Biology subjects

Meyer, A. S.

Publications and source records attributed to Meyer, A. S..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood

Cytokines mediate cell-to-cell communication across the immune system and therefore are critical to immunosurveillance in cancer and other diseases. Several cytokines show dysregulated abundance or signaling responses in breast cancer, associated with the disease and differences in survival and progression. Cytokines operate in a coordinated manner to affect immune surveillance and regulate one another, necessitating a systems approach for a complete picture of this dysregulation. Here, we profiled cytokine signaling responses of peripheral immune cells from breast cancer patients as compared to healthy controls in a multidimensional manner across ligands, cell populations, and responsive pathways. We find alterations in cytokine responsiveness across pathways and cell types that are best defined by integrated signatures across dimensions. Alterations in the abundance of a cytokines cognate receptor do not explain differences in responsiveness. Rather, alterations in baseline signaling and receptor abundance suggesting immune cell reprogramming are associated with altered responses. These integrated features suggest a global reprogramming of immune cell communication in breast cancer. Significance StatementWhile individual cytokine responses have previously been observed to be altered in breast cancer, cytokine signaling responses are tightly interconnected in a way that has not been previously characterized. Here, we profile cytokine signaling responses and find alterations that are shared across both pathways and cell types. The signatures across these measurements better define the alterations and point to a broad immunosuppression response. HighlightsO_LIBaseline and post-stimulation cytokine signaling profiles differ between healthy donors and breast cancer patients. C_LIO_LIChanges in cytokine response are not explained by differences in abundance of the cognate receptor C_LIO_LIFeatures of signaling response and receptor abundance dysregulation are coordinated across patients C_LIO_LIIntegrated patterns of dysregulation in breast cancer patients share features of Th17 like-response as well as regulatory-like B and CD8+ T cells C_LI

cancer biology↗

bioRxiv2023

Dissecting signaling regulators driving AXL-mediated bypass resistance and associated phenotypes by phosphosite perturbations

Receptor tyrosine kinase (RTK)-targeted therapies are often effective but invariably limited by drug resistance. A major mechanism of acquired resistance involves "bypass" switching to alternative pathways driven by non-targeted RTKs that restore proliferation. One such RTK is AXL whose overexpression, frequently observed in bypass resistant tumors, drives both cell survival and associated malignant phenotypes such as epithelial-to-mesenchymal (EMT) transition and migration. However, the signaling molecules and pathways eliciting these responses have remained elusive. To explore these coordinated effects, we generated a panel of mutant lung adenocarcinoma PC9 cell lines in which each AXL intracellular tyrosine residue was mutated to phenylalanine. By integrating measurements of phosphorylation signaling and other phenotypic changes associated with resistance through multivariate modeling, we mapped signaling perturbations to specific resistant phenotypes. Our results suggest that AXL signaling can be summarized into two clusters associated with progressive disease and poor clinical outcomes in lung cancer patients. These clusters displayed favorable Abl1 and SFK motifs and their phosphorylation was consistently decreased by dasatinib. High-throughput kinase specificity profiling showed that AXL likely activates the SFK cluster through FAK1 which is known to complex with Src. Moreover, the SFK cluster overlapped with a previously established focal adhesion kinase (FAK1) signature conferring EMT-mediated erlotinib resistance in lung cancer cells. Finally, we show that downstream of this kinase signaling, AXL and YAP form a positive feedback loop that sustains drug tolerant persister cells. Altogether, this work demonstrates an approach for dissecting signaling regulators by which AXL drives erlotinib resistance-associated phenotypic changes. One-sentence summaryA systems biology approach elucidates the signaling pathways driving AXL-mediated erlotinib resistance in lung cancer.

cancer biology↗

bioRxiv2023

Mixed IgG Fc immune complexes exhibit blended binding profiles and refine FcR affinity estimates

Immunoglobulin (Ig)G antibodies coordinate immune effector responses by selectively binding to target antigens and then interacting with various effector cells via the Fc{gamma} receptors. The Fc domain of IgG can promote or inhibit distinct effector responses across several different immune cell types through variation based on subclass and Fc domain glycosylation. Extensive characterization of these interactions has revealed how the inclusion of certain Fc subclasses or glycans results in distinct immune responses. During an immune response, however, IgG is produced with mixtures of Fc domain properties, so antigen-IgG immune complexes are likely to almost always be comprised of a combination of Fc forms. Whether and how this mixed composition influences immune effector responses has not been examined. Here, we measured Fc{gamma} receptor binding to immune complexes of mixed Fc domain composition. We found that the binding properties of the mixed-composition immune complexes fell along a continuum between those of the corresponding pure cases. Binding quantitatively matched a mechanistic binding model, except for several low-affinity interactions mostly involving IgG2. We found that the affinities of these interactions are different than previously reported, and that the binding model could be used to provide refined estimates of these affinities. Finally, we demonstrated that the binding model can predict effector-cell elicited platelet depletion in humanized mice, with the model inferring the relevant effector cell populations. Contrary to the previous view in which IgG2 poorly engages with effector populations, we observe appreciable binding through avidity, but insufficient amounts to observe immune effector responses. Overall, this work demonstrates a quantitative framework for reasoning about effector response regulation arising from IgG of mixed Fc composition. Summary pointsO_LIThe binding behavior of mixed Fc immune complexes is a blend of the binding properties for each constituent IgG species. C_LIO_LIAn equilibrium, multivalent binding model can be generalized to incorporate immune complexes of mixed Fc composition. C_LIO_LIParticularly for low-affinity IgG-Fc{gamma} receptor interactions, immune complexes provide better estimates of affinities. C_LIO_LIThe Fc{gamma}R binding model predicts effector-elicited cell clearance in humanized mice. C_LI

immunology↗

bioRxiv2022

Cytokine-expression patterns reveal coordinated immunological programs associated with persistent MRSA bacteremia

Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is a common, life-threatening infection that imposes up to 30% mortality even when appropriate therapy is used. Despite in vitro efficacy, antibiotics often fail to resolve the infection in vivo, resulting in persistent MRSA bacteremia. Recently, several genetic, epigenetic, and proteomic correlates of persistent outcomes have been identified. However, the extent to which single variables or composite patterns operate as independent predictors of outcome or reflect shared underlying mechanisms of persistence is unknown. To explore this question, we employed a tensor-based integration of host transcriptional and proteomic data across a well-characterized cohort of patients with persistent and resolving MRSA bacteremia outcomes. Tensor-based data integration yielded high correlative accuracy with persistence and revealed immunologic signatures shared across both the transcriptomic and proteomic datasets. We find that elevated proliferation of mature granulocytes associates with resolving bacteremia outcomes. In contrast, patients with persistent bacteremia heterogeneously exhibit correlates of granulocyte dysfunction or immature granulocyte proliferation. Collectively, these results suggest that transcriptional and proteomic correlates of persistent versus resolving bacteremia outcomes are complex and may not be disclosed by conventional modeling. However, a tensor-based integration approach can help to reveal consensus molecular mechanisms in an interpretable manner. Significance StatementWhile antibacterial therapies effectively resolve MRSA in vitro, these treatments often fail to clear MRSA bacteremia in vivo, suggesting that host-pathogen interactions are essential to persistent MRSA bacteremia. Recent studies have identified genetic, transcriptomic, and proteomic determinants of MRSA persistence. These determinants independently, however, provide insufficient mechanistic insight and it is unclear if they indicate unique or overlapping persistence mechanisms. Here, we use tensor-based decomposition to jointly analyze cytokine and transcriptomic measurements from patients with MRSA bacteremia. Results indicate that persistence mechanisms integrated across biological modalities reflect diverging mechanisms of persistent bacteremia. Ultimately, these results may help to identify future therapeutic targets for treating persistent MRSA bacteremia.

immunology↗

bioRxiv2022

A stimulus-contingent positive feedback loop enables IFN-β dose-dependent activation of pro-inflammatory genes

