Tensor modeling of MRSA bacteremia cytokine and transcriptional patterns reveals coordinated, outcome-associated immunological programs.

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 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 (PB) exhibit signals of granulocyte dysfunction, suppressed antigen presentation, and deviated lymphocyte polarization. In contrast, patients with resolving bacteremia (RB) 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 PB vs. RB 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.

Baseline Conceptual-Semantic Impairment Predicts Longitudinal Treatment Effects for Anomia in Primary Progressive Aphasia and Alzheimer's Disease.

Background: An individual's diagnostic subtype may fail to predict the efficacy of a given type of treatment for anomia. Classification by conceptual-semantic impairment may be more informative. Aims: This study examined the effects of conceptual-semantic impairment and diagnostic subtype on anomia treatment effects in primary progressive aphasia (PPA) and Alzheimer's disease (AD). Methods & Procedures: At baseline, the picture and word versions of the Pyramids and Palm Trees and Kissing and Dancing tests were used to measure conceptual-semantic processing. Based on norming that was conducted with unimpaired older adults, participants were classified as being impaired on both the picture and word versions (i.e., modality-general conceptual-semantic impairment), the picture version (Objects or Actions) only (i.e., visual-conceptual impairment), the word version (Nouns or Verbs) only (i.e., lexical-semantic impairment), or neither the picture nor the word version (i.e., no impairment). Following baseline testing, a lexical treatment and a semantic treatment were administered to all participants. The treatment stimuli consisted of nouns and verbs that were consistently named correctly at baseline (Prophylaxis items) and/or nouns and verbs that were consistently named incorrectly at baseline (Remediation items). Naming accuracy was measured at baseline, and it was measured at three, seven, eleven, fourteen, eighteen, and twenty-one months. Outcomes & Results: Compared to baseline naming performance, lexical and semantic treatments both improved naming accuracy for treated Remediation nouns and verbs. For Prophylaxis items, lexical treatment was effective for both nouns and verbs, and semantic treatment was effective for verbs, but the pattern of results was different for nouns -- the effect of semantic treatment was initially nonsignificant or marginally significant, but it was significant beginning at 11 Months, suggesting that the effects of prophylactic semantic treatment may become more apparent as the disorder progresses. Furthermore, the interaction between baseline Conceptual-Semantic Impairment and the Treatment Condition (Lexical vs. Semantic) was significant for verb Prophylaxis items at 3 and 18 Months, and it was significant for noun Prophylaxis items at 14 and 18 Months. Conclusions: The pattern of results suggested that individuals who have modality-general conceptual-semantic impairment at baseline are more likely to benefit from lexical treatment, while individuals who have unimpaired conceptual-semantic processing at baseline are more likely to benefit from semantic treatment as the disorder progresses. In contrast to conceptual-semantic impairment, diagnostic subtype did not typically predict the treatment effects.

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.

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 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.

Multivalent, asymmetric IL-2-Fc fusions show enhanced selectivity for regulatory T cells.

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, ß, 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/ß 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.

Disulfide-HMGB1 signals through TLR4 and TLR9 to induce inflammatory macrophages capable of innate-adaptive crosstalk in human liver transplantation.

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 toll-like receptor (TLR) 4 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 reactive oxygen species production. TLR4 inhibition primarily impeded cytokine/chemokine and costimulatory molecule programs, whereas TLR9 inhibition decreased HLA-DR and reactive oxygen species production. diS-HMGB1-polarized macrophages also showed increased capacity to present antigens and activate T memory cells. In murine OLT, diS-HMGB1 treatment potentiated ischemia-reperfusion-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.

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

Immunoglobulin G (IgG) antibodies coordinate immune effector responses by interacting with effector cells via fragment crystallizable γ (Fcγ) receptors. The IgG Fc domain directs effector responses through subclass and glycosylation variation. Although each Fc variant has been extensively characterized in isolation, during immune responses, IgG is almost always produced in Fc mixtures. How this influences effector responses has not been examined. Here, we measure Fcγ receptor binding to mixed Fc immune complexes. Binding of these mixtures falls along a continuum between pure cases and quantitatively matches a mechanistic model, except for several low-affinity interactions mostly involving IgG2. We find that the binding model provides refined estimates of their affinities. Finally, we demonstrate that the model predicts effector cell-elicited platelet depletion in humanized mice. Contrary to previous views, IgG2 exhibits appreciable binding through avidity, though it is insufficient to induce effector responses. Overall, this work demonstrates a quantitative framework for modeling mixed IgG Fc-effector cell regulation.

Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects.

Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.

Design of cell-type-specific hyperstable IL-4 mimetics via modular de novo scaffolds.

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.

Tensor-based insights into systems immunity and infectious disease.

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 analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.

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

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-ß 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.

