The prognosis for the most common type of pancreatic cancer, pancreatic ductal adenocarcinoma, also known as PDAC, remains poor. Only around 4% of PDAC patients are likely to live 5 years after being diagnosed. The immune system plays a part in the progression of PDAC, as increased inflammation contributes to the growth of the tumour and its ability to resist treatment.

The NF-κB proteins of the immune system are transcription factors that control when and how much certain other proteins are produced. The protein RELA is an important member of the NF-κB family. It is known to be overactivated in PDAC tumours and may be responsible for the high levels of inflammation found in this type of pancreatic cancer. A better understanding of how RELA is activated in PDAC cells will help develop medicines targeting this process.

Butera et al. combined experimental and computational approaches to model a network of interactions between key molecules in lines of human PDAC cells grown in the laboratory to investigate how RELA is controlled. Usually, when the RELA protein is activated, it is located in the cell’s nucleus, this means that tracking the location of RELA within the cell can reveal its activated form. To follow RELA, it was labelled with a fluorescent marker and visualised using live, high-throughput fluorescence microscopy.

When RELA was activated with a pro-inflammatory molecule TNFα, it changed how it moved in and out of the cell’s nucleus. Using computational modelling approaches, Butera et al. could build a statistical model that revealed that the location and activity of RELA is affected by actin, a key component of the cell’s molecular scaffolding called the cytoskeleton.

When actin was disrupted, RELA's activity was altered. Moreover, profiling targets of RELA using RNA sequencing revealed two genes that encode proteins related to the regulation of actin. This indicates that RELA activity – which is itself affected by actin organisation – can feed back to regulate actin.

Butera et al. have identified genes regulated by RELA in PDAC cells that could be used as targets for anti-cancer drug development. Further research into the feedback between RELA and actin will help untangle the complex network that causes inflammation in pancreatic cancer.

The NF-κB transcription factor RELA is an essential mediator of the inflammatory and immune responses in all mammals (Hayden et al., 2006) and is central to the canonical NF-κB signalling pathway (Ghosh et al., 1998). As a transcription factor, RELA activation is controlled in large part through its localisation. Inactive RELA is sequestered in the cytoplasm by IκB proteins and IκB degradation by upstream cues, such as the potent inflammatory cytokine tumour necrosis factor α (TNFα), enables RELA translocation to the nucleus where RELA regulates gene expression (DiDonato et al., 1997; Zandi et al., 1997).

Live imaging experiments of fluorescently labelled RELA and electrophoretic mobility shift assays have shown that RELA oscillates between the nucleus and cytoplasm in response to TNFα (Hoffmann et al., 2002; Nelson et al., 2004; Sung et al., 2009; Tay et al., 2010; Sero et al., 2015; Zambrano et al., 2016). Oscillations are driven by a negative feedback loop between RELA and particular IκB isoforms since the genes encoding IκB proteins are RELA transcriptional targets (Brown et al., 1993; Scott et al., 1993; Sun et al., 1993; Hoffmann et al., 2002). The pattern of RELA translocation has been shown to dictate the specificity and timing of RELA target gene expression, including the genes encoding IκBα, IκBε, and the chemokine RANTES (Ashall et al., 2009; Zambrano et al., 2016; Lane et al., 2017).

Previously, we showed that cell shape is a regulator of RELA dynamics in breast cancer cell lines and that breast cancer cells with mesenchymal cell shape (protrusive with low cell–cell contacts) have higher RELA nuclear translocation (Sero et al., 2015). We also identified that RELA activity, coupled to cell shape, is predictive of breast cancer progression (Sailem and Bakal, 2017). Although the mechanistic basis for how cell shape regulates RELA remains poorly understood, studies have shown that chemically inhibiting actin or tubulin dynamics can increase RELA binding to DNA and RELA-dependent gene expression (Rosette and Karin, 1995; Bourgarel-Rey et al., 2001; Németh et al., 2004).

