Deciphering the immunopeptidome in vivo reveals new tumour antigens.

Immunosurveillance of cancer requires the presentation of peptide antigens on major histocompatibility complex class I (MHC-I) molecules1-5. Current approaches to profiling of MHC-I-associated peptides, collectively known as the immunopeptidome, are limited to in vitro investigation or bulk tumour lysates, which limits our understanding of cancer-specific patterns of antigen presentation in vivo6. To overcome these limitations, we engineered an inducible affinity tag into the mouse MHC-I gene (H2-K1) and targeted this allele to the KrasLSL-G12D/+Trp53fl/fl mouse model (KP/KbStrep)7. This approach enabled us to precisely isolate MHC-I peptides from autochthonous pancreatic ductal adenocarcinoma and from lung adenocarcinoma (LUAD) in vivo. In addition, we profiled the LUAD immunopeptidome from the alveolar type 2 cell of origin up to late-stage disease. Differential peptide presentation in LUAD was not predictable by mRNA expression or translation efficiency and is probably driven by post-translational mechanisms. Vaccination with peptides presented by LUAD in vivo induced CD8+ T cell responses in naive mice and tumour-bearing mice. Many peptides specific to LUAD, including immunogenic peptides, exhibited minimal expression of the cognate mRNA, which prompts the reconsideration of antigen prediction pipelines that triage peptides according to transcript abundance8. Beyond cancer, the KbStrep allele is compatible with other Cre-driver lines to explore antigen presentation in vivo in the pursuit of understanding basic immunology, infectious disease and autoimmunity.

Antigen discovery for the development of cancer immunotherapy.

Central to successful cancer immunotherapy is effective T cell antitumor immunity. Multiple targeted immunotherapies engineered to invigorate T cell-driven antitumor immunity rely on identifying the repertoire of T cell antigens expressed on the tumor cell surface. Mass spectrometry-based survey of such antigens ("immunopeptidomics") combined with other omics platforms and computational algorithms has been instrumental in identifying and quantifying tumor-derived T cell antigens. In this review, we discuss the types of tumor antigens that have emerged for targeted cancer immunotherapy and the immunopeptidomics methods that are central in MHC peptide identification and quantification. We provide an overview of the strength and limitations of mass spectrometry-driven approaches and how they have been integrated with other technologies to discover targetable T cell antigens for cancer immunotherapy. We highlight some of the emerging cancer immunotherapies that successfully capitalized on immunopeptidomics, their challenges, and mass spectrometry-based strategies that can support their development.

MEK inhibition enhances presentation of targetable MHC-I tumor antigens in mutant melanomas.

Combining multiple therapeutic strategies in NRAS/BRAF mutant melanoma-namely MEK/BRAF kinase inhibitors, immune checkpoint inhibitors (ICIs), and targeted immunotherapies-may offer an improved survival benefit by overcoming limitations associated with any individual therapy. Still, optimal combination, order, and timing of administration remains under investigation. Here, we measure how MEK inhibition (MEKi) alters anti-tumor immunity by utilizing quantitative immunopeptidomics to profile changes in the peptide major histocompatibility molecules (pMHC) repertoire. These data reveal a collection of tumor antigens whose presentation levels are selectively augmented following therapy, including several epitopes present at over 1,000 copies per cell. We leveraged the tunable abundance of MEKi-modulated antigens by targeting four epitopes with pMHC-specific T cell engagers and antibody drug conjugates, enhancing cell killing in tumor cells following MEK inhibition. These results highlight drug treatment as a means to enhance immunotherapy efficacy by targeting specific upregulated pMHCs and provide a methodological framework for identifying, quantifying, and therapeutically targeting additional epitopes of interest.

Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer.

Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present.In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.

Absolute quantification of tumor antigens using embedded MHC-I isotopologue calibrants.

Absolute quantification measurements (copies per cell) of peptide major histocompatibility complex (pMHC) antigens are necessary to inform targeted immunotherapy drug design; however, existing methods for absolute quantification have critical limitations. Here, we present a platform termed SureQuant-IsoMHC, utilizing a series of pMHC isotopologues and internal standard-triggered targeted mass spectrometry to generate an embedded multipoint calibration curve to determine endogenous pMHC concentrations for a panel of 18 tumor antigens. We apply SureQuant-IsoMHC to measure changes in expression of our target panel in a melanoma cell line treated with a MEK inhibitor and translate this approach to estimate antigen concentrations in melanoma tumor biopsies.

