Abstract: Systems serology aims to broadly profile the antigen binding, Fc biophysical features, immune receptor engagement, and effector functions of antibodies. This experimental approach excels at identifying antibody functional features that are relevant to a particular disease. However, a crucial limitation of this approach is its incomplete description of what structural features of the antibodies are responsible for the observed immune receptor engagement and effector functions. Knowing these antibody features is important for both understanding how effector responses are naturally controlled through antibody Fc structure and designing antibody therapies with specific effector profiles. Here, we address this limitation by modeling the molecular interactions occurring in these assays and using this model to infer quantities of specific antibody species among the antibodies being profiled. We used several validation strategies to show that the model accurately infers antibody properties. We then applied the model to infer previously unavailable antibody fucosylation information from existing systems serology data. Using this capability, we find that COVID-19 vaccine efficacy is associated with the induction of afucosylated spike protein-targeting IgG. Our results also question an existing assumption that controllers of HIV exhibit gp120-targeting IgG that are less fucosylated than those of progressors. Additionally, we confirm that afucosylated IgG is associated with membrane-associated antigens for COVID-19 and HIV, and present new evidence indicating that this relationship is specific to the host cell membrane. Finally, we use the model to identify redundant assay measurements and subsets of information-rich measurements from which they can be inferred. In total, our modeling approach provides a quantitative framework for the reasoning typically applied in these studies, improving the ability to draw mechanistic conclusions from these data.

Abstract: 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 cytokine’s 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.

Abstract: Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, “the medium is the message.” In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We review how tensor-structured analyses and decompositions can preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.

Abstract: Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

Abstract: Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These methods are useful for identifying patterns within a variety of biomedical datasets due to their ability to preserve the organizational structure of experiments and therefore aid in generating meaningful insights. However, missing data in the datasets being analyzed can impose challenges. Tensor factorization can be performed with some level of missing data and reconstruct a complete tensor. However, while tensor methods may impute these missing values, the choice of fitting algorithm may influence the fidelity of these imputations. Previous approaches, based on alternating least squares with prefilled values or direct optimization, suffer from introduced bias or slow computational performance. In this study, we propose that censored least squares can better handle missing values with data structured in tensor form. We ran censored least squares on four different biological datasets and compared its performance against alternating least squares with prefilled values and direct optimization. We used the error of imputation and the ability to infer masked values to benchmark their missing data performance. Censored least squares appeared best suited for the analysis of high-dimensional biological data by accuracy and convergence metrics across several studies.

Abstract: Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is a common and life-threatening infection that imposes up to 30% mortality even when appropriate therapy is used. Despite in vitro efficacy determined by minimum inhibitory concentration (MIC) breakpoints, antibiotics often fail to resolve these infections 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 their 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 cytokine datasets across a well-characterized cohort of patients with persistent or resolving MRSA bacteremia outcomes. This method yielded high correlative accuracy with outcomes and immunologic signatures united by transcriptomic and cytokine datasets. Results reveal that patients with persistent MRSA bacteremia exhibit signals of granulocyte dysfunction, suppressed antigen presentation, and deviated lymphocyte polarization. In contrast, patients with resolving bacteremia heterogeneously exhibit correlates of robust antigen-presenting cell trafficking and enhanced neutrophil maturation corresponding to appropriate T lymphocyte polarization and B lymphocyte response. These results suggest that transcriptional and cytokine correlates of persistent versus resolving bacteremia outcomes are complex and may not be disclosed by conventional modeling. In this respect, a tensor-based integration approach may help to reveal consensus molecular and cellular mechanisms and their biological interpretation.

Abstract: 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.

Abstract: The cytokine interleukin-2 (IL-2) has the potential to treat autoimmune disease but is limited by its modest specificity toward immunosuppressive regulatory T (Treg) cells. IL-2 receptors consist of combinations of α, β, and γ chains of variable affinity and cell specificity. Engineering IL-2 to treat autoimmunity has primarily focused on retaining binding to the relatively Treg-selective, high-affinity receptor while reducing binding to the less selective, low-affinity receptor. However, we found that refining the designs to focus on targeting the high-affinity receptor through avidity effects is key to optimizing Treg selectivity. We profiled the dynamics and dose dependency of signaling responses in primary human immune cells induced by engineered fusions composed of either wild-type IL-2 or mutant forms with altered affinity, valency, and fusion to the antibody Fc region for stability. Treg selectivity and signaling response variations were explained by a model of multivalent binding and dimer-enhanced avidity—a combined measure of the strength, number, and conformation of interaction sites—from which we designed tetravalent IL-2–Fc fusions that had greater Treg selectivity in culture than do current designs. Biasing avidity toward IL2Rα with an asymmetrical multivalent design consisting of one α/β chain–binding and one α chain–binding mutant further enhanced Treg selectivity. Comparative analysis revealed that IL2Rα was the optimal cell surface target for Treg selectivity, indicating that avidity for IL2Rα may be the optimal route to producing IL-2 variants that selectively target Tregs.

