Protein interactome of 3',5'-cAMP reveals its role in regulating the actin cytoskeleton.

Identification of protein interactors is ideally suited for the functional characterization of small molecules. 3',5'-cAMP is an evolutionary ancient signaling metabolite largely uncharacterized in plants. To tap into the physiological roles of 3',5'-cAMP, we used a chemo-proteomics approach, thermal proteome profiling (TPP), for the unbiased identification of 3',5'-cAMP protein targets. TPP measures shifts in the protein thermal stability upon ligand binding. Comprehensive proteomics analysis yielded a list of 51 proteins significantly altered in their thermal stability upon incubation with 3',5'-cAMP. The list contained metabolic enzymes, ribosomal subunits, translation initiation factors, and proteins associated with the regulation of plant growth such as CELL DIVISION CYCLE 48. To functionally validate obtained results, we focused on the role of 3',5'-cAMP in regulating the actin cytoskeleton suggested by the presence of actin among the 51 identified proteins. 3',5'-cAMP supplementation affected actin organization by inducing actin-bundling. Consistent with these results, the increase in 3',5'-cAMP levels, obtained either by feeding or by chemical modulation of 3',5'-cAMP metabolism, was sufficient to partially rescue the short hypocotyl phenotype of the actin2 actin7 mutant, severely compromised in actin level. The observed rescue was specific to 3',5'-cAMP, as demonstrated using a positional isomer 2',3'-cAMP, and true for the nanomolar 3',5'-cAMP concentrations reported for plant cells. In vitro characterization of the 3',5'-cAMP-actin pairing argues against a direct interaction between actin and 3',5'-cAMP. Alternative mechanisms by which 3',5'-cAMP would affect actin dynamics, such as by interfering with calcium signaling, are discussed. In summary, our work provides a specific resource, 3',5'-cAMP interactome, as well as functional insight into 3',5'-cAMP-mediated regulation in plants.

Tyr-Asp inhibition of glyceraldehyde 3-phosphate dehydrogenase affects plant redox metabolism.

How organisms integrate metabolism with the external environment is a central question in biology. Here, we describe a novel regulatory small molecule, a proteogenic dipeptide Tyr-Asp, which improves plant tolerance to oxidative stress by directly interfering with glucose metabolism. Specifically, Tyr-Asp inhibits the activity of a key glycolytic enzyme, glyceraldehyde 3-phosphate dehydrogenase (GAPC), and redirects glucose toward pentose phosphate pathway (PPP) and NADPH production. In line with the metabolic data, Tyr-Asp supplementation improved the growth performance of both Arabidopsis and tobacco seedlings subjected to oxidative stress conditions. Moreover, inhibition of Arabidopsis phosphoenolpyruvate carboxykinase (PEPCK) activity by a group of branched-chain amino acid-containing dipeptides, but not by Tyr-Asp, points to a multisite regulation of glycolytic/gluconeogenic pathway by dipeptides. In summary, our results open the intriguing possibility that proteogenic dipeptides act as evolutionarily conserved small-molecule regulators at the nexus of stress, protein degradation, and metabolism.

Global mapping of protein-metabolite interactions in Saccharomyces cerevisiae reveals that Ser-Leu dipeptide regulates phosphoglycerate kinase activity.

Protein-metabolite interactions are of crucial importance for all cellular processes but remain understudied. Here, we applied a biochemical approach named PROMIS, to address the complexity of the protein-small molecule interactome in the model yeast Saccharomyces cerevisiae. By doing so, we provide a unique dataset, which can be queried for interactions between 74 small molecules and 3982 proteins using a user-friendly interface available at https://promis.mpimp-golm.mpg.de/yeastpmi/ . By interpolating PROMIS with the list of predicted protein-metabolite interactions, we provided experimental validation for 225 binding events. Remarkably, of the 74 small molecules co-eluting with proteins, 36 were proteogenic dipeptides. Targeted analysis of a representative dipeptide, Ser-Leu, revealed numerous protein interactors comprising chaperones, proteasomal subunits, and metabolic enzymes. We could further demonstrate that Ser-Leu binding increases activity of a glycolytic enzyme phosphoglycerate kinase (Pgk1). Consistent with the binding analysis, Ser-Leu supplementation leads to the acute metabolic changes and delays timing of a diauxic shift. Supported by the dipeptide accumulation analysis our work attests to the role of Ser-Leu as a metabolic regulator at the interface of protein degradation and central metabolism.

Adaptive T-cell immunity controls senescence-prone MyD88- or CARD11-mutant B-cell lymphomas.

