non-neuronal cells to neurons [33,34,35]. However, the contributing role of MYT1L in carcinogenesis remains poorly understood. Our previous studies have uncovered that miR-141 was down-regulated in glioblastoma and was able to suppress cell proliferation by directly targeting MYT1L, implicating an oncogenic role of MYT1L in glioblastoma. In this study, we found that in normal DNA-PK glioblastoma M059K cell lines, knockdown of MYT1L attenuated cell proliferation and induced apoptosis and S-phase cell cycle arrest (Figure 1A–D). In contrast, the ectopic expression of MYT1L promoted cell proliferation, inhibited apoptosis, and shortened the S phase (Figure 1E,F,H,J). Similar phenotypic alterations were also noted in HEK293 cells in response to ectopic MYT1L (Figure 1E–G,I). These results indicated that in cooperation with DNA-PK, MYT1L may function as an oncogene in the progression of glioblastoma. However, in the loss-of-function DNA-PK M059J cell line, the knockdown of MYT1L promoted cell proliferation, attenuated apoptosis, and shortened the S phase (Figure 2A–D). Conversely, the enforced expression of MYT1L inhibited cell proliferation and induced a G1 cell cycle arrest (Figure 2E–G), suggesting a tumor suppressor role of MYT1L in DNA-PK-deficient glioblastoma cells, although it suppressed apoptosis (Figure 2H). Mechanically, the sustained activation of the AKT pathway in both HEK293 and M059K cells and the activation of the ERK1/2 pathway in M059K cells (Figure 1K) may contribute to cell proliferation induced by ectopic expression of MYT1L, which supports the key role of these two well-studied oncogenic signaling pathways in the development of human malignancies [36]. However, the proliferative inhibition mediated by ectopic expression of MYT1L in M059J cells may not be attributed to the activation of these two pathways (Figure 2I). A total of 13% apoptotic cells were also found in the negative control siRNA group (Figure 1C), which may reflect the cytotoxic effect of either transfection reagent (Lipofectamine 3000) or negative control siRNA. The cell cycle is tightly regulated by cyclins, CDKs, and CDK inhibitors. Although cyclin E1 was down-regulated in HEK293, while it was up-regulated in M059K cells in response to MYT1L expression (Figure 1K), that may play a role in shortening the S phase (Figure 1G,H) because of the critical requirement of cyclin E in the S phase progression [37,38]. Furthermore, p21, a well-known CDK inhibitor, was down-regulated in HEK293 and M059K cells in response to MYT1L (Figure 1K), which may also contribute to shortening the S phase. Moreover, p21 has been shown to promote cell death in cancer cells via the activation of autophagy [39,40,41]; the down-regulated p21 may be a causal player in attenuating apoptosis in HEK293 cells that express MYT1L (Figure 1I,J). Interestingly, CDK6 was down-regulated in M059J cells in response to MYT1L (Figure 2I), which may play a role in a G1 cell cycle arrest (Figure 2G). Ectopic MYT1L induced down-regulation of caspase 3 (Figure 1K, Supplementary Figure S1) in both HEK293 and M509K cells, which may play a contributing role in apoptotic inhibition (Figure 1I,J). However, this is not the case in M059J due to the absence of caspase 3 in both GFP- and MYT1L-M059J cells (Figure 2I). As a transcription factor, the transcriptional targets of MYT1L, unfortunately, remain largely unknown. Here, we discovered that MYT1L could directly regulate the transcription of CXCR1 through a novel cis-acting element (−2114/−2103) at the CXCR1 promoter. We noted that the overexpression of MYT1L correlated with CXCR1 mRNA levels in 4 out of 6 brain cancer cell lines (Figure 3A,B) and the induction of CXCR1 in the normal DNA-PK HEK293 and M059K cells in response to the ectopically expressed MYT1L (Figure 3C,D), supporting the opinion that CXCR1 is a transcriptional target of MYT1L. Surprisingly, in M059J cells with loss of DNA-PK function (Supplementary Figure S4), the enforced expression of MYT1L failed to induce CXCR1 transcription (Figure 3C), although the protein levels of CXCR1 were not consistent with its mRNA levels (Figure 3C,D). Taken together, our results demonstrate that MYT1L functionality depends on DNA-PK activity. Furthermore, the ectopically expressed MYT1L increased the luciferase activity of the wild-type CXCR1 promoter/luciferase reporter in HEK-293 and M059K cells with the normal expression of DNA-PK, which was abolished when the mutant construct was used (Figure 3G). However, the ectopic expression of MYT1L suppressed the luciferase activity in the DNA-PK-deficient M059J cells (Figure 3G). Moreover, the enforced expression of MYT1L resulted in the sustained activation of the AKT pathway in all three cell lines examined (Figure 1K and Figure 2I). The ERK1/2 pathway was activated in M059K and M059J cells, while it was inhibited in HEK293 cells in response to MYT1L (Figure 1K and Figure 2I). The activation of the ERK1/2 pathway was CXCR1-dependent, whereas the AKT pathway was not (Figure 4J). Based on these findings, we may propose that under non-stimulated conditions, MYT1L may act as a transcriptional repressor suppressing CXCR1 transcription; DNA-PK may function as a coactivator, and once it is phosphorylated by the CXCR1-dependent activation of the ERK1/2 kinase, it, in turn, phosphorylates MYT1L; the phosphorylated MYT1L recruits histone acetyltransferases (HATs) and transcription factors (TFs) to the CXCR1 promoter and promotes CXCR1 transcription (Figure 5). DNA-PK (also known as PRKDC and p350) is a nuclear serine/threonine protein kinase complex that is composed of a catalytic subunit of DNA-PKcs and a heterodimer of Ku proteins (Ku70/Ku80). Although one of the most well-defined functions of DNA-PK is to govern the repair of DNA double-strand breaks through both non-homologous end-joining (NHEJ) and homologous recombination (HR) [42], it was originally identified as a component of the Sp1 transcription complex in which it may modulate the transcriptional activity of the complex by phosphorylating Sp1 [43]. For the first time, our findings demonstrated that DNA-PK may functionally modulate the transcriptional activity of MYT1L by phosphorylation, eventually leading to the transcriptional activation of CXCR1, although this requires further validation using chromatin immunoprecipitation quantitative PCR (ChIP-qPCR) and the electrophoretic mobility shift assay (EMSA) when a ChIP-grade antibody to the phosphorylated MYT1L is commercially available. Our previous work demonstrated that DNA-PK may also function as a repressor of MYT1L, resulting in the transcriptional inhibition of tumor suppressor p21, a transcriptional target of MYT1L [29]. Several publications support the dual role of DNA-PK in the transcriptional control of gene expression. DNA-PK inhibited the transcription of the xanthine oxidoreductase gene via E-box/TATA-like elements [44]. Simultaneously, Ku proteins may also act as a transcriptional recycling coactivator of the androgen receptor [45]. Chemokines are a superfamily of small (about 8 to 14 kDa) cytokine-like proteins that selectively regulate the recruitment and trafficking of leukocytes to inflammatory sites through chemoattraction [46]. Members of this family have been divided into four subfamilies, CXC, CC, C, and CX3C, based on the arrangement of the first two of the four conserved cysteine residues in the amino terminus of the proteins, which can bind CXCR, CCR, XCR1, and CX3CR1, respectively. Chemokines play a critical role in inflammatory reactions. IL-8 is a well-characterized ELR+ CXC chemokine that attracts neutrophils through binding CXCR1 and CXCR2 receptors on the cell surface [47]. It is suspected that the signature of chemokines that persist at inflammatory sites may be important in the development of chronic diseases and can contribute to the development of malignancies [48]. There is sufficient evidence to show that chemokines also play a significant role in cancer, including melanoma [49], breast [50], colon [51], esophageal [52], prostate [53], and non-small cell lung [54] cancers, in addition to its role in the development of inflammatory responses. Here, we showed that IL-8 was significantly up-regulated in HEK293 and M059J cells, while it was only slightly increased in M059K cells in response to the ectopically expressed MYT1L (Figure 4A). Unlike IL-8, GROα more selectively binds to the CXCR2 receptor, while the affinity to CXCR1 is significantly lower [55]. Since the CXCR2 receptor was undetectable in HEK293 and M059K cells (Figure 3E), GROα in these cells could only function through CXCR1. The enforced expression of MYT1L caused an elevation of GROα in HEK293 and M059J cells (Figure 4B–E). Although GROα mRNA was down-regulated, its protein level was unaffected in M059K cells in response to the ectopically expressed MYT1L (Figure 4F,G). IL-8 and/or GROα bind to CXCR1, eventually promoting glioblastoma proliferation via the ERK1/2 pathway that can be blocked by CXCR1 siRNA (Figure 4J,K), suggesting that the activation of the proliferative ERK1/2 pathway in glioblastoma is, at least in part, CXCR1-dependent. Although this is the first report regarding the critical role of DNA-PK/MYT1L in the proliferative IL-8/GROα-CXCR1 signaling loop in glioblastoma, the significant contributing role of IL-8-CXCR1/2 axes in the proliferation and angiogenesis of glioblastoma cells has been demonstrated before [28]. Taken together, we propose a potential DNA-PK/MYT1L-CXCR1 signaling loop in the progression of glioblastoma (Figure 5). We speculate that the DNA-PK/MYT1L-CXCR1-ERK1/2 might be up-regulated at WHO grade IV and/or recurrent glioblastomas due to chemo- or radio-resistance. DNA-PK is a key player in DNA damage response on double-strand breaks. Activation of DNA damage response facilitates glioma stem cells to develop radio-resistance [56]. Furthermore, DNA-PK is a master kinase of the proliferative/progenitor subtype of glioblastoma, guiding targeted cancer therapy [57]. Immunohistochemical staining has indicated the expression of IL-8 in 66.7–67.3% of WHO grade IV glioblastoma tissues, with the expression of its receptor CXCR1 found primarily in both tumor-associated vessels and grade IV glioblastomas [28], supporting the previous report showing a crucial role of IL-8/CXCR1/2 signaling in glioblastoma stem cells [58]. Moreover, the IL-8/CXCR1/STAT3 pathway is crucial for the maintenance of glioblastoma stem cells [59]. Glioblastoma and neuroblastoma are two common types of brain tumors that occur in different aged populations. Our previous studies indicated that MYT1L was overexpressed in glioblastoma cell lines and 46.9% of malignant glioma tissue samples (n = 32) [29], functioning as an oncogene in glioblastoma cells with normal DNA-PK activity. To see whether MYT1L is also up-regulated in neuroblastoma, we compared its expression in both glioblastoma and neuroblastoma cell lines. It was found that MYT1L was overexpressed in 3 out of 7 neuroblastoma cell lines examined (Figure 3A), suggesting that MYT1L up-regulation may be a common event in both glioblastoma and neuroblastoma. Although the SH-SY5Y cell line is a subclone of the SK-N-SH