Antibody protection from SARS-CoV-2 respiratory tract exposure and infection.

The COVID-19 pandemic has underscored the need to understand the dynamics of SARS-CoV-2 respiratory infection and protection provided by the immune response. SARS-CoV-2 infections are characterized by a particularly high viral load, and further by the small number of inhaled virions sufficient to generate a high viral titer in the nasal passage a few days after exposure. SARS-CoV-2 specific antibodies (Ab), induced from vaccines, previous infection, or inhaled monoclonal Ab, have proven effective against SARS-CoV-2 infection. Our goal in this work is to model the protective mechanisms that Ab can provide and to assess the degree of protection from individual and combined mechanisms at different locations in the respiratory tract. Neutralization, in which Ab bind to virion spikes and inhibit them from binding to and infecting target cells, is one widely reported protective mechanism. A second mechanism of Ab protection is muco-trapping, in which Ab crosslink virions to domains on mucin polymers, effectively immobilizing them in the mucus layer. When muco-trapped, the continuous clearance of the mucus barrier by coordinated ciliary propulsion entrains the trapped viral load toward the esophagus to be swallowed. We model and simulate the protection provided by either and both mechanisms at different locations in the respiratory tract, parametrized by the Ab titer and binding-unbinding rates of Ab to viral spikes and mucin domains. Our results illustrate limits in the degree of protection by neutralizing Ab alone, the powerful protection afforded by muco-trapping Ab, and the potential for dual protection by muco-trapping and neutralizing Ab to arrest a SARS-CoV-2 infection. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection.

The SARS-CoV-2 coronavirus continues to evolve with scores of mutations of the spike, membrane, envelope, and nucleocapsid structural proteins that impact pathogenesis. Infection data from nasal swabs, nasal PCR assays, upper respiratory samples, ex vivo cell cultures and nasal epithelial organoids reveal extreme variabilities in SARS-CoV-2 RNA titers within and between the variants. Some variabilities are naturally prone to clinical testing protocols and experimental controls. Here we focus on nasal viral load sensitivity arising from the timing of sample collection relative to onset of infection and from heterogeneity in the kinetics of cellular infection, uptake, replication, and shedding of viral RNA copies. The sources of between-variant variability are likely due to SARS-CoV-2 structural protein mutations, whereas within-variant population variability is likely due to heterogeneity in cellular response to that particular variant. With the physiologically faithful, agent-based mechanistic model of inhaled exposure and infection from (Chen et al., 2022), we perform statistical sensitivity analyses of the progression of nasal viral titers in the first 0-48 h post infection, focusing on three kinetic mechanisms. Model simulations reveal shorter latency times of infected cells (including cellular uptake, viral RNA replication, until the onset of viral RNA shedding) exponentially accelerate nasal viral load. Further, the rate of infectious RNA copies shed per day has a proportional influence on nasal viral load. Finally, there is a very weak, negative correlation of viral load with the probability of infection per virus-cell encounter, the model proxy for spike-receptor binding affinity.

Quantitative Consequences of Protein Carriers in Immunopeptidomics and Tyrosine Phosphorylation MS2 Analyses.

Utilizing a protein carrier in combination with isobaric labeling to "boost" the signal of other low-level samples in multiplexed analyses has emerged as an attractive strategy to enhance data quantity while minimizing protein input in mass spectrometry analyses. Recent applications of this approach include pMHC profiling and tyrosine phosphoproteomics, two applications that are often limited by large sample requirements. While including a protein carrier has been shown to increase the number of identifiable peptides in both applications, the impact of a protein carrier on quantitative accuracy remains to be thoroughly explored, particularly in relevant biological contexts where samples exhibit dynamic changes in abundance across peptides. Here, we describe two sets of analyses comparing MS2-based quantitation using a 20× protein carrier in pMHC analyses and a high (~100×) and low (~9×) protein carrier in pTyr analyses, using CDK4/6 inhibitors and EGF stimulation to drive dynamic changes in the immunopeptidome and phosphoproteome, respectively. In both applications, inclusion of a protein carrier resulted in an increased number of MHC peptide or phosphopeptide identifications, as expected. At the same time, quantitative accuracy was adversely affected by the presence of the protein carrier, altering interpretation of the underlying biological response to perturbation. Moreover, for tyrosine phosphoproteomics, the presence of high levels of protein carrier led to a large number of missing values for endogenous phosphopeptides, leading to fewer quantifiable peptides relative to the "no-boost" condition. These data highlight the unique limitations and future experimental considerations for both analysis types and provide a framework for assessing quantitative accuracy in protein carrier experiments moving forward.

