A mechanistic pharmacokinetic model for intrathecal administration of antisense oligonucleotides

Intrathecal administration is an important mode for delivering biological agents targeting central nervous system (CNS) diseases. However, current clinical practices lack a sound theorical basis for a quantitative understanding of the variables and conditions that govern the delivery efficiency and specific tissue targeting especially in the brain. This work presents a distributed mechanistic pharmacokinetic model (DMPK) for predictive analysis of intrathecal drug delivery to CNS. The proposed DMPK model captures the spatiotemporal dispersion of antisense oligonucleotides (ASO) along the neuraxis over clinically relevant time scales of days and weeks as a function of infusion, physiological and molecular properties. We demonstrate its prediction capability using biodistribution data of antisense oligonucleotide (ASO) administration in non-human primates. The results are in close agreement with the observed ASO pharmacokinetics in all key compartments of the central nervous system. The model enables determination of optimal injection parameters such as intrathecal infusion volume and duration for maximum ASO delivery to the brain. Our quantitative model-guided analysis is suitable for identifying optimal parameter settings to target specific brain regions with therapeutic drugs such as ASOs.

Inter-morph pollen flow and reproductive success in a self-compatible species with stigma-height dimorphism: the influence of herkogamy and reciprocity

Inter-morph pollen transfer and its dependence on herkogamy and reciprocity are not completely understood in species with stigma-height dimorphism. We asked whether total stigmatic pollen loads, inter-morph fraction of pollen load and reproductive success differed between morphs in Jasminum malabaricum, a species exhibiting stigma-height dimorphism. We tested whether higher herkogamy and reciprocity resulted in higher inter-morph pollen deposition and reproductive success. We quantified individual-level estimates of herkogamy, reciprocity, total stigmatic pollen load, inter-morph stigmatic pollen fraction and fruit set for both morphs in naturally occurring populations of J. malabaricum. Total pollen load was higher in the long-styled morph, inter-morph pollen fraction was higher in the short-styled morph, but fruit set did not differ between morphs. Higher herkogamy resulted in a higher inter-morph fraction of pollen load and fruit set in the long-styled morph of one population. In the other population, only reciprocity was found to be related to inter-morph pollen deposition. This study is the first to quantify and report natural inter-morph stigmatic pollen load in a species with stigma-height dimorphism. Morph-specific differences in pollen load were similar to patterns commonly observed in heterostylous species. The results highlight the importance of both herkogamy and reciprocity in facilitating inter-morph pollen transfer. Population-specific patterns indicate that local environmental factors determine the relative functional importance of herkogamy and reciprocity.

High herkogamy but low reciprocity characterizes isoplethic populations of Jasminum malabaricum, a species with stigma-height dimorphism

Studies of floral polymorphisms have focused on heterostyly, while stigma-height dimorphism has received considerably less attention. Few studies have examined the reproductive biology of species with stigma-height dimorphism to understand how factors influencing mate availability and pollen transfer are related to morph ratios in populations. Floral morphological traits, especially herkogamy and reciprocity, pollinator visitation, breeding system and spatiotemporal mate availability, are known to affect inter-morph pollination and morph ratios in species with stigma-height dimorphism. In this study, we investigated the presence of stigma-height dimorphism and estimated morph ratios in four naturally occurring populations of Jasminum malabaricum. We quantified morph- and population-specific differences in the abovementioned factors in these populations to understand the observed morph ratios. The positions of anthers and stigmas were characteristic of stigma-height dimorphism, the first report of this polymorphism in the genus. All study populations were isoplethic, implying equal fitness of both morphs. Herkogamy was higher in the short-styled morph, while reciprocity was higher between the long-styled stigma and short-styled anthers. Long- and short-tongued pollinators were common floral visitors, and we observed no differences between morphs in spatiotemporal mate availability or pollinator visitation. Neither morph exhibited self- or heteromorphic incompatibility. The short-styled stigma had lower reciprocity but likely receives sufficient inter-morph pollen from long-tongued pollinators, and also by avoiding self-pollination due to higher herkogamy. These results highlight the importance of sufficient effective pollinators and floral morphological features, particularly herkogamy, in maintaining isoplethy in species with stigma-height dimorphism.

Dissecting Particle Uptake Heterogeneity in a Cell Population Using Bayesian Analysis

Individual cells in a solution display variable uptake of nanomaterials, peptides, and nutrients. Such variability reflects their heterogeneity