Despite frequent upregulation of both TNFα and RELA in PDAC tumours (Weichert et al., 2007; Zhao et al., 2016), the dynamics and regulation of single-cell RELA translocation, as well as RELA transcriptional output, are poorly understood for PDAC cells. Here, we used CRISPR-CAS9 to tag RELA with GFP in the human PDAC cell lines MIA PaCa2 and PANC1 and identified rapid, non-oscillatory, and heterogeneous RELA dynamics by live imaging. To explore potential cytoskeletal or cell shape regulation of RELA in PDAC, we constructed Bayesian models using single-cell datasets from TNFα-stimulated PDAC cells with variation in RELA, actin, and tubulin measurements for hypothesis generation: one dataset with five PDAC cell lines and another with diverse small molecules targeting cytoskeletal components. Notably, nuclear RELA was statistically predicted as dependent on actin features across Bayesian models. Finally, we used RNA-seq to identify genes with distinct patterns of expression based on dependence on TNFα dose and RELA activation. Using live imaging with knockdown of selected targets, our results uncover novel mechanisms regulating inflammation-associated RELA dynamics in PDAC, including negative feedback loops between RELA and the actin modulators NUAK2 and ARHGAP31, and between RELA and the non-canonical NF-κB protein RELB.

To characterise previously unknown single-cell RELA dynamics in PDAC cells, we used CRISPR-CAS9 genome editing to tag RELA endogenously with GFP in the human PDAC cell lines MIA PaCa2 and PANC1. Using live imaging and automated image analysis, we characterised RELA responses to the cytokine TNFα, which is upregulated with PDAC progression (Zhao et al., 2016). Strikingly, PDAC cells show atypical RELA dynamics compared to reports in cells from other tissues (Ashall et al., 2009; Tay et al., 2010; Sero et al., 2015), as we observed that PDAC cells maintain prolonged nuclear RELA localisation and RELA responses are cell cycle independent. However, we identified that a key difference between MIA PaCa2 and PANC1 cells is that MIA PaCa2 cells respond with higher and sustained RELA nuclear localisation compared to PANC1 cells, while PANC1 cells have a damped RELA response over time. Similar to other cell lines (Ashall et al., 2009; Tay et al., 2010; Sero et al., 2015), we found that RELA nuclear translocation in PDAC cells occurs immediately following TNFα addition and peak nuclear RELA localisation occurs around 1 hr post-TNFα.

The distinct RELA dynamics between PDAC cells with reports from other tissues may be attributed to the use of knock-in RELA-GFP in this study since many studies used exogenous RELA fusion constructs (Ashall et al., 2009; Tay et al., 2010; Sero et al., 2015). As RELA upregulates several genes involved in positive and negative feedback (Collart et al., 1990; Libermann and Baltimore, 1990; Brown et al., 1993; Scott et al., 1993; Sun et al., 1993), which we observed in the present study using RNA-seq, RELA overexpression could interfere with its intrinsic dynamics. Nonetheless, other studies that have tagged RELA endogenously did detect oscillations, including MEFs (Sung et al., 2009; Zambrano et al., 2016) and MCF7 breast cancer cells (Stewart-Ornstein and Lahav, 2016).

Here, we identified that MIA PaCa2 and PANC1 cells in all cell cycle stages are responsive to 0.1 and 10 ng/ml TNFα, with minimal dissimilarities in RELA translocation responses between cell cycle stages in terms of mean nuclear RELA or the amplitude and timing of peak RELA. In contrast, RELA translocation responses to TNFα were identified in HeLa cells by Ankers et al., 2016 as dependent on the cell cycle phase at the time of TNFα addition in a study using double thymidine block to synchronise cells, or Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) labelling to infer cell cycle phase. HeLa cells treated with 10 ng/ml TNFα in late G1 displayed a stronger response than the population average, while S-phase cells showed a suppressed or delayed response. Furthermore, RELA was found to interact with E2F1, a transcription factor regulating the G1 to S transition, in late G1 when E2F1 levels are highest during the cell cycle (Ankers et al., 2016). Conflicting reports of whether TNFα-induced RELA translocation is cell cycle regulated may be due to cancer cell type-specific deregulation of cell cycle proteins (Cordon-Cardo, 1995; Otto and Sicinski, 2017) or RELA may not physically interact with E2F1 in PDAC cells in general or specifically in unsynchronised cells.