Type I interferons (IFN) induce powerful anti-viral and innate immune responses via the transcription factor, IFN-stimulated gene factor (ISGF3). However, in some pathological contexts type I IFNs are responsible for exacerbating inflammation. Here, we show that a high dose of IFN-{beta} also activates an inflammatory gene expression program in contrast to IFN-{lambda}3, a type III IFN, which elicits only the common anti-viral gene program. We show that the inflammatory gene program depends on a second, potentiated phase in ISGF3 activation. Iterating between mathematical modeling and experimental analysis we show that the ISGF3 activation network may engage a positive feedback loop with its subunits IRF9 and STAT2. This network motif mediates stimulus-specific ISGF3 dynamics that are dependent on ligand, dose, and duration of exposure, and when engaged activates the inflammatory gene expression program. Our results reveal a previously underappreciated dynamical control of the JAK-STAT/IRF signaling network that may produce distinct biological responses, and suggest that studies of type I IFN dysregulation, and in turn therapeutic remedies, may focus on feedback regulators within it. HIGHLIGHTSO_LIHigh dose IFN-{beta} activates a pro-inflammatory gene program in epithelial cells. C_LIO_LIIFN-{beta}, but not IFN-{lambda}3, induces a second, potentiated phase in ISGF3 activity. C_LIO_LIISGF3 induces its subunits to form a stimulus-contingent positive feedback loop. C_LIO_LIThe positive feedback motif is required for the pro-inflammatory gene program. C_LI

immunology↗

bioRxiv2022

Engineered human cytokine/antibody fusion proteins expand regulatory T cells and confer autoimmune disease protection

Low dose human interleukin-2 (hIL-2) treatment is used clinically to treat autoimmune disorders due to the cytokines preferential expansion of immunosuppressive regulatory T cells (TRegs). However, high toxicity, short serum half-life, and off-target immune cell activation limit the clinical potential of IL-2 treatment. Recent work showed that complexes comprising hIL-2 and the anti-hIL-2 antibody F5111 overcome these limitations by preferentially stimulating TRegs over immune effector cells. Although promising, therapeutic translation of this approach is complicated by the need to optimize dosing ratios and by the instability of the cytokine/antibody complex. We leveraged structural insights to engineer a single-chain hIL-2/F5111 antibody fusion protein, termed F5111 immunocytokine (IC), that potently and selectively activates and expands TRegs. F5111 IC conferred protection in mouse models of colitis and checkpoint inhibitor-induced diabetes mellitus. These results provide a roadmap for IC design and establish a TReg-biased immunotherapy that could be clinically translated for autoimmune disease treatment.

bioengineering↗

bioRxiv2022

A genetic model for in vivo proximity labeling of the mammalian secretome

Organ functions are highly specialized and interdependent. Secreted factors regulate organ development and mediate homeostasis through serum trafficking and inter-organ communication. Enzyme-catalyzed proximity labeling enables the identification of proteins within a specific cellular compartment. Here, we report a BirA*G3 mouse strain that enables CRE-dependent promiscuous biotinylation of proteins trafficking through the endoplasmic reticulum. When broadly activated throughout the mouse, widespread labeling of proteins was observed within the secretory pathway. Streptavidin affinity purification and peptide mapping by quantitative mass spectrometry (MS) proteomics revealed organ-specific secretory profiles and serum trafficking. As expected, secretory proteomes were highly enriched for signal peptide-containing proteins, highlighting both conventional and non-conventional secretory processes, and ectodomain shedding. Lower-abundance proteins with hormone-like properties were recovered and validated using orthogonal approaches. Hepatocyte-specific activation of BirA*G3 highlighted liver-specific biotinylated secretome profiles. The BirA*G3 mouse model demonstrates enhanced labeling efficiency and tissue specificity over viral transduction approaches and will facilitate a deeper understanding of secretory protein interplay in development, and healthy and diseased adult states.