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 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. Summary points: The binding behavior of mixed Fc immune complexes is a blend of the binding properties for each constituent IgG species.An equilibrium, multivalent binding model can be generalized to incorporate immune complexes of mixed Fc composition.Particularly for low-affinity IgG-Fcγ receptor interactions, immune complexes provide better estimates of affinities.The FcγR binding model predicts effector-elicited cell clearance in humanized mice.

Live Cell Lineage Tracing of Dormant Cancer Cells.

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.

Addressing the genetic/nongenetic duality in cancer with systems biology.

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.

Association of Regional Atrophy With Naming Decline in Primary Progressive Aphasia.

BACKGROUND AND OBJECTIVES: Primary progressive aphasia (PPA) is a neurodegenerative condition that predominantly impairs language. Most investigations of how focal atrophy affects language consider 1 time point compared with healthy controls. However, true atrophy quantification requires comparing individual brains over time. In this observational cohort study, we identified areas where focal atrophy was associated with contemporaneous decline in naming in the same individuals. METHODS: Cross-sectional analyses-related Boston Naming Test (BNT) performance and volume in 22 regions of interests (ROIs) at each time point using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Longitudinal analysis evaluated changes in BNT performance and change in volume in the same ROIs. RESULTS: Participants (N = 62; 50% female; mean age = 66.8 ± 7.4 years) with PPA completed the BNT and MRI twice (mean = 343.9 ± 209.0 days apart). In cross-sectional left inferior frontal gyrus pars opercularis, superior temporal pole, middle temporal gyrus, and inferior temporal gyrus were identified as critical for naming at all time points. Longitudinal analysis revealed that increasing atrophy in the left supramarginal gyrus and middle temporal pole predicted greater naming decline, as did female sex and longer intervals between time points. DISCUSSION: Although cross-sectional analyses identified classic language areas that were consistently related to poor performance at multiple time points, it was not increasing atrophy in these areas that lead to further decline: longitudinal analysis of each person's atrophy over time instead identified nearby but distinct regions where increased atrophy was related to decreasing performance. The results demonstrate that directly examining atrophy (in each individual) over time furthers understanding of decline in PPA and reveal the importance of left supramarginal gyrus and middle temporal pole in maintaining naming when areas normally critical for language degenerate. The novel results provide insight into how the underlying disease progresses to result in the clinical decline in naming, the deficit most common among all 3 PPA variants.

Systems approaches to uncovering the contribution of environment-mediated drug resistance.

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.

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity.

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.

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 cytokine's preferential expansion of immunosuppressive regulatory T cells (Tregs). However, off-target immune cell activation and short serum half-life 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 leverage structural insights to engineer a single-chain hIL-2/F5111 antibody fusion protein, termed F5111 immunocytokine (IC), which potently and selectively activates and expands Tregs. F5111 IC confers 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.

Free-energy landscapes and insertion pathways for peptides in membrane environment.

Free-energy landscapes for short peptides-specifically for variants of the pH low insertion peptide (pHLIP)-in the heterogeneous environment of a lipid bilayer or cell membrane are constructed, taking into account a set of dominant interactions and the conformational preferences of the peptide backbone. Our methodology interprets broken internal H-bonds along the backbone of a polypeptide as statistically interacting quasiparticles, activated from the helix reference state. The favored conformation depends on the local environment (ranging from polar to nonpolar), specifically on the availability of external H-bonds (with H_{2}O molecules or lipid headgroups) to replace internal H-bonds. The dominant side-chain contribution is accounted for by residue-specific transfer free energies between polar and nonpolar environments. The free-energy landscape is sensitive to the level of pH in the aqueous environment surrounding the membrane. For high pH, we identify pathways of descending free energy that suggest a coexistence of membrane-adsorbed peptides with peptides in solution. A drop in pH raises the degree of protonation of negatively charged residues and thus increases the hydrophobicity of peptide segments near the C terminus. For low pH, we identify insertion pathways between the membrane-adsorbed state and a stable trans-membrane state with the C terminus having crossed the membrane.

Mechanical response to tension and torque of molecular chains via statistically interacting particles associated with extension, contraction, twist, and supercoiling.

A methodology for the statistical mechanical analysis of polymeric chains under tension introduced previously is extended to include torque. The response of individual bonds between monomers or of entire groups of monomers to a combination of tension and torque involves, in the framework of this method of analysis, the (thermal or mechanical) activation of a specific mix of statistically interacting particles carrying quanta of extension or contraction and quanta of twist or supercoiling. The methodology, which is elucidated in applications of increasing complexity, is capable of describing the conversion between twist chirality and plectonemic chirality in quasistatic processes. The control variables are force or extension and torque or linkage (a combination of twist and writhe). The versatility of this approach is demonstrated in two applications relevant and promising for double-stranded DNA under controlled tension and torque. One application describes conformational transformations between (native) B-DNA, (underwound) S-DNA, and (overwound) P-DNA in accord with experimental data. The other application describes how the conversion between a twisted chain and a supercoiled chain accommodates variations of linkage and excess length in a buckling transition.