The early proximal αβ TCR signalosome specifies thymic selection outcome through a quantitative protein interaction network

SRC family kinase inhibition rescues molecular and behavioral phenotypes, but not protein interaction network dynamics, in a mouse model of Fragile X syndrome

Glutamatergic synapses encode information from extracellular inputs using dynamic protein interaction networks (PINs) that undergo widespread reorganization following synaptic activity, allowing cells to distinguish between signaling inputs and generate coordinated cellular responses. Here, we investigate how Fragile X Messenger Ribonucleoprotein (FMRP) deficiency disrupts signal transduction through a glutamatergic synapse PIN downstream of NMDA receptor or metabotropic glutamate receptor (mGluR) stimulation. In cultured cortical neurons or acute cortical slices from P7, P17 and P60 FMR1-/y mice, the unstimulated protein interaction network state resembled that of wildtype littermates stimulated with mGluR agonists, demonstrating resting state pre-activation of mGluR signaling networks. In contrast, interactions downstream of NMDAR stimulation were similar to WT. We identified the Src family kinase (SFK) Fyn as a network hub, because many interactions involving Fyn were pre-activated in FMR1-/y animals. We tested whether targeting SFKs in FMR1-/y mice could modify disease phenotypes, and found that Saracatinib (SCB), an SFK inhibitor, normalized elevated basal protein synthesis, novel object recognition memory and social behavior in FMR1-/y mice. However, SCB treatment did not normalize the PIN to a wild-type-like state in vitro or in vivo, but rather induced extensive changes to protein complexes containing Shank3, NMDARs and Fyn. We conclude that targeting abnormal nodes of a PIN can identify potential disease-modifying drugs, but behavioral rescue does not correlate with PIN normalization.

Differential protein-protein interactions underlie signaling mediated by the TCR and a 4-1BB domain-containing CAR

Cells rely on activity-dependent protein-protein interactions to convey biological signals. For chimeric antigen receptor (CAR) T cells containing a 4-1BB costimulatory domain, receptor engagement is thought to stimulate the formation of protein complexes similar to those stimulated by T cell receptor (TCR)-mediated signaling, but the number and type of protein interaction-mediating binding domains differ between CARs and TCRs. Here, we performed coimmunoprecipitation mass spectrometry analysis of a second-generation, CD19-directed 4-1BB:ζ CAR (referred to as bbζCAR) and identified 128 proteins that increased their coassociation after target engagement. We compared activity-induced TCR and CAR signalosomes by quantitative multiplex coimmunoprecipitation and showed that bbζCAR engagement led to the activation of two modules of protein interactions, one similar to TCR signaling that was more weakly engaged by bbζCAR as compared with the TCR and one composed of TRAF signaling complexes that was not engaged by the TCR. Batch-to-batch and interindividual variations in production of the cytokine IL-2 correlated with differences in the magnitude of protein network activation. Future CAR T cell manufacturing protocols could measure, and eventually control, biological variation by monitoring these signalosome activation markers.

Protein interaction network analysis of mTOR signaling reveals modular organization

The mammalian target of rapamycin (mTOR) is a serine-threonine kinase that acts as a central mediator of translation and plays important roles in cell growth, synaptic plasticity, cancer, and a wide range of developmental disorders. The signaling cascade linking lipid kinases (phosphoinositide 3-kinases), protein kinases (AKT), and translation initiation complexes (EIFs) to mTOR has been extensively modeled, but does not fully describe mTOR system behavior. Here, we use quantitative multiplex coimmunoprecipitation to monitor a protein interaction network (PIN) composed of 300+ binary interactions among mTOR-related proteins. Using a simple model system of serum-deprived or fresh-media-fed mouse 3T3 fibroblasts, we observed extensive PIN remodeling involving 27+ individual protein interactions after 1 h, despite phosphorylation changes observed after only 5 min. Using small molecule inhibitors of phosphoinositide 3-kinase, AKT, mTOR, MEK and ERK, we define subsets of the PIN, termed "modules", that respond differently to each inhibitor. Using primary fibroblasts from individuals with overgrowth disorders caused by pathogenic PIK3CA or MTOR variants, we find that hyperactivation of mTOR pathway components is reflected in a hyperactive PIN. Our data define a "modular" organization of the mTOR PIN in which coordinated groups of interactions respond to the activation or inhibition of distinct nodes, and demonstrate that kinase inhibitors affect the modular network architecture in a complex manner, inconsistent with simple linear models of signal transduction.

PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network

Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the protein. However, there is still a challenge to selecting the near-native decoys generated by protein-protein docking. Here, we propose a docking evaluation method using 3D point cloud neural network named PointDE. PointDE transforms protein structure to the point cloud. Using the state-of-the-art point cloud network architecture and a novel grouping mechanism, PointDE can capture the geometries of the point cloud and learn the interaction information from the protein interface. On public datasets, PointDE surpasses the state-of-the-art method using deep learning. To further explore the ability of our method in different types of protein structures, we developed a new dataset generated by high-quality antibody-antigen complexes. The result in this antibody-antigen dataset shows the strong performance of PointDE, which will be helpful for the understanding of PPI mechanisms.

APLs and Oranges: Induction of T Cell Anergy by Altered Peptide Ligands

This Pillars of Immunology article is a commentary on “Induction of T-cell anergy by altered T-cell-receptor ligand on live antigen-presenting cells,” a pivotal article written by J. Sloan-Lancaster, B. D. Evavold, and P. M. Allen, and published in Nature, in 1993. https://www.nature.com/articles/363156a0

Protein interaction network analysis of mTOR signaling reveals modular organization

The mammalian target of rapamycin (mTOR) is a serine-threonine kinase that acts as a central mediator of translation, and plays important roles in cell growth, synaptic plasticity, cancer, and a wide range of developmental disorders. The signaling cascade linking lipid kinases (PI3Ks), protein kinases (AKT) and translation initiation complexes (EIFs) to mTOR has been extensively modeled, but does not fully describe mTOR system behavior. Here, we use quantitative multiplex co-immunoprecipitation to monitor a protein interaction network (PIN) composed of 300+ binary interactions among mTOR-related proteins. Using a simple model system of serum deprived or fresh-media-fed mouse 3T3 fibroblasts, we observed extensive PIN remodeling involving 27+ individual protein interactions after one hour, despite phosphorylation changes observed after only five minutes. Using small molecule inhibitors of PI3K, AKT, mTOR, MEK and ERK, we define subsets of the PIN, termed 'modules', that respond differently to each inhibitor. Using primary fibroblasts from individuals with overgrowth disorders caused by pathogenic PIK3CA or MTOR variants, we find that hyperactivation of mTOR pathway components is reflected in a hyperactive PIN. Our data define a "modular" organization of the mTOR PIN in which coordinated groups of interactions respond to activation or inhibition of distinct nodes, and demonstrate that kinase inhibitors affect the modular network architecture in a complex manner, inconsistent with simple linear models of signal transduction.

In Vitro Analysis of Thymocyte Signaling

When a developing thymocyte expresses a TCR, it is subjected to numerous interactions with self-peptide/MHC complexes that determine its fate. These include death by neglect, negative selection (apoptosis and lineage deviation), positive selection, and lineage commitment. Identifying signals that govern these unique cell fates requires the ability to assess the activity, level of expression, subcellular location, and molecular associations between numerous proteins within the developing T cell. Given the unique, temporal, and developmental changes that occur during development, isolating and analyzing small populations of thymocytes are necessary to get a complete picture of the development process. Thus, this chapter describes methods designed to analyze thymocyte signaling under various types of peptide-based stimulation in vitro.

Fetal Thymic Organ Culture and Negative Selection

Negative selection removes potentially harmful T cell precursors from the conventional T cell pool. This process can involve the induction of apoptosis, anergy, receptor editing, or deviation into a regulatory T cell lineage. As such, this process is essential for the health of an organism through its contribution to central and peripheral tolerance. While a great deal is known about the process, the precise mechanisms that regulate these various forms of negative selection are not clear. Numerous models exist with the potential to address these questions in vitro and in vivo. This chapter describes fetal thymic organ culture methods designed to analyze the signals that determine these unique cell fates.

Early expression of mature αβ TCR in CD4-CD8- T cell progenitors enables MHC to drive development of T-ALL bearing NOTCH mutations

T cell development and immune responses are directed by major histocompatibility complex:T cell antigen receptor (MHC:TCR) signaling, but aberrant signals can cause T cell tumors to form. We show that in mice and humans, a low-frequency progenitor cell population expresses early αβ TCR while coreceptor double-negative (EADN), and these EADN cells can transform to thymic leukemia. Mouse models showed that EADN cells did not require MHC to develop but when presented with MHC they could respond with high sensitivity. Transformation to leukemia occurred and required MHC, although with extended tumor growth this requirement could be lost. Thus, MHC:TCR signaling can initiate a leukemia phenotype from an understudied developmental state that appears to be represented in the mouse and human disease spectrum.

During normal T cell development in mouse and human, a low-frequency population of immature CD4⁻CD8⁻ double-negative (DN) thymocytes expresses early, mature αβ T cell antigen receptor (TCR). We report that these early αβ TCR+ DN (EADN) cells are DN3b-DN4 stage and require CD3δ but not major histocompatibility complex (MHC) for their generation/detection. When MHC - is present, however, EADN cells can respond to it, displaying a degree of coreceptor-independent MHC reactivity not typical of mature, conventional αβ T cells. We found these data to be connected with observations that EADN cells were susceptible to T cell acute lymphoblastic leukemia (T-ALL) transformation in both humans and mice. Using the OT-1 TCR transgenic system to model EADN-stage αβ TCR expression, we found that EADN leukemogenesis required MHC to induce development of T-ALL bearing NOTCH1 mutations. This leukemia-driving MHC requirement could be lost, however, upon passaging the tumors in vivo, even when matching MHC was continuously present in recipient animals and on the tumor cells themselves. These data demonstrate that MHC:TCR signaling can be required to initiate a cancer phenotype from an understudied developmental state that appears to be represented in the mouse and human disease spectrum.

TRScore: a three-dimensional RepVGG-based scoring method for ranking protein docking models

MOTIVATION

Protein-protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications.

RESULTS

We present a new deep learning-based scoring method for ranking protein-protein docking models based on a three-dimensional (3D) RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein-protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein-protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set, and overall obtained a significant improvement over existing methods in cross validation and independent evaluations.

AVAILABILITY

Codes available at: https://github.com/BioinformaticsCSU/TRScore.

Synaptic protein interaction networks encode experience by assuming stimulus-specific and brain-region-specific states

A core network of widely expressed proteins within the glutamatergic post-synapse mediates activity-dependent synaptic plasticity throughout the brain, but the specific proteomic composition of synapses differs between brain regions. Here, we address the question, how does proteomic composition affect activity-dependent protein-protein interaction networks (PINs) downstream of synaptic activity? Using quantitative multiplex co-immunoprecipitation, we compare the PIN response of in vivo or ex vivo neurons derived from different brain regions to activation by different agonists or different forms of eyeblink conditioning. We report that PINs discriminate between incoming stimuli using differential kinetics of overlapping and non-overlapping PIN parameters. Further, these "molecular logic rules" differ by brain region. We conclude that although the PIN of the glutamatergic post-synapse is expressed widely throughout the brain, its activity-dependent dynamics show remarkable stimulus-specific and brain-region-specific diversity. This diversity may help explain the challenges in developing molecule-specific drug therapies for neurological disorders.