cell biology↗

bioRxiv2021

Neural encoding of phrases and sentences in spoken language comprehension

Speech stands out in the natural world as a biological signal that communicates formally-specifiable complex meanings. However, the acoustic and physical dynamics of speech do not injectively mark the linguistic structure and meaning that we perceive. Linguistic structure must therefore be inferred through the human brains endogenous mechanisms, which remain poorly understood. Using electroencephalography, we investigated the neural response to synthesized spoken phrases and sentences that were closely physically-matched but differed in syntactic structure, under either linguistic or non-linguistic task conditions. Differences in syntactic structure were well-captured in theta band ([~] 2 to 7 Hz) phase coherence, phase connectivity degree at low frequencies (< [~] 2 Hz), and in both intensity and degree of power connectivity of induced neural response in the alpha band ([~] 7.5 to 13.5 Hz). Theta-gamma phase-amplitude coupling was found when participants listened to speech, but it did not discriminate between syntactic structures. Spectral-temporal response function modelling suggested different encoding states in both temporal and spectral dimensions as a function of the amount and type of linguistic structure perceived, over and above the acoustically-driven neural response. Our findings provide a comprehensive description of how the brain separates linguistic structures in the dynamics of neural responses, and imply that phase synchronization and strength of connectivity can be used as readouts for constituent structure, providing a novel basis for future neurophysiological research on linguistic structure in the brain.

neuroscience↗

bioRxiv2021

Multivalency enhances the specificity of Fc-cytokine fusions

The common {gamma}-chain receptor cytokines coordinate the proliferation and function of immune cell populations. One of these cytokines, interleukin (IL)-2, has potential as a therapy in autoimmune disease but is limited in effectiveness by its modest specificity toward regulatory T cells (Tregs). Engineering Treg-selective IL-2 has primarily focused on retaining binding to the high-affinity receptor, expressed more highly on Tregs, while reducing binding to the lower affinity receptor with broader expression. However, other parameters, such as the orientation and valency of Fc fusion, have signaling effects that have never been systematically explored. Here, we systematically profiled the signaling responses to a panel of wild type and mutein IL-2-Fc fusions across time, cell types, and concentrations. Exploring these responses, we found that dimeric muteins have unique specificity for Tregs through binding avidity. A mechanistic model of receptor interactions could capture these effects and directed the design of tetravalent IL-2-Fc fusions with greater Treg specificity than possible with current design strategies. Exploration of other surface targets on Tregs revealed that there are no other binding moieties that could be fused to IL-2 for greater selectivity. Instead, IL2R itself is a maximally unique surface target for Tregs, and so avidity is likely the only route to more selective Treg interaction. However, the binding model revealed that asymmetrical, multivalent IL-2 fusions can bias avidity effects toward IL2R for even further enhanced Treg selectivity. These findings present a comprehensive analysis of how ligand properties and their effects on surface receptor-ligand interactions translate to selective activation of immune cell populations, and consequently reveals two new routes toward therapeutic cytokines with superior Treg selectivity that can be exploited for designing selective therapies in many other contexts. Significance StatementSignaling in off-target immune cells has hindered the effectiveness of IL-2 as an immunotherapy. We show that IL-2-Fc fusions with higher valency can exhibit enhanced regulatory T cell selectivity. This altered selectivity is explained by the kinetics of surface receptor-ligand binding and can be quantitatively predicted using a multivalent binding model. Using these insights, we successfully develop two new strategies for IL-2 therapies with unprecedented selectivity. HighlightsO_LICurrent IL-2 therapies are limited by a selectivity/target potency tradeoff. C_LIO_LIMultivalency enhances selectivity for Tregs through IL2R avidity. C_LIO_LITreg selectivity cannot be enhanced by targeting other surface protein markers. C_LIO_LIMultivalency can decouple selectivity from signaling using asymmetric cytokine fusions. C_LI

immunology↗

bioRxiv2021

A lineage tree-based hidden Markov model to quantify cellular heterogeneity and plasticity