Abstract: Living materials, which are made either of or by living cells, or are synthetic materials with programmable elements catered to cells, are environmentally responsive and can self-repair, allowing controlled and predictable interactions with biological systems. Such features can be achieved in purely synthetic materials using chemical approaches to create dynamic and responsive materials that can undergo programmed changes, be remodelled by cells in a predictive way, sense their microenvironment and report back, or respond to remote triggers to rearrange in physical or chemical ways. In this Perspective, we discuss synthetic approaches to design such cell-responsive and environment-responsive living materials, with a particular focus on their applications in cancer. We highlight how synthetic and systems biology approaches can be implemented in the design of synthetic living materials, and we outline key cancer-related applications, including modelling of tumour heterogeneity, the tumour microenvironment and tumour evolution in response to therapy. Finally, we emphasize the importance of inclusive designs that should be based on an understanding of how health and disease manifest in and affect humans from all racial and ethnic backgrounds, skin colours, sexes and genders.

Abstract: Ischemia-reperfusion injury (IRI) during orthotopic liver transplantation (OLT) contributes to graft rejection and poor clinical outcomes. The disulfide form of high mobility group box 1 (diS-HMGB1), an intracellular protein released during OLT-IRI, induces pro-inflammatory macrophages. How diS-HMGB1 differentiates human monocytes into macrophages capable of activating adaptive immunity remains unknown. We investigated if diS-HMGB1 binds TLR4 and TLR9 to differentiate monocytes into pro-inflammatory macrophages that activate adaptive immunity and promote graft injury and dysfunction. Assessment of 106 clinical liver tissue and longitudinal blood samples revealed that OLT recipients were more likely to experience IRI and graft dysfunction with increased diS-HMGB1 released during reperfusion. Increased diS-HMGB1 concentration also correlated with TLR4/TLR9 activation, polarization of monocytes into pro-inflammatory macrophages, and production of anti-donor antibodies. In vitro, healthy volunteer monocytes stimulated with purified diS-HMGB1 had increased inflammatory cytokine secretion, antigen presentation machinery, and ROS production. TLR4 inhibition primarily impeded cytokine/chemokine and costimulatory molecule programs, whereas TLR9 inhibition decreased HLA-DR/ROS production. diS-HMGB1-polarized macrophages also showed increased capacity to present antigens and activate T memory cells. In murine OLT, diS-HMGB1 treatment potentiated IR-mediated hepatocellular injury, accompanied by increased serum alanine transaminase levels. This translational study identifies the diS-HMGB1/TLR4/TLR9 axis as potential therapeutic targets in OLT-IRI recipients.

Abstract: Immunoglobulin (Ig)G antibodies coordinate immune effector responses by selectively binding to target antigens and then interacting with various effector cells via the Fcγ 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 Fcγ 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.

Abstract: A major challenge to improving outcomes for patients with cancer is the identification of effective therapeutic strategies that can prevent tumor cell proliferation. Here we sought to gain a deeper understanding of how anti-cancer agents modulate cell cycle progression in HER2+ breast cancer, a disease subtype that accounts for 20% of all breast cancers. We treated HER2+ breast cancer cells with a panel of drugs and tracked changes in cell number and cell cycle phase, which revealed drug-specific cell cycle effects that varied across time. This suggested that a computational model that could account for cell cycle phase durations would provide a framework to explore drug-induced changes in cell cycle changes. Toward that goal, we developed a linear chain trick (LCT) computational model, in which the cell cycle is partitioned into subphases that faithfully captured drug-induced dynamic responses. The model inferred phase-specific drug effects and independent modulation of cell cycle phases, which we confirmed experimentally. We then used our LCT model to predict the effect of unseen drug combinations that target cells in different cell cycle phases. Experimental testing confirmed several model predictions and identified combination treatment strategies that may improve therapeutic response in patients with HER2+ breast cancer. Overall, this integrated experimental and modeling approach opens new avenues for assessing drug responses, predicting effective drug combinations, and identifying optimal drug sequencing strategies.

Abstract: Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analysis approaches to realize their full potential. We highlight the recent application of tensor methods and discuss several future opportunities.

Abstract: The interleukin-4 (IL-4) cytokine plays a critical role in modulating immune homeostasis. Although there is great interest in harnessing this cytokine as a therapeutic in natural or engineered formats, the clinical potential of native IL-4 is limited by its instability and pleiotropic actions. Here, we design IL-4 cytokine mimetics (denoted Neo-4) based on a de novo engineered IL-2 mimetic scaffold and demonstrate that these cytokines can recapitulate physiological functions of IL-4 in cellular and animal models. In contrast with natural IL-4, Neo-4 is hyperstable and signals exclusively through the type I IL-4 receptor complex, providing previously inaccessible insights into differential IL-4 signaling through type I versus type II receptors. Because of their hyperstability, our computationally designed mimetics can directly incorporate into sophisticated biomaterials that require heat processing, such as three-dimensional-printed scaffolds. Neo-4 should be broadly useful for interrogating IL-4 biology, and the design workflow will inform targeted cytokine therapeutic development.