cell line, the global gene expression microarray showed a profound differential expression of genes in these two cell lines [60]. However, the mechanism involved is unclear. In the present study, we noted that MYT1L was up-regulated in the SH-SY5Y cell line at both mRNA and protein levels (Figure 3A,B), while in its parental line, SK-N-SH, only MYT1L mRNA was up-regulated. The MYT1L protein was undetectable, which may implicate the involvement of post-transcriptional regulation, such as miRNA(s), in MYT1L expression. Some of the molecules analyzed in this manuscript can be molecular targets in cancer therapy. The IL-8-CXCR1/2 axis has been proposed as a potential therapeutic target for numerous cancers due to its contributing roles in proliferation, migration/invasion, angiogenesis, and tumor immunosuppression [61,62]. The tumor-produced IL-8 is a potent chemoattractant that recruits CXCR1/2-expressing human myeloid-derived suppressor cells to tumor foci, playing a crucial role in immune resistance [63]. IL-8 receptor CXCR1 may be a biomarker for cancer stem cells [64], including glioblastoma stem cells [58]. Targeting myeloid cell CXCR1/2 enhances antitumor immunity in pancreatic cancer [65]. Neutralizing IL-8 or inhibiting its receptor CXCR1/2 potentiates anti-PD-1-mediated antitumor immunotherapy for glioma [66]. These findings highlight IL-8 and CXCR1/2 as potential therapeutic targets for cancer. McClelland and colleagues have summarized numerous inhibitors developed to target IL-8 or its receptor CXCR1/2 for the treatment of chronic obstructive pulmonary disease (COPD), asthma, diabetes, pneumonia, and solid tumors, including prostate cancer [62]. Phase 1 and 2 clinical trials in prostate cancer with Navarixin, a small-molecule inhibitor of CXCR1/2, have been completed. Phase 1 and 2 clinical trials in prostate cancer with BMS-986253, a monoclonal antibody against IL-8, are still active. However, none of these inhibitors have yet been approved to treat cancer. The ERK1/2 signaling pathway contributes to numerous biological and pathological processes. Excessive activation of the ERK1/2 pathway is a hallmark of human malignancies [67,68,69,70,71,72,73] due to its crucial role in maintaining sustained cellular proliferation, resistance to cell death, angiogenesis, invasion, and metastasis. Many FDA-approved drugs target upstream regulators of the ERK1/2 pathway, resulting in an indirect inhibition of ERK1/2 kinase [74]. ERK1/2 signaling may be reactivated upon the development of drug resistance. Liu and colleagues have described 10 small-molecule inhibitors of ERK1/2 which were developed recently and have undergone clinical trials in cancer patients [75]. Some of them have shown promising results in the treatment of cancers. Surprisingly, a large body of evidence also indicates a contributing role of ERK1/2 activation in cancer cell apoptosis primarily induced by chemical compounds, including glioblastoma, bladder, breast, colon, endometrial, head and neck, lung, renal cell, and testicular germ cell carcinomas [76,77,78,79,80,81,82,83,84,85], and mechanically, through activation of caspase 3 and induction of reactive oxygen species. Both oncogene and non-oncogene addiction have been demonstrated as fantastic targets for cancer therapy due to their crucial role in supporting cancer progression [86]. Using a unique glioblastoma cell model system, M059J with deficient DNA-PK, while M059K with normal DNA-PK, we demonstrate in vitro that MYT1L-overexpressing cells show non-oncogene addiction to DNA-PK. Genomic instability due to defects in DNA repair genes is a key hallmark of cancer that drives the development of human malignancies [87]. However, these defects may also offer cancer cells opportunities to evolve a dependency on a non-oncogene addiction gene. For instance, BRCA2-deficient cancer cells are more dependent on PARP1 to repair DNA for survival, resulting in an increased sensitivity to PARP1 inhibitors [88], which kill the cancer cells through the genetic concept of synthetic lethality [89]. Targeting mediators of DNA repair has become a state-of-the-art strategy for cancer [90], leading to the development of inhibitors of key mediators of DNA repair, including DNA-PK. Interestingly, genetic and pharmacological targeting of a strong non-oncogene addiction to DNA-PKcs (DNA-dependent protein kinase catalytic subunit) leads to the accumulation of DNA double-strand breaks in ATM-defective cancer cells, which triggers cell apoptosis via the proapoptotic signaling pathway [91]. These results are supported by our findings showing the dependence of MYT1L-overexpressing glioblastoma cells on DNA-PK. VX-984 and M3814 are two selective DNA-PK inhibitors [92,93] that profoundly inhibit tumor growth in tumor xenograft models by enhancing radiotherapy. These two inhibitors are currently in ongoing clinical trials. The DNA-PK/MYT1L-CXCR1-ERK1/2 proliferative signaling loop we proposed here was primarily based on data obtained from glioblastoma cell lines. It is interesting to look at the expression of DNA-PK, MYT1L, and CXCR1 and the levels of phosphorylated ERK1/2 in a large cohort of glioblastoma tissue samples and correlate the levels of these molecules to clinicopathological parameters of this disease. It is also interesting to look at the effect of MYT1L on tumor growth in an in vivo tumor xenograft animal model using GFP-/MYT1L-M059J and GFP-/MYT1L M059K cells, further confirming our in vitro findings. 4. materials and methods 4.5. western blot analysis The cells were rinsed twice with ice-cold PBS and scraped off the plate in radioimmunoprecipitation assay buffer (RIPA). The total protein lysate prepared from human adult brain normal tissues was purchased from BioChain (Newark, CA, USA) and served as a normal control for human cell lines. The whole cellular lysates (30–100 µg per sample) were electrophoresed via 10% SDS-PAGE and electrophoretically transferred to PVDF membranes (Amersham HybondTM-P, GE Healthcare, Chicago, IL, USA) at 4 °C for 1.5 h. The blots were incubated for 1 h with 5% nonfat dry milk to block the nonspecific binding sites and subsequently incubated with polyclonal/monoclonal antibodies specific to MYT1L (Abnova, Taipei, China) or AKT1 (Abcam, Cambridge, GH, UK), CDK2 (Abcam, Cambridge, GH, UK), DNA-PKcs (Abcam, Cambridge, GH, UK), p21 (Abcam, Cambridge, GH, UK) or CDK4 (Cell Signaling Technology, Danvers, MA, USA), CDK6 (Cell Signaling Technology, Danvers, MA, USA), cyclin A2 (Cell Signaling Technology, Danvers, MA, USA), cyclin D1 (Cell Signaling Technology, Danvers, MA, USA), cyclin E1 (Cell Signaling Technology, Danvers, MA, USA), ERK1/2 (Cell Signaling Technology, Danvers, MA, USA), p27 (Cell Signaling Technology, Danvers, MA, USA), pERK1/2 (Cell Signaling Technology, Danvers, MA, USA) or BAX (Santa Cruz Biotechnology, Dallas, TX, USA), BCL2 (Santa Cruz Biotechnology, Dallas, TX, USA), GROα (Santa Cruz Biotechnology, Dallas, TX, USA), IL8RB (Santa Cruz Biotechnology, Dallas, TX, USA), pAKT1/2/3 (Santa Cruz Biotechnology, Dallas, TX, USA), or CXCR1 (LSBio, Lynnwood, WA, USA) at 4 °C overnight. Immunoreactivity was detected using a peroxidase-conjugated antibody and visualized using the ECL Plus Western Blotting Detection System (GE Healthcare, Chicago, IL, USA). The blots were stripped before reprobing with an antibody against actin (Abcam, Cambridge, GH, UK) or GAPDH (Santa Cruz Biotechnology, Dallas, TX, USA). 4.9. bioinformatics analysis The MYT1L binding sites in the CXCR1 promoter were predicted using Zinc Finger Protein-DNA Scoring Form, a DNA binding site predictor for Cys2His2 zinc finger proteins (http://zf.princeton.edu/form.php, accessed on 14 March 2025). The Cancer Genome Atlas (TCGA) datasets were used to analyze a relationship between MYT1L expression and CXCR1 expression in glioblastoma and neuroblastoma. 4.12. statistical analysis The Student’s t-test was used to determine the statistical significance of differences in GROα expression, IL-8 expression, cell growth, apoptosis, cell cycle, and luciferase activity between groups. Luciferase activity was measured in duplicate, while other experiments were performed in triplicate. A value of p ***5. Conclusions*** This work, for the first time, reveals that MYT1L functions as a transcription factor governing the expression of CXC chemokine receptor CXCR1. The CXCR1 signaling promotes proliferation, inhibits apoptosis, and shortens the S phase in DNA-PK+ glioblastoma cells via activation of the ERK1/2 pathway, which can be blocked by CXCR1 knockdown. The function of MYT1L is DNA-PK-dependent, highlighting a key role of DNA-PK modulating the transcriptional activity of MYT1L. Our findings have demonstrated a positive feedback DNA-PK/MYT1L-CXCR1-ERK1/2 proliferative signaling loop in glioblastoma cells and might have significant therapeutic implications. In the future, our studies can be extended by analyzing the effect of CXCR1 knockdown in M059J cells with impaired DNA-PK. This would allow us to understand the interlink between DNA-PK-MYT1L-CXCR1-pERK1/2. Source paper: PMC12072392	2025-09-23T11:51:44.173489Z
8	1	2025-09-18T09:14:33.603471Z	8	[{"start":8299,"end":8310,"text":"NSCLC cells","labels":["CellType"]},{"start":10874,"end":10885,"text":"NSCLC cells","labels":["CellType"]},{"start":11309,"end":11320,"text":"NSCLC cells","labels":["CellType"]},{"start":668,"end":678,"text":"A549 cells","labels":["CellLine"]},{"start":2501,"end":2511,"text":"A549 cells","labels":["CellLine"]},{"start":4816,"end":4826,"text":"A549 cells","labels":["CellLine"]},{"start":5308,"end":5318,"text":"A549 cells","labels":["CellLine"]},{"start":7528,"end":7538,"text":"A549 cells","labels":["CellLine"]},{"start":10438,"end":10448,"text":"A549 cells","labels":["CellLine"]},{"start":6750,"end":6757,"text":"T cells","labels":["CellType"]},{"start":4584,"end":4595,"text":"tumor cells","labels":["VagueCellCategory"]},{"start":7262,"end":7283,"text":"STAT1-deficient cells","labels":["CellType"]},{"start":10117,"end":10128,"text":"tumor cells","labels":["VagueCellCategory"]},{"start":8454,"end":8471,"text":"lung cancer cells","labels":["CellType"]},{"start":9229,"end":9246,"text":"lung cancer cells","labels":["CellType"]}]	286.076	Results IFN-γ significantly induced STAT1 phosphorylation, leading to a time-dependent upregulation of PD-L1 expression. Immunofluorescence confirmed that p-STAT1 is translocated to nucleus. Curcumin treatment inhibited STAT1 phosphorylation by 68% (p p ***Conclusion*** Curcumin effectively inhibits IFN-γ-induced STAT1 phosphorylation and PD-L1 expression, downregulates ISGs, and enhances IFN-γ-mediated tumor suppression. These findings suggest that curcumin may serve as a therapeutic adjuvant in NSCLC, potentially improving immune checkpoint inhibitor (ICI) efficacy. Material and methods Western blot analysis Total protein lysates were extracted from treated A549 cells using ice-cold radioimmunoprecipitation assay (RIPA) buffer supplemented with protease and phosphatase inhibitors and centrifuged at 10,000 × g for 10 min at 4◦C. The total protein concentrations were measured with the bicinchoninic acid (BCA) protein assay kit (Pierce, Thermo Fisher