Cell plasticity operates alongside other sources of cell-to-cell heterogeneity, such as genetic mutations and variation in signaling, together preventing most cancer therapies from being curative. The predominant methods of quantifying tumor-drug response operate on snapshot, population-level measurements and therefore lack evolutionary dynamics, which are particularly critical for dynamic processes such as plasticity. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of single cell phenotypic heterogeneity and state transitions in an unsupervised fashion. To benchmark our model, we paired cell fate with either cell lifetimes or individual cell cycle phase lengths on synthetic data and demonstrated that the model successfully classifies cells within experimentally tractable dataset sizes. As an application, we analyzed experimental measurements of same measurements in cancer and non-cancer cell populations under various treatments. We find that in each case multiple phenotypically distinct states exist, with significant heterogeneity and unique drug responses. In total, this framework allows for the flexible classification of single cell heterogeneity across lineages.

systems biology↗

bioRxiv2021

Motif-based phosphoproteome clustering improves modeling and interpretation

Cell signaling is orchestrated in part through a network of protein kinases and phosphatases. Dysregulation of kinase signaling is widespread in diseases such as cancer and is readily targetable through inhibitors of kinase enzymatic activity. Mass spectrometry-based analysis of kinase signaling can provide a global view of kinase signaling regulation but making sense of these data is complicated by its stochastic coverage of the proteome, measurement of substrates rather than kinase signaling itself, and the scale of the data collected. Here, we implement a dual data and motif clustering strategy (DDMC) that simultaneously clusters substrate peptides into similarly regulated groups based on their variation within an experiment and their sequence profile. We show that this can help to identify putative upstream kinases and supply more robust clustering. We apply this clustering to large-scale clinical proteomic profiling of lung cancer and identify conserved proteomic signatures of tumorigenicity, genetic mutations, and tumor immune infiltration. We propose that DDMC provides a general and flexible clustering strategy for the analysis of phosphoproteomic data. One-sentence SummaryDDMC is a general and flexible strategy for phosphoproteomic analysis by clustering phosphopeptides using both their phosphorylation abundance and sequence motifs.

bioinformatics↗

bioRxiv2021

A general model of multivalent binding with ligands of heterotypic subunits and multiple surface receptors

Multivalent cell surface receptor binding is a ubiquitous biological phenomenon with functional and therapeutic significance. Predicting the amount of ligand binding for a cell remains an important question in computational biology as it can provide great insight into cell-to-cell communication and rational drug design toward specific targets. In this study, we extend a mechanistic, two-step multivalent binding model. This model predicts the behavior of a mixture of different multivalent ligand complexes binding to cells expressing various types of receptors. It accounts for the combinatorially large number of interactions between multiple ligands and receptors, optionally allowing a mixture of complexes with different valencies and complexes that contain heterogeneous ligand units. We derive the macroscopic predictions and demonstrate how this model enables large-scale predictions on mixture binding and the binding space of a ligand. This model thus provides an elegant and computationally efficient framework for analyzing multivalent binding.

systems biology↗

bioRxiv2021

Structured decomposition improves systems serology prediction and interpretation

Systems serology measurements provide a comprehensive view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. Identifying patterns in these measurements will help to guide vaccine and therapeutic antibody development, and improve our understanding of disorders. Furthermore, consistent patterns across diseases may reflect conserved regulatory mechanisms; recognizing these may help to combine modalities such as vaccines, antibody-based interventions, and other immunotherapies to maximize protection. A common feature of systems serology studies is structured biophysical profiling across disease-relevant antigen targets, properties of antibodies interaction with the immune system, and serological samples. These are typically produced alongside additional measurements that are not antigen-specific. Here, we report a new form of tensor factorization, total tensor-matrix factorization (TMTF), which can greatly reduce these data into consistently observed patterns by recognizing the structure of these data. We use a previous study of HIV-infected subjects as an example. TMTF outperforms standard methods like principal components analysis in the extent of reduction possible. Data reduction, in turn, improves the prediction of immune functional responses, classification of subjects based on their HIV control status, and interpretation of these resulting models. Interpretability is improved specifically by applying further data reduction, separation of the Fc from antigen-binding effects, and recognizing consistent patterns across individual measurements. Therefore, we propose that TMTF will be an effective general strategy for exploring and using systems serology. Summary pointsO_LIStructured decomposition provides substantial data reduction without loss of information. C_LIO_LIPredictions based on decomposed factors are accurate and robust to missing measurements. C_LIO_LIDecomposition structure improves the interpretability of modeling results. C_LIO_LIDecomposed factors represent meaningful patterns in the HIV humoral response. C_LI

