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Expression kinetics from a synthetic model system for ERK signal duration HEK293 with a stably transfected ∆RAF1:ER fusion protein were treated with ER antagonist 4‐hydroxytamoxifen (4OHT, ON scenario in B). To generate pulses, ERK signalling was turned off using the MEK inhibitor U0126 (ON/OFF scenario in B). To distinguish between primary and secondary response genes, translation was blocked with cycloheximide (CYHX) in parallel to 4OHT stimulation (ON/CYHX scenario in B). In addition, we used actinomycin D (ActD) to determine mRNA half‐lives via transcriptional shutdown and 4‐thiouridine (4SU) to determine mRNA half‐lives via metabolic labelling. Log2 gene expression fold changes of significantly induced genes (FDR = 1%) across different treatment scenarios. Gene induction of immediate, delayed and late responding genes is sustained upon constant activation (ON scenario) and transient upon two‐hour pulse activation (ON/OFF scenario). Genes still significantly induced upon parallel CYHX treatment were considered primary response genes. Genes were ranked by their model‐derived response time.
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The RAF‐MEK‐ERK signalling pathway controls fundamental, often opposing cellular processes such as proliferation and apoptosis. Signal duration has been identified to play a decisive role in these cell fate decisions. However, it remains unclear how the different early and late responding gene expression modules can discriminate short and long sign...
Citations
... For instance, systems biological approaches combining quantitative experiments with dynamic modeling have shown that coherent FFL, where both inputs directly and indirectly induce gene expression networks, allow for decoding the amplitude and duration of input signals (Alon, 2007). Furthermore, signal decoding at the level of target gene expression may be related to mRNA half-life (Uhlitz et al, 2017). ...
... Considering the experimentally measured SMAD input and fitting the model parameters to describe the RNA-sequencing time courses, we could explain 64% of the 4,823 differentially expressed target genes by this simple model (Fig 3). Similarly, gene expression models with an experimentally measured TF input disentangled input decoding in other cellular networks, such as the p53 and MAPK signaling pathways (Purvis et al, 2012;Uhlitz et al, 2017), and were also used to describe the dynamics of a small set of 12 TGFβ target genes in liver cells (Lucarelli et al, 2018). Likewise, similar dynamic models were used to infer mechanisms of biological regulation from multi-OMICS time course data (Peshkin et al, 2015;Becker et al, 2018). ...
TGFβ-signaling regulates cancer progression by controlling cell division, migration, and death. These outcomes are mediated by gene expression changes, but the mechanisms of decision-making toward specific fates remain unclear. Here, we combine SMAD transcription factor imaging, genome-wide RNA sequencing, and morphological assays to quantitatively link signaling, gene expression, and fate decisions in mammary epithelial cells. Fitting genome-wide kinetic models to our time-resolved data, we find that most of the TGFβ target genes can be explained as direct targets of SMAD transcription factors, whereas the remainder show signs of complex regulation, involving delayed regulation and strong amplification at high TGFβ doses. Knockdown experiments followed by global RNA sequencing revealed transcription factors interacting with SMADs in feedforward loops to control delayed and dose-discriminating target genes, thereby reinforcing the specific epithelial-to-mesenchymal transition at high TGFβ doses. We identified early repressors, preventing premature activation, and a late activator, boosting gene expression responses for a sufficiently strong TGFβ stimulus. Taken together, we present a global view of TGFβ-dependent gene regulation and describe specificity mechanisms reinforcing cellular decision-making.
... For instance, systems biological approaches combining quantitative experiments with dynamic modelling have shown that coherent FFL, where both inputs directly and indirectly induce gene expression networks, allow for decoding the amplitude and duration of input signals (Alon, 2007). Furthermore, signal decoding at the level of target gene expression may be related to mRNA half-life (Uhlitz et al., 2017). ...
... Considering the experimentally measured SMAD input and fitting the model parameters to describe the RNA sequencing time courses, we could explain 64 % of the 4823 differentially expressed target genes by this simple model (Figure 3). Similarly, gene expression models with an experimentally measured TF input disentangled input decoding in other cellular networks such as the p53 and MAPK signaling pathways (Purvis et al., 2012;Uhlitz et al., 2017) and were also used to describe the dynamics of a small set of 12 TGFβ target genes in liver cells (Lucarelli et al., 2018). Likewise, similar dynamic models were used to infer mechanisms of biological regulation from multi-OMICS time course data (Peshkin et al., 2015;Becker et al., 2018). ...
TGFβ-signaling regulates cancer progression by controlling cell division, migration and death. These outcomes are mediated by gene expression changes, but the mechanisms of decision making towards specific fates remain unclear. Here, we combine SMAD transcription factor imaging, genome-wide RNA sequencing and morphological assays to quantitatively link signaling, gene expression and fate decisions in mammary epithelial cells. Fitting genome-wide kinetic models to our time-resolved data, we find that the majority of TGFβ target genes can be explained as direct targets of SMAD transcription factors, whereas the remainder show signs of complex regulation, involving delayed regulation and strong amplification at high TGFβ doses. Knockdown experiments followed by global RNA sequencing revealed transcription factors interacting with SMADs in feedforward loops to control delayed and dose-discriminating target genes, thereby reinforcing the specific epithelial-to-mesenchymal transition at high TGFβ doses. We identified early repressors, preventing premature activation, and a late activator, boosting gene expression responses for a sufficiently strong TGFβ stimulus. Taken together, we present a global view of TGFβ-dependent gene regulation and describe specificity mechanisms reinforcing cellular decision making.
... Many immediate-early genes are transcription factors, which then induce secondary targets. Interestingly, delayed primary genes often encode negative feedback regulators (Amit et al, 2007;Avraham & Yarden, 2011;Legewie et al, 2008), and the timing of primary response genes is strongly determined by mRNA half-lives (Uhlitz et al, 2017). While the kinetics of the transcriptome response to MAPK activation has been characterized in quantitative depth (Tullai et al, 2007;Amit et al, 2007;Uhlitz et al, 2017;Legewie et al, 2008), the wiring of how immediate-early transcription factors induce secondary response genes and the understanding of their interaction remain cryptic. ...
... Interestingly, delayed primary genes often encode negative feedback regulators (Amit et al, 2007;Avraham & Yarden, 2011;Legewie et al, 2008), and the timing of primary response genes is strongly determined by mRNA half-lives (Uhlitz et al, 2017). While the kinetics of the transcriptome response to MAPK activation has been characterized in quantitative depth (Tullai et al, 2007;Amit et al, 2007;Uhlitz et al, 2017;Legewie et al, 2008), the wiring of how immediate-early transcription factors induce secondary response genes and the understanding of their interaction remain cryptic. ...
... Thus the selected transcription factors cover different dynamic aspects of the RAF1-induced transcriptional response, which include transcripts previously characterized as the immediate-early (IEG), immediate-late (ILG), delayed-early (DEG), and secondary response genes (SRG) (Uhlitz et al, 2017). The classes and half-maximal induction times of each of the 22 candidate TFs are shown in Table 1. ...
The MAPK pathway is an important cellular signaling cascade whose dysregulation causes a variety of diseases. While the upstream regulators of this cascade have been extensively characterized, the understanding of how its activation translates into different transcriptional responses remains poorly understood. This study attempts to fill this knowledge gap by using targeted Perturb-seq against 22 transcription factors in an inducible model system for RAF-MAPK signaling. A topology-based modeling approach is applied to the obtained data to construct a directional interaction network. By removing coherent feed-forward loops and integrating the expression kinetics of transcription factors, a parsimonious network structure is derived that distinguishes direct from indirect interactions between the investigated transcription factors and their targets. In particular, EGR1 and FOS are found to act as orthogonal upstream regulators of the RAF-MAPK response. The results presented here provide valuable insights into the organization of the transcriptional network downstream of RAF-MAPK signaling and thus provide a basis for a better understanding of this complex process.
... ERK activity can stimulate cell proliferation, differentiation, metabolism, and drug resistance, by modulating the expression of target genes including CCND1 (encoding Cyclin D1), FOS (FOS), MYC (MYC), and FOSL1 (FRA-1). ERK target genes have been categorized into rapidly responding immediate-early genes (IEGs), immediate-late genes (ILGs), and delayed early genes (DEGs) based on their timing of expression following ERK activation [26,27]. Here, we discuss how ERK activity dynamics can lead to differential or selective expression of these genes, which provides a mechanism for the induction of distinct cellular processes. ...
... Additional modeling analysis has added depth to this concept, demonstrating that persistence detection depends critically on the kinetic parameters of RNA induction and degradation [26,37]. Effective persistence detection by a genewhether an IEG, ILG, or DEGrequires that mRNA and protein production be very low in the unstimulated state because any pre-existing pool of protein can be directly phosphorylated and stabilized by ERK, bypassing the feedforward requirement for ERK duration. ...