Scientific, USA). Equal amounts of protein (20 μg per sample) were separated on 10% sodium dodecyl sulfate–polyacrylamide gels (SDS-PAGE) and transferred onto 0.45 μM nitrocellulose membranes (Merck). Membranes were blocked with 5% BSA in Tris-buffered saline with 0.1% Tween- 20 (TBST) for 1 h at room temperature to prevent non-specific binding and incubated overnight at 4 °C with primary antibodies against phospho-STAT1 (Tyr701), total STAT1, PD-L1, and β-actin (Santa Cruz Biotechnology, Dallas, TX, USA). After washing with TBST, membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary anti-mouse IgG antibody or anti-rabbit IgG, HRP-linked antibody, for 1 h at room temperature. Protein bands were detected using enhanced chemiluminescence (ECL) reagent (GE Healthcare, USA) and visualized using a ChemiDoc imaging system (Bio-Rad, USA). Analysis of protein bands was performed using ImageJ software (NIH, Bethesda, MD, USA). The primary and secondary antibodies are listed in Table 1.Table 1The description of primary and secondary antibodies AntibodySpeciesCloneDilutionCa#SourcePD-L1RabbitMonoclonal1:1000ab213524AbcamSTAT1MouseMonoclonal1:10009176Cell Signaling TechnologypSTAT1RabbitMonoclonal1:10009177SCell Signaling Technologyβ-actinMouseMonoclonal1:500047,778Santa Cruz Biotechnology, Dallas, TX, USAAnti-mouseHorse-1:10,0007076Cell Signaling, Danvers, MA, USAAnti-RabbitGoat-1:10,0007074Cell Signaling, Danvers, MA, USA Quantitative real-time pcr (qrt-pcr) analysis Total RNA was extracted from A549 cells using the RNeasy Mini-Kit (Qiagen, #74,104) according to the manufacturer’s instructions, and the eluted RNA purity and concentration were assessed using a NanoDrop One spectrophotometer (Thermo Scientific, USA). For cDNA synthesis, 500 ng of RNA was reverse transcribed using the RevertAid First Strand cDNA Synthesis Kit to cDNA as per the manufacturer’s instructions. Quantitative real-time PCR (qRT-PCR) was conducted using the SYBR Green PCR Master Mix (Applied Biosystems™) on a QuantStudio 5 Machine (Thermo Fisher Scientific, Inc.). The relative expression levels of PD-L1, STAT1, and ISGs (ISG15 and SOCS1) were normalized to the housekeeping gene GAPDH and analyzed using the 2^ − ΔΔCt method. The PCR reactions were carried out in duplicate with 40 cycles of denaturation (15 s at 95 °C), annealing (20 s at 65 °C), and elongation (20 s at 72 °C) after an initial enzyme activation (15 min at 95 °C). The primer sequences used are presented in Table 2.Table 2List of PCR primers designed using NCBI/Primer-BLAST program PrimerPrimer sequencesForwardReverseCD274 (PD−L1)5′-TGGCATTTGCTGAACGCATTT- 3′5′-AGTGCAGCCAGGTCTAATTGT- 3′ISG155′-ATCACCCAGAAGATCGGCGT- 3′5′-TCGCATTTGTCCACCACCAG- 3′SOCS15’- TTCGCCCTTAGCGTGAAGATGG- 3′5’- TAGTGCTCCAGCAGCTCGAAGA- 3′GAPDH5′-GGAAGGTGAAGGTCGGAGTC- 3′5′-TGAAGGGGTCATTGATGGCA- 3′ Statistical analysis All experiments were performed in triplicate, and data are presented as mean ± standard deviation (SD). Statistical analyses were conducted using GraphPad Prism 10 (GraphPad Software, USA). One-way analysis of variance (ANOVA) followed by Tukey’s post-hoc test was used to compare multiple groups, and an unpaired Student’s t-test was used for pairwise comparisons. Differences were considered statistically significant at p ***Results*** Discussion Immune evasion remains a significant challenge in the treatment of NSCLC, with PD-L1 upregulation being one of the primary mechanisms by which tumors escape immune surveillance (Cui et al. 2024). The IFN-γ/STAT1 signaling pathway plays a crucial role in PD-L1 regulation, enabling tumor cells to suppress T-cell-mediated immune responses and resist immune checkpoint blockade therapy (Padmanabhan et al. 2022). In this study, we investigated how IFN-γ-induced STAT1 phosphorylation leads to PD-L1 upregulation in A549 cells and explored the potential of curcumin, a bioactive polyphenol with known anti-inflammatory and anti-cancer properties, as a therapeutic agent capable of modulating this pathway. Our findings provide strong evidence that curcumin inhibits IFN-γ-induced STAT1 activation, thereby reducing PD-L1 expression and enhancing the anti-proliferative effects of IFN-γ in NSCLC. Consistent with previous studies, we observed that IFN-γ induces a robust phosphorylation of STAT1 at Tyr701 in A549 cells, leading to its nuclear translocation and subsequent activation of target genes, including PD-L1. The kinetics of STAT1 phosphorylation followed a time-dependent pattern, with phosphorylation detected as early as 2 h, peaking at 6 h, and remaining elevated up to 24 h. These results are in agreement with earlier reports that demonstrated a similar pattern of IFN-γ-induced STAT1 activation in various cancer models, including melanoma (Schmitt et al. 2012), colorectal cancer (Zhao et al. 2020), and lung adenocarcinoma (Gao et al. 2018). STAT1 phosphorylation is a prerequisite for its dimerization and nuclear translocation, which is required for the transcriptional activation of ISGs (Wang et al. 2017). Immunofluorescence analysis confirmed that IFN-γ treatment led to a marked accumulation of p-STAT1 in the nucleus, reinforcing the notion that STAT1 plays a crucial role in IFN-γ-mediated transcriptional regulation. These findings align with studies showing that sustained STAT1 activation promotes an immunosuppressive tumor microenvironment by inducing PD-L1 expression and other immune-regulatory genes. The upregulation of PD-L1 in response to IFN-γ was confirmed at both the mRNA and protein levels, as demonstrated by qRT-PCR and Western blotting. The increased surface expression of PD-L1 following IFN-γ treatment highlights the functional significance of this regulation, as surface PD-L1 interacts with PD- 1 on T cells to inhibit anti-tumor immune responses (Arak et al. 2021). These results are in accordance with previous reports showing that IFN-γ is one of the most potent inducers of PD-L1 in NSCLC, facilitating immune escape and tumor progression (Pawelczyk et al. 2019). Additionally, the use of fludarabine, a STAT1 inhibitor, significantly attenuated IFN-γ-induced PD-L1 expression, confirming that STAT1 is the primary mediator of this regulatory axis. This finding corroborates prior studies demonstrating that STAT1-deficient cells fail to upregulate PD-L1 in response to IFN-γ, emphasizing the centrality of STAT1 in this pathway. One of the most significant findings of this study is the ability of curcumin to inhibit IFN-γ-induced STAT1 activation and PD-L1 expression in A549 cells. Western blot analysis revealed that curcumin suppressed STAT1 phosphorylation in a dose-dependent manner, with the highest concentration (50 µM) reducing phosphorylation by 68%. This effect was not due to a decrease in total STAT1 protein levels, indicating that curcumin selectively inhibits STAT1 activation rather than its expression. Previous studies have reported that curcumin can interfere with JAK-STAT signaling in other cancer types. Curcumin directly inhibits the phosphorylation of STAT3, a key component of the JAK-STAT signaling pathway in breast cancer (Golmohammadi et al. 2024) and prostate cancer (Li et al. 2024), as well as the downregulation of the STAT1 in melanoma (Xu et al. 2018), but its specific effect on STAT1 in IFN-γ-stimulated NSCLC cells had not been previously explored. Our findings extend these observations by demonstrating that curcumin effectively blocks STAT1 activation in lung cancer cells, preventing the downstream induction of PD-L1. The suppression of PD-L1 expression by curcumin was observed at both the transcriptional and translational levels, as evidenced by qRT-PCR and Western blotting. This finding is particularly relevant in the context of NSCLC, where high PD-L1 expression correlates with poor prognosis and resistance to immunotherapy. Previous studies have reported that curcumin downregulates PD-L1 in other cancer models, such as melanoma (Xu et al. 2018) and hepatocellular carcinoma (Guo et al. 2021), but the specific inhibition of IFN-γ-induced PD-L1 expression in NSCLC had not been thoroughly investigated. Our study provides the first evidence that curcumin can effectively suppress IFN-γ-mediated PD-L1 upregulation in lung cancer cells via STAT1 pathway, highlighting its potential as an immune-modulatory agent. In addition to PD-L1, STAT1 regulates the expression of multiple ISGs involved in immune evasion, including SOCS1 (Ilangumaran et al. 2024) and ISG15 (Desai 2015). Our results demonstrated that IFN-γ significantly upregulated both SOCS1 and ISG15, reinforcing the notion that IFN-γ signaling contributes to an immunosuppressive tumor microenvironment. Curcumin pretreatment, however, led to a significant reduction in both SOCS1 and ISG15 expression, further supporting its ability to interfere with IFN-γ-driven STAT1 signaling. SOCS1 is known to act as a feedback inhibitor of JAK-STAT signaling (Liau et al. 2018), but paradoxically, its overexpression in tumors has been associated with immune escape mechanisms. By suppressing SOCS1 expression, curcumin may enhance the responsiveness of tumor cells to immune-mediated clearance. Similarly, ISG15 has been implicated in tumor progression and resistance to therapy (Meng et al. 2024), suggesting that its downregulation by curcumin may have additional therapeutic benefits. Finally, we observed that curcumin enhances the anti-proliferative effect of IFN-γ in A549 cells. While IFN-γ alone resulted in a modest reduction in cell viability (21%), the combination of IFN-γ and curcumin led to a significantly greater reduction (47%), suggesting a synergistic effect. These findings align with previous reports that curcumin enhances the anti-tumor activity of cytokines by modulating cell cycle regulators and apoptotic pathways (Hu et al. 2018). The precise mechanism by which curcumin sensitizes NSCLC cells to IFN-γ-induced growth suppression remains to be elucidated, but it may involve inhibition of survival pathways downstream of STAT1 activation. Given that STAT1 has been implicated in both pro-apoptotic and pro-survival signaling, the net effect of its inhibition may depend on the cellular context and additional regulatory factors. In conclusion, our study provides novel evidence that IFN-γ induces PD-L1 expression in NSCLC cells via STAT1 activation and that curcumin effectively inhibits this process by suppressing STAT1 phosphorylation and nuclear translocation. Furthermore, curcumin downregulates IFN-γ-induced ISGs and enhances IFN-γ-mediated tumor cell growth suppression, highlighting its potential as a therapeutic adjuvant in NSCLC. These findings suggest that curcumin could be used to improve the efficacy of immune checkpoint inhibitors by reducing tumor immune evasion. Future studies should focus on elucidating the precise molecular mechanisms underlying curcumin’s effects on STAT1 signaling and investigating its potential synergistic effects with existing immunotherapies in preclinical and clinical settings. Source paper: PMC12141184	2025-09-22T13:46:32.762897Z