Aberrant B-cell receptor/NF-κB signaling is a hallmark feature of B-cell non-Hodgkin lymphomas, especially in diffuse large B-cell lymphoma (DLBCL). Recurrent mutations in this cascade, for example, in CD79B, CARD11, or NFKBIZ, and also in the Toll-like receptor pathway transducer MyD88, all deregulate NF-κB, but their differential impact on lymphoma development and biology remains to be determined. Here, we functionally investigate primary mouse lymphomas that formed in recipient mice of Eµ-myc transgenic hematopoietic stem cells stably transduced with naturally occurring NF-κB mutants. Although most mutants supported Myc-driven lymphoma formation through repressed apoptosis, CARD11- or MyD88-mutant lymphoma cells selectively presented with a macrophage-activating secretion profile, which, in turn, strongly enforced transforming growth factor ß (TGF-ß)-mediated senescence in the lymphoma cell compartment. However, MyD88- or CARD11-mutant Eµ-myc lymphomas exhibited high-level expression of the immune-checkpoint mediator programmed cell death ligand 1 (PD-L1), thus preventing their efficient clearance by adaptive host immunity. Conversely, these mutant-specific dependencies were therapeutically exploitable by anti-programmed cell death 1 checkpoint blockade, leading to direct T-cell-mediated lysis of predominantly but not exclusively senescent lymphoma cells. Importantly, mouse-based mutant MyD88- and CARD11-derived signatures marked DLBCL subgroups exhibiting mirroring phenotypes with respect to the triad of senescence induction, macrophage attraction, and evasion of cytotoxic T-cell immunity. Complementing genomic subclassification approaches, our functional, cross-species investigation unveils pathogenic principles and therapeutic vulnerabilities applicable to and testable in human DLBCL subsets that may inform future personalized treatment strategies.

Reduced proteasome activity in the aging brain results in ribosome stoichiometry loss and aggregation.

A progressive loss of protein homeostasis is characteristic of aging and a driver of neurodegeneration. To investigate this process quantitatively, we characterized proteome dynamics during brain aging in the short-lived vertebrate Nothobranchius furzeri combining transcriptomics and proteomics. We detected a progressive reduction in the correlation between protein and mRNA, mainly due to post-transcriptional mechanisms that account for over 40% of the age-regulated proteins. These changes cause a progressive loss of stoichiometry in several protein complexes, including ribosomes, which show impaired assembly/disassembly and are enriched in protein aggregates in old brains. Mechanistically, we show that reduction of proteasome activity is an early event during brain aging and is sufficient to induce proteomic signatures of aging and loss of stoichiometry in vivo. Using longitudinal transcriptomic data, we show that the magnitude of early life decline in proteasome levels is a major risk factor for mortality. Our work defines causative events in the aging process that can be targeted to prevent loss of protein homeostasis and delay the onset of age-related neurodegeneration.

Identifying drug targets in tissues and whole blood with thermal-shift profiling.

Monitoring drug-target interactions with methods such as the cellular thermal-shift assay (CETSA) is well established for simple cell systems but remains challenging in vivo. Here we introduce tissue thermal proteome profiling (tissue-TPP), which measures binding of small-molecule drugs to proteins in tissue samples from drug-treated animals by detecting changes in protein thermal stability using quantitative mass spectrometry. We report organ-specific, proteome-wide thermal stability maps and derive target profiles of the non-covalent histone deacetylase inhibitor panobinostat in rat liver, lung, kidney and spleen and of the B-Raf inhibitor vemurafenib in mouse testis. In addition, we devised blood-CETSA and blood-TPP and applied it to measure target and off-target engagement of panobinostat and the BET family inhibitor JQ1 directly in whole blood. Blood-TPP analysis of panobinostat confirmed its binding to known targets and also revealed thermal stabilization of the zinc-finger transcription factor ZNF512. These methods will help to elucidate the mechanisms of drug action in vivo.

Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins.

Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present nonparametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP data sets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. An open source software implementation of NPARC is provided.

Thermal proteome profiling monitors ligand interactions with cellular membrane proteins.

We extended thermal proteome profiling to detect transmembrane protein-small molecule interactions in cultured human cells. When we assessed the effects of detergents on ATP-binding profiles, we observed shifts in denaturation temperature for ATP-binding transmembrane proteins. We also observed cellular thermal shifts in pervanadate-induced T cell-receptor signaling, delineating the membrane target CD45 and components of the downstream pathway, and with drugs affecting the transmembrane transporters ATP1A1 and MDR1.

Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry.

The direct detection of drug-protein interactions in living cells is a major challenge in drug discovery research. Recently, we introduced an approach termed thermal proteome profiling (TPP), which enables the monitoring of changes in protein thermal stability across the proteome using quantitative mass spectrometry. We determined the intracellular thermal profiles for up to 7,000 proteins, and by comparing profiles derived from cultured mammalian cells in the presence or absence of a drug we showed that it was possible to identify direct and indirect targets of drugs in living cells in an unbiased manner. Here we demonstrate the complete workflow using the histone deacetylase inhibitor panobinostat. The key to this approach is the use of isobaric tandem mass tag 10-plex (TMT10) reagents to label digested protein samples corresponding to each temperature point in the melting curve so that the samples can be analyzed by multiplexed quantitative mass spectrometry. Important steps in the bioinformatic analysis include data normalization, melting curve fitting and statistical significance determination of compound concentration-dependent changes in protein stability. All analysis tools are made freely available as R and Python packages. The workflow can be completed in 2 weeks.

Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns.

MOTIVATION: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system's Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling. RESULTS: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase.