TNF preconditioning alters kinase determinants of thrombin-induced barrier perturbations

To investigate kinase signaling pathways induced by thrombin and explore how the phosphosignaling environment may be altered by sequential inflammatory stimuli, we exploited our published KiR dataset of HBMEC monolayers stimulated with thrombin, with or without TNF preconditioning (Dankwa et al, 2021). Thrombin causes an acute drop in HBMEC barrier integrity that peaks around 20–30 min and a slower barrier recovery over the next 1.5 h (Figs 1A and S1A and B). TNF preconditioning exacerbated thrombin-induced barrier disruption, as demonstrated by the increased maximum barrier permeability and prolonged barrier recovery (Figs 1A and S1A and B). Although this finding is consistent with previous reports (Anrather et al, 1997; Tiruppathi et al, 2001; Liu et al, 2004), the molecular networks that regulate these differences remained unknown.

To define kinase signaling mechanisms underlying TNF potentiation of thrombin-induced barrier disruption, we reanalyzed the dataset from a small chemical compound screen against human kinases. In the original KiR analysis, HBMEC monolayers were treated with thrombin (−/+TNF preconditioning), and 28 kinase inhibitors were added 6 min after thrombin treatment (Fig 1B) (Dankwa et al, 2021). The endothelial barrier integrity was assessed in real-time using the xCELLigence system, which measures cellular impedance across adherent cells in electronic microplate wells. Decreases in the cell index correlate with the initial strong retraction of endothelial cells after thrombin treatment during the barrier disruption phase, whereas increased cell index values are associated with recovery of monolayer integrity (Dankwa et al, 2021). We observed that the DMSO vehicle alone had no effect on HBMECs treated with thrombin or TNF (Fig S1C) but that different kinase inhibitors blunt, have minimal effect, or exacerbate thrombin-induced barrier disruption and exhibit temporal features during the 6-h time window (Fig 1B) (Dankwa et al, 2021).

We reasoned that if similar kinases control barrier integrity in the two inflammatory conditions within a given time window, there should be a stronger correlation between the activities of the 28 kinase inhibitors in thrombin-alone and TNF-preconditioned settings in that temporal period. In contrast, if different kinases control the barrier integrity in the two conditions, we expect less correlation between the activities of the kinase inhibitors. Thus, the KiR screen can be used to probe the underlying barrier regulatory phosphosignaling networks across conditions. Using area under the curve (AUC) of the normalized cell index as the metric, we compared the two inflammatory conditions across four different 10-min time windows: barrier disruption (6–16 min after thrombin treatment), early barrier recovery (31–41 min after thrombin treatment), mid barrier recovery (121–131 min after thrombin treatment), and late barrier recovery (241–251 min after thrombin treatment) (Fig 1C). We also evaluated the correlation between the activities of the 28 kinase inhibitors in +thrombin and TNF preconditioning+thrombin conditions within a 5-min time window that slides at 1-min steps for the first 2 h after kinase inhibitor treatment and at 5-min steps afterward (Fig 1D). The correlation was the lowest in the early barrier disruption phase (Pearson’s r = 0.72) and increased during early barrier recovery (Pearson’s r = 0.90 to 0.92) (Fig 1D), suggesting that the phosphosignaling networks are more divergent during barrier disruption and more similar during the initial barrier restoration phase. Of note, differences in the kinetics of action of the kinase inhibitors may also contribute to the reduction of the correlation at the earliest time points. Notably, the correlation declined again at ∼120 min and remained divergent throughout the entire mid-to-late barrier recovery phases (Pearson’s r = 0.91 to 0.77), a period when the two inflammatory conditions diverged in their slopes of recovery and final cell index (compare Fig 1A and D). This analysis is consistent with a model where TNF preconditioning alters kinase regulatory networks that dictate the extent and kinetics of thrombin-induced barrier disruption and recovery in brain endothelial cells.

Time-resolved predictions of barrier-weakening and barrier-strengthening kinases between the two inflammatory conditions

Previously, we predicted 29 and 25 kinases that regulate the HBMEC barrier in response to thrombin treatment with or without TNF preconditioning, respectively (Dankwa et al, 2021). However, these KiR predictions were based on the full AUC (6-h time window, post-thrombin treatment) and did not consider time windows of barrier activity. To address this lack of temporal resolution, we performed temporal KiR (tKiR) using a sliding-window analysis (Fig S2, top, schematic overview). For this approach, a 5-min sliding time window was applied to the normalized cell index data, which slides at 1-min steps for the first 2 h after kinase inhibitor treatment and at 5-min steps afterward (Figs 2A and S2). Among the 291 protein kinases used for tKiR predictions (Anastassiadis et al, 2011) (Fig S2, see methodology), the number of functional kinases increased to 120 and 108 in HBMECs treated with thrombin −/+TNF preconditioning, respectively (Fig 2A; Table S1). The higher resolution of tKiR is because the sliding-window analysis allows for the detection of kinases whose activity is brief or sporadic. In total, 159 kinases (∼30% of the total human kinome) were predicted to regulate barrier function within the 6-h timeframe, with 69 kinases being common to the two conditions (Fig 2A).