immunology↗

bioRxiv2020

A quantitative view of strategies to engineer cell-selective ligand binding

A critical property of many therapies is their selective binding to specific target populations. Exceptional specificity can arise from high-affinity binding to unique cell surface targets. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multi-valent molecules, but these create a combinatorial explosion of design possibilities. Therefore, guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we extend a multi-valent binding model to multi-ligand and multi-receptor interactions. Using this model, we explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, and valency changes. Each of these strategies maximizes selectivity in distinct cases, leading to synergistic improvements when used in combination. Finally, we identify situations in which selectivity cannot be derived through passive binding alone to highlight areas in need of new developments. In total, this work uses a quantitative model to unify a comprehensive set of design guidelines for engineering cell-specific therapies. Summary pointsO_LIAffinity, valency, and other alterations to target cell binding provide enhanced selectivity in specific situations. C_LIO_LIEvidence for the effectiveness and limitations of each strategy are abundant within the drug development literature. C_LIO_LICombining strategies can offer enhanced selectivity. C_LIO_LIA simple, multivalent ligand-receptor binding model can help to direct therapeutic engineering. C_LI

bioengineering↗

bioRxiv2020

Iron can be microbially extracted from Lunar and Martian regolith simulants and 3D printed into tough structural materials

In-situ resource utilization (ISRU) is increasingly acknowledged as an essential requirement for the construction of sustainable extra-terrestrial colonies. Even with decreasing launch costs, the ultimate goal of establishing colonies must be the usage of resources found at the destination of interest. Typical approaches towards ISRU are often constrained by the mass and energy requirements of transporting processing machineries, such as rovers and massive reactors, and the vast amount of consumables needed. Application of self-reproducing bacteria for the extraction of resources is a promising approach to avoid these pitfalls. In this work, the bacterium Shewanella oneidensis was used to reduce three different types of Lunar and Martian regolith simulants, allowing for the magnetic extraction of iron-rich materials. The quantity of bacterially extracted material was up to 5.8 times higher and the total iron concentration was up to 43.6% higher in comparison to untreated material. The materials were 3D printed into cylinders and the mechanical properties were tested, resulting in a 396 {+/-} 115% improvement in compressive strength in the bacterially treated samples. This work demonstrates a proof of concept for the on-demand production of construction and replacement parts in space exploration.

microbiology↗

bioRxiv2020

Structure and meaning organize neural oscillations into a content-specific hierarchy

Neural oscillations track linguistic information during speech comprehension (e.g., Ding et al., 2016; Keitel et al., 2018), and are known to be modulated by acoustic landmarks and speech intelligibility (e.g., Zoefel & VanRullen, 2015). But, it is unclear what information (e.g., timing, rhythm, or content) the brain utilizes to generate linguistic structure and meaning beyond the information that is present in the physical stimulus. We used electroencephalography (EEG) to investigate whether oscillations are modulated by linguistic content over and above the speech stimulus rhythmicity and temporal distribution. We manipulated the presence of semantic and syntactic information apart from the timescale of their occurrence, and controlled for the acoustic-prosodic and lexical-semantic information in the signal. EEG was recorded while 29 adult native speakers of all genders listened to naturally-spoken Dutch sentences, jabberwocky controls with a sentence-like prosodic rhythm and morphemes, word lists with lexical content but no phrase structure, and backwards acoustically-matched controls. Mutual information (MI) analysis revealed sensitivity to linguistic content: Phase MI was highest for sentences at the phrasal (0.8-1.1 Hz) and lexical timescale (1.9-2.8 Hz), suggesting that the delta-band is modulated by lexically-driven combinatorial processing beyond prosody, and that linguistic content (i.e., structure and meaning) organizes the phase of neural oscillations beyond the timescale and rhythmicity of the stimulus. This pattern is consistent with neurophysiologically-inspired models of language comprehension (Martin, 2016, 2020; Martin & Doumas, 2017) where oscillations encode endogenously-generated linguistic content over and above exogenous or stimulus-driven timing and rhythm information. Significance StatementBiological systems like the brain encode their environment not only by reacting in a series of stimulus-driven responses, but by combining stimulus-driven information with endogenous, internally-generated, inferential knowledge and meaning. Understanding language from speech is the human benchmark for this. Much research focusses on the purely stimulus-driven response, but here, we focus on the goal of language behavior: conveying structure and meaning. To that end, we use naturalistic stimuli that contrast acoustic-prosodic and lexical-semantic information to show that, during spoken language comprehension, oscillatory modulations reflect computations related to inferring structure and meaning from the acoustic signal. Our experiment provides the first evidence to date that compositional structure and meaning organize the oscillatory response, above and beyond acoustic and lexical controls.

