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Major challenges to chimeric antigen receptor (CAR) T cell therapies include uncontrolled immune activity, off-tumor toxicities and tumor heterogeneity. To overcome these challenges, we engineered CARs directed against small molecules. By conjugating the same small molecule to distinct tumor-targeting antibodies, we show that small molecule specific-CAR T cells can be redirected to different tumor antigens. Such binary switches allow control over the degree of CAR T cell activity and enables simultaneous targeting of multiple tumor-associated antigens. We also demonstrate that ultraviolet light-sensitive caging of small molecules blocks CAR T cell activation. Exposure to ultraviolet light, uncaged small molecules and restored CAR T cell-mediated killing. Together, our data demonstrate that a light-sensitive caging system enables an additional level of control over tumor cell killing, which could improve the therapeutic index of CAR T cell therapies., (© 2022 The Authors. ChemMedChem published by Wiley-VCH GmbH.)

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.

Melanoma is the deadliest form of skin cancer, affecting men more frequently and severely than women. Although recent studies suggest that differences in activity of the androgen receptor (AR) underlie the observed sex bias, little is known about AR activity in melanoma. Here we show that AR and EGR1 bind to the long non-coding RNA SLNCR and increase melanoma proliferation through coordinated transcriptional regulation of several growth-regulatory genes. ChIP-seq reveals that ligand-free AR is enriched on SLNCR-regulated melanoma genes and that AR genomic occupancy significantly overlaps with EGR1 at consensus EGR1 binding sites. We present a model in which SLNCR recruits AR to EGR1-bound genomic loci and switches EGR1-mediated transcriptional activation to repression of the tumor suppressor p21 Waf1/Cip1 . Our data implicate the regulatory triad of SLNCR, AR, and EGR1 in promoting oncogenesis and may help explain why men have a higher incidence of and more rapidly progressive melanomas compared with women., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

During αβ T cell development, T cell antigen receptor (TCR) engagement transduces biochemical signals through a protein-protein interaction (PPI) network that dictates dichotomous cell fate decisions. It remains unclear how signal specificity is communicated, instructing either positive selection to advance cell differentiation or death by negative selection. Early signal discrimination might occur by PPI signatures differing qualitatively (customized, unique PPI combinations for each signal), quantitatively (graded amounts of a single PPI series), or kinetically (speed of PPI pathway progression). Using a novel PPI network analysis, we found that early TCR-proximal signals distinguishing positive from negative selection appeared to be primarily quantitative in nature. Furthermore, the signal intensity of this PPI network was used to find an antigen dose that caused a classic negative selection ligand to induce positive selection of conventional αβ T cells, suggesting that the quantity of TCR triggering was sufficient to program selection outcome. Because previous work had suggested that positive selection might involve a qualitatively unique signal through CD3δ, we reexamined the block in positive selection observed in CD3δ 0 mice. We found that CD3δ 0 thymocytes were inhibited but capable of signaling positive selection, generating low numbers of MHC-dependent αβ T cells that expressed diverse TCR repertoires and participated in immune responses against infection. We conclude that the major role for CD3δ in positive selection is to quantitatively boost the signal for maximal generation of αβ T cells. Together, these data indicate that a quantitative network signaling mechanism through the early proximal TCR signalosome determines thymic selection outcome., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background: Autism spectrum disorders (ASDs) are a heterogeneous group of behaviorally defined disorders and are associated with hundreds of rare genetic mutations and several environmental risk factors. Mouse models of specific risk factors have been successful in identifying molecular mechanisms associated with a given factor. However, comparisons among different models to elucidate underlying common pathways or to define clusters of biologically relevant disease subtypes have been complicated by different methodological approaches or different brain regions examined by the labs that developed each model. Here, we use a novel proteomic technique, quantitative multiplex co-immunoprecipitation or QMI, to make a series of identical measurements of a synaptic protein interaction network in seven different animal models. We aim to identify molecular disruptions that are common to multiple models., Methods: QMI was performed on 92 hippocampal and cortical samples taken from seven mouse models of ASD: Shank3B, Shank3Δex4-9, Ube3a 2xTG , TSC2, FMR1, and CNTNAP2 mutants, as well as E12.5 VPA (maternal valproic acid injection on day 12.5 post-conception). The QMI panel targeted a network of 16 interacting, ASD-linked, synaptic proteins, probing 240 potential co-associations. A custom non-parametric statistical test was used to call significant differences between ASD models and littermate controls, and Hierarchical Clustering by Principal Components was used to cluster the models using mean log 2 fold change values., Results: Each model displayed a unique set of disrupted interactions, but some interactions were disrupted in multiple models. These tended to be interactions that are known to change with synaptic activity. Clustering revealed potential relationships among models and suggested deficits in AKT signaling in Ube3a 2xTG mice, which were confirmed by phospho-western blots., Conclusions: These data highlight the great heterogeneity among models, but suggest that high-dimensional measures of a synaptic protein network may allow differentiation of subtypes of ASD with shared molecular pathology., Competing Interests: All animal studies were approved by the IACUC of Seattle Children’s Research Institute and/or McMaster University.Not applicableThe authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Human immunity exhibits remarkable heterogeneity among individuals, which engenders variable responses to immune perturbations in human populations. Population studies reveal that, in addition to interindividual heterogeneity, systemic immune signatures display longitudinal stability within individuals, and these signatures may reliably dictate how given individuals respond to immune perturbations. We hypothesize that analyzing relationships among these signatures at the population level may uncover baseline immune phenotypes that correspond with response outcomes to immune stimuli. To test this, we quantified global gene expression in peripheral blood CD4 + cells from healthy individuals at baseline and following CD3/CD28 stimulation at two time points 1 mo apart. Systemic CD4 + cell baseline and poststimulation molecular immune response signatures (MIRS) were defined by identifying genes expressed at levels that were stable between time points within individuals and differential among individuals in each state. Iterative differential gene expression analyses between all possible phenotypic groupings of at least three individuals using the baseline and stimulated MIRS gene sets revealed shared baseline and response phenotypic groupings, indicating the baseline MIRS contained determinants of immune responsiveness. Furthermore, significant numbers of shared phenotype-defining sets of determinants were identified in baseline data across independent healthy cohorts. Combining the cohorts and repeating the analyses resulted in identification of over 6000 baseline immune phenotypic groups, implying that the MIRS concept may be useful in many immune perturbation contexts. These findings demonstrate that patterns in complex gene expression variability can be used to define immune phenotypes and discover determinants of immune responsiveness., (Copyright © 2018 by The American Association of Immunologists, Inc.)