Abstract: Type I interferons (IFN) induce powerful antiviral 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-β also activates an inflammatory gene expression program in contrast to IFN-λ3, a type III IFN, which elicits only the common antiviral 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.

Abstract: The dogma that cancer is a genetic disease is being questioned. Recent findings suggest that genetic/nongenetic duality is necessary for cancer progression. A think tank organized by the Shraman Foundation’s Institute for Theoretical Biology compiled key challenges and opportunities that theoreticians, experimentalists, and clinicians can explore from a systems biology perspective to provide a better understanding of the disease as well as help discover new treatment options and therapeutic strategies.

Abstract: Abstract Breast cancer is a leading cause of global cancer-related deaths, and metastasis is the overwhelming culprit of poor patient prognosis. The most nefarious aspect of metastasis is dormancy, a prolonged period between primary tumor resection and relapse. Current therapies are insufficient at killing dormant cells; thus, they can remain quiescent in the body for decades until eventually undergoing a phenotypic switch, resulting in metastases that are more adaptable and drug resistant. Unfortunately, dormancy has few in vitro models, largely because lab-derived cell lines are highly proliferative. Existing models address tumor dormancy, not cellular dormancy, because tracking individual cells is technically challenging. To combat this problem, a live cell lineage approach to find and track individual dormant cells, distinguishing them from proliferative and dying cells over multiple days, is adapted. This approach is applied across a range of different in vitro microenvironments. This approach reveals that the proportion of cells that exhibit long-term quiescence is regulated by both cell intrinsic and extrinsic factors, with the most dormant cells found in 3D collagen gels. This paper envisions that this approach will prove useful to biologists and bioengineers in the dormancy community to identify, quantify, and study dormant tumor cells.

Abstract: Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to therapeutic stress. Such phenotypic plasticity may confer resistance, but also presents opportunities to identify molecular programs that could be targeted for therapeutic benefit. Approaches to quantify tumor-drug responses typically focus on snapshot, population-level measurements. While informative, these methods lack lineage and temporal information, which are particularly critical for understanding dynamic processes such as cell state switching. As new technologies have become available to measure lineage relationships, modeling approaches will be needed to identify the forms of cell-to-cell heterogeneity present in these data. 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 phenotypic heterogeneity and state transitions. In benchmarking studies, we demonstrated that the model successfully classifies cells within experimentally-tractable dataset sizes. As an application, we analyzed experimental measurements in cancer and non-cancer cell populations under various treatments. We find evidence of multiple phenotypically distinct states, with considerable heterogeneity and unique drug responses. In total, this framework allows for the flexible modeling of single cell heterogeneity across lineages to quantify, understand, and control cell state switching.

Abstract: Low dose human interleukin-2 (hIL-2) treatment is used clinically to treat autoimmune disorders due to the cytokine’s 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.

Abstract: Cancer drug response is heavily influenced by the extracellular matrix (ECM) environment. Despite a clear appreciation that the ECM influences cancer drug response and progression, a unified view of how, where, and when environment-mediated drug resistance contributes to cancer progression has not coalesced. Here, we survey some specific ways in which the ECM contributes to cancer resistance with a focus on how materials development can coincide with systems biology approaches to better understand and perturb this contribution. We argue that part of the reason that environment-mediated resistance remains a perplexing problem is our lack of a wholistic view of the entire range of environments and their impacts on cell behavior. We cover a series of recent experimental and computational tools that will aid exploration of ECM reactions space, and how they might be synergistically integrated.

Abstract: 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. Mass spectrometry-based analysis can provide a global view of kinase regulation but mining these data is complicated by its stochastic coverage of the proteome, measurement of substrates rather than kinases, and the scale of the data. Here, we implement a Dual Data and Motif Clustering (DDMC) strategy that simultaneously clusters peptides into similarly regulated groups based on their variation 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 clinical proteomic profiling of lung cancer and identify conserved proteomic signatures of tumorigenicity, genetic mutations, and immune infiltration. We propose that DDMC provides a general and flexible clustering strategy for the analysis of phosphoproteomic data.

Abstract: To better understand the signaling complexity of AXL, a member of the TAM receptor tyrosine kinase family, we created a physical and functional map of AXL signaling interactions, phosphorylation events, and target-engagement of three AXL tyrosine kinase inhibitors (TKI). We assessed AXL protein-complexes using BioID, effects of AXL TKI on global phosphoproteins using mass spectrometry, and target engagement of AXL TKI using activity-based protein profiling. BioID identifies AXL-interacting proteins that are mostly involved in cell adhesion/migration. Global phosphoproteomics show that AXL inhibition decreases phosphorylation of peptides involved in phosphatidylinositol-mediated signaling and cell adhesion/migration. Comparison of three AXL inhibitors reveals that TKI RXDX-106 inhibits pAXL, pAKT and migration/invasion of these cells without reducing their viability, while Bemcentinib exerts AXL-independent phenotypic effects on viability. Proteomic characterization of these TKIs demonstrates that they inhibit diverse targets in addition to AXL, with Bemcentinib having the most off-targets. AXL and EGFR TKI co-treatment did not reverse resistance in cell line models of Erlotinib-resistance. However, a unique vulnerability was identified in one resistant clone, wherein combination of Bemcentinib and Erlotinib inhibited cell viability and signaling. We also show that AXL is overexpressed in  30-40% of non-small but rarely in small-cell lung cancer. Cell lines have a wide range of AXL expression, with basal activation detected rarely.