As a further refinement, tKiR models were used to predict the kinase functionality (promoting barrier weakening or promoting barrier strengthening) in a time-resolved manner for the two inflammatory conditions (Fig 2B and C). The barrier activity of individual kinases was inferred by the linear regression slope of the kinase inhibitors targeting that kinase from the KiR analysis, where barrier-weakening or barrier-strengthening kinases are defined as the kinases that promote disruption or strengthening of barrier integrity, respectively (Fig S2, top). For HBMECs not preconditioned with TNF, 53 and 60 kinases were predicted to play a barrier-weakening and barrier-strengthening role, respectively (Fig 2B). In addition, seven kinases were predicted to play both barrier-weakening and barrier-strengthening roles during different time windows and were termed “switch kinases” (Fig 2B). For HBMECs preconditioned with TNF, 58 and 47 kinases were predicted, respectively, to be barrier-weakening and barrier-strengthening, and three switch kinases were predicted (Fig 2C). Overall, a greater number of barrier-weakening kinases were predicted during the initial disruption phase, whereas the proportion of barrier-strengthening kinases increased over barrier recovery (Fig 2D). Nevertheless, cells propagate both types of barrier activities concurrently, and it is the balance that changes over time (Fig 2D).

The tKiR predictions of kinase activity recapitulate information flow through canonical MAPK signaling pathways involved in barrier regulation

Previous work has established that MAPK cascades are activated after thrombin stimulation, including the extracellular signal-regulated kinases (ERKs), Jun N-terminal kinases (JNKs), and p38 MAPKs (Minami et al, 2004; Mehta & Malik, 2006; Radeva & Waschke, 2018). The topology of MAPKs is well-established; cell surface receptors such as receptor tyrosine kinases (RTKs) provide a link between extracellular stimuli and the cascade (Morrison, 2012). After activation, most MAPK pathways have a four-tier kinase architecture (MAP3K-MAP2K-MAPK-MAPKAPK) (Pimienta & Pascual, 2007). Given the strict directionality of the network, we reasoned that RTKs would be predicted to regulate the barrier during the earliest time points after thrombin treatment, followed sequentially by MAP3Ks, MAP2Ks, MAPKs, and finally MAPKAPKs. Furthermore, kinases involved in a signaling cascade would be more likely to have the same barrier activity over a given time window, especially if they were acting to propagate a specific barrier phenotype.

Overall, the ERK, JNK, and p38 pathways were all implicated in thrombin signaling, with multiple kinases in each signaling cascade predicted by tKiR (Fig 3). Specifically, within the ERK and the JNK pathways, most of the predicted kinases were barrier-weakening, and the windows when their activity was predicted followed the expected order of MAP3Ks or MAP2Ks preceding MAPKs, which preceded downstream MAPKAPKs (Fig 3). Although the ERK pathway had both an early and a late wave of barrier-weakening activity, the JNK pathway was predicted to have primarily early barrier activity (Fig 3). By comparison, the p38 pathway had a mixture of early barrier-weakening activity followed by a switch to late barrier-strengthening activity, which was mediated by different p38 isoforms (Fig 3). For instance, MAPKAPK2/MK2 was one of the seven switch kinases in the +thrombin condition (Fig 2B) and had an early barrier-weakening activity and late barrier-strengthening activity (Fig 3). MAPKAPK2/MK2 is a primary target of MAPK14/p38α, although it can also be activated by MAPK1/ERK2 and JNK signaling (Ronkina et al, 2008; Johnson et al, 2023). Of interest, a mid-to-late–acting barrier-strengthening p38 pathway could be assembled from known components, consisting of MAP3K20/ZAK-MAPK14/p38α-MAPKAPK2/MK2, but different upstream kinases appeared to be involved in the early barrier activity of MAPKAPK2/MK2 under thrombin stimulation (Fig 3). Thus, our tKiR model predicted different MAPK kinases were responsible for early and late periods of MAPKAPK2/MK2 activation.