... production integrates total ERK activity over time, regardless of its duration ( Figure 1B). Thus, ERK target genes vary in their capacity for persistence detection and in their responses to ERK activity dynamics [26,37]. ...
Signaling by the extracellular signal-regulated kinase (ERK) pathway controls many cellular processes, including cell division, death, and differentiation. In this second installment of a two-part review, we address the question of how the ERK pathway exerts distinct and context-specific effects on multiple processes. We discuss how the dynamics of ERK activity induce selective changes in gene expression programs, with insights from both experiments and computational models. With a focus on single-cell biosensor-based studies, we summarize four major functional modes for ERK signaling in tissues: adjusting the size of cell populations, gradient-based patterning, wave propagation of morphological changes, and diversification of cellular gene expression states. These modes of operation are disrupted in cancer and other related diseases and represent potential targets for therapeutic intervention. By understanding the dynamic mechanisms involved in ERK signaling, there is potential for pharmacological strategies that not only simply inhibit ERK, but also restore functional activity patterns and improve disease outcomes.
... As the key activators of MAPK pathway, ERK1/2 are connected to induction of hundreds of gene targets, including cFos, early growth response proteins 1/2 (EGR1/2), and dual-specificity phosphatases 1/6 (DUSP1/6). 50 Quantitative reverse-transcription PCR (qRT-PCR), following pretreatment of HepG2 cells with DMSO, CMA, or PalmB and stimulation with EGF (0, 30, and 60 min), revealed inhibitor-dependent changes in the induction pattern of these genes ( Figures 4D and S4E). In the case of the immediate-early response genes cFos and EGR1/2, treatment with CMA and PalmB limited the rapid mRNA transcription typically observed after 30 min of EGF treatment compared with the DMSO control. ...
Extracellular signal-regulated kinases (ERK1/2) are key effector proteins of the mitogen-activated protein kinase pathway, choreographing essential processes of cellular physiology. Here, we discover that ERK1/2 are subject to S-acylation, a reversible lipid modification of cysteine residues, at C271/C254. The levels of ERK1/2 S-acylation are modulated by epidermal growth factor (EGF) signaling, mirroring its phosphorylation dynamics, and acylation-deficient ERK2 displays altered phosphorylation patterns. We show that ERK1/2 S-acylation is mediated by “writer” protein acyl transferases (PATs) and “eraser” acyl protein thioesterases (APTs) and that chemical inhibition of either lipid addition or removal alters ERK1/2’s EGF-triggered transcriptional program. Finally, in a mouse model of metabolic syndrome, we find that ERK1/2 lipidation levels correlate with alterations in ERK1/2 lipidation writer/eraser expression, solidifying a link between ERK1/2 activity, ERK1/2 lipidation, and organismal health. This study describes how lipidation regulates ERK1/2 and offers insight into the role of dynamic S-acylation in cell signaling more broadly.
... Temporal regulation of gene expression is an essential attribute of transcriptional control for cellular processes such as cell fate transitions (Basma et al., 2009;Chamberlain et al., 2008;Konstantinides et al., 2022) and responses to signals (Behar and Hoffmann, 2010;Krakauer et al., 2002;Uribe et al., 2021). Specific genes, often termed immediate-early genes, are rapidly activated in response to a signal, while other genes change expression more gradually (Sheng and Greenberg, 1990;Uhlitz et al., 2017;Uribe et al., 2021). Genes that show coordinated trajectories are often functionally related, driving diverse phenotypes at different timescales (Gandhi et al., 2011;Krakauer et al., 2002;Schnoes et al., 2008;Szustakowski et al., 2007). ...
Cis-Regulatory Elements (CREs) control transcription levels, temporal dynamics, and cell-cell variation — often referred to as transcriptional noise. However, the combination of regulatory proteins and epigenetic features necessary to control different transcription attributes is not fully understood. Here, single-cell RNA-seq (scRNA-seq) is conducted during a time course of estrogen treatment to identify genomic predictors of expression timing and noise. We find that genes associated with multiple active enhancers exhibit faster temporal responses. Synthetic modulation of enhancer activity verifies that activating enhancers accelerates expression responses, while inhibiting enhancers results in a more gradual response. Noise is controlled by a balance of promoter and enhancer activity. Active promoters are found at genes with low noise levels, whereas active enhancers are associated with high noise. Finally, we observe that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise levels. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.