Multiprotein complexes transduce cellular signals through extensive interaction networks, but the ability to analyze these networks in cells from small clinical biopsies is limited. To address this, we applied an adaptable multiplex matrix system to physiologically relevant signaling protein complexes isolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR) signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by exposed surface epitopes). Upon stimulation of Jurkat cells with superantigen-loaded antigen-presenting cells, this system produced high-dimensional data that enabled visualization of network activity. A comprehensive analysis platform generated PiSCES biosignatures by applying unsupervised hierarchical clustering, principal component analysis, an adaptive nonparametric with empirical cutoff analysis, and weighted correlation network analysis. We generated PiSCES biosignatures from 4-mm skin punch biopsies from control patients or patients with the autoimmune skin disease alopecia areata. This analysis distinguished disease patients from the controls, detected enhanced basal TCR signaling in the autoimmune patients, and identified a potential signaling network signature that may be indicative of disease. Thus, generation of PiSCES biosignatures represents an approach that can provide information about the activity of protein signaling networks in samples including low-abundance primary cells from clinical biopsies., (Copyright © 2016, American Association for the Advancement of Science.)

This Podcast features an interview with Adam Schrum and Steven Neier, authors of a Research Article that appears in the 2 August 2016 issue of Science Signaling, about a method for identifying protein-protein interactions in patient tissue samples. The authors used this method to compare signaling complexes downstream of the T cell receptor in T cells from healthy skin with those in T cells from the skin of patients with the autoimmune disease alopecia areata. The study revealed differences in the relative abundance of some protein complexes between T cells from the control and patient groups. This technique could be adapted for use as a diagnostic tool to stratify patients by molecular phenotype and predict the therapeutic strategy that is likely to work best for each patient.Listen to Podcast., (Copyright © 2016, American Association for the Advancement of Science.)

Development of better therapies for the T cell-mediated autoimmune disease alopecia areata (AA) could be expedited by an improved understanding of the immunologic signals underlying its pathogenesis. To approach this, our group is mounting a new technological and analytical platform, multiplex immunoprecipitation detected by flow cytometry (MIF). MIF is designed to allow analysis of collections of protein-protein interactions that participate in T cell signaling webs. Early experiments suggest that MIF can detect the increased protein-protein interaction network activity that occurs under conditions of T cell antigenic stimulation. Future experiments will focus on application of MIF to T cells isolated from AA or control patient samples, to identify critical T cell signaling complexes associated with the disorder.

Background: There is significant interest in the generation of improved assays to clearly identify experimental mice possessing functional vision, a property that could qualify mice for inclusion in behavioral and neuroscience studies. Widely employed current methods rely on mouse responses to visual cues in assays of reflexes, depth perception, or cognitive memory. However, commonly assessed mouse reflexes can sometimes be ambiguous in their expression, while depth perception assays are sometimes confounded by variation in anxiety responses and exploratory conduct. Furthermore, in situations where experimental groups vary in their cognitive memory capacity, memory assays may not be ideal for assessing differences in vision., Results: We have optimized a non-invasive behavioral assay that relies on an untrained, innate response to identify individual experimental mice possessing functional vision: slow angled-descent forepaw grasping (SLAG). First, we verified that SLAG performance depends on vision and not olfaction. Next, all members of an age-ranged cohort of 158 C57BL/6 mice (57 wild-type, 101 knockout, age range 44-241 days) were assessed for functional vision using the SLAG test without training or conditioning. Subjecting the population to a second innate behavioral test, Dark Chamber preference, corroborated that the functional vision assessment of SLAG was valid., Conclusions: We propose that the SLAG assay is immediately useful to quickly and clearly identify experimental mice possessing functional vision. SLAG is based on a behavioral readout with a significant innate component with no requirement for training. This will facilitate the selection of mice of known sighted status in vision-dependent experiments that focus on other types of behavior, neuroscience, and/or cognitive memory.