The same overall MAPK pathways were predicted with TNF preconditioning; however, the timing and duration of the ERK and the JNK pathways were shifted earlier and shortened for most kinases (Fig 3). TNF preconditioning also modified barrier-regulatory signaling cascades within the three canonical MAPK pathways. In the ERK pathway, there was a second wave of barrier-weakening activity by MAPK1/ERK2, and in the JNK pathway, the barrier-weakening activity of TAOK1 was also shifted from an early to a later timeframe. Of interest, TAOK1 can promote activation of the ERK1/2 pathway in macrophages (Zhu et al, 2020), suggesting that TAOK1 may also function in the ERK pathway in regulating barrier properties. In the JNK pathway, the number of barrier-regulatory kinases was increased, consistent with previous work indicating that combined TNF and thrombin treatment alters the magnitude and duration of JNK activation in endothelial cells (Liu et al, 2004). In the p38 pathway, the barrier activity of the p38 isoforms was rewired. Specifically, although MAPK13/p38δ retained a brief barrier-weakening activity, the late barrier-strengthening MAPK14/p38α-MAPKAPK2/MK2 pathway was absent and other p38 isoforms replaced it. Therefore, we termed MAPKAPK2/MK2 a condition-specific, switch kinase. It was predicted to have early barrier-weakening activity in both inflammatory conditions but late barrier-strengthening activity only in the thrombin-alone condition. Consequently, this analysis suggests that core MAPK signaling pathways involved in thrombin-induced barrier disruption remain intact after TNF preconditioning, but the timing, duration, and importance of specific MAPKs are altered.

Kinase activation state is largely consistent with tKiR-predicted barrier activity in the ERK and the JNK pathways

To investigate the tKiR predictions about MAPK signaling pathways regulating barrier phenotypes in the two inflammatory conditions, we probed kinase phosphorylation over time via Western blot. We reasoned that there should be a rise in phosphorylation at kinase activation sites immediately preceding and/or during periods of predicted barrier activity. For this analysis, HBMECs (−/+TNF preconditioning) were treated with thrombin for 0, 5, 15, 30, 60, 120, 180, 240, or 360 min. In each experimental condition, the level of phosphorylated kinase after thrombin treatment was compared with its phosphorylation at the basal level (media only) after normalization to GAPDH. Initially, we probed the ERK and JNK pathways that were primarily predicted to be involved in barrier-disruptive signaling cascades from the tKiR analysis. Control Western blots showed that the total levels of the ERK1/2 and JNK1/2 isoforms did not change in this period (Fig S3A) and therefore did not change the normalization of phosphorylated protein levels compared with normalization to GAPDH (Fig S3B). Within the canonical ERK cascade, there was a sequential activation of kinases and statistically significant increase in activation-associated phosphorylation in MAP2K1/2 (MEK1/2), MAPK1/ERK2, MAPK3/ERK1, and the downstream MAPKAPK target MKNK1 prior or during predicted early barrier-weakening activity in both inflammatory conditions (Fig 4A). Likewise, there was a second wave of MAPK1/ERK2 and MAPK3/ERK1 late barrier activity from 180 min to 360 min after thrombin treatment in both inflammatory conditions that was independent of late MAP2K1/2 (MEK1/2) activation (Fig 4A), as predicted by the tKiR model (Fig 3). As MAP2K1/2 (MEK1/2) were only predicted to have early barrier activity (Fig 3), this raises the possibility that another kinase(s), such as TAOK1, is responsible for the late MAPK1/3 (ERK2/1) activation or there is a change in the activity of a phosphatase(s) that can dephosphorylate MAPK1/3 (ERK2/1).

Results from temporal Western blot analysis also showed that activation marks increased in both MAPK8/JNK1 and MAPK9/JNK2 before the predicted early barrier-weakening activity in both inflammatory conditions (Fig 4B). Moreover, the timing, magnitude, and duration of early MAPK8/JNK1 activation increased with TNF preconditioning, consistent with a shift in the timing of barrier activity predicted by tKiR (Fig 4B). There were also some notable discrepancies between activation marks and predicted barrier activity. In +thrombin condition, we only observed a very modest and statistically significant increase in the activity of MAPK8/JNK1 and MAPK9/JNK2 at early time points where they were predicted to have barrier-strengthening activity (Fig 4B). In addition, there was an increase in MAPK8/JNK1 and MAPK9/JNK2 activation marks at late time points (180 and 240 min after thrombin treatment) in +thrombin condition (Fig 4B), despite the lack of predicted barrier activity. One possibility is that the late JNK1/JNK2 activation marks may be related to a non-barrier function of JNK kinases. Alternatively, this may be a false-negative tKiR prediction. Overall, in most cases, increasing phosphorylation levels were observed immediately preceding or overlapping with the corresponding time windows of tKiR-predicted barrier activity for the ERK and the JNK pathways.