The high sensitivity of PDAC cells to TNFα and lack of oscillations suggest that PDAC cells may suppress negative feedback imposed by IκBα, as Hoffmann et al., 2002 demonstrated that oscillations in NF-κB DNA binding are due to negative feedback with IκBα, which is encoded by a gene (NFKBIA) upregulated by NF-κB factors. Hoffman et al. also identified that cells with high IκBβ and IκBε expression, or absence of IκBα, lose oscillations stimulated by TNFα. These findings motivated our inspection into RELA interaction with IκB proteins and upregulation of IκB genes by TNFα in PDAC cells. Using co-immunoprecipitation of RELA with MIA PaCa2 cells, we found that RELA binds to IκBα, IκBβ, and IκBε and binding to each is suppressed in high TNFα concentrations. However, contrary to our expectations based on the findings by Hoffmann et al., we did not observe RELA oscillations with NFKBIB or NFKBIE depletion in MIA PaCa2 or PANC1 cells, suggesting that the lack of RELA oscillations in these cell lines may be due to enhanced positive feedback and/or insufficient NFKBIA expression.

Co-immunoprecipitation also revealed that the NF-κB protein REL had reduced interaction with RELA with TNFα. This is in line with a recent study (Rahman et al., 2022) that endogenously fluorescently labelled RelA and c-Rel in double knockin mice and used fluorescence cross-correlation spectroscopy to probe for binding/dimerisation in primary ear fibroblasts to quantify the abundance of RelA:c-Rel heterodimers pre- and post-TNFα. Rahman et al., 2022 identified that the relative abundance of the RelA:c-Rel dimer was higher in the nucleus of resting cells then higher in the cytoplasm of TNFα-stimulated cells, despite the individual concentrations of RelA and c-Rel increasing in the nucleus with TNFα. Our combined results suggest that binding of RELA and REL is reduced with TNFα in multiple cell types and in both human and mouse models.

We used Bayesian modelling as an unbiased and high dimensional approach to determine whether descriptors of cell shape and the cytoskeleton correlate with the observed heterogeneity in RELA localisation, having previously used Bayesian modelling to show that RELA localisation in breast cells is strongly dependent on neighbour contact, cell area, and protrusiveness in the presence and absence of TNFα (Sero et al., 2015). In the present study, we extended the analysis to include measurements of actin and tubulin organisation. Since Bayesian modelling relies on heterogeneity in measurements to make predictions, we used a dataset with five PDAC cell lines with high intra- and inter-line variability in RELA, as well as distinct cell shape and cytoskeleton features between the cell lines. We independently ran Bayesian modelling with a dataset of cells with perturbation of the cytoskeleton using small molecules, based on the seminal study by Sachs et al., 2005 that used Bayesian inference to derive cell signalling networks. Consistently among our models, differences in cytoplasmic actin intensity, as well as measures of actin localisation (cortical versus cytoplasmic actin), were predictive of differences in nuclear RELA between single PDAC cells.

Interestingly, the Bayesian-inferred arrows in the RELA network generated from TNFα-treated cells (Figure 2C) sometimes point in opposite directions compared to the network from cells treated with biochemical inhibitors (Figure 2F). This arises from the inherent properties and flexibility of Bayesian networks (Scutari et al., 2018). These networks use a directed acyclic graph (DAG) to represent global probability distributions broken down into smaller local distributions, ensuring no loops or cycles. Multiple valid DAG configurations can exist, so the dependence between two variables A and B can be represented as either A→B or B→A if both are probabilistically equivalent (Heckerman et al., 1995; Scutari, 2010; Scutari et al., 2019). Thus, reversing an arc can leave the overall network structure unchanged as long as local distributions remain consistent with the data.

One route through which cytoskeletal structures may influence RELA dynamics is by inducing changes in TNF receptor turnover or conformation. In the literature, evidence exists for the modulation of nuclear RELA by actin and tubulin inhibitors (Bourgarel-Rey et al., 2001; Németh et al., 2004), in addition to TNFα/RELA regulation of the cytoskeleton (Georgouli et al., 2019; Huber et al., 2004). However, there is limited evidence for cytoskeletal regulation of TNF receptors. One example is that the actin-binding protein Filamin interacts with TNF receptor-associated factor 2 (TRAF2), which is not itself a TNF receptor but is involved in TNF receptor intracellular signal transduction (Leonardi et al., 2000). On a related topic, a recently published study (Alraies et al., 2024) subjected dendritic cells to space confinement and found upregulation of the chemokine receptor Ccr7, in a manner dependent on expression and function of IKKβ and the lipid metabolism enzyme cPLA₂. In turn, the prostaglandin E₂ receptor (PGE₂) receptor, which is in the cPLA₂ pathway, induces NF-κB nuclear translocation. This study also identifies the role of ARP2/3 activity in regulating cPLA₂ activation via nuclear envelope tensioning. Interestingly, transcriptomics of confined cPLA₂^WT and cPLA₂ ^KO dendritic cells revealed correlated expression between Ccr7, the gene encoding IKKβ (Ikbkb), and the major subunits of ARP2/3 (Actr2 and Actr3). Overall, the impact of cytoskeletal dynamics on TNF receptors remains a source for further study.