... To gain additional insight into how nilotinib reverses acquired trametinib resistance, we performed RNA-seq comparing resistant cells treated with trametinib to the same cells treated with trametinib+nilotinib. Since nilotinib reverses resistance in SK-MEL-2MR and SK-MEL-30MR by targeting ABL1/2 and DDR1, we identified changes in common in the two lines. The expression of many ERK targets and regulators (FOS, FRA1-FOSL1, ETV4/5, EGR1, DUSPs, SPRYs, MAFF, EPHA2, HMGA2, KCCN4, UBALD2, NEDD9) [34][35][36][37][38][39][40] were reduced by nilotinib as were cell cycle genes (CCND1-cyclin D1, CDC25, CDKN2D, PCNA), whereas the PRUNE2 tumor suppressor was upregulated (Table S3 and Dataset S3) [41]. Moreover, nilotinib induced downregulation of many Biocarta pathways including Cell Cycle, G2 checkpoint, Proteosome, and EIF ( Figure 4G and S5, Dataset S3). ...
Simple Summary
NRAS-mutant melanoma is a highly aggressive subtype with few treatment options. Although both BRAF-mutant and NRAS-mutant melanomas have activation of the MEK/ERK pathway, MEK inhibitors (MEKi) are only effective for the BRAF-mutant subtype. The aim of this study was to understand why MEKi are ineffective in NRAS-mutant melanomas with the long-term goal of identifying new treatment regimens. Here, we show that ABL and DDR kinases are critically important for MEKi resistance because they cooperate to promote the stability of key proteins involved in driving melanoma growth and survival. FDA-approved drugs that inhibit ABL1/2 and DDR1 have been used for decades to treat leukemia. We showed that one such inhibitor prevents MEKi resistance from developing in a NRAS-mutant melanoma animal model. Thus, the data in this study provide the rationale for testing the use of drugs targeting ABL1/2 and DDR1 in combination with MEKi for patients with NRAS-mutant melanomas who have failed to respond to immunotherapy.
Abstract
Melanomas harboring NRAS mutations are a particularly aggressive and deadly subtype. If patients cannot tolerate or the melanomas are insensitive to immune checkpoint blockade, there are no effective 2nd-line treatment options. Drugs targeting the RAF/MEK/ERK pathway, which are used for BRAF-mutant melanomas, do little to increase progression-free survival (PFS). Here, using both loss-of-function and gain-of-function approaches, we show that ABL1/2 and DDR1 are critical nodes during NRAS-mutant melanoma intrinsic and acquired MEK inhibitor (MEKi) resistance. In some acquired resistance cells, ABL1/2 and DDR1 cooperate to stabilize RAF proteins, activate ERK cytoplasmic and nuclear signaling, repress p27/KIP1 expression, and drive RAF homodimerization. In contrast, other acquired resistance cells depend solely on ABL1/2 for their survival, and are sensitive to highly specific allosteric ABL1/2 inhibitors, which prevent β-catenin nuclear localization and destabilize MYC and ETS1 in an ERK-independent manner. Significantly, targeting ABL1/2 and DDR1 with an FDA-approved anti-leukemic drug, reverses intrinsic MEKi resistance, delays acquisition of acquired resistance, and doubles the survival time in a NRAS-mutant mouse model. These data indicate that repurposing FDA-approved drugs targeting ABL1/2 and DDR1 may be a novel and effective strategy for treating patients with treatment-refractory NRAS-driven melanomas.
... Among the genes that were high in the high 24 h-p-ERK1/2 groups were HOXA9, which is highly expressed in AML and is known to be a poor prognostic factor 33 . Further on, we focused our analysis on previously reported primary response genes (immediate early genes (IEGs), immediate late genes (ILGs), delayed early genes (DEGs)) and secondary response genes (SRGs) induced by ERK (n = 189) or by p38 (n = 501) signaling 34,35 . In total, this included 689 genes (Supplementary Data 4), of which 525 were identified in our dataset (Fig. 5a). ...
... This resulted in 62 significantly differentially expressed genes, of which 29 were upregulated in 24 h-p-ERK1/2 high post-treatment group, including FOSL1 (FOS like 1) (Fig. 5c). FOSL1 is an activator protein-1 (AP-1) transcription factor and an immediate late gene downstream of ERK 34,36 . Patients in the 24 h-p-ERK1/2 high group had a higher induction of FOSL1, especially 4 h posttreatment (Fig. 5d). ...