Abstract: A critical property of many therapies is their selective binding to target populations. Exceptional specificity can arise from high-affinity binding to surface targets expressed exclusively on target cell types. 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 multivalent molecules, creating a combinatorial explosion of design possibilities. Guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we employ a uniquely general multivalent binding model to dissect multi-ligand and multi-receptor interactions. This model allows us to analyze and explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, valency changes, multi-specific molecules, and ligand competition. Each of these strategies can optimize selectivity in distinct cases, leading to enhanced selectivity when employed together. The proposed model, therefore, provides a comprehensive toolkit for the model-driven design of selectively binding therapies.

Abstract: Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix-tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV- and SARS-CoV-2-infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.

Abstract: The γ-chain receptor dimerizes with complexes of the cytokines interleukin-2 (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 because of their ability to regulate the abundance and function of specific immune cell populations. Here, we build a binding reaction model for the ligand-receptor interactions of common γ-chain cytokines, which includes receptor trafficking dynamics, enabling quantitative predictions of cell-type-specific response to natural and engineered cytokines. We then show that tensor factorization is a powerful tool to visualize changes in the input-output behavior of the family across time, cell types, ligands, and concentrations. These results present a more accurate model of ligand response validated across a panel of immune cell types as well as a general approach for generating interpretable guidelines for manipulation of cell-type-specific targeting of engineered ligands.

Abstract: 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.

Abstract: 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 divergent effects on the tumor microenvironment, immune response, 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, additive compound pairs with distinct growth/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.

Abstract: The pharmacokinetic properties of antibodies are largely dictated by the pH-dependent binding of the IgG fragment crystallizable (Fc) domain to the human neonatal Fc receptor (hFcRn). Engineered Fc domains that confer a longer circulation half-life by virtue of more favorable pH-dependent binding to hFcRn are of great therapeutic interest. Here we developed a pH Toggle switch Fc variant containing the L309D/Q311H/N434S (DHS) substitutions, which exhibits markedly improved pharmacokinetics relative to both native IgG1 and widely used half-life extension variants, both in conventional hFcRn transgenic mice and in new knock-in mouse strains. engineered specifically to recapitulate all the key processes relevant to human antibody persistence in circulation, namely: (i) physiological expression of hFcRn, (ii) the impact of hFcγRs on antibody clearance and (iii) the role of competing endogenous IgG. DHS-IgG retains intact effector functions, which are important for the clearance of target pathogenic cells and also has favorable developability.

Abstract: The recent wide-spread adoption of single cell profiling technologies has revealed that individual cancers are not homogenous collections of deregulated cells, but instead are comprised of multiple genetically and phenotypically distinct cell subpopulations that exhibit a wide range of responses to extracellular signals and therapeutic insult. Such observations point to the urgent need to understand cancer as a complex, adaptive system. Cancer systems biology studies seek to develop the experimental and theoretical methods required to understand how biological components work together to determine how cancer cells function. Ultimately, such approaches will lead to improvements in how cancer is managed and treated. In this review, we discuss recent advances in cancer systems biology approaches to quantify, model, and elucidate mechanisms of heterogeneity.

Abstract: Conjugating certain types of lentiviral vectors with targeting ligands can redirect the vectors to specifically transduce desired cell types. However, extensive genetic and/or biochemical manipulations are required for conjugation, which hinders applications for targeting lentiviral vectors for broader research fields. We developed envelope proteins fused with biotin-binding molecules to conjugate the pseudotyped vectors with biotinylated targeting molecules by simply mixing them. The envelope proteins fused with the monomeric, but not tetrameric, biotin-binding molecules can pseudotype lentiviral vectors and be conjugated with biotinylated targeting ligands. The conjugation is stable enough to redirect lentiviral transduction in the presence of serum, indicating their potential in in vivo . When a signaling molecule is conjugated with the vector, the conjugation facilitates transduction and signaling in a receptor-specific manner. This simple method of ligand conjugation and ease of obtaining various types of biotinylated ligands will make targeted lentiviral transduction easily applicable to broad fields of research.

Abstract: Targeted cancer therapeutics have demonstrated more limited clinical efficacy than anticipated, due to both intrinsic and acquired drug resistance. Underlying mechanisms have been largely attributed to genetic changes, but a substantial proportion of resistance observations remain unexplained by genomic properties. Emerging evidence shows that receptor tyrosine kinase (RTK) reprogramming is a major alternative process causing targeted drug resistance, separate from genetic alterations. Hence, the contributions of mechanisms leading to this process need to be more rigorously assessed.