The power of weak, transient interactions across biology: A paradigm of emergent behavior.

A growing list of diverse biological systems and their equally diverse functionalities provides realizations of a paradigm of emergent behavior. In each of these biological systems, pervasive ensembles of weak, short-lived, spatially local interactions act autonomously to convey functionalities at larger spatial and temporal scales. In this article, a range of diverse systems and functionalities are presented in a cursory manner with literature citations for further details. Then two systems and their properties are discussed in more detail: yeast chromosome biology and human respiratory mucus.

Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection.

Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate of infectious virions. We apply this baseline model in the absence of immune protection to explore immediate, short-term outcomes from novel SARS-CoV-2 depositions onto the air-ASL interface. For each RT location, we compute probability to clear versus infect; per infected cell, we compute dynamics of viral load and cell infection. Results reveal that nasal infections are highly likely within 1-2 days from minimal exposure, and alveolar pneumonia occurs only if infectious virions are deposited directly into alveolar ducts and sacs, not via retrograde propagation to the deep lung. Furthermore, to infect just 1% of the 140 m2 of alveolar surface area within 1 week, either 103 boluses each with 106 infectious virions or 106 aerosols with one infectious virion, all physically separated, must be directly deposited. These results strongly suggest that COVID-19 disease occurs in stages: a nasal/upper RT infection, followed by self-transmission of infection to the deep lung. Two mechanisms of self-transmission are persistent aspiration of infected nasal boluses that drain to the deep lung and repeated rupture of nasal aerosols from infected mucosal membranes by speaking, singing, or cheering that are partially inhaled, exhaled, and re-inhaled, to the deep lung.

A hybrid discrete-continuum model of immune responses to SARS-CoV-2 infection in the lung alveolar region, with a focus on interferon induced innate response.

We develop a lattice-based, hybrid discrete-continuum modeling framework for SARS-CoV-2 exposure and infection in the human lung alveolar region, or parenchyma, the massive surface area for gas exchange. COVID-19 pneumonia is alveolar infection by the SARS-CoV-2 virus significant enough to compromise gas exchange. The modeling framework orchestrates the onset and progression of alveolar infection, spatially and temporally, beginning with a pre-immunity baseline, upon which we superimpose multiple mechanisms of immune protection conveyed by interferons and antibodies. The modeling framework is tunable to individual profiles, focusing here on degrees of innate immunity, and to the evolving infection-replication properties of SARS-CoV-2 variant strains. The model employs partial differential equations for virion, interferon, and antibody concentrations governed by diffusion in the thin fluid coating of alveolar cells, species and lattice interactions corresponding to sources and sinks for each species, and multiple immune protections signaled by interferons. The spatial domain is a two-dimensional, rectangular lattice of alveolar type I (non-infectable) and type II (infectable) cells with a stochastic, species-concentration-governed, switching dynamics of type II lattice sites from healthy to infected. Once infected, type II cells evolve through three phases: an eclipse phase during which RNA copies (virions) are assembled; a shedding phase during which virions and interferons are released; and then cell death. Model simulations yield the dynamic spread of, and immune protection against, alveolar infection and viral load from initial sites of exposure. We focus in this paper on model illustrations of the diversity of outcomes possible from alveolar infection, first absent of immune protection, and then with varying degrees of four known mechanisms of interferon-induced innate immune protection. We defer model illustrations of antibody protection to future studies. Results presented reinforce previous recognition that interferons produced solely by infected cells are insufficient to maintain a high efficacy level of immune protection, compelling additional mechanisms to clear alveolar infection, such as interferon production by immune cells and adaptive immunity (e.g., T cells). This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Statistical mechanics of chromosomes: in vivo and in silico approaches reveal high-level organization and structure arise exclusively through mechanical feedback between loop extruders and chromatin substrate properties.