To identify potential regulatory loops involving RELA in PDAC, we used RNA-seq analysis with MIA PaCa2 and PANC1 cells treated with varying TNFα doses +/-IκB-SR. We identified 254 genes significantly regulated by TNFα, which may be viewed as candidates for targeting NF-κB signalling or output in PDAC. Identifying novel targets for PDAC is important as the 5-year survival rate of non-resected PDAC patients has remained unchanged from 1975 to 2011 (Bengtsson et al., 2020). Moreover, RELA is hyperactive in 50–70% of PDAC tumours (Wang et al., 1999; Weichert et al., 2007) and contributes to both cancer progression (Fujioka et al., 2003; Melisi et al., 2009) and resistance to chemotherapy (Bold et al., 2001; Kunnumakkara et al., 2007).

Of note, we present the first evidence that ARHGAP31 is a transcriptional target of TNFα or RELA. Moreover, ARHGAP31 is not previously associated with pancreatic cancer, but has well-studied roles in actin modulation, since ARHGAP31 is the human orthologue for the mouse protein CdGAP (mCdc42 GTPase-activating protein) and ARHGAP31 inactivates the GTPases CDC42 and RAC1 (Lamarche-Vane and Hall, 1998; Tcherkezian et al., 2006). CDC42 is involved in the formation of premigratory filopodia in PDAC cells and promotes invasiveness (Razidlo et al., 2018), while RAC1 expression is required for the development of PDAC tumours, in addition to the formation of the ADM and PanIN precursors in KRAS^G12D mouse models (Heid et al., 2011), indicating that RAC1 may play a role in actin remodelling during PDAC initiation.

Our results also demonstrate that TNFα upregulates RNA expression of the kinase-encoding gene NUAK2 (SNARK), while NUAK2 expression is abrogated in the presence of IκB-SR. Our identification of a feedback loop between RELA nuclear translocation and NUAK2 expression is analogous to prior findings that NUAK2 increases the activity of the mechanosensitive transcriptional coactivator YAP through stimulation of actin polymerisation and myosin activity (Yuan et al., 2018). A potential source for further study could consider myosin phosphatase target subunit 1 (MYPT1), a substrate for NUAK2 (Yamamoto et al., 2008) that is reported to mediate NUAK2 regulation of actin stress fibres in growing cells (Vallenius et al., 2011). From a therapeutic perspective, NUAK2 may be a promising target to follow-up for potential use in PDAC therapy as kinases contain an ATP-binding cleft that is a druggable pocket and can contain additional druggable sites distal to the ATP or substrate binding pockets conferring inhibitor specificity (Lamba and Ghosh, 2012). However, NUAK2 is a prognostic marker for PDAC and is associated with a favourable prognosis according to The Human Protein Atlas, which proposes a potential tumour suppressor role for NUAK2 in PDAC, suggesting that enhancing NUAK2 may be a favourable strategy for PDAC. Therefore, our data provide an incentive for the exploration of a RELA-NUAK2 signalling axis in PDAC progression and response to therapy.

Further information and requests for resources should be directed to and will be fulfilled by Chris Bakal (chris.bakal@icr.ac.uk).

All data generated for this study have been included as source data files. RNAseq data (FASTQ files and processed counts) are available from GEO under accession GSE268743.

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Author details

1. Francesca Butera

   Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, London, United Kingdom

   Present address

   Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   frankie.butera@mcri.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6606-4678

2. Julia E Sero

   Department of Life Sciences, University of Bath, Bath, United Kingdom

   Contribution

   Conceptualization, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0299-9212

3. Lucas G Dent

   Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, London, United Kingdom

   Contribution

   Supervision, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8573-4617

4. Chris Bakal

   Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, London, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   cbakal@icr.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0413-6744

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