Abstract: Maternal Zika virus (ZIKV) infection during pregnancy is recognized as the cause of an epidemic of microcephaly and other neurological anomalies in human fetuses. It remains unclear how ZIKV accesses the highly vulnerable population of neural progenitors of the fetal central nervous system (CNS), and which cell types of the CNS may be viral reservoirs. In contrast, the related dengue virus (DENV) does not elicit teratogenicity. To model viral interaction with cells of the fetal CNS in vitro, we investigated the tropism of ZIKV and DENV for different induced pluripotent stem cell-derived human cells, with a particular focus on microglia-like cells. We show that ZIKV infected isogenic neural progenitors, astrocytes, and microglia-like cells (pMGLs), but was only cytotoxic to neural progenitors. Infected glial cells propagated ZIKV and maintained ZIKV load over time, leading to viral spread to susceptible cells. DENV triggered stronger immune responses and could be cleared by neural and glial cells more efficiently. pMGLs, when cocultured with neural spheroids, invaded the tissue and, when infected with ZIKV, initiated neural infection. Since microglia derive from primitive macrophages originating in proximity to the maternal vasculature, they may act as a viral reservoir for ZIKV and establish infection of the fetal brain. Infection of immature neural stem cells by invading microglia may occur in the early stages of pregnancy, before angiogenesis in the brain rudiments. Our data are also consistent with ZIKV and DENV affecting the integrity of the blood–brain barrier, thus allowing infection of the brain later in life.

Abstract: Many immune receptors transduce activation across the plasma membrane through their clustering. With Fcγ receptors (FcγRs), this clustering is driven by binding to antibodies of differing affinities that are in turn bound to multivalent antigen. As a consequence of this activation mechanism, accounting for and rationally manipulating immunoglobulin (Ig)G effector function is complicated by, among other factors, differing affinities between FcγR species and changes in the valency of antigen binding. In this study, we show that a model of multivalent receptor-ligand binding can effectively account for the contribution of IgG-FcγR affinity and immune complex valency. This model in turn enables us to make specific predictions about the effect of immune complexes of defined composition. In total, these results enable both rational immune complex design for a desired IgG effector function and the deconvolution of effector function by immune complexes.

Abstract: Metastases are a major cause of cancer mortality. AXL, a receptor tyrosine kinase (RTK) aberrantly expressed in many tumors, is a potent oncogenic driver of metastatic cell motility and has been identified as broadly relevant in cancer drug resistance. Despite its frequent association with changes in cancer phenotypes, the precise mechanism leading to AXL activation is incompletely understood. In addition to its ligand growth arrest specific-6 (Gas6), activation of AXL requires the lipid moiety phosphatidylserine (PS). PS is only available to mediate AXL activation when it is externalized on cell membranes, an event that occurs during certain physiologic processes such as apoptosis. Here it is reported that exposure of cancer cells to PS-containing vesicles, including synthetic liposomes and apoptotic bodies, contributes to enhanced migration of tumor cells via a PS-Gas6-AXL signaling axis. These findings suggest that anti-cancer treatments that induce fractional cell killing enhance the motility of surviving cells in AXL-expressing tumors, which may explain the widespread role of AXL in limiting therapeutic efficacy. Implications: This study demonstrates that motility behavior of AXL-expressing tumor cells can be elicited by Gas6-bearing apoptotic bodies generated from tumor treatment with therapeutics that produce killing of a portion of the tumor cells present but not all, hence generating potentially problematic invasive and metastatic behavior of the surviving tumor cells.

Abstract: Traditional drug screening methods lack features of the tumor microenvironment that can contribute to resistance. There remains a gap in whether extracellular signals, such as stiffness, dimensionality, and cell-cell contacts act independently, or are integrated within a cell, to affect drug sensitizations or resistance. This is critically important, as adaptive resistance is mediated, at least in part, by the extracellular matrix (ECM) of the tumor microenvironment. We developed an approach to screen drug responses in cells cultured on 2D and in 3D biomaterial environments to explore how key features of ECM mediate drug response. This approach uncovered that cells on 2D hydrogels and as spheroids encapsulated in 3D hydrogels were less responsive to receptor tyrosine kinase (RTK)-targeting drugs sorafenib and lapatinib, but not cytotoxic drugs, compared to single cells in hydrogels and cells on plastic. Transcriptomic differences between these in vitro models and tumor xenografts did not reveal mechanisms of ECM-mediated resistance. However, a systems biology analysis of phospho-kinome data suggested that MEK phosphorylation was associated with RTK-targeted drug resistance. Using sorafenib as a model drug, we found that co-administration with a MEK inhibitor decreased ECM-mediated resistance in vitro and reduced in vivo tumor burden compared to sorafenib alone. In sum, we provide a novel strategy for identifying and overcoming ECM-mediated resistance mechanisms by performing drug screening, phospho-kinome analysis, and systems biology across multiple biomaterial environments.

Abstract: Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic.