Public and private human T-cell clones respond differentially to HCMV antigen when boosted by CD3 copotentiation

Human cytomegalovirus (HCMV) induces long-lasting T-cell immune responses that control but do not clear infection. Typical responses involve private T-cell clones, expressing T-cell antigen receptors (TCRs) unique to a person, and public T-cell clones with identical TCRs active in different people. Here, we report the development of a pretherapeutic immunostimulation modality against HCMV for human T cells, CD3 copotentiation, and the clonal analysis of its effects in recall assays at single-cell resolution. CD3 copotentiation of human T cells required identification of an intrinsically inert anti-CD3 Fab fragment that conditionally augmented signaling only when TCR was coengaged with antigen. When applied in recall assays, CD3 copotentiation enhanced the expansion of both public and private T-cell clones responding to autologous HLA-A2(+) antigen-presenting cells and immunodominant NLVPMVATV (NLV) peptide from HCMV pp65 protein. Interestingly, public vs private TCR expression was associated with distinct clonal expansion signatures in response to recall stimulus. This implied that besides possible differences in their generation and selection in an immune response, public and private T cells may respond differently to pharmacoimmunomodulation. Furthermore, a third clonal expansion profile was observed upon CD3 copotentiation of T-cell clones from HLA-A2(-) donors and 1 HLA-A2(+) presumed-uninfected donor, where NLV was of low intrinsic potency. We conclude that human T-cell copotentiation can increase the expansion of different classes of T-cell clones responding to recall antigens of different strengths, and this may be exploitable for therapeutic development against chronic, persistent infections such as HCMV.

FOLFOX Chemotherapy Ameliorates CD8 T Lymphocyte Exhaustion and Enhances Checkpoint Blockade Efficacy in Colorectal Cancer

Background: Colorectal cancer (CRC) is the third most common malignancy worldwide. The presence of CD8 tumor-infiltrating T lymphocytes (TILs) is associated with improved prognosis and therapeutic response in CRC patients. FOLFOX chemotherapy is a standard first-line treatment for patients with CRC. Yet, the effect of FOLFOX on TILs is poorly understood. Specifically, it is unclear whether FOLFOX therapy impacts the phenotype and functionality of tumor antigen specific TILs. Immune checkpoint blockade (ICB) has significantly improved clinical outcome of cancer treatment but has shown limited efficacy in CRC patients. Recently, ICB efficiency has been linked to reinvigoration of T cells with a non-terminally dysfunctional phenotype. Here, we investigate the effect of FOLFOX on CD8 T cell tumor accumulation, phenotype and function and tested the combination of FOLFOX and ICB to improve tumor regression. Methods: A mouse model of CRC expressing a human tumor antigen was used to study the effect of FOLFOX on tumor growth and TILs phenotype and function. Tetramers were used to identify and monitor phenotype and function of tumor specific TILs. The phenotype and function of TILs were compared between FOLFOX and control treatment through flow cytometry, in vivo depletion and ex vivo stimulation. Furthermore, the anti-tumor effect of the single drug or combined therapy with anti-PD1 were also assessed. Results: We show that FOLFOX treatment effectively controlled tumor burden and this was dependent on CD8 T cells. FOLFOX enabled TILs to remain in a functional differentiation state characterized by lower levels of inhibitory receptors PD-1 and TIM-3 and a CD38^loCD101^loTIM-3^-TCF-1^hi phenotype. Consistent with this, TILs from FOLFOX treated tumors exhibited higher effector function. Importantly, while anti-PD-1 treatment alone had no significant effect on tumor burden, FOLFOX and PD-1 checkpoint blockade combination showed significant tumor control. Conclusions: FOLFOX treatment impacts the phenotype and function of TILs making them more responsive to checkpoint blockade. This study highlights the importance of combining chemotherapy and ICB to optimize treatment efficacy in patients with colorectal cancer.

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.