9	1	2025-09-18T09:14:33.603605Z	9	[{"start":920,"end":936,"text":"cell line MIO-M1","labels":["CellLine"]},{"start":5334,"end":5351,"text":"retinal microglia","labels":["CellType"]},{"start":5634,"end":5660,"text":"inner blood-retina barrier","labels":["Tissue"]},{"start":2285,"end":2300,"text":"retinal tissues","labels":["Tissue"]},{"start":5792,"end":5797,"text":"heart","labels":["Tissue"]},{"start":6954,"end":6960,"text":"retina","labels":["Tissue"]},{"start":2315,"end":2334,"text":"retinal vasculature","labels":["Tissue"]},{"start":2425,"end":2444,"text":"retinal vasculature","labels":["Tissue"]},{"start":5801,"end":5807,"text":"kidney","labels":["Tissue"]},{"start":4577,"end":4597,"text":"human umbilical cord","labels":["Tissue"]},{"start":965,"end":978,"text":"Mueller cells","labels":["CellType"]},{"start":2336,"end":2349,"text":"Mueller cells","labels":["CellType"]},{"start":2648,"end":2661,"text":"Mueller cells","labels":["CellType"]},{"start":3534,"end":3547,"text":"Mueller cells","labels":["CellType"]},{"start":3599,"end":3612,"text":"Mueller cells","labels":["CellType"]},{"start":5906,"end":5919,"text":"Mueller cells","labels":["CellType"]},{"start":6799,"end":6812,"text":"Mueller cells","labels":["CellType"]},{"start":7269,"end":7282,"text":"Mueller cells","labels":["CellType"]},{"start":7436,"end":7449,"text":"Mueller cells","labels":["CellType"]},{"start":7809,"end":7822,"text":"Mueller cells","labels":["CellType"]},{"start":197,"end":210,"text":"treated cells","labels":["VagueCellCategory"]},{"start":273,"end":286,"text":"treated cells","labels":["VagueCellCategory"]},{"start":347,"end":360,"text":"control cells","labels":["VagueCellCategory"]},{"start":1176,"end":1181,"text":"cells","labels":["VagueCellCategory"]},{"start":1255,"end":1260,"text":"cells","labels":["VagueCellCategory"]},{"start":1834,"end":1839,"text":"cells","labels":["VagueCellCategory"]},{"start":2978,"end":2990,"text":"MIO-M1 cells","labels":["CellLine"]},{"start":3237,"end":3242,"text":"cells","labels":["VagueCellCategory"]},{"start":3775,"end":3787,"text":"MIO-M1 cells","labels":["CellLine"]},{"start":4086,"end":4091,"text":"cells","labels":["VagueCellCategory"]},{"start":4652,"end":4664,"text":"MIO-M1 cells","labels":["CellLine"]},{"start":4776,"end":4789,"text":"control cells","labels":["VagueCellCategory"]},{"start":6247,"end":6259,"text":"MIO-M1 cells","labels":["CellLine"]},{"start":6523,"end":6528,"text":"cells","labels":["VagueCellCategory"]},{"start":6712,"end":6724,"text":"MIO-M1 cells","labels":["CellLine"]},{"start":5053,"end":5065,"text":"MIO-M1 cells","labels":["CellLine"]},{"start":2565,"end":2590,"text":"retinal endothelial cells","labels":["CellType"]},{"start":2773,"end":2798,"text":"retinal endothelial cells","labels":["CellType"]},{"start":4550,"end":4567,"text":"endothelial cells","labels":["CellType"]},{"start":5519,"end":5544,"text":"retinal endothelial cells","labels":["CellType"]},{"start":6814,"end":6839,"text":"retinal endothelial cells","labels":["CellType"]},{"start":6845,"end":6862,"text":"retinal pericytes","labels":["CellType"]},{"start":2359,"end":2385,"text":"retinal pigment epithelium","labels":["Tissue"]},{"start":6882,"end":6900,"text":"neurovascular unit","labels":["Tissue"]}]	7366.209	Materials and methods Statistical analysis All experiments were performed at least three times. A one-way ANOVA followed by a Tukey's post-hoc test was used to compare the RT-qPCR signals from the treated cells and the ELISA results. For comparison of RT-qPCR signals from treated cells to the hypothetical value of 1 (normalized to the signal of control cells), a one-sample t-test was used, as in this type of statistical analysis, the variability of the values obtained from control cell signals is taken into consideration, although they appear without standard deviations (SD=0). P#x003C;0.05 was considered to indicate a statistically significant difference. All statistical analyses were performed in GraphPad Prism version 9 (GraphPad Software, Inc.); means and standard deviations are provided as numbers or as scatter plots. Results Discussion Confirming previously published data, it was shown that the human cell line MIO-M1, an accepted model of human Mueller cells, expresses AGT, ACE, ACE2, angiotensin II receptors AT1 and AT2, as well as the receptor of angiotensin (1-7) MAS1 (17-19,21-24,28). The mRNA expression levels of these were largely unchanged when cells were exposed to hyperglycemic or hypoxic conditions or both. Exposure of cells to these conditions seemed to not result in induction of cellular stress, which may adversely affect the outcome of the investigations, since expression of IL-6 mRNA, a marker of cellular stress, remained stable. It could be suggested that an incubation time of 6 h is too short, but due to the short half-life of angiotensin II, longer exposure times would likely not result in more relevant data. However, expression and secretion of VEGF-A were substantially increased by hypoxia alone or in combination with hyperglycemia confirming the expected strong response of the cells to their altered environment within the studied time span. Angiotensin II and aldosterone did not modulate VEGF-A levels, proving the dominant role of hypoxia in the regulation of the growth factors' expression and secretion. It was to be expected that hyperglycemia alone did not modulate VEGF-A expression and secretion within 6 h, as possible changes likely manifest only after extended exposure (32). VEGF receptors are expressed in various retinal tissues including the retinal vasculature, Mueller cells, and the retinal pigment epithelium (33,34). Upregulation of VEGFR2 in the retinal vasculature is associated with the development of DR and its activation by the ligand VEGF-A165 results in elevated permeability of retinal endothelial cells or increased expression of pro-inflammatory mediators in Mueller cells in vitro (29,34,35). Inhibitors of ACE and/or AT1, at least in part, prevent VEGF-A165-induced permeability of retinal endothelial cells in vitro and in vivo as well as retinal neovascularization, thereby proving an interaction between both signaling pathways (36-39). However, expression and secretion of VEGF-A by MIO-M1 cells were not altered by angiotensin II likely reflecting the different behaviors of both cell types. mRNAs coding for proteins of the RAAS indeed exhibited differential expression patterns in the presence of either angiotensin II or aldosterone when cells were cultured under hyperglycemic and/or hypoxic conditions. It is of interest, that angiotensin II did not alter the expression of its precursor AGT under any of the tested conditions, which may indicate that the peptide hormone cannot, directly or indirectly, induce its own expression in Mueller cells. To assess a possible pro-inflammatory response of Mueller cells, the changes in the expression of mRNA as well as secretion of the pro-inflammatory cytokine IL-6, which is constitutively expressed by this cell type (including MIO-M1 cells), was assessed (31). However, under normoxic or hypoxic conditions, the amounts of the secreted cytokine and its mRNA expression levels were not significantly altered by the treatment with angiotensin II or aldosterone, suggesting that the hormones do not induce a pro-inflammatory response of the cells under these circumstances. Interestingly, aldosterone significantly enhanced the expression of ACE mRNA under hypoxic conditions, thus not resulting in an inflammatory response, that is enhanced expression or secretion of IL-6 via the angiotensin II/AT1-axis. Angiotensin II, on the other hand, increased the expression of ACE2, resulting in its own inactivation by the formation of angiotensin (1-7), which does not activate AT1. Similar to the behavior of endothelial cells from the human umbilical cord, the mRNA expression levels of IL-6 were increased in MIO-M1 cells exposed to hyperglycemia and aldosterone, although the amount of the secreted cytokine remained unchanged from control cells (40). However, increased IL-6 expression is likely independent of the angiotensin II/AT1-axis, as possibly endogenously produced peptide hormone is inactivated by high levels of ACE2. Although angiotensin II did not significantly increase IL-6 mRNA expression in MIO-M1 cells cultured under hyperglycemic conditions, more IL-6 was secreted under these conditions. Higher expression of AT1 mRNA could lead to stronger activation of the pro-inflammatory angiotensin II/AT1 signaling cascade, similar to that observed for angiotensin II-activated retinal microglia, which express higher quantities of various pro-inflammatory cytokines and chemokines including IL-6, a process that is mediated by AT1 (41). Elevated permeability of retinal endothelial cells due to IL-6-mediated trans-signaling in vitro likely contributes to the breakdown of the inner blood-retina barrier in vivo (42). The protective ACE2/angiotensin (1-7)/MAS1 signaling cascade is upregulated during acute and chronic diseases of the heart or kidney to counteract detrimental processes (43,44). This signaling cascade seems to also be activated by Mueller cells exposed to hyperglycemia and angiotensin II, as expression of ACE2 and MAS1 RNA was elevated. Thus, the concentrations of the vasodilator angiotensin (1-7) formed by protease ACE2 may be higher, and through its interaction with receptor MAS1, anti-angiogenic and anti-inflammatory processes can be induced (24-27). However, as MIO-M1 cells secreted increased quantities of IL-6 when exposed to hyperglycemia and angiotensin II, the pro-inflammatory axis seems to exceed the anti-inflammatory response. A similar inflammatory response to angiotensin II was also observed after additional exposure of the cells to hyperglycemia plus hypoxia, as the expression of IL-6 mRNA was substantially upregulated. However, the observed lower expression of ACE mRNA is in line with an assumed capacity of MIO-M1 cells to counteract angiotensin II-induced pro-inflammatory signaling. In vivo, Mueller cells, retinal endothelial cells, and retinal pericytes form the so-called neurovascular unit, which tightly regulates vascular homeostasis in the retina (45). Whether the cellular interactions change their individual responses to angiotensin II could not be evaluated in the present study. However, inhibitors of ACE or AT1 were found to, at least in part, improve the outcomes of DME in diabetic patients, which supports the findings of the present study that Mueller cells likely contribute to angiotensin II-mediated inflammatory responses present in the early development of this disease (46,47). In contrast, the impact of Mueller cells on angiotensin II-mediated inflammatory responses observed in the early development of RVO is likely low when hypoxia plays a dominant role accompanied by induction of expression and secretion of the angiogenic and permeability-inducing growth factor VEGF-A (9). In conclusion, the results of the present in vitro study provide evidence that the responses of Mueller cells to activation of the RAAS by angiotensin II depend on the environment: A pro-inflammatory response is observed under hyperglycemic (plus hypoxic) conditions, whereas changes induced by hypoxia are not modulated by angiotensin II. Source paper: PMC10442740	2025-09-22T16:13:48.125477Z
