Decoding the function of Atg13 phosphorylation reveals a role of Atg11 in bulk autophagy initiation.

Autophagy is initiated by the assembly of multiple autophagy-related proteins that form the phagophore assembly site where autophagosomes are formed. Atg13 is essential early in this process, and a hub of extensive phosphorylation. How these multiple phosphorylations contribute to autophagy initiation, however, is not well understood. Here we comprehensively analyze the role of phosphorylation events on Atg13 during nutrient-rich conditions and nitrogen starvation. We identify and functionally characterize 48 in vivo phosphorylation sites on Atg13. By generating reciprocal mutants, which mimic the dephosphorylated active and phosphorylated inactive state of Atg13, we observe that disrupting the dynamic regulation of Atg13 leads to insufficient or excessive autophagy, which are both detrimental to cell survival. We furthermore demonstrate an involvement of Atg11 in bulk autophagy even during nitrogen starvation, where it contributes together with Atg1 to the multivalency that drives phase separation of the phagophore assembly site. These findings reveal the importance of post-translational regulation on Atg13 early during autophagy initiation, which provides additional layers of regulation to control bulk autophagy activity and integrate cellular signals.

A Causal Model of Ion Interference Enables Assessment and Correction of Ratio Compression in Multiplex Proteomics.

Multiplex proteomics using isobaric labeling tags has emerged as a powerful tool for the simultaneous relative quantification of peptides and proteins across multiple experimental conditions. However, the quantitative accuracy of the approach is largely compromised by ion interference, a phenomenon that causes fold changes to appear compressed. The degree of compression is generally unknown, and the contributing factors are poorly understood. In this study, we thoroughly characterized ion interference at the MS2 level using a defined two-proteome experimental system with known ground-truth. We discovered remarkably poor agreement between the apparent precursor purity in the isolation window and the actual level of observed reporter ion interference in MS2 scans-a discrepancy that we found resolved by considering cofragmentation of peptide ions hidden within the spectral "noise" of the MS1 isolation window. To address this issue, we developed a regression modeling strategy to accurately predict reporter ion interference in any dataset. Finally, we demonstrate the utility of our procedure for improved fold change estimation and unbiased PTM site-to-protein normalization. All computational tools and code required to apply this method to any MS2 TMT dataset are documented and freely available.

SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery.

This paper introduces the "SurgT: Surgical Tracking" challenge which was organized in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). There were two purposes for the creation of this challenge: (1) the establishment of the first standardized benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided. Participants were assigned the task of developing algorithms to track the movement of soft tissues, represented by bounding boxes, in stereo endoscopic videos. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The metric used for ranking the methods was the Expected Average Overlap (EAO) score, which measures the average overlap between a tracker's and the ground truth bounding boxes. Coming first in the challenge was the deep learning submission by ICVS-2Ai with a superior EAO score of 0.617. This method employs ARFlow to estimate unsupervised dense optical flow from cropped images, using photometric and regularization losses. Second, Jmees with an EAO of 0.583, uses deep learning for surgical tool segmentation on top of a non-deep learning baseline method: CSRT. CSRT by itself scores a similar EAO of 0.563. The results from this challenge show that currently, non-deep learning methods are still competitive. The dataset and benchmarking tool created for this challenge have been made publicly available at https://surgt.grand-challenge.org/. This challenge is expected to contribute to the development of autonomous robotic surgery and other digital surgical technologies.

GSE1 links the HDAC1/CoREST co-repressor complex to DNA damage.

Post-translational modifications of histones are important regulators of the DNA damage response (DDR). By using affinity purification mass spectrometry (AP-MS) we discovered that genetic suppressor element 1 (GSE1) forms a complex with the HDAC1/CoREST deacetylase/demethylase co-repressor complex. In-depth phosphorylome analysis revealed that loss of GSE1 results in impaired DDR, ATR signalling and γH2AX formation upon DNA damage induction. Altered profiles of ATR target serine-glutamine motifs (SQ) on DDR-related hallmark proteins point to a defect in DNA damage sensing. In addition, GSE1 knock-out cells show hampered DNA damage-induced phosphorylation on SQ motifs of regulators of histone post-translational modifications, suggesting altered histone modification. While loss of GSE1 does not affect the histone deacetylation activity of CoREST, GSE1 appears to be essential for binding of the deubiquitinase USP22 to CoREST and for the deubiquitination of H2B K120 in response to DNA damage. The combination of deacetylase, demethylase, and deubiquitinase activity makes the USP22-GSE1-CoREST subcomplex a multi-enzymatic eraser that seems to play an important role during DDR. Since GSE1 has been previously associated with cancer progression and survival our findings are potentially of high medical relevance.