Our tKiR model predicted that MAPKAPK2/MK2 had a complex barrier activity that differed between the two inflammatory conditions. In the thrombin-alone condition, MAPKAPK2/MK2 was predicted to be a switch kinase with early barrier-weakening and late barrier-strengthening activity. However, the late barrier-strengthening activity was predicted to be disconnected after TNF preconditioning (Fig 3). As MAPKAPK2/MK2 can be activated by ERK, JNK, and p38 pathways, we used Western blots to investigate how the three MAPKs signaling pathways may regulate this complex barrier phenotype. Consistent with the tKiR model (Fig 3), MAPKAPK2/MK2 activation increased at both early and late time points in +thrombin condition, but the late MAPKAPK2/MK2 activation was substantially reduced with TNF preconditioning (Fig 4B). The early MAPKAPK2/MK2 activation correlated with a rise in MAPK1/ERK2 and MAPK3/ERK1 activation (Fig 4A), and the late MAPKAPK2/MK2 activation correlated with a rise in MAPK8/JNK1 and MAPK9/JNK2 activation (Fig 4B). Moreover, the late MAPK8/9 (JNK1/2) activation marks were substantially reduced after TNF preconditioning (Fig 4B). This analysis indicates that the ERK pathway is more likely to contribute to early MAPKAPK2/MK2 activation, whereas the JNK pathway is more likely to contribute to late MAPKAPK2/MK2 activation.

Differential p38-driven activation of the MAPKAPK2/MK2 switch kinase in the two inflammatory conditions

To further investigate the MAPKAPK2/MK2 switch kinase, we examined the p38 pathway. The tKiR methodology predicted that a MAP3K20/ZAK-MAPK14/p38α-MAPKAPK2/MK2 pathway was involved in late barrier strengthening in +thrombin condition and that this pathway is disconnected with TNF preconditioning (Figs 3 and 5A). To test the model predictions, we probed the phosphorylation levels of MAP3K20/ZAK and the p38 isoforms by Western blot. Control Western blots showed that the total levels of p38 isoforms did not change in this period (Fig S3A). The MAP3K20/ZAK gene is alternatively spliced into large (∼100 kD) and small (∼55 kD) isoforms (Gotoh et al, 2001). In HBMECs, phosphorylated MAP3K20/ZAK ran as two large isoforms (labeled isoforms 1 and 2) and a smaller isoform (labeled isoform 3) (Fig 5B). Although the smaller MAP3K20/ZAK isoform 3 (∼70 kD) was activated at early time points (5 and 10 min after thrombin treatment), the larger MAP3K20/ZAK isoform 2 was activated at late time points (240 and 360 min after thrombin treatment), coincident with a period of predicted MAP3K20/ZAK barrier-strengthening activity in both inflammatory conditions (Fig 5B). Likewise, MAPK14/p38α was activated at both early and late time points in +thrombin condition, but the late activation was dampened with TNF preconditioning (Fig 5B). Taken together, these findings suggest that the smaller MAP3K20/ZAK isoform 3 is likely responsible for the early p38 activation and the larger MAP3K20/ZAK isoform 2 for the late barrier-strengthening functionality of MAPK14/p38α-MAPKAPK2/MK2. Furthermore, the dampening of MAPK14/p38α and MAPKAPK2/MK2 activation in the late barrier recovery phase upon TNF preconditioning is consistent with the tKiR model predictions that sequential treatment with TNF and thrombin led to rewiring of this late-stage barrier-strengthening function to other p38 isoforms that are unable to activate MAPKAPK2/MK2. Consequently, there is a dampening of both late JNK (Fig 4B) and p38α activation (Fig 5B) after TNF preconditioning, which could contribute to the reprogramming of the MAPKAPK2/MK2 switch kinase.

Construction of phosphosignaling networks that regulate barrier function

Among the kinases predicted by tKiR, there are many non-MAPK kinases (Table S1). However, much less is known about non-MAPK signaling cascades, and many kinases have been rarely studied. To discover new signaling pathways and crosstalk between pathways, we reasoned that kinases that are temporally related in barrier activity are more likely to act within a connected signaling module. Thus, we used self-organizing maps (SOMs) to systematically group kinases according to their temporal barrier activity. SOM is a dimensionality reduction method that can capture the topographic relationships of time series data (Kohonen, 1982). Kinases of similar temporal functionality were clustered into one group (called a “neuron”), and neurons having similar mean temporal behavior are more closely located on the SOM (Figs 6A, S2 [bottom] and S4A and Tables S2 and S3). To generate the SOMs, we used a grid size of 6 × 6 (resulting in up to 36 neurons). In +thrombin condition, kinases were grouped into 30 neurons, and each neuron contained between 1 and 11 kinases (Figs 6A and S4B; Table S2). In TNF preconditioning+thrombin condition, kinases were grouped into 35 neurons, and each neuron contained between 1 and 9 kinases (Figs 6A and S4B; Table S2).