Abstract: Resistance limits the effectiveness of receptor tyrosine kinase (RTK)-targeted therapies. Combination therapies targeting resistance mechanisms can considerably improve response, but will require an improved understanding of when particular combinations will be effective. One common form of resistance is bypass signaling, wherein RTKs not targeted by an inhibitor can direct reactivation of pathways essential for survival. Although this mechanism of resistance is well appreciated, it is unclear which downstream signaling events are responsible. Here, we apply a combined experimental- and statistical modeling-based approach to identify a set of pathway reactivation essential for RTK-mediated bypass resistance. Differences in the downstream pathway activation provided by particular RTKs lead to qualitative differences in the capacity of each receptor to drive therapeutic resistance. We identify and validate that the JNK pathway is activated during and strongly modulates bypass resistance. These results identify effective therapeutic combinations that block bypass-mediated resistance and provide a basic understanding of this network-level change in kinase dependence that will inform the design of prognostic assays for identifying effective therapeutic combinations in individual patients.

Abstract: Kinase inhibitor resistance often involves upregulation of poorly understood “bypass” signaling pathways. Here, we show that extracellular proteomic adaptation is one path to bypass signaling and drug resistance. Proteolytic shedding of surface receptors, which can provide negative feedback on signaling activity, is blocked by kinase inhibitor treatment and enhances bypass signaling. In particular, MEK inhibition broadly decreases shedding of multiple receptor tyrosine kinases (RTK), including HER4, MET, and most prominently AXL, an ADAM10 and ADAM17 substrate, thus increasing surface RTK levels and mitogenic signaling. Progression-free survival of patients with melanoma treated with clinical BRAF/MEK inhibitors inversely correlates with RTK shedding reduction following treatment, as measured noninvasively in blood plasma. Disrupting protease inhibition by neutralizing TIMP1 improves MAPK inhibitor efficacy, and combined MAPK/AXL inhibition synergistically reduces tumor growth and metastasis in xenograft models. Altogether, extracellular proteomic rewiring through reduced RTK shedding represents a surprising mechanism for bypass signaling in cancer drug resistance.

Abstract: Axons navigate long distances through complex 3D environments to interconnect the nervous system during development. Although the precise spatiotemporal effects of most axon guidance cues remain poorly characterized, a prevailing model posits that attractive guidance cues stimulate actin polymerization in neuronal growth cones whereas repulsive cues induce actin disassembly. Contrary to this model, we find that the repulsive guidance cue Slit stimulates the formation and elongation of actin-based filopodia from mouse dorsal root ganglion growth cones. Surprisingly, filopodia form and elongate toward sources of Slit, a response that we find is required for subsequent axonal repulsion away from Slit. Mechanistically, Slit evokes changes in filopodium dynamics by increasing direct binding of its receptor, Robo, to members of the actin-regulatory Ena/VASP family. Perturbing filopodium dynamics pharmacologically or genetically disrupts Slit-mediated repulsion and produces severe axon guidance defects in vivo. Thus, Slit locally stimulates directional filopodial extension, a process that is required for subsequent axonal repulsion downstream of the Robo receptor.

Abstract: Dysregulation of ErbB-family signaling underlies numerous pathologies and has been therapeutically targeted through inhibiting ErbB-receptors themselves or their cognate ligands. For the latter, “decoy” antibodies have been developed to sequester ligands including heparin-binding epidermal growth factor (HB-EGF); however, demonstrating sufficient efficacy has been difficult. Here, we hypothesized that this strategy depends on properties such as ligand-receptor binding affinity, which varies widely across the known ErbB-family ligands. Guided by computational modeling, we found that high-affinity ligands such as HB-EGF are more difficult to target with decoy antibodies compared to low-affinity ligands such as amphiregulin (AREG). To address this issue, we developed an alternative method for inhibiting HB-EGF activity by targeting its cleavage from the cell surface. In a model of the invasive disease endometriosis, we identified A Disintegrin and Metalloproteinase 12 (ADAM12) as a protease implicated in HB-EGF shedding. We designed a specific inhibitor of ADAM12 based on its recombinant prodomain (PA12), which selectively inhibits ADAM12 but not ADAM10 or ADAM17. In endometriotic cells, PA12 significantly reduced HB-EGF shedding and resultant cellular migration. Overall, specific inhibition of ligand shedding represents a possible alternative to decoy antibodies, especially for ligands such as HB-EGF that exhibit high binding affinity and localized signaling.

Abstract: The members of the actin regulatory family of Ena/VASP proteins form stable tetramers. The vertebrate members of the Ena/VASP family, VASP, Mena, and EVL, have many overlapping properties and expression patterns, but functional and regulatory differences between paralogs have been observed. The formation of mixed oligomers may serve a regulatory role to refine Ena/VASP activity. While it has been assumed that family members can form mixed oligomers, this possibility has not been investigated systematically. Using cells expressing controlled combinations of VASP, Mena, and EVL, we evaluated the composition of Ena/VASP oligomers and found that VASP forms oligomers without apparent bias with itself, Mena, or EVL. However, Mena and EVL showed only weak hetero-oligomerization, suggesting specificity in the association of Ena/VASP family members. Co-expression of VASP increased the ability of Mena and EVL to form mixed oligomers. Additionally, we found that the tetramerization domain at the C-termini of Ena/VASP proteins conferred the observed selectivity. Finally, we demonstrate that replacement of the TD with a synthetic tetramerizing coiled-coil sequence supports homo-oligomerization and normal VASP subcellular localization.