Aberrant pro-survival signaling is a hallmark of cancer cells, but the response to chemotherapy is poorly understood. In this study, we investigate the initial signaling response to standard induction chemotherapy in a cohort of 32 acute myeloid leukemia (AML) patients, using 36-dimensional mass cytometry. Through supervised and unsupervised machine learning approaches, we find that reduction of extracellular-signal-regulated kinase (ERK) 1/2 and p38 mitogen-activated protein kinase (MAPK) phosphorylation in the myeloid cell compartment 24 h post-chemotherapy is a significant predictor of patient 5-year overall survival in this cohort. Validation by RNA sequencing shows induction of MAPK target gene expression in patients with high phospho-ERK1/2 24 h post-chemotherapy, while proteomics confirm an increase of the p38 prime target MAPK activated protein kinase 2 (MAPKAPK2). In this study, we demonstrate that mass cytometry can be a valuable tool for early response evaluation in AML and elucidate the potential of functional signaling analyses in precision oncology diagnostics. The molecular mechanisms underlying response to chemotherapy in Acute myeloid leukemia (AML) remain to be explored. Here, the authors perform 36-dimensional mass cytometry in 32 AML patients during intensive chemotherapy and suggest functional signalling analysis for prognosis prediction early after treatment in AML.
... These changes included the upregulation of several well-established MAPK target genes (Ccnd1, Etv4, Egr2, Dusp2, and Ereg), confirming that pathogenic RIT1 regulates MAPK signaling in this cell type ( fig. S7A) (37). In addition, GO and KEGG analyses revealed an enrichment of genes critical for proper cardiac function (Fig. 8B,C) and whose dysregulation may contribute to the cardiomyopathy-like phenotype exhibited by RIT1 NS murine models (13,14). ...
RIT1 belongs to the family of Ras guanosine triphosphatases (GTPases) that regulate many aspects of signal transduction and are drivers of cancer and congenital disorders. RIT1 gain-of-function mutations are found in lung cancer, leukemia, and in the germline of Noonan syndrome individuals with an increased prevalence of cardiac hypertrophy and other congenital heart defects. Pathogenic RIT1 proteins evade proteasomal degradation and promote MEK/ERK mitogen-activated protein kinase (MAPK) hyperactivation, yet the mechanism remains poorly understood. Here we show that RAF kinases are putative mutant RIT1 effectors necessary for MAPK activation and characterize RIT1 association with plasma membrane lipids and interaction with RAF kinases. We identify critical residues present in the RIT1 hypervariable region that facilitate interaction with negatively charged membrane lipids and show that these are necessary for association with RAF kinases. Although mutant RIT1 binds to RAF kinases directly, it fails to activate RAF-MAPK signaling in the absence of classical Ras proteins. Consistent with aberrant RAF/MEK/ERK activation as a driver of disease, we show that MEK inhibition alleviates cardiac hypertrophy in a mouse model of RIT1-mutant Noonan syndrome. These data shed light on pathogenic RIT1 function and identify avenues for therapeutic intervention.
One Sentence Summary
Electrostatic plasma membrane association facilitates RIT1-mediated Ras-dependent RAF kinase activation to promote pathogenic MAPK signaling.
... On a single cell level, both ERK and Akt activity dynamics have substantial cell-to-cell and dynamic variation, exhibiting complex pulses and more simple steady activity 1,24,[40][41][42][43][44][45][46][47][48] . Such variation, when coupled with the observations that cell cycle progression is also heterogeneous 1,49,50 , have prompted investigations into the correlation between dynamics and cell cycle fate in single cells. ...
... www.nature.com/scientificreports/ Although the median in the 8.5-40 h post-growth factor window was analyzed here, we cannot rule out the importance of other dynamic features such as pulsing, although in our datasets we did not observe significant pulsing 1,24,[40][41][42][43][44][45][46][47][48] (perhaps due to experimental differences such as subconfluency and serum-starvation-see "Discussion"). Moreover, because of strong growth factor stimulation and potential probe saturation in the 0-8.5 h window, we cannot rule out the importance of this earlier signaling window either. ...
Biochemical correlates of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window (~ 8.5–40 h). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Simple metrics of central tendency in this time window are associated with subsequent cell division fates. ERK activity was more strongly associated with division fates than Akt activity, suggesting Akt activity dynamics may contribute less to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics may be more important than Akt activity dynamics for driving cell division in this non-transformed context.