Chemical Acetylation of Ligands and Two-Step Digestion Protocol for Reducing Codigestion in Affinity Purification-Mass Spectrometry.

We present an effective, fast, and user-friendly method to reduce codigestion of bead-bound ligands, such as antibodies or streptavidin, in affinity purification-mass spectrometry experiments. A short preincubation of beads with Sulfo-NHS-Acetate leads to chemical acetylation of lysine residues, making ligands insusceptible to Lys-C-mediated proteolysis. In contrast to similar approaches, our procedure offers the advantage of exclusively using nontoxic chemicals and employing mild chemical reaction conditions. After binding of bait proteins to Sulfo-NHS-Acetate treated beads, we employ a two-step digestion protocol with the sequential use of Lys-C protease for on-bead digestion followed by in-solution digestion of the released proteins with trypsin. The implementation of this protocol results in a strong reduction of contaminating ligand peptides, which allows significantly higher amounts of sample to be subjected to LC-MS analysis, improving sensitivity and quantitative accuracy.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark.

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery.

Domain generalization improves end-to-end object detection for real-time surgical tool detection.

PURPOSE: Computer assistance for endoscopic surgery depends on knowledge about the contents in an endoscopic scene. An important step of analysing the video contents is real-time surgical tool detection. Most methods for tool detection nevertheless depend on multi-step algorithms building upon prior knowledge like anchor boxes or non-maximum suppression which ultimately decrease performance. A real-world difficulty encountered by learning-based methods are limited datasets. Training a neural network on data matching a specific distribution (e.g. from a single hospital or showing a specific type of surgery) can result in a lack of generalization. METHODS: In this paper, we propose the application of a transformer based architecture for end-to-end tool detection. This architecture promises state-of-the-art accuracy while decreasing the complexity resulting in improved run-time performance. To improve the lack of cross-domain generalization due to limited datasets, we enhance the architecture with a latent feature space via variational encoding to capture common intra-domain information. This feature space models the linear dependencies between domains by constraining their rank. RESULTS: The trained neural networks show a distinct improvement on out-of-domain data indicating better generalization to unseen domains. Inference with the end-to-end architecture can be performed at up to 138 frames per second (FPS) achieving a speedup in comparison to older approaches. CONCLUSIONS: Experimental results on three representative datasets demonstrate the performance of the method. We also show that our approach leads to better domain generalization.

A Proteomic Approach for the Quantification of Posttranslational Protein Lysine Acetylation in Candida albicans.

Candida albicans is a normal component of the human microflora that colonizes mucosal/epithelial surfaces and the gastrointestinal tract as a commensal organism. However, in an immunocompromised host, it can cause life-threatening infections of high mortality and morbidity. Virulence as well as antifungal drug resistance of C. albicans is often regulated by posttranslational modifications (PTM) of proteins via lysine acetylation by lysine acetyltransferases. Here, we report an experimental approach using tandem mass tag (TMT) labeling for the detection and quantification of lysine acetylation of histone and nonhistone proteins in C. albicans.

In Search of a Universal Method: A Comparative Survey of Bottom-Up Proteomics Sample Preparation Methods.

Robust, efficient, and reproducible protein extraction and sample processing is a key step for bottom-up proteomics analyses. While many sample preparation protocols for mass spectrometry have been described, selecting an appropriate method remains challenging since some protein classes may require specialized solubilization, precipitation, and digestion procedures. Here, we present a comprehensive comparison of the 16 most widely used sample preparation methods, covering in-solution digests, device-based methods, and commercially available kits. We find a remarkably good performance of the majority of the protocols with high reproducibility, little method dependency, and low levels of artifact formation. However, we revealed method-dependent differences in the recovery of specific protein features, which we summarized in a descriptive guide matrix. Our work thereby provides a solid basis for the selection of MS sample preparation strategies for a given proteomics project.

A toolbox for class I HDACs reveals isoform specific roles in gene regulation and protein acetylation.