Abstract: The AXL receptor is a TAM (Tyro3, AXL, MerTK) receptor tyrosine kinase (RTK) important in physiological inflammatory processes such as blood clotting, viral infection, and innate immune-mediated cell clearance. Overexpression of the receptor in a number of solid tumors is increasingly appreciated as a key drug resistance and tumor dissemination mechanism. Although the ligand-receptor (Gas6-AXL) complex structure is known, literature reports on ligand-mediated signaling have provided conflicting conclusions regarding the influence of other factors such as phosphatidylserine binding, and a detailed, mechanistic picture of AXL activation has not emerged. Integrating quantitative experiments with mathematical modeling, we show here that AXL operates to sense local spatial heterogeneity in ligand concentration, a feature consistent with its physiological role in inflammatory cell responses. This effect arises as a result of an intricate reaction-diffusion interaction. Our results demonstrate that AXL functions distinctly from other RTK families, a vital insight for the envisioned design of AXL-targeted therapeutic intervention.

Abstract: The relationship between drug resistance, changes in signaling, and emergence of an invasive phenotype is well appreciated, but the underlying mechanisms are not well understood. Using machine learning analysis applied to the Cancer Cell Line Encyclopedia database, we identified expression of AXL, the gene that encodes the epithelial-to-mesenchymal transition (EMT)-associated receptor tyrosine kinase (RTK) AXL, as exceptionally predictive of lack of response to ErbB family receptor-targeted inhibitors. Activation of EGFR (epidermal growth factor receptor) transactivated AXL, and this ligand-independent AXL activity diversified EGFR-induced signaling into additional downstream pathways beyond those triggered by EGFR alone. AXL-mediated signaling diversification was required for EGF (epidermal growth factor)-elicited motility responses in AXL-positive TNBC (triple-negative breast cancer) cells. Using cross-linking coimmunoprecipitation assays, we determined that AXL associated with EGFR, other ErbB receptor family members, MET (hepatocyte growth factor receptor), and PDGFR (platelet-derived growth factor receptor) but not IGF1R (insulin-like growth factor 1 receptor) or INSR (insulin receptor). From these AXL interaction data, we predicted AXL-mediated signaling synergy for additional RTKs and validated these predictions in cells. This alternative mechanism of receptor activation limits the use of ligand-blocking therapies and indicates against therapy withdrawal after acquired resistance. Further, subadditive interaction between EGFR- and AXL-targeted inhibitors across all AXL-positive TNBC cell lines may indicate that increased abundance of EGFR is principally a means to transactivation-mediated signaling.

Abstract: A Disintegrin and Metalloproteinases (ADAMs) are the principal enzymes for shedding receptor tyrosine kinase (RTK) ectodomains and ligands from the cell surface. Multiple layers of activity regulation, feedback, and catalytic promiscuity impede our understanding of context-dependent ADAM "sheddase" function and our ability to predictably target that function in disease. This study uses combined measurement and computational modeling to examine how various growth factor environments influence sheddase activity and cell migration in the invasive disease of endometriosis. We find that ADAM-10 and -17 dynamically integrate numerous signaling pathways to direct cell motility. Data-driven modeling reveals that induced cell migration is a quantitative function of positive feedback through EGF ligand release and negative feedback through RTK shedding. Although sheddase inhibition prevents autocrine ligand shedding and resultant EGF receptor transactivation, it also leads to an accumulation of phosphorylated receptors (HER2, HER4, and MET) on the cell surface, which subsequently enhances Jnk/p38 signaling. Jnk/p38 inhibition reduces cell migration by blocking sheddase activity while additionally preventing the compensatory signaling from accumulated RTKs. In contrast, Mek inhibition reduces ADAM-10 and -17 activities but fails to inhibit compensatory signaling from accumulated RTKs, which actually enhances cell motility in some contexts. Thus, here we present a sheddase-based mechanism of rapidly acquired resistance to Mek inhibition through reduced RTK shedding that can be overcome with rationally directed combination inhibitor treatment. We investigate the clinical relevance of these findings using targeted proteomics of peritoneal fluid from endometriosis patients and find growth-factor-driven ADAM-10 activity and MET shedding are jointly dysregulated with disease.

Abstract: Growth factor–induced migration is a critical step in the dissemination and metastasis of solid tumors. Although differences in properties characterizing cell migration on two-dimensional (2D) substrata versus within three-dimensional (3D) matrices have been noted for particular growth factor stimuli, the 2D approach remains in more common use as an efficient surrogate, especially for high-throughput experiments. We therefore were motivated to investigate which migration properties measured in various 2D assays might be reflective of 3D migratory behavioral responses. We used human triple-negative breast cancer lines stimulated by a panel of receptor tyrosine kinase ligands relevant to mammary carcinoma progression. Whereas 2D migration properties did not correlate well with 3D behavior across multiple growth factors, we found that increased membrane protrusion elicited by growth factor stimulation did relate robustly to enhanced 3D migration properties of the MDA-MB-231 and MDA-MB-157 lines. Interestingly, we observed this to be a more reliable relationship than cognate receptor expression or activation levels across these and two additional mammary tumor lines.