10	1	2025-09-18T09:14:33.603847Z	10	[{"start":1969,"end":1979,"text":"HeLa cells","labels":["CellLine"]},{"start":2255,"end":2265,"text":"HeLa cells","labels":["CellLine"]},{"start":2344,"end":2348,"text":"HeLa","labels":["CellLine"]},{"start":2495,"end":2505,"text":"HeLa cells","labels":["CellLine"]},{"start":2582,"end":2591,"text":"HeLa cell","labels":["CellLine"]},{"start":2658,"end":2668,"text":"HeLa cells","labels":["CellLine"]},{"start":2721,"end":2731,"text":"HeLa cells","labels":["CellLine"]},{"start":2898,"end":2908,"text":"HeLa cells","labels":["CellLine"]},{"start":3024,"end":3034,"text":"HeLa cells","labels":["CellLine"]},{"start":6043,"end":6053,"text":"HeLa cells","labels":["CellLine"]},{"start":6387,"end":6397,"text":"HeLa cells","labels":["CellLine"]},{"start":6789,"end":6799,"text":"HeLa cells","labels":["CellLine"]},{"start":8667,"end":8671,"text":"HeLa","labels":["CellLine"]},{"start":9278,"end":9282,"text":"HeLa","labels":["CellLine"]},{"start":9935,"end":9945,"text":"HeLa cells","labels":["CellLine"]},{"start":10139,"end":10149,"text":"HeLa cells","labels":["CellLine"]},{"start":11306,"end":11316,"text":"HeLa cells","labels":["CellLine"]},{"start":11576,"end":11585,"text":"HeLa cell","labels":["CellLine"]},{"start":11722,"end":11732,"text":"HeLa cells","labels":["CellLine"]},{"start":11847,"end":11856,"text":"HeLa cell","labels":["CellLine"]},{"start":12127,"end":12131,"text":"HeLa","labels":["CellLine"]},{"start":14531,"end":14541,"text":"HeLa cells","labels":["CellLine"]},{"start":14959,"end":14963,"text":"HeLa","labels":["CellLine"]},{"start":15710,"end":15720,"text":"HeLa cells","labels":["CellLine"]},{"start":15787,"end":15797,"text":"HeLa cells","labels":["CellLine"]},{"start":15863,"end":15873,"text":"HeLa cells","labels":["CellLine"]},{"start":10449,"end":10453,"text":"MCF7","labels":["CellLine"]},{"start":10818,"end":10824,"text":"Jurkat","labels":["CellLine"]},{"start":10785,"end":10811,"text":"MOLT4 human leukemia cells","labels":["CellLine"]},{"start":10952,"end":10976,"text":"HT-29 colon cancer cells","labels":["CellLine"]},{"start":12610,"end":12615,"text":"roots","labels":["Tissue"]},{"start":10877,"end":10912,"text":"diffuse large B-cell lymphoma cells","labels":["CellType"]},{"start":6920,"end":6962,"text":"H2452 malignant pleural mesothelioma cells","labels":["CellLine"]},{"start":7173,"end":7185,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":7467,"end":7479,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":7938,"end":7950,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":9614,"end":9629,"text":"apoptotic cells","labels":["VagueCellCategory"]},{"start":9689,"end":9694,"text":"cells","labels":["VagueCellCategory"]},{"start":10552,"end":10566,"text":"neuro-2a cells","labels":["CellLine"]},{"start":10676,"end":10709,"text":"HT1376 human bladder cancer cells","labels":["CellLine"]},{"start":11010,"end":11019,"text":"AGS cells","labels":["CellLine"]},{"start":12046,"end":12056,"text":"dead cells","labels":["VagueCellCategory"]},{"start":11076,"end":11110,"text":"RBE human cholangiocarcinoma cells","labels":["CellLine"]},{"start":13061,"end":13077,"text":"skeletal systems","labels":["Tissue"]},{"start":10472,"end":10476,"text":"HCE7","labels":["CellLine"]},{"start":10508,"end":10541,"text":"HL60 promyelocytic leukemia cells","labels":["CellLine"]},{"start":10580,"end":10585,"text":"C1300","labels":["CellLine"]},{"start":10668,"end":10671,"text":"T24","labels":["CellLine"]},{"start":10829,"end":10866,"text":"Hut-78 T-cell leukemia\/lymphoma cells","labels":["CellLine"]},{"start":11062,"end":11071,"text":"HCCC-9810","labels":["CellLine"]},{"start":13019,"end":13035,"text":"gastrointestinal","labels":["Tissue"]},{"start":13037,"end":13042,"text":"renal","labels":["Tissue"]},{"start":13044,"end":13055,"text":"respiratory","labels":["Tissue"]},{"start":25,"end":37,"text":"Conifer bark","labels":["Tissue"]},{"start":265,"end":277,"text":"conifer bark","labels":["Tissue"]},{"start":830,"end":834,"text":"bark","labels":["Tissue"]},{"start":1609,"end":1621,"text":"conifer bark","labels":["Tissue"]},{"start":1872,"end":1884,"text":"conifer bark","labels":["Tissue"]},{"start":10426,"end":10431,"text":"SW480","labels":["CellLine"]}]	5727.084	2. results 3. discussion Conifer bark, the medicinal use of which dates back more than 2000 years, contains bioactive alkaloids, flavonoids, lignans, phenolic acids, proanthocyanidins, stilbenes, and terpenoids that contribute to its therapeutic potential. Various conifer bark extracts are used nowadays in the nutraceutical, food, and pharmaceutical industries because of their health-promoting effects in numerous ailments and diseases [27,28,29]. Such extracts are Pycnogenol®, Flavangenol®, and Oligopin® derived from Pinus pinaster Ait. bark [29], Enzogenol® produced from Pinus radiata D. Don bark [30], and Abigenol® originating from Abies alba Mill. bark [31]. Pinus cembra L. (Pinaceae, Swiss stone pine, Arolla pine, cembran pine, cedar pine) is a coniferous tree growing in the Alps and Carpathian Mountains [32]. The bark has been scarcely investigated for its chemical composition and biological activity. We have previously assessed the antioxidant potential of the raw bark extract (80% methanolic bark extract) and found EC50 values of 71.1 ± 0.5 and 26.0 ± 0.3 μg/mL in the DPPH radical scavenging and reducing power assays, respectively. The antioxidant potential of the raw bark extract is strongly associated with the total phenolic content, quantified as 299.3 ± 1.4 mg/g. In the same assays, catechin was more effective (EC50 = 5.56 ± 0.05 and 3.70 ± 0.03 μg/mL, respectively) [26]. Other polar conifer bark extracts (80% methanolic, aqueous) scavenged the DPPH radical with EC50 values ranging from 6.46 ± 0.36 to 100.1 ± 0.1 μg/mL [29,33,34,35,36]. In the reducing power assay, polar conifer bark extracts exhibited EC50 values ranging from 9.17 ± 0.13 to 25.32 ± 0.62 μg/mL [29,35,37]. Overall, the EC50 values of the raw extract of cembran pine bark in the DPPH and reducing power assays fall within the range of values reported for other polar conifer bark extracts. The cytotoxic potential of the raw bark extract was further investigated. HeLa cells were used for this purpose as they have advantages over other tumor cell lines, for example, high adaptive capacity and proliferation rate [38]. The study revealed moderate or weak cytotoxicity. At 25 and 50 μg/mL, the extract had an insignificant impact on the viability of HeLa cells, lacked apoptosis-inducing effects, and induced slight increases (≤5%) in the HeLa cell percentages in the S, G2/M, and sub-G1 phases in comparison with the control. Other polar conifer bark extracts exhibited higher activity on HeLa cells. The aqueous extract of Pinus massoniana Lamb. bark significantly inhibited HeLa cell viability and caused a substantial increase in the proportions of HeLa cells in the sub-G1 and G2/M phases [39]. Accumulation of HeLa cells in the sub-G1 phase is considered an indicator of a pro-apoptotic effect [39,40]. In other studies, the extract significantly inhibited the migration and invasion of HeLa cells, respectively, the latter effect being attributed to cathepsin B down-regulation [41,42]. Pro-apoptotic effects in HeLa cells were also reported for the 80% methanolic extract of Pinus sylvestris L. bark [43], ethanolic extract of Pinus merkusii Jung. & de Vriese bark [44], and procyanidin-rich extract of Pinus koraiensis Siebold & Zucc. bark [45]. The pro-apoptotic effects of conifer bark extracts were found to be mediated by activation of caspase-9 and -3, up-regulation of the pro-apoptotic protein Bax, and down-regulation of the anti-apoptotic protein Bcl-2 and survivin [39,44,45]. Purification of the cembran pine raw bark extract resulted in the isolation of two stilbene glycosides, namely resveratroloside (1) and pinostilbenoside (2), the structures of which were confirmed through spectroscopic techniques. To the best of our knowledge, this is the first report on the presence of resveratroloside (1) and pinostilbenoside (2) in Pinus cembra L. bark. Both compounds were previously isolated from other conifer barks: resveratroloside from Pinus sibirica R. Mayr bark [46] and pinostilbenoside from Pinus sibirica R. Mayr bark [46] and Pinus koraiensis Siebold & Zucc. bark [18]. The evaluation of the antioxidant potential of resveratroloside (1) and pinostilbenoside (2) demonstrated weaker effects than the raw bark extract, indicating a potential synergistic interaction among the components of the extract. Previous studies have reported similar synergistic interactions in pine bark extracts. Pycnogenol, a standardized extract obtained from the bark of French maritime pine (Pinus maritima Lam.), exhibits stronger biological effects than its components when tested individually [27]. In contrast to our findings, Dar et al. (2016) reported a strong antioxidant potential for resveratroloside in the DPPH assay (IC50 = 14.0 μg/mL) [47]. The explanation lies in the fact that Dar et al. [47] used another experimental protocol. Resveratroloside (1) exhibited higher activity than pinostilbenoside (2) in both assays. The findings align with previous studies reporting a higher antioxidant capacity (evaluated as oxygen radical absorbance capacity, ORAC) for resveratroloside (4.01 ± 0.71 Trolox equivalents/μM) than pinostilbenoside (1.89 ± 0.25 Trolox equivalents/μM). In the same assay, the aglycones, resveratrol and pinostilbene, were more active than the corresponding glycosides, showing ORAC values of 5.26 ± 0.26 and 5.01 ± 0.27 Trolox equivalents/μM, respectively [48]. Glycosylation and methylation negatively impact the antioxidant capacity of stilbenes by blocking the free phenolic hydroxyl groups responsible for the antioxidant activity [49]. On the other hand, glycosylation and methylation of the stilbene hydroxyl groups might enhance other bioactivities such as tyrosinase inhibitory activity [48] and anticancer activity, respectively [3]. Glycosylation enhances the stability of stilbenes, while methylation increases their lipophilicity, leading to improved bioavailability [3]. In this study, resveratroloside (1) and pinostilbenoside (2) demonstrated promising cytotoxic activity against HeLa cells. The activity was evaluated after 48 h of incubation with 25 or 50 μg/mL of each compound (equivalent to 64 or 128 μM of resveratroloside (1) and 62 or 124 μM of pinostilbenoside (2)). The selection of the concentrations to be tested and incubation time was based on previous studies investigating the cytotoxicity of resveratrol in HeLa cells [50,51,52]. In addition, this study revealed pronounced cytotoxicity (less than 30% cell viability) for both compounds at 100 μg/mL. This served as additional support for selecting lower doses (25 and 50 μg/mL) in cell-based assays. To the best of current knowledge, this is the first study evaluating the effects of resveratroloside (1) and pinostilbenoside (2) on human cervical carcinoma HeLa cells. Resveratroloside (1) has been scarcely investigated for its antitumor potential. Only its antiproliferative effects on H2452 malignant pleural mesothelioma cells (approximately 30% inhibition at 200 μM) were reported so far [53]. To the