The class I histone deacetylases are essential regulators of cell fate decisions in health and disease. While pan- and class-specific HDAC inhibitors are available, these drugs do not allow a comprehensive understanding of individual HDAC function, or the therapeutic potential of isoform-specific targeting. To systematically compare the impact of individual catalytic functions of HDAC1, HDAC2 and HDAC3, we generated human HAP1 cell lines expressing catalytically inactive HDAC enzymes. Using this genetic toolbox we compare the effect of individual HDAC inhibition with the effects of class I specific inhibitors on cell viability, protein acetylation and gene expression. Individual inactivation of HDAC1 or HDAC2 has only mild effects on cell viability, while HDAC3 inactivation or loss results in DNA damage and apoptosis. Inactivation of HDAC1/HDAC2 led to increased acetylation of components of the COREST co-repressor complex, reduced deacetylase activity associated with this complex and derepression of neuronal genes. HDAC3 controls the acetylation of nuclear hormone receptor associated proteins and the expression of nuclear hormone receptor regulated genes. Acetylation of specific histone acetyltransferases and HDACs is sensitive to inactivation of HDAC1/HDAC2. Over a wide range of assays, we determined that in particular HDAC1 or HDAC2 catalytic inactivation mimics class I specific HDAC inhibitors. Importantly, we further demonstrate that catalytic inactivation of HDAC1 or HDAC2 sensitizes cells to specific cancer drugs. In summary, our systematic study revealed isoform-specific roles of HDAC1/2/3 catalytic functions. We suggest that targeted genetic inactivation of particular isoforms effectively mimics pharmacological HDAC inhibition allowing the identification of relevant HDACs as targets for therapeutic intervention.

Regulation of Pkc1 Hyper-Phosphorylation by Genotoxic Stress.

The cell wall integrity (CWI) signaling pathway is best known for its roles in cell wall biogenesis. However, it is also thought to participate in the response to genotoxic stress. The stress-activated protein kinase Mpk1 (Slt2, is activated by DNA damaging agents through an intracellular mechanism that does not involve the activation of upstream components of the CWI pathway. Additional observations suggest that protein kinase C (Pkc1), the top kinase in the CWI signaling cascade, also has a role in the response to genotoxic stress that is independent of its recognized function in the activation of Mpk1. Pkc1 undergoes hyper-phosphorylation specifically in response to genotoxic stress; we have found that this requires the DNA damage checkpoint kinases Mec1 (Mitosis Entry Checkpoint) and Tel1 (TELomere maintenance), but not their effector kinases. We demonstrate that the casein kinase 1 (CK1) ortholog, Hrr25 (HO and Radiation Repair), previously implicated in the DNA damage transcriptional response, associates with Pkc1 under conditions of genotoxic stress. We also found that the induced association of Hrr25 with Pkc1 requires Mec1 and Tel1, and that Hrr25 catalytic activity is required for Pkc1-hyperphosphorylation, thereby delineating a pathway from the checkpoint kinases to Pkc1. We used SILAC mass spectrometry to identify three residues within Pkc1 the phosphorylation of which was stimulated by genotoxic stress. We mutated these residues as well as a collection of 13 phosphorylation sites within the regulatory domain of Pkc1 that fit the consensus for CK1 sites. Mutation of the 13 Pkc1 phosphorylation sites blocked hyper-phosphorylation and diminished RNR3 (RiboNucleotide Reductase) basal expression and induction by genotoxic stress, suggesting that Pkc1 plays a role in the DNA damage transcriptional response.

MIcro-surgical anastomose workflow recognition challenge report.

BACKGROUND AND OBJECTIVE: Automatic surgical workflow recognition is an essential step in developing context-aware computer-assisted surgical systems. Video recordings of surgeries are becoming widely accessible, as the operational field view is captured during laparoscopic surgeries. Head and ceiling mounted cameras are also increasingly being used to record videos in open surgeries. This makes videos a common choice in surgical workflow recognition. Additional modalities, such as kinematic data captured during robot-assisted surgeries, could also improve workflow recognition. This paper presents the design and results of the MIcro-Surgical Anastomose Workflow recognition on training sessions (MISAW) challenge whose objective was to develop workflow recognition models based on kinematic data and/or videos. METHODS: The MISAW challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations. The latter described the sequences at three different granularity levels: phase, step, and activity. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score. RESULTS: Six teams participated in at least one task. All models employed deep learning models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), or a combination of both. The best models achieved accuracy above 95%, 80%, 60%, and 75% respectively for recognition of phases, steps, activities, and multi-granularity. The RNN-based models outperformed the CNN-based ones as well as the dedicated modality models compared to the multi-granularity except for activity recognition. CONCLUSION: For high levels of granularity, the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available at http://www.synapse.org/MISAW to encourage further research in surgical workflow recognition.