Abstract: In bioprocess development, the 96-well plate format has been widely used for high-throughput screening of production cell line or culture conditions. However, suspension cell cultures in conventional 96-well plates often fail to reach high cell density under normal agitation presumably due to constraints in oxygen transfer. Although more vigorous agitation can improve gas transfer in 96-well plate format, it often requires specialized instruments. In this report, we employed Fluorinert, a biologically inert perfluorocarbon, to improve oxygen transfer in 96-well plate and to enable the growth of a Chinese Hamster Ovary cell line expressing a recombinant monoclonal antibody. When different amounts of Fluorinert were added to the cell culture medium, a dose-dependent improvement in cell growth was observed in both conventional and deep square 96-well plates. When sufficient Fluorinert was present in the culture, the cell growth rate, the peak cell density, and recombinant protein production levels achieved in deep square 96-wells were comparable to cultures in ventilated shake flasks. Although Fluorinert is known to dissolve gases such as oxygen and CO2, it does not dissolve nor extract medium components, such as glucose, lactate, or amino acids. We conclude that mixing Fluorinert with culture media is a suitable model for miniaturization of cell line development and process optimization. Proper cell growth and cellular productivity can be obtained with a standard shaker without the need for any additional aeration or vigorous agitation. \copyright 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2012

Abstract: Epithelial-mesenchymal transition (EMT), whether in developmental morphogenesis or malignant transformation, prominently involves modified cell motility behavior. While major advances have transpired in understanding the molecular pathways regulating the process of EMT induction per se by certain environmental stimuli, an important outstanding question is how the activities of signaling pathways governing motility yield the diverse movement behaviors characteristic of pre-induction versus post-induction states across a broad landscape of growth factor contexts. For the particular case of EMT induction in human mammary cells by ectopic expression of the transcription factor Twist, we found the migration responses to a panel of growth factors (EGF, HRG, IGF, HGF) dramatically disparate between confluent pre-Twist epithelial cells and sparsely distributed post-Twist mesenchymal cells - but that a computational model quantitatively integrating multiple key signaling node activities could nonetheless account for this full range of behavior. Moreover, motility in both conditions was successfully predicted a priori for an additional growth factor treatment (PDGF). While this signaling network state model could comprehend motility behavior globally, modulation of the network interactions underlying the altered pathway activities was identified by ascertaining differences in quantitative topological influences among the nodes between the two conditions.

Abstract: The concentration of biomarkers, such as DNA, prior to a subsequent detection step may facilitate the early detection of cancer, which could significantly increase chances for survival. In this study, the partitioning behavior of mammalian genomic DNA fragments in a two-phase aqueous micellar system was investigated using both experiment and theory. The micellar system was generated using the nonionic surfactant Triton X-114 and phosphate-buffered saline (PBS). Partition coefficients were measured under a variety of conditions and compared with our theoretical predictions. With this comparison, we demonstrated that the partitioning behavior of DNA fragments in this system is primarily driven by repulsive, steric, excluded-volume interactions that operate between the micelles and the DNA fragments, but is limited by the entrainment of micelle-poor, DNA-rich domains in the macroscopic micelle-rich phase. Furthermore, the volume ratio, that is, the volume of the top, micelle-poor phase divided by that of the bottom, micelle-rich phase, was manipulated to concentrate DNA fragments in the top phase. Specifically, by decreasing the volume ratio from 1 to 1/10, we demonstrated proof-of-principle that the concentration of DNA fragments in the top phase could be increased two- to nine-fold in a predictive manner.

Abstract: Urinary tract infections (UTIs) are a leading cause of health expenditures, in part due to the expensive and lengthy diagnostic method that involves culturing bacteria. A new technique has been developed allowing for faster UTI diagnosis through an electrochemical chip to measure unique ribosomal RNA (rRNA) found in bacteria associated with UTIs. Although this technique has had success, concentrating the bacterial rRNA through the use of two-phase aqueous micellar systems in the urine sample prior to utilizing the chip may provide increased sensitivity. This approach is appealing because these systems are relatively inexpensive, easily scalable, and simple to use. In this study, we examined the partitioning behavior of bacterial RNA fragments, puri ed from Enteroccocus faecalis, in a two-phase micellar system comprised of the nonionic surfactant Triton X-114 and phosphate-buffered saline. Experimentally measured results were compared to theoretical values to determine the governing factors involved in RNA partitioning. We demonstrated that RNA fragments partition primarily due to steric, excluded-volume interactions that exist between the RNA and micelles. However, the presence of entrained micelle-poor, RNA-rich domains in the macroscopic micelle-rich, RNA-poor phase limits the extent of RNA partitioning in this system. Additionally, by manipulating the volume ratio, or the volume of the top, micelle-poor phase divided by that of the bottom, micelle-rich phase, we demonstrated that RNA fragments can be concentrated up to four-fold in a predictive manner. Concentrating the bacterial RNA with these two-phase micellar systems prior to detection with the UTI chip may facilitate the earlier detection of UTIs.