To investigate systems-level interconnections, we built TREKING models of barrier phosphosignaling networks (Fig S2, bottom). Specifically, for each neuron, a local phosphosignaling network was built to describe the paths through which phosphosignals may propagate, using the kinase–substrate phosphorylation database PhosphoSitePlus (Hornbeck et al, 2015) to predict upstream and downstream kinases and to infer intermediate kinases in the pathways (Fig S4C). We were able to build 26 local networks from the 30 neurons generated in +thrombin condition and 27 local networks from the 35 neurons generated in TNF preconditioning+thrombin condition (Table S2). The remaining neurons included only a single kinase that does not self-phosphorylate or multiple unconnected kinases. The size and topology of the phosphosignaling networks varied across neurons. In +thrombin condition, most networks had a maximum node-to-node shortest path length below 10 kinases, whereas the deepest networks built from neurons (0,0) and (3,3) had a maximum shortest path length of 14 kinases (Fig S4C). In TNF preconditioning+thrombin condition, most networks had a maximum shortest path length between 3 and 9 kinases, except for the network built from neuron (2,4) that had a maximum shortest path length of 16 kinases (Fig S4C).

TREKING maps alternate routes for activation of the MAPKAPK2/MK2 switch kinase

We used the TREKING models to further investigate the regulation of the MAPKAPK2/MK2 switch kinase. In the +thrombin condition, MAPKAPK2/MK2 was clustered within a mid-to-late barrier-strengthening neuron (5,5). This neuron contained the MAP3K20/ZAK, MAPK14/p38α, and MAPKAPK2/MK2 components in the p38 pathway and G-protein–coupled receptors 3 and 4 (GRK3 and GRK4), C-terminal Src kinase (CSK; a negative regulator of Src-family kinases), and two members of noncanonical NF-κB signaling pathways (MAP3K14/NIK and inhibitor of nuclear factor kappa B kinase subunit beta [IKBKB/IKKβ]) (Fig 6B). In addition to the canonical MAP3K20/ZAK-MAPK14/p38α-MAPKAPK2/MK2 pathway (Figs 3 and 5), the TREKING model predicted several alternative pathways for late MAPKAPK2/MK2 activation (Fig 6C). One alternative pathway was predicted to link MAP3K20/ZAK and MAPK14/p38α via checkpoint kinase 2 (CHEK2), TTK protein kinase (TTK), ABL proto-oncogene 1 non-RTK (ABL1/c-Abl), and zeta chain of T-cell receptor–associated protein kinase 70 (ZAP70), instead via the canonical MAPK pathway topology (MAP3K-MAP2K-MAPK) (Fig 6C). Other routes included a CSK–LCK pathway and a noncanonical NF-κB signaling pathway involving MAP3K14/NIK and the inhibitor of nuclear factor kappa B kinase subunit alpha (CHUK/IKKα) and MAPK1/ERK2 (Fig 6C). Consistent with TREKING model predictions, activation marks increased in MAPK1/ERK2, ABL1/c-Abl, and CHUK/IKKα by Western blot in the late barrier recovery phase (Fig 6D). The TREKING model also predicted that activated MAPKAPK2/MK2 leads to activation of MAP3K5/ASK1, suggesting a potential positive feedback loop, via AKT serine/threonine kinase 1 (AKT1), which exhibited increased phosphorylation in the late barrier recovery phase (Fig 6C and D).

MAPKAPK2/MK2 was no longer predicted to be a switch kinase with TNF preconditioning and clustered with an early barrier-weakening neuron (5,3) that also included MAPK3/ERK1, MKNK1, MAPK8/JNK1, p21 (Rac1) activated kinase 3 (PAK3), and Janus kinase 1 (JAK1) (Fig S5A). To better understand the early barrier-weakening activity of MAPKAPK2/MK2, we built a composite phosphosignaling network from neurons (5,3) and (5,4) (Fig S5B). This TREKING model showed that ERK- and JNK-associated signaling may contribute to the activation of MAPKAPK2/MK2 in the barrier disruption phase and revealed crosstalk between ERK and JNK signaling pathways (Fig S5B). One of the barrier-strengthening p38 MAPKs, MAPK14/p38α, was also inferred by the TREKING model that describes barrier-disruptive signaling, highlighting the complexity of kinase functionality and kinase-mediated signaling during different phases of barrier perturbation. Consistent with model predictions, increased levels of activated kinases in ERK, JNK, and p38 pathways and the noncanonical, inferred kinase ABL1/c-Abl were detected within the first 15 min of thrombin treatment by Western blot (Fig S5C).