neuroscience↗

bioRxiv2019

Measurement and models accounting for cell death capture hidden variation in compound response

Cancer cell sensitivity or resistance is almost universally quantified through a direct or surrogate measure of cell number. However, compound responses can occur through many distinct phenotypic outcomes including changes in cell growth, apoptosis, and non-apoptotic cell death. These outcomes have distinct effects on the tumor microenvironment, immune responses, and resistance mechanisms. Here, we show that quantifying cell viability alone is insufficient to distinguish between these compound responses. Using an alternative assay and drug response analysis amenable to high-throughput measurement, we find that compounds with identical viability outcomes can have very different effects on cell growth and death. Moreover, compound pairs with additive cell growth and death effects can appear synergistic when only assessed by viability. Overall, these results demonstrate an approach to incorporating measurements of cell death when characterizing a pharmacologic response. Summary PointsO_LIMeasurements of solely live cell numbers mask important differences in compound effects. C_LIO_LIAdditive effects on growth and death rates can appear synergistic when analyzed solely via live cell number. C_LIO_LIAutomated imaging can provide reasonable throughput to analyze cell response in terms of cell growth and death, and endpoint analysis is similarly informative. C_LI

cancer biology↗

bioRxiv2019

Modeling Cell-Specific Dynamics and Regulation of the Common Gamma Chain Cytokines

Many receptor families exhibit both pleiotropy and redundancy in their regulation, with multiple ligands, receptors, and responding cell populations. Any intervention, therefore, has multiple effects, confounding intuition about how to precisely manipulate signaling for therapeutic purposes. The common {gamma}-chain cytokine receptor dimerizes with complexes of the cytokines interleukin (IL)-2, IL-4, IL-7, IL-9, IL-15, and IL-21 and their corresponding "private" receptors. These cytokines have existing uses and future potential as immune therapies due to their ability to regulate the abundance and function of specific immune cell populations. However, engineering cell specificity into a therapy is confounded by the complexity of the family across responsive cell types. Here, we build a binding-reaction model for the ligand-receptor interactions of common {gamma}-chain cytokines enabling quantitative predictions of response. We show that accounting for receptor-ligand trafficking is essential to accurately model cell response. This model accurately predicts ligand response across a wide panel of cell types under diverse experimental designs. Further, we can predict the effect and specificity of natural or engineered ligands across cell types. We then show that tensor factorization is a uniquely powerful tool to visualize changes in the input-output behavior of the family across time, cell types, ligands, and concentration. In total, these results present a more accurate model of ligand response validated across a panel of immune cell types, and demonstrate an approach for generating interpretable guidelines to manipulate the cell type-specific targeting of engineered ligands. These techniques will in turn help to study and therapeutically manipulate many other complex receptor-ligand families. Summary pointsO_LIA dynamical model of the {gamma}-chain cytokines accurately models responses to IL-2, IL-15, IL-4, and IL-7. C_LIO_LIReceptor trafficking is necessary for capturing ligand response. C_LIO_LITensor factorization maps responses across cell populations, receptors, cytokines, and dynamics to visualize cytokine specificity. C_LIO_LIAn activation model coupled with tensor factorization provides design specifications for engineering cell-specific responses. C_LI

immunology↗