The revolution in understanding higher order chromosome dynamics and organization derives from treating the chromosome as a chain polymer and adapting appropriate polymer-based physical principles. Using basic principles, such as entropic fluctuations and timescales of relaxation of Rouse polymer chains, one can recapitulate the dominant features of chromatin motion observed in vivo. An emerging challenge is to relate the mechanical properties of chromatin to more nuanced organizational principles such as ubiquitous DNA loops. Toward this goal, we introduce a real-time numerical simulation model of a long chain polymer in the presence of histones and condensin, encoding physical principles of chromosome dynamics with coupled histone and condensin sources of transient loop generation. An exact experimental correlate of the model was obtained through analysis of a model-matching fluorescently labeled circular chromosome in live yeast cells. We show that experimentally observed chromosome compaction and variance in compaction are reproduced only with tandem interactions between histone and condensin, not from either individually. The hierarchical loop structures that emerge upon incorporation of histone and condensin activities significantly impact the dynamic and structural properties of chromatin. Moreover, simulations reveal that tandem condensin-histone activity is responsible for higher order chromosomal structures, including recently observed Z-loops.

Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells.

INTRODUCTION: Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions. AREAS COVERED: We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation. EXPERT OPINION: Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.

Phosphoproteomics identifies microglial Siglec-F inflammatory response during neurodegeneration.

Alzheimer's disease (AD) is characterized by the appearance of amyloid-ß plaques, neurofibrillary tangles, and inflammation in brain regions involved in memory. Using mass spectrometry, we have quantified the phosphoproteome of the CK-p25, 5XFAD, and Tau P301S mouse models of neurodegeneration. We identified a shared response involving Siglec-F which was upregulated on a subset of reactive microglia. The human paralog Siglec-8 was also upregulated on microglia in AD. Siglec-F and Siglec-8 were upregulated following microglial activation with interferon gamma (IFNγ) in BV-2 cell line and human stem cell-derived microglia models. Siglec-F overexpression activates an endocytic and pyroptotic inflammatory response in BV-2 cells, dependent on its sialic acid substrates and immunoreceptor tyrosine-based inhibition motif (ITIM) phosphorylation sites. Related human Siglecs induced a similar response in BV-2 cells. Collectively, our results point to an important role for mouse Siglec-F and human Siglec-8 in regulating microglial activation during neurodegeneration.

Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery.

Physiologically-based pharmacokinetic (PBPK) modeling is a popular drug development tool that integrates physiology, drug physicochemical properties, preclinical data, and clinical information to predict drug systemic disposition. Since PBPK models seek to capture complex physiology, parameter uncertainty and variability is a prevailing challenge: there are often more compartments (e.g., organs, each with drug flux and retention mechanisms, and associated model parameters) than can be simultaneously measured. To improve the fidelity of PBPK modeling, one approach is to search and optimize within the high-dimensional model parameter space, based on experimental time-series measurements of drug distributions. Here, we employ Latin Hypercube Sampling (LHS) on a PBPK model of PEG-liposomes (PL) that tracks biodistribution in an 8-compartment mouse circulatory system, in the presence (APA+) or absence (naïve) of anti-PEG antibodies (APA). Near-continuous experimental measurements of PL concentration during the first hour post-injection from the liver, spleen, kidney, muscle, lung, and blood plasma, based on PET/CT imaging in live mice, are used as truth sets with LHS to infer optimal parameter ranges for the full PBPK model. The data and model quantify that PL retention in the liver is the primary differentiator of biodistribution patterns in naïve versus APA+ mice, and spleen the secondary differentiator. Retention of PEGylated nanomedicines is substantially amplified in APA+ mice, likely due to PL-bound APA engaging specific receptors in the liver and spleen that bind antibody Fc domains. Our work illustrates how applying LHS to PBPK models can further mechanistic understanding of the biodistribution and antibody-mediated clearance of specific drugs.

Centromere tethering confines chromosome domains.

The organization of chromosomes into territories plays an important role in a wide range of cellular processes, including gene expression, transcription, and DNA repair. Current understanding has largely excluded the spatiotemporal dynamic fluctuations of the chromatin polymer. We combine in vivo chromatin motion analysis with mathematical modeling to elucidate the physical properties that underlie the formation and fluctuations of territories. Chromosome motion varies in predicted ways along the length of the chromosome, dependent on tethering at the centromere. Detachment of a tether upon inactivation of the centromere results in increased spatial mobility. A confined bead-spring chain tethered at both ends provides a mechanism to generate observed variations in local mobility as a function of distance from the tether. These predictions are realized in experimentally determined higher effective spring constants closer to the centromere. The dynamic fluctuations and territorial organization of chromosomes are, in part, dictated by tethering at the centromere.

Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D.

Particle tracking is a powerful biophysical tool that requires conversion of large video files into position time series, i.e., traces of the species of interest for data analysis. Current tracking methods, based on a limited set of input parameters to identify bright objects, are ill-equipped to handle the spectrum of spatiotemporal heterogeneity and poor signal-to-noise ratios typically presented by submicron species in complex biological environments. Extensive user involvement is frequently necessary to optimize and execute tracking methods, which is not only inefficient but introduces user bias. To develop a fully automated tracking method, we developed a convolutional neural network for particle localization from image data, comprising over 6,000 parameters, and used machine learning techniques to train the network on a diverse portfolio of video conditions. The neural network tracker provides unprecedented automation and accuracy, with exceptionally low false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species.

A developmentally regulated translational control pathway establishes the meiotic chromosome segregation pattern.

Production of haploid gametes from diploid progenitor cells is mediated by a specialized cell division, meiosis, where two divisions, meiosis I and II, follow a single S phase. Errors in progression from meiosis I to meiosis II lead to aneuploid and polyploid gametes, but the regulatory mechanisms controlling this transition are poorly understood. Here, we demonstrate that the conserved kinase Ime2 regulates the timing and order of the meiotic divisions by controlling translation. Ime2 coordinates translational activation of a cluster of genes at the meiosis I-meiosis II transition, including the critical determinant of the meiotic chromosome segregation pattern CLB3. We further show that Ime2 mediates translational control through the meiosis-specific RNA-binding protein Rim4. Rim4 inhibits translation of CLB3 during meiosis I by interacting with the 5' untranslated region (UTR) of CLB3. At the onset of meiosis II, Ime2 kinase activity rises and triggers a decrease in Rim4 protein levels, thereby alleviating translational repression. Our results elucidate a novel developmentally regulated translational control pathway that establishes the meiotic chromosome segregation pattern.

Similar yet different: phylogenomic analysis to delineate Salmonella and Citrobacter species boundaries.

BACKGROUND: Salmonella enterica is a leading cause of foodborne illness worldwide resulting in considerable public health and economic costs. Testing for the presence of this pathogen in food is often hampered by the presence of background microflora that may present as Salmonella (false positives). False positive isolates belonging to the genus Citrobacter can be difficult to distinguish from Salmonella due to similarities in their genetics, cell surface antigens, and other phenotypes. In order to understand the genetic basis of these similarities, a comparative genomic approach was used to define the pan-, core, accessory, and unique coding sequences of a representative population of Salmonella and Citrobacter strains. RESULTS: Analysis of the genomic content of 58 S. enterica strains and 37 Citrobacter strains revealed the presence of 31,130 and 1540 coding sequences within the pan- and core genome of this population. Amino acid sequences unique to either Salmonella (n = 1112) or Citrobacter (n = 195) were identified and revealed potential niche-specific adaptations. Phylogenetic network analysis of the protein families encoded by the pan-genome indicated that genetic exchange between Salmonella and Citrobacter may have led to the acquisition of similar traits and also diversification within the genera. CONCLUSIONS: Core genome analysis suggests that the Salmonella enterica and Citrobacter populations investigated here share a common evolutionary history. Comparative analysis of the core and pan-genomes was able to define the genetic features that distinguish Salmonella from Citrobacter and highlight niche specific adaptations.

High-fat diet in a mouse insulin-resistant model induces widespread rewiring of the phosphotyrosine signaling network.

Obesity-associated type 2 diabetes and accompanying diseases have developed into a leading human health risk across industrialized and developing countries. The complex molecular underpinnings of how lipid overload and lipid metabolites lead to the deregulation of metabolic processes are incompletely understood. We assessed hepatic post-translational alterations in response to treatment of cells with saturated and unsaturated free fatty acids and the consumption of a high-fat diet by mice. These data revealed widespread tyrosine phosphorylation changes affecting a large number of enzymes involved in metabolic processes as well as canonical receptor-mediated signal transduction networks. Targeting two of the most prominently affected molecular features in our data, SRC-family kinase activity and elevated reactive oxygen species, significantly abrogated the effects of saturated fat exposure in vitro and high-fat diet in vivo. In summary, we present a comprehensive view of diet-induced alterations of tyrosine signaling networks, including proteins involved in fundamental metabolic pathways.