best of our knowledge, the antitumor potential of pinostilbenoside (2) has not been investigated before. Cytotoxic therapies eliminate cancer cells by triggering various pathways of cell death. Induction of apoptosis (programmed cell death) has been a primary objective in cancer therapy for more than 30 years [54]. In recent years, many drugs, including natural compounds, have been reported to trigger other types of death in cancer cells such as autophagy, ferroptosis, necroptosis, pyroptosis, paraptosis, lysosome-dependent cell death, oncosis, and necrosis [55,56]. Resveratroloside (1) and pinostilbenoside (2) are not the sole stilbenes that cause tumor cell death by triggering non-apoptotic mechanisms. Resveratrol was reported to induce tumor cell death by apoptosis, autophagy, necroptosis, and necrosis [56,57]. Pterostilbene was found to activate apoptosis, autophagy, and necrosis in cancer cells, apoptosis being the major mechanism involved in cancer cell death [58]. Combrestatins (diaryl stilbenoids) are effective promoters of tumor necrosis [59]. Dysregulation of the cell cycle, a process involving cell growth, DNA replication, and cell division, is a hallmark of cancer. An important strategy in cancer therapy is the induction of cell cycle arrest. Flavopiridol, abemaciclib, and palbociclib are a few examples of antitumor drugs that suppress the cell cycle via inhibition of enzymes/proteins (cyclin-dependent kinases/cyclins) responsible for driving the progression of the cell cycle from one phase to the next one [60]. According to this study, resveratroloside (1) and pinostilbenoside (2) impeded HeLa cell proliferation, with pinostilbenoside (2) being more active than resveratroloside (1) at 25 μg/mL. This result aligns with previous studies showing that stilbenes inhibit the proliferation of tumor cell lines. Resveratrol [57], piceatannol [10], pterostilbene [61,62,63], and polydatin (piceid) [64] were reported to suppress the proliferation of various cancer cell lines (lung, prostate, breast, colorectal, liver, pancreatic, cervical, ovarian, bladder, leukemia, multiple myeloma, bone, oral, esophageal, head and neck). Regarding the impact of resveratroloside (1) and pinostilbenoside (2) on the HeLa cell cycle, both compounds induced a significant dose-dependent increase in the sub-G1 phase population. In addition, both compounds (25 μg/mL) induced cell cycle arrest at the S phase, indicating a blockage of DNA replication [60]. As mentioned earlier, the sub-G1 population is a hallmark of apoptosis [39,40]. In fact, not only apoptotic cells accumulate in the sub-G1 phase, but this phase consists of cells showing DNA fragmentation, a process observed in both apoptosis and necrosis [65,66,67]. When exploring a potential pro-apoptotic effect, only resveratroloside (1) showed activity (approximately 11% increase in the early and late apoptotic HeLa cells following 48 h treatment with resveratroloside (1) at 50 μg/mL). The results of this study indicate that resveratroloside (1) and pinostilbenoside (2) impact the viability and proliferation of HeLa cells by triggering mainly non-apoptotic (highly likely necrotic) cell death, as well as S-phase cell cycle arrest. Similar results have been reported for other stilbene derivatives. Resveratrol was found to induce apoptosis and block cell cycle progression in the S phase in human SW480 colon carcinoma, MCF7 breast carcinoma, HCE7 esophageal squamous carcinoma, HL60 promyelocytic leukemia cells [68], and neuro-2a cells derived from C1300 murine neuroblastoma [69]. Piceatannol caused apoptosis and G0/G1 phase arrest in T24 and HT1376 human bladder cancer cells [70]. Pterostilbene was reported to induce apoptosis and S-phase arrest in MOLT4 human leukemia cells [71], Jurkat and Hut-78 T-cell leukemia/lymphoma cells [72], and diffuse large B-cell lymphoma cells [73], apoptosis and G1 phase arrest in HT-29 colon cancer cells [58] and human gastric carcinoma AGS cells [74], and autophagy and S phase arrest in HCCC-9810 and RBE human cholangiocarcinoma cells [62]. To conclude the cytotoxicity assays, resveratroloside (1) and pinostilbenoside (2) reduced viability (mostly via non-apoptotic routes) and proliferation (via sub-G1- and S-phase arrest) in HeLa cells. The results are consistent with previous studies on the antitumor potential of stilbenes. Resveratrol, the basic scaffold of resveratroloside (1) and pinostilbenoside (2), was reported to promote cell cycle arrest at the S phase, apoptosis, and autophagy in HeLa cells [52]. Polydatin, a glycoside of resveratrol, namely resveratrol-3-O-β-mono-D-glucoside, reduced proliferation and induced apoptosis in HeLa cells [64]. In this study, resveratroloside (1) and pinostilbenoside (2) exhibited comparable activity in arresting the HeLa cell cycle at the S and sub-G1 phases (at 25 and 50 μg/mL, respectively). On the other hand, pinostilbenoside (2) exhibited higher activity than resveratroloside (1) in increasing the number of dead cells through non-apoptotic mechanisms (at 25 and 50 μg/mL) and in reducing HeLa cell proliferation (at 25 μg/mL). The latter findings are consistent with earlier studies reporting increased cytotoxic activity for the methoxylated analogs of resveratrol compared to resveratrol itself [75]. The two compounds (1 and 2) isolated in this study are stilbene glycosides. Glycosylation is known to positively impact the water solubility, intestinal absorption, and bioactivity of stilbenes [76,77]. A notable example is polydatin, one of the main compounds in the roots of Polygonum cuspidatum Sieb. et Zucc., identified in other plant species across the Liliaceae, Fabaceae, and Vitaceae families. Based on its anti-inflammatory, antioxidant, and apoptosis-modulating potential, polydatin displays diverse biological activities (anticancer, antidiabetic, antimicrobial, cardioprotective, hepatoprotective, and neuroprotective effects, as well as protective effects on the gastrointestinal, renal, respiratory, and skeletal systems). A large number of studies conducted on polydatin has revealed versatility in modulating numerous targets related to oxidative stress (Nrf2 and Akt pathways, glutathione, catalase (CAT), SOD, GPx, GST, MPO), inflammation (NF-κB, phospholipase A2 (PLA2), COX-2, iNOS, TNF-α, IL-1β, IL-6, ICAM-1, MAPKs, ERK1/2, JNK1/2), and apoptosis (p53/MAPK/JNK and PI3K/Akt/mTOR pathways, B-cell lymphoma 2 (Bcl-2), Bcl-2-associated x (Bax), D-cyclins, caspase-3, cytochrome c). Clinical trials support the benefits of polydatin in chronic pelvic pain, liver diseases, inflammatory bowel syndrome, and EGFR-tyrosine kinase inhibitor (TKI)-related ashes. Moreover, various drug delivery systems (liposomes, micelles, nanoparticles, polymeric nanocapsules) have been developed to improve the bioavailability, biocompatibility, and efficacy of polydatin [64,78]. The results of the present study, along with the remarkable biological potential of the stilbene scaffold and the broad bioactivity of polydatin, a resveratrol glycoside, indicate that the bioactive properties of resveratroloside (1) and pinostilbenoside (2) require further in-depth investigation. Future studies should explore the ability of resveratroloside (1) and pinostilbenoside (2) to modulate cellular signaling pathways, enzymes, and other molecules involved in the antioxidant defense and oxidative damage repair. Research on the antitumor potential (mechanisms underlying cytotoxic activity in HeLa cells, cytotoxicity against other tumor cell lines) should also continue. Exploration of additional bioactivities and development of appropriate delivery systems are crucial for the therapeutic valorization of resveratroloside (1) and pinostilbenoside (2). 4. materials and methods 4.7. statistical analysis Antioxidant assays were performed in triplicate, and the results were expressed as mean ± standard deviation (SD). HeLa cell-based assays were performed in triplicate; the results were expressed as mean ± standard error (SE). The differences between the results were tested using one-way ANOVA with Tukey’s HSD test (SPSS version 18.0); p ***5. Conclusions*** In this study, resveratroloside (1) and pinostilbenoside (2) were first isolated from Pinus cembra L. bark. This is the first report of these compounds in this species. Their structures were confirmed by 1H-NMR, 13C-NMR, and HRESIMS. Compared to the raw bark extract, resveratroloside (1) and pinostilbenoside (2) showed lower activity as free radical scavengers and reducing agents. However, they were more effective in reducing the viability and suppressing the proliferation of human cervical carcinoma HeLa cells. At 25 µg/mL, both compounds induced S-phase cell cycle arrest in HeLa cells. At 25 and 50 µg/mL, they significantly reduced the viability of HeLa cells, mainly through non-apoptotic mechanisms. Glycosylated stilbene scaffolds have great potential for therapeutic applications, so further studies are needed to assess the bioactive potential of resveratroloside (1) and pinostilbenoside (2). Source paper: PMC12115102	2025-09-22T18:17:46.605439Z
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glia","labels":["CellType"]},{"start":2804,"end":2815,"text":"Müller glia","labels":["CellType"]},{"start":3008,"end":3019,"text":"Müller glia","labels":["CellType"]},{"start":5547,"end":5557,"text":"astrocytes","labels":["CellType"]},{"start":5006,"end":5016,"text":"astrocytes","labels":["CellType"]},{"start":3840,"end":3851,"text":"MIO-M1cells","labels":["CellLine"]},{"start":7027,"end":7038,"text":"MIO-M1cells","labels":["CellLine"]}]	3388.618	2. results 3. discussion Müller glial cells play fundamental roles in retinal tissue functions. Therefore, studies into their biology and functions may contribute to understanding the causes of retinal pathologies and to develop strategies to alleviate their outcomes. The aim of this study was to investigate the impact of high glucose and glucose fluctuations on critical cellular processes, such as gliosis and reprogramming in Müller cells. These processes are of pivotal importance in the onset and progression of retinal neurodegeneration and diabetic retinopathy. In the present study, we observed a significant increase in GFAP expression in NG MIO-M1 cells exposed to sustained high-glucose and GF treatments, indicating a glucose stress-induced gliotic response. This response was associated with other markers of reactive gliosis, such as the morphological changes associated with the transition from a bipolar to a radial morphology and the overexpression/reorganization of vimentin intermediate filament. These findings indicate that NG MIO-M1 cells are sensitive to glucose changes in their surrounding environment, potentially functioning as a protective or reactive mechanism in response to metabolic stress. The remarkable sensitivity to glucose stress and the prompt reactive gliosis observed in MIO-M1 cells were found to be similar to those observed in Müller cells of diabetic subjects [8]. This finding confirms the suitability of MIO-M1 cells as an in vitro model to study Müller cell response. In contrast, HG MIO-M1 cells maintain a basal high level of both GFAP and Vimentin together to less organized intermediate filaments across the different glucose treatments, suggesting a limited and static response to sustained high-glucose and GF treatments. These findings highlight that MIO-M1 cells in chronic hyperglycemic conditions may adapt or develop enhanced tolerance over time due to constant and prolonged exposure to high glucose levels. The adaptation or desensitization of HG MIO-M1 cells to sustained high-glucose and GFs may indicate a saturation of the gliotic response, which is characteristic of prolonged hyperglycemic states, as observed in uncontrolled diabetes [34]. This adaptation or desensitization may result in a reduction in the cellular capacity to respond to additional metabolic challenges or stress. To understand the clinical consequences of Müller cell gliosis it is essential to also consider the reprogramming process, which may include dedifferentiation of Müller cells and regeneration of various retinal neurons, contributing to retinal tissues repair under certain conditions. In species such as birds [35], zebrafish [36,37], and rodents [38], Müller glia contribute as the primary source of retinal regeneration. When the retina is damaged, Müller glia can dedifferentiate into Müller glia-derived progenitor cells (MGPCs), acquiring a progenitor-like phenotype and starting to proliferate, thus contributing to retinal repair [39,40]. Notably, Müller glia in lower vertebrates exhibit a remarkable ability to regenerate retinal neurons, contrasting with the limited regenerative capacity seen in mammals, including humans [40], where Müller cells typically respond to injury with reactive gliosis, leading to scarring rather than regeneration [11,20]. The SHH signaling pathway is essential for the proper development of all vertebrate retinas [41,42,43,44,45,46,47], and several studies have demonstrated the involvement of SHH signaling in the proliferation and differentiation of MGPCs, contributing to retinal regeneration in lower vertebrate [18,19,31,32,34]. However, in mammals, the SHH pathway alone may not be sufficient to overcome the intrinsic limitations of Müller cells to regenerate neuronal cells. In our study, we observed an increase in SHH expression in NG MIO-M1cells exposed to sustained high-glucose and GF treatments. This suggests that these cells are engaging in pathways associated with cellular dedifferentiation and potential reprogramming towards a progenitor state. The increased SHH protein expression in NG cells was accompanied by significant changes in the intracellular localization of SHH, exhibiting a more spotted/punctate pattern in the cytoplasm and in close proximity to the plasma membrane. These findings suggest an enhanced state of cellular activity with increased synthesis and potential accumulation of SHH, aligning with the findings in lower vertebrates and rodents, where increased SHH signaling plays a role in Müller cell reprogramming and retinal regeneration [18,48,49]. Furthermore, significantly higher levels of SHH were observed in HG MIO-M1 cells compared to NG MIO-M1 cells. Conversely, HG MIO-M1 cells exposed to GF treatments showed a decrease in SHH protein expression levels. A trend toward a decrease was also observed in response to sustained high-glucose. This finding is consistent with a previous study in which the authors observed a decrease in SHH signaling in reactive astrocytes of the cerebral cortex after acute, focal injury, particularly in cells proximal to the lesion site [50]. This highlights that the negative regulation of SHH activity in astrocytes is context-dependent and varies with the degree of cellular damage. In our study, the decreased SHH levels observed in HG MIO-M1 cells exposed to GFs indicate that the severity of metabolic stress may influence SHH signaling pathways through a similar mechanism. These observations underscore the notion that glial cells, including Müller cells and astrocytes, exhibit differential SHH responses depending on the extent of glucose stress, highlighting the dynamic regulation of SHH signaling in glial cells. Although the SHH level has decreased, the cells that express it maintain a dot-like distribution of SHH inside the cells. Our findings suggest that, under NG condition, exposure to sustained high-glucose and GFs stimulates SHH expression, potentially promoting the dedifferentiation and reprogramming of Müller cells. However, in HG-adapted cells, additional metabolic stress from GF treatments leads to decreased SHH expression, possibly impairing regenerative capacity and enhancing gliotic responses. These observations indicate that the severity and fluctuation of glucose stress influence SHH signaling pathways, affecting the balance between neuroprotection and gliosis in the retina. A previous study demonstrated that high-glucose conditions have been associated with increased SOX2 levels in human Müller glial cells, which may support cell survival and regeneration under stress conditions [51]. In our experiments, SOX2 was significantly upregulated in HG MIO-M1 cells, compared to NG MIO-M1 cells. Furthermore, NG MIO-M1 cells have the capacity to modulate this gene when exposed to sustained high-glucose and GF treatments, exhibiting a consistent upregulation of SOX2. These findings are consistent with the observed upregulation of SHH in NG MIO-M1 cells exposed to different glucose treatments. Unlike NG MIO-M1cells, HG MIO-M1 cells exposed to the same glucose treatments do not show variations in SOX2 expression. This different behavior of HG MIO-M1 cells, with regards to SOX2 and SHH expression, may be due to the different roles and regulatory mechanisms in the glial cells of these two genes. SOX2 is a transcription factor crucial for maintaining stemness and promoting cell progenitor proliferation, and its upregulation could be a response to cellular stress in order to maintain or enhance regenerative capacity. In contrast, SHH signaling, which is involved in cell differentiation and tissue patterning, may be more sensitive to metabolic perturbations with a more dynamic regulation, which can lead to its downregulation under glucose fluctuations treatments. However, despite the observed upregulation of SHH and SOX2 in NG MIO-M1 cells, the expression of rhodopsin, a marker of mature neuronal cells, was not detected. This indicates that, although the cells possess the potential to differentiate into neuronal cells, the conditions employed in our protocol were not sufficient to fully induce this differentiation. The lack of mature neuronal marker expression is likely due to the timing and duration of the experimental conditions. It may therefore be worthwhile to optimize the protocol in order to more accurately reflect the diabetic conditions in humans, with a view to promoting full neuronal differentiation. Given the role of SHH in Müller cells reprogramming and the observed upregulation under sustained high-glucose and GF treatments, future experiments could investigate the effect of exogenous SHH treatment on human Müller cells. This approach would provide insights into the therapeutic potential of modulating SHH signaling to mitigate the adverse effects of reactive gliosis and enhance regenerative processes in the diabetic retina. Although the current study provides important insights into the gliotic and reprogramming potential of Müller cells under different glucose treatments, some limitations should be considered. The study employed the MIO-M1 cell line, which, although well established in human retinal research, may not fully capture the physiological complexity of Müller cells found in human retina. Future research could benefit from using primary Müller cells to better reflect the in vivo context of human diabetic retinopathy. In addition, the effects of other stressors, such as oxidative stress or hypoxia, which also play a role in diabetic retinal damage, were not included in our model. Inclusion of these factors could provide a more comprehensive understanding of the cellular mechanisms involved. Despite these limitations, the current study contributes important insights into the gliotic and reprogramming potential of Müller cells under varying glucose conditions. 4. materials and methods 4.5. statistical analysis All the results are expressed as the mean ± SEM (standard error of the mean) of at least three independent experiments. Statistically significant differences were assessed using Prism 6.05 (GraphPad PRISM Software, Inc., La Jolla, CA, USA) with Student’s t-test for statistical comparison between groups. Differences between means were considered statistically significant when p-values were at least ***5. Conclusions*** The results showed that MIO-M1 Müller cells exhibit distinct responses to different glucose treatments, which are strongly dependent on their metabolic environment. This differential response could have implications for the onset and progression of diabetic retinopathy. The increased levels of activation and dedifferentiation markers observed in NG MIO-M1 cells in response to different glucose treatments suggest a protective response that may occur in early or well-controlled diabetes. In contrast, the lack of response observed in HG MIO-M1 cells exposed to different glucose treatments may reflect an exhausted gliotic and reprogramming capacity, which could contribute to the development and progression of diabetic complications such as retinal neurodegeneration and diabetic retinopathy observed in uncontrolled diabetic patients exposed to prolonged metabolic glucose stress. Although our results provide valuable insights, further in-depth studies are needed to elucidate the mechanisms involved and to explore the potential therapeutic implications for mitigating retinal neurodegeneration associated with diabetes. Extending these studies to primary Müller cells in future research would provide a more physiologically relevant system, helping to validate and refine our findings. This approach would provide a deeper understanding of the cellular response to sustained high-glucose and glucose fluctuations and could improve the development of targeted therapeutic strategies for retinal pathologies in human patients. Source paper: PMC11641291	2025-09-23T10:47:32.994118Z
12	1	2025-09-18T09:14:33.604209Z	12	[{"start":1723,"end":1730,"text":"MCF-10A","labels":["CellLine"]},{"start":2081,"end":2085,"text":"HeLa","labels":["CellLine"]},{"start":8248,"end":8259,"text":"hepatocytes","labels":["CellType"]},{"start":1699,"end":1722,"text":"breast epithelial cells","labels":["CellType"]},{"start":2526,"end":2531,"text":"HepG2","labels":["CellLine"]},{"start":2678,"end":2689,"text":"HepG2 cells","labels":["CellLine"]},{"start":3457,"end":3468,"text":"HepG2 cells","labels":["CellLine"]},{"start":5063,"end":5074,"text":"HepG2 cells","labels":["CellLine"]},{"start":7840,"end":7851,"text":"HepG2 cells","labels":["CellLine"]},{"start":8982,"end":8993,"text":"HepG2 cells","labels":["CellLine"]},{"start":9033,"end":9038,"text":"HepG2","labels":["CellLine"]},{"start":9089,"end":9100,"text":"HepG2 cells","labels":["CellLine"]},{"start":2090,"end":2100,"text":"T98G cells","labels":["CellLine"]},{"start":6099,"end":6108,"text":"L02 cells","labels":["CellLine"]},{"start":9047,"end":9050,"text":"L02","labels":["CellLine"]},{"start":6064,"end":6070,"text":"livers","labels":["Tissue"]},{"start":8300,"end":8305,"text":"liver","labels":["Tissue"]},{"start":5293,"end":5302,"text":"HCC cells","labels":["CellType"]},{"start":7554,"end":7563,"text":"HCC cells","labels":["CellType"]},{"start":7628,"end":7637,"text":"HCC cells","labels":["CellType"]},{"start":7992,"end":8001,"text":"HCC cells","labels":["CellType"]},{"start":8665,"end":8674,"text":"HCC cells","labels":["CellType"]},{"start":8850,"end":8859,"text":"HCC cells","labels":["CellType"]},{"start":1779,"end":1798,"text":"breast cancer cells","labels":["CellType"]},{"start":4869,"end":4888,"text":"breast cancer cells","labels":["CellType"]},{"start":1603,"end":1615,"text":"normal cells","labels":["VagueCellCategory"]},{"start":1859,"end":1903,"text":"invasive human metastatic colon cancer cells","labels":["CellType"]},{"start":1924,"end":1943,"text":"primary tumor cells","labels":["CellType"]},{"start":2186,"end":2198,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":3473,"end":3486,"text":"control cells","labels":["VagueCellCategory"]},{"start":3586,"end":3591,"text":"cells","labels":["VagueCellCategory"]},{"start":4920,"end":4932,"text":"cancer cells","labels":["VagueCellCategory"]},{"start":6207,"end":6212,"text":"cells","labels":["VagueCellCategory"]},{"start":7355,"end":7373,"text":"liver cancer cells","labels":["CellType"]},{"start":7815,"end":7820,"text":"cells","labels":["VagueCellCategory"]},{"start":8049,"end":8054,"text":"cells","labels":["VagueCellCategory"]},{"start":2494,"end":2524,"text":"hepatocellular carcinoma cells","labels":["CellType"]},{"start":1800,"end":1805,"text":"MCF-7","labels":["CellLine"]},{"start":4890,"end":4895,"text":"MCF-7","labels":["CellLine"]},{"start":5173,"end":5178,"text":"MCF-7","labels":["CellLine"]},{"start":5183,"end":5212,"text":"invasive human metastatic CRC","labels":["Tissue"]}]	1530.254	Introduction Hepatocellular carcinoma (HCC) is an exceedingly fatal malignancies, with mortalities that approximate the incidence rates worldwide [1]. HCC is usually diagnosed at advanced stages owing to late symptom manifestations with limited therapeutic options, leading to ineffective intervention and poor prognosis [2]. An increasing number of studies have focused on the progression, pathological features, and prognosis of liver cancer [3–5]. HCC epidemiology is rapidly evolving, one of the most common causes is non-alcoholic fatty liver disease [6], further proving that lipid metabolism plays a crucial role in HCC occurrence. Therefore, the identification of novel therapeutic targets is urgently needed to improve the treatment of patients with HCC [7]. Phosphatidylethanolamine (PE), also known as cephalin, is the most abundant lipid in the cytoplasmic layer of cell membranes and is involved in cellular processes such as membrane fusion [8], autophagy and apoptosis [9–11]. For eukaryotic PE in vivo, two main synthetic pathways exist [12]: including the Kennedy pathway of CDP-ethanolamine(CDP-Etn) and mitochondrial phosphatidylserine decarboxylation pathway [13]. Phosphatidylethanolamine cytidyltransferase 2(PCYT2) is the rate-limiting enzyme of the CDP-Etn pathway. Previous studies have shown that PCYT2 is highly specific, and is present only in the rough endoplasmic reticulum of eukaryotes [14]. P-Eth is then catalyzed by PCYT2 to form CDP-Etn, leading to PE synthesis [15]. Generally, PCYT2 expression is reduced in various epithelial-derived cancer cell lines compared to normal cells [16,17]. Compared epithelial-derived cancer cell lines PCYT2 activity with that of breast epithelial cells MCF-10A showed that its PCYT2 activity was inhibited in breast cancer cells (MCF-7) [16]. PCYT2 expression was significantly reduced in invasive human metastatic colon cancer cells compared to that in primary tumor cells [17], and previous studies have shown that PCYT2 knockdown under nutrient-rich conditions significantly facilitated the proliferation of HeLa and T98G cells and promoted in vivo tumor growth. The inhibition of PCYT2 increases P-Etn levels in cancer cells and stimulates tumor growth [18]. However, in organoid models, PCYT2 knockdown inhibits cell growth [19]. Despite these findings, no evidence exists that suggests that PCYT2 expression is associated with HCC occurrence and development. This study aimed to investigate the role of PCYT2 in human hepatocellular carcinoma cells (HepG2) by inhibiting their proliferation, invasion, and migration abilities and promoting cell apoptosis. Materials and methods Cell viability analysis HepG2 cells were seeded into 96-well plates and incubated with a Cell Counting kit (CCK-8) solution for 40 min at 37°C. Absorbance was measured at 450 nm using a Spectra MAX M5 microplate spectrophotometer to detect cell viability. Invasion experiment Dilute the Matrigel with basal medium (1:8), lay it flat on a Transwell membrane, and incubate at 37°C for 4 h. The subsequent experimental steps were identical to those used for the migration assay, including cell culture, paraformaldehyde fixation and 0.1% crystal violet staining. Animal experiments Five-week-old BALB/c female nude mice were purchased from Jiangsu Jicui Pharmaceutical Biotechnology Co, passed SPF level training and assessment, and were routinely reared using standard SPF conditions. PCYT2 overexpressed HepG2 cells and control cells were subcutaneously injected into the left and right sides of the nude mice (approximately 1 × 107 cells on each side, n = 6 tumors in each group). Following 20–25 d, the tumors achieved a certain size and the micewere anesthetized with isopentane and sacrificed; the tumors were collected for follow-up evaluation. The animal experiments were approved by the Animal Ethics Committee of Anhui Medical University and conducted in accordance with the guidelines for the care and use of laboratory animals. Statistical analysis Data were analyzed using GraphPad Prism software (version 8.0), and the results were expressed as mean ± standard deviation (SD). T-test was performed to determine the significant of differences between two groups. A one-way analysis of variance was used for comparison across groups. Statistical significance was set at P ***Results*** Discussion PCYT2 is a rate-limiting enzyme in PE synthesis that is commonly used in the study of obesity-related diseases, such as non-alcoholic fatty liver disease and type 2 diabetes [12,20–22]. Recently, interest in the role of PCYT2 in cancer has been growing [18,23,24]. Previous studies show that PCYT2 has different roles in various cancers and cancer settings. For instance, in metastatic colorectal cancer (CRC), PCYT2 is significantly downregulated and functions as a tumor metastasis inhibitor [25]. In human breast cancer cells (MCF-7), the level of PCYT2 in cancer cells is elevated in response to the stressful environment [26]. Our findings show that PCYT2 expression is abnormally downregulated in HepG2 cells, which is consistent with that of previous studies where in PCYT2 expression was downregulated in MCF-7 and invasive human metastatic CRC [16,17]. Although PCYT2 regulates several human cancers [18,19,23], its role in HCC cells remains unknown. Based on the literature and our findings, PCYT2 in human cancers appears to have a consistent expression profile in human cancers, irrespective of tumor origin or location [27]. Regarding the mechanism whereby PCYT2 influences cancer development, a previous report showed that PCYT2 downregulation-induced phosphoethanolamine (PEtn) accumulation correlated with tumor growth under nutrient starvation, thereby PCYT2 overexpression reduced PEtn levels and tumor growth [18]. However, in the present, we found that CDP-Etn supplementation inhibited HCC migration, invasion and proliferation. In our previous study, we showed that the levels of BAX and cleaved caspase-3 were significantly increased whereas Bcl-2 was significantly reduced in the livers of type 2 diabetic mice and L02 cells after stimulation with high glucose and free fatty acids (HG&FFA)(12). CDP-Etn (100 μM) protected cells from HG&FFA-induced apoptosis by reducing BAX and cleaved caspase-3 levels as well as and increasing Bcl-2 levels [12]. Whether PCYT2 downregulation induced PEtn accumulation contributes to HCC development further study. Increasing evidence suggests that PCYT2 is aberrantly expressed in various models of liver disease and may predict clinical outcomes in patients. PCYT2 is instrumental in the deregulation of these processes leading to the development of obesity, insulin resistance, liver steatosis and dyslipidemia [28]. CDP-Etn supplementation has been reported to alleviate PCYT2 deficiency engendering age-dependent and insulin-resistant non-alcoholic steatohepatitis to improve patient prognosis [20,29–31]. Chronic administration of peroxisome proliferators can increase the content of hepatic PC and PE for hepatomegaly and proliferation as well as cause liver cancer in rodents [32,33]. Based on the current studies, we hypothesized that PCYT2 may be involved in the regulation of cellular processes in HCC. Therefore, we utilized in vivo and in vitro validation methods to assess the expression and mechanistic roles of PCYT2 in liver cancer cells. Herein, PCYT2 overexpression was determined to inhibit HCC cell proliferation, migration and invasion both in vitro and in vivo. And, the number and morphology of mitochondria in HCC cells overexpressing PCYT2 were significantly different from those in HCC cells without any treatment, such as a decrease in the number of mitochondria and swelling of the mitochondria. These changes suggest that PCYT2 affects the mitochondrial function of cells. Notably, when the HepG2 cells received CDP-Etn supplementation, their proliferation, migration, and invasion were inhibited in vitro. Notably, the ATP level decreased in HCC cells following the overexpression of PCYT2, and the cells were found to be accompanied by mitochondrial damage using transmission electron microscopy. However, previous reports have indicated that PCYT2 is present only in the endoplasmic reticulum of hepatocytes [14,34]. Additionally, the phenotype of liver PCYT2-/- knockout mice showed no signs of liver injury, however, they experienced massive accumulation of liver triglycerides (TAG) [35]. Therefore, we hypothesized that the influence of PCYT2 on mitochondrial function is mediated by metabolites such as TAG, DAG, and PE. However, further studies are needed to clarify whether the PCYT2 exerted inhibition of HCC cells alleviates mitochondrial damage. As understanding, the mechanism whereby PCYT2 overexpression causes mitochondrial damage will deepen our understanding of PCYT2 regulation in HCC cells. In conclusion, this study provided compelling data demonstrating the aberrant expression and functional role of PCYT2 in HepG2 cells. PCYT2 expression levels were lower in HepG2 than in L02. Furthermore, PCYT2 overexpression in HepG2 cells induced mitochondrial damage; inhibited proliferation, invasion, and migration; and promoted cell apoptosis. Source paper: PMC12118871	2025-09-23T11:20:43.939020Z