A phosphatase-centric mechanism drives stress signaling response.

Changing environmental cues lead to the adjustment of cellular physiology by phosphorylation signaling networks that typically center around kinases as active effectors and phosphatases as antagonistic elements. Here, we report a signaling mechanism that reverses this principle. Using the hyperosmotic stress response in Saccharomyces cerevisiae as a model system, we find that a phosphatase-driven mechanism causes induction of phosphorylation. The key activating step that triggers this phospho-proteomic response is the Endosulfine-mediated inhibition of protein phosphatase 2A-Cdc55 (PP2ACdc55 ), while we do not observe concurrent kinase activation. In fact, many of the stress-induced phosphorylation sites appear to be direct substrates of the phosphatase, rendering PP2ACdc55 the main downstream effector of a signaling response that operates in parallel and independent of the well-established kinase-centric stress signaling pathways. This response affects multiple cellular processes and is required for stress survival. Our results demonstrate how a phosphatase can assume the role of active downstream effectors during signaling and allow re-evaluating the impact of phosphatases on shaping the phosphorylome.

Co-occurrence balanced time series classification for the semi-supervised recognition of surgical smoke.

PURPOSE: Automatic recognition and removal of smoke in surgical procedures can reduce risks to the patient by supporting the surgeon. Surgical smoke changes its visibility over time, impacting the vision depending on its amount and the volume of the body cavity. While modern deep learning algorithms for computer vision require large amounts of data, annotations for training are scarce. This paper investigates the use of unlabeled training data with a modern time-based deep learning algorithm. METHODS: We propose to improve the state of the art in smoke recognition by enhancing a image classifier based on convolutional neural networks with a recurrent architecture thereby providing temporal context to the algorithm. We enrich the training with unlabeled recordings from similar procedures. The influence of surgical tools on the smoke recognition task is studied to reduce a possible bias. RESULTS: The evaluations show that smoke recognition benefits from the additional temporal information during training. The use of unlabeled data from the same domain in a semi-supervised training procedure shows additional improvements reaching an accuracy of 86.8%. The proposed balancing policy is shown to have a positive impact on learning the discrimination of co-occurring surgical tools. CONCLUSIONS: This study presents, to the best of our knowledge, the first use of a time series algorithm for the recognition of surgical smoke and the first use of this algorithm in the described semi-supervised setting. We show that the performance improvements with unlabeled data can be enhanced by integrating temporal context. We also show that adaption of the data distribution is beneficial to avoid learning biases.

Peroxiredoxin promotes longevity and H2O2-resistance in yeast through redox-modulation of protein kinase A.

Peroxiredoxins are H2O2 scavenging enzymes that also carry out H2O2 signaling and chaperone functions. In yeast, the major cytosolic peroxiredoxin, Tsa1 is required for both promoting resistance to H2O2 and extending lifespan upon caloric restriction. We show here that Tsa1 effects both these functions not by scavenging H2O2, but by repressing the nutrient signaling Ras-cAMP-PKA pathway at the level of the protein kinase A (PKA) enzyme. Tsa1 stimulates sulfenylation of cysteines in the PKA catalytic subunit by H2O2 and a significant proportion of the catalytic subunits are glutathionylated on two cysteine residues. Redox modification of the conserved Cys243 inhibits the phosphorylation of a conserved Thr241 in the kinase activation loop and enzyme activity, and preventing Thr241 phosphorylation can overcome the H2O2 sensitivity of Tsa1-deficient cells. Results support a model of aging where nutrient signaling pathways constitute hubs integrating information from multiple aging-related conduits, including a peroxiredoxin-dependent response to H2O2.

Mechanical stress impairs pheromone signaling via Pkc1-mediated regulation of the MAPK scaffold Ste5.