MAPK14/p38α regulates the late activation of MAPKAPK2/MK2

Our models predicted that early and late barrier activity of MAPKAPK2/MK2 was regulated by distinct upstream MAPK kinases and that the late barrier strengthening activity in the thrombin-alone condition involved a MAP3K20/ZAK-MAPK14/p38α signaling pathway. To evaluate our predictions and ask if MAPK14/p38α is responsible for the increased activity of MAPKAPK2/MK2 in the barrier recovery phase, we used both genetic and pharmacological approaches (Fig 7A and Tables S4 and S5). We performed lentiviral knockdowns in HBMECs by targeting MAP3K20 or MAPK14, and the knockdown efficiency was evaluated by quantitative reverse-transcription PCR (qRT–PCR) (Fig 7B; Table S4). We then measured the activity of MAPKAPK2/MK2 across 6-h time course after thrombin treatment by Western blot. Because knocking down MAP3K20/ZAK resulted in low cell viability and permeable barrier at the basal level, we did not pursue it further. Knocking down MAPK14/p38α eliminated the second wave of MAPKAPK2/MK2 activation compared with the scrambled control but preserved the first wave of MAPKAPK2/MK2 activation in the barrier disruption phase (Fig 7B), consistent with the model prediction that MAPK14/p38α contributes to the barrier activity of MAPKAPK2/MK2 primarily in the barrier recovery phase. We then used a small-molecule inhibitor, zunsemetinib, to selectively block the MAPK14/p38α activation of MAPKAPK2/MK2 (Wang et al, 2018) at 2 h after thrombin treatment. In the zunsemetinib-treated cells, there was an immediate decrease in the MAPKAPK2/MK2 phosphorylation mark as compared with the DMSO control (Fig 7C), consistent with the model that signaling from MAPK14/p38α is important for activating MAPKAPK2/MK2 in the barrier recovery phase.

Overall, results from both genetic knockdown and small-molecule inhibitor treatment support the model prediction that in +thrombin condition, MAP3K20/ZAK-MAPK14/p38α signaling contributes to the second wave of MAPKAPK2/MK2 signaling (Fig 7D). Our analysis also indicates that in +thrombin condition, both JNK and ERK signaling likely contribute to the early MAPKAPK2/MK2 activation, and the late barrier-strengthening pathway is rewired with TNF preconditioning (Fig 7D). In TNF preconditioning+thrombin condition, ERK and JNK signaling still both regulate early MAPKAPK2/MK2 activation, but TNF preconditioning increased early JNK activation (Fig 4). However, the late MAPK14/p38α-MAPKAPK2/MK2 barrier recovery pathway is reduced, and different p38 MAPK isoforms (MAPK11/p38β and MAPK13/p38δ) are predicted to play a more important role in barrier recovery in cells that are preconditioned with TNF (Fig 7D).

tKiR and TREKING expand the current understanding of kinases and associated phosphosignaling in barrier regulation

Most previous and ongoing research has focused on certain stages of barrier perturbation or sampled sparsely during a time course. We therefore asked how TREKING predictions compared with the existing literature in their breadth and temporal resolution. To do this, we visualized portions of the phosphosignaling networks that had been reported in the literature to regulate the endothelial barrier in response to thrombin (Table S6). We then performed the same visualization with kinases that had been predicted by tKiR and finally, by TREKING. Compared with the literature, TREKING substantially increased the number of kinases associated with thrombin-induced barrier regulation and was able to assemble most predicted kinases into time-resolved phosphosignaling networks (Fig 8; Video 1 and Supplemental Data 1). This highlights the power of TREKING to broadly dissect functionally important phosphosignaling in barrier regulation, with high temporal resolution.

Cell lines

Primary HBMECs (Cat# ACBRI 376; Cell Systems) were cultured on rat tail collagen type I (5 μg/cm²; Cat# 354236; Corning) in HBMEC culture media (Cat# CC-3202; Lonza) at 37°C and 5% CO₂. HBMECs were obtained at passage 3 and used until passage 9. HEK293-FT cells (Arang et al, 2017) were maintained in Gibco DMEM (Cat# 10569010; Thermo Fisher Scientific) supplemented with 10% FBS (Cat# F31016HI; SeraPrime), 25 mM HEPES, 2 mM L-glutamine (Cat# 25-005-CI; Corning), Gibco 1x MEM nonessential amino acids solution (Cat# 11140050; Thermo Fisher Scientific), and 100 μg/ml primocin (Cat# ant-pm; InvivoGen) at 37°C and 5% CO₂. HEK293-FT cells were used until passage 12.

xCELLigence data acquisition

The kinetic data in this study are published and were acquired from xCELLigence assays using the xCELLigence Real Time Cell Analysis Single Plate device (Agilent Technologies) (Dankwa et al, 2021). The assays are described here in brief. HBMECs were grown to confluency in xCELLigence 96 PET E-plates (Cat# 300600900; Agilent Technologies). On the day of the assay, HBMECs were equilibrated in serum-free culture media for 1–2 h, and thrombin (Cat# T6884; Sigma-Aldrich) was added at 5 nM. To capture thrombin-induced barrier disruption, the cell index was measured every minute for 6 min, after which kinase inhibitors were added in triplicate at 0.5 μM. The cell index was then measured every minute for 2 h and thereafter every 5 min for 4 h. The assays with TNF preconditioning were performed identically except that HBMECs were activated with 10 ng/ml TNF (Cat# 10291-TA; R&D Systems) for ∼22 h before media equilibration. Data analysis was performed as described previously (Dankwa et al, 2021) with some modifications. The cell index was normalized to baseline (0) at the time point before addition of thrombin, and AUC was determined for each kinase inhibitor treatment. For this study, the AUC was determined within 5-min sliding windows over the 6-h time course. AUC values were normalized by subtracting the AUC of cells treated with thrombin+DMSO or TNF preconditioning+thrombin+DMSO. These values were then linearly transformed, with the most negative-normalized AUC value within a 5-min window being set to 0 and the normalized value for the control sample being set to 100.

Lentivirus production

HEK293-FT cells were plated in 10-cm² tissue culture-treated dishes at 4 × 10⁶ cells per dish and incubated at 37°C and 5% CO₂. The following day, cells were transfected at 70–80% confluency with 1.5 μg of pCMV-VSV-G envelope plasmid, 3 μg of psPax2 packaging plasmid, and 6 μg of pLKO.1 plasmid harboring shRNA against human MAPK14 gene (Sigma-Aldrich MISSION shRNA library clones TRCN0000000509 and TRCN0000000511) or with a non-targeting scrambled shRNA (Sigma-Aldrich). 21 μl of 1 mg/ml polyethylenimine MAX (Cat# 24765; Polysciences) was mixed with pCMV-VSV-G, psPax2, and pLKO.1 in serum-free DMEM (Cat# 10569010; Thermo Fisher Scientific) and incubated for 10 min. Transfection mixtures were added dropwise to HEK293-FT cells. Media was changed the following day. Culture supernatant containing lentiviral particles was harvested ∼24 h after the media change and stored at −80°C until use.

Lentiviral transduction of HBMECs

Lentiviral transduction of HBMECs for knockdown of MAPK14/p38α was performed directly in the vessels at the time of cell seeding. 12-well plates (Cat# 3513; Corning) were coated with rat tail collagen type I (Cat# 354236; Corning) at 5 μg/cm² for 30 min at 37°C. HBMECs were seeded in 12-well plates at 40,000 cells/well with 1 mg/ml polybrene infection/transfection reagent (Cat# TR-1003-G; MilliporeSigma). Pooled lentiviruses were added to the corresponding wells at 0.5 ml per well. 24 h after transduction, puromycin (Cat# A1113803; Thermo Fisher Scientific) was added to the media at 2 μg/ml of final concentration. Cells were grown under puromycin selection for 2–3 d at which time cells were harvested for RNA extraction and TNF preconditioning for time course lysate collection was performed.

Quantitative reverse-transcription PCR

RNA was extracted from TRIzol (Cat# 15596018; Thermo Fisher Scientific) homogenates by chloroform-based separation. Briefly, 80 μl of chloroform was added for every 400 μl of TRIzol reagent used. Samples were shaken gently for 15 s, incubated at room temperature for 3 min, and then centrifuged at 12,000g and 4°C for 15 min. RNA contained in the upper aqueous phase was transferred to new tubes, 200 μl of isopropanol (Cat# T036181000; Thermo Fisher Scientific) was added for every 400 μl of TRIzol reagent used for RNA extraction, incubated at room temperature for 10 min, and then centrifuged at 12,000g and 4°C for 10 min to pellet RNA. RNA pellets were washed three times using 75% ethanol (Cat# T038181000; Thermo Fisher Scientific), resuspended in 20 μl RNAase-free water, and incubated at 55–60°C for 15 min. cDNA was prepared from equal amounts of DNase-treated RNA of each sample using the iScript cDNA Synthesis Kit (Cat# 1708891; Bio-Rad) according to the manufacturer’s protocol. qRT-PCR was performed using Power SYBR Green PCR Master Mix (Cat# 4367659; Thermo Fisher Scientific) and primers targeting human MAPK14 gene (PrimerBank: https://pga.mgh.harvard.edu/primerbank/; forward primer: 5′-CCCGAGCGTTACCAGAACC-3′ and reverse primer: 5′-TCGCATGAATGATGGACTGAAAT-3′). Samples were run on an Applied Biosystems QuantStudio 3 real-time PCR system (Cat# A28567; Thermo Fisher Scientific) with the following amplification conditions: a hold stage with 50°C for 2 min followed by 95°C for 10 min and a PCR stage with 40 cycles of 95°C for 15 s and 60°C for 1 min. Relative transcript abundance was determined by normalizing to the housekeeping gene GAPDH followed by normalizing to kinase expression in the scrambled control sample according to the 2^−ΔΔCT method.