Cells continuously adapt cellular processes by integrating external and internal signals. In yeast, multiple stress signals regulate pheromone signaling to prevent mating under unfavorable conditions. However, the underlying crosstalk mechanisms remain poorly understood. Here, we show that mechanical stress activates Pkc1, which prevents lysis of pheromone-treated cells by inhibiting polarized growth. In vitro Pkc1 phosphorylates conserved residues within the RING-H2 domains of the scaffold proteins Far1 and Ste5, which are also phosphorylated in vivo. Interestingly, Pkc1 triggers dispersal of Ste5 from mating projections upon mechanically induced stress and during cell-cell fusion, leading to inhibition of the MAPK Fus3. Indeed, RING phosphorylation interferes with Ste5 membrane association by preventing binding to the receptor-linked Gßγ protein. Cells expressing nonphosphorylatable Ste5 undergo increased lysis upon mechanical stress and exhibit defects in cell-cell fusion during mating, which is exacerbated by simultaneous expression of nonphosphorylatable Far1. These results uncover a mechanical stress-triggered crosstalk mechanism modulating pheromone signaling, polarized growth, and cell-cell fusion during mating.

Novel interconnections of HOG signaling revealed by combined use of two proteomic software packages.

Modern quantitative mass spectrometry (MS)-based proteomics enables researchers to unravel signaling networks by monitoring proteome-wide cellular responses to different stimuli. MS-based analysis of signaling systems usually requires an integration of multiple quantitative MS experiments, which remains challenging, given that the overlap between these datasets is not necessarily comprehensive. In a previous study we analyzed the impact of the yeast mitogen-activated protein kinase (MAPK) Hog1 on the hyperosmotic stress-affected phosphorylome. Using a combination of a series of hyperosmotic stress and kinase inhibition experiments, we identified a broad range of direct and indirect substrates of the MAPK. Here we re-evaluate this extensive MS dataset and demonstrate that a combined analysis based on two software packages, MaxQuant and Proteome Discoverer, increases the coverage of Hog1-target proteins by 30%. Using protein-protein proximity assays we show that the majority of new targets gained by this analysis are indeed Hog1-interactors. Additionally, kinetic profiles indicate differential trends of Hog1-dependent versus Hog1-independent phosphorylation sites. Our findings highlight a previously unrecognized interconnection between Hog1 signaling and the RAM signaling network, as well as sphingolipid homeostasis.

A constitutive active allele of the transcription factor Msn2 mimicking low PKA activity dictates metabolic remodeling in yeast.

In yeast, protein kinase A (PKA) adjusts transcriptional profiles, metabolic rates, and cell growth in accord with carbon source availability. PKA affects gene expression mostly via the transcription factors Msn2 and Msn4, two key regulators of the environmental stress response. Here we analyze the role of the PKA-Msn2 signaling module using an Msn2 allele that harbors serine-to-alanine substitutions at six functionally important PKA motifs (Msn2A6) . Expression of Msn2A6 mimics low PKA activity, entails a transcription profile similar to that of respiring cells, and prevents formation of colonies on glucose-containing medium. Furthermore, Msn2A6 leads to high oxygen consumption and hence high respiratory activity. Substantially increased intracellular concentrations of several carbon metabolites, such as trehalose, point to a metabolic adjustment similar to diauxic shift. This partial metabolic switch is the likely cause for the slow-growth phenotype in the presence of glucose. Consistently, Msn2A6 expression does not interfere with growth on ethanol and tolerated is to a limited degree in deletion mutant strains with a gene expression signature corresponding to nonfermentative growth. We propose that the lethality observed in mutants with hampered PKA activity resides in metabolic reprogramming that is initiated by Msn2 hyperactivity.

CDK and MAPK Synergistically Regulate Signaling Dynamics via a Shared Multi-site Phosphorylation Region on the Scaffold Protein Ste5.

We report an unanticipated system of joint regulation by cyclin-dependent kinase (CDK) and mitogen-activated protein kinase (MAPK), involving collaborative multi-site phosphorylation of a single substrate. In budding yeast, the protein Ste5 controls signaling through a G1 arrest pathway. Upon cell-cycle entry, CDK inhibits Ste5 via multiple phosphorylation sites, disrupting its membrane association. Using quantitative time-lapse microscopy, we examined Ste5 membrane recruitment dynamics at different cell-cycle stages. Surprisingly, in S phase, where Ste5 recruitment should be blocked, we observed an initial recruitment followed by a steep drop-off. This delayed inhibition revealed a requirement for both CDK activity and negative feedback from the pathway MAPK Fus3. Mutagenesis, mass spectrometry, and electrophoretic analyses suggest that the CDK and MAPK modify shared sites, which are most extensively phosphorylated when both kinases are active and able to bind their docking sites on Ste5. Such collaborative phosphorylation can broaden regulatory inputs and diversify output dynamics of signaling pathways.