Small-molecule inhibitor treatment

HBMECs were seeded in 12-well plates (Cat# 3513; Corning) at 35,000 cells/well and grown for 4 d. On the day of lysate collection, cells were equilibrated in serum-free culture media for 1 h and then treated with thrombin at a final concentration of 5 nM. Zunsemetinib (Cat# HY-139553; MedChemExpress) was added 2 h after thrombin treatment at a final concentration of 10 μM (0.1% DMSO). As controls, DMSO was added to another set of wells 2 h after thrombin treatment at a final concentration of 0.1%. Cell lysates were collected right before thrombin treatment and at 5, 15, 30, 60, 120, 180, 240, 300, and 360 min after thrombin treatment.

Lysate preparation

HBMECs were seeded in six-well plates (Cat# 353046; Corning) at 55,000 cells/well and grown for 3 d. Cells were then activated with 10 ng/ml TNF for 21 h or kept in media for the same period. On the day of lysate collection, cells were equilibrated in serum-free culture media for 1 h and then treated with thrombin in triplicate at a final concentration of 5 nM for 5, 15, 30, 60, 120, 180, 240, and 360 min. After the indicated incubation periods, cells were washed twice with ice-cold PBS and lysed in SDS lysis buffer (50 mM Tris–HCl, 2% SDS, 5% glycerol, 5 mM ethylenediaminetetraacetic acid, 1 mM sodium fluoride, 10 mM β-glycerophosphate, 1 mM phenylmethylsulfonyl fluoride, 1 mM sodium orthovanadate, 1 mM dithiothreitol, supplemented with a cocktail of protease inhibitors [Cat# 4693159001; Roche] and phosphatase inhibitors [Cat# P5726; Sigma-Aldrich]). Cell lysates were clarified in filter plates (Cat# 8075; Pall) at 2,671g for 30 min, after which they were stored at −80°C until use. Cell lysates from three biological replicates were collected.

Western blot

All the gel electrophoresis was performed using Bolt 4–12% Bis-Tris mini protein gels. Proteins were transferred to PVDF membranes using the iBlot 2 (Cat# IB21001; Thermo Fisher Scientific) or iBlot 3 (Cat# A56727; Thermo Fisher Scientific) dry blotting system. Primary antibodies were used at concentrations recommended by the manufacturer (see Tables S3 and S5 for antibody information). The antibody to GAPDH (Cat# 97166, RRID AB_2756824; Cell Signaling Technology) was used as the loading control at 1:2,000 dilution. Blots were imaged using the Bio-Rad ChemiDoc imaging system, and signals were quantified using ImageJ2 (https://imagej.nih.gov/ij/, version 2.3.0). Background correction was performed for each band by subtracting background signals nearby the band. The signals from proteins of interest were first normalized to the signals from GAPDH, and then the signals at each time point were normalized to the signals at time zero for the fold change of phosphorylation from the basal level. Western blot was performed on cell lysates collected from three biological replicates.

TREKING

To build TREKING models, we incorporated multiple methodologies, the details of which can be found in the methods that follow. Specifically, TREKING models are built by first employing elastic net regularization in a temporally resolved way that generates a list of predicted functional kinases within each time window. This generates a temporal trace for each predicted kinase, and these temporal traces are then organized into “neurons” using SOM methodology. To generate local networks for the kinases within each “neuron,” we performed network generation steps, which resulted in the comprehensive TREKING models that are described within this study.

Literature search

To compare the scope of previous research with the current study, a comprehensive literature search on protein kinases reported to regulate barrier function was performed using the free search engine PubMed (https://pubmed.ncbi.nlm.nih.gov). The search terms were “endothelial barrier thrombin” plus kinase gene names, and the search results were filtered so that only articles published on or after year 2000 were shown. The search was performed on each of the 518 human protein kinases. The search was not restricted to studies on HBMECs; instead, studies on thrombin-induced barrier disruption using endothelial cell lines, primary endothelial cells isolated from different organs, or in vivo studies using mice or rats were all included. To compare with our work, studies or data beyond a 6-h time window (after thrombin treatment) were excluded.

Statistical analysis

A t test was used to evaluate the difference of kinase activity between non-treated and thrombin-treated conditions. SciPy statistical package (https://github.com/scipy/scipy, version 1.8.0) was used to perform the t tests. At each time point, both P-value and fold change of phosphorylation with respect to non-treated cells were reported to determine if the two sets of data are different from each other. A P-value below 0.05 or all biological replicates reporting a fold change increasing or decreasing by at least 20% compared with non-treated cells indicates that the kinase activity is different between non-treated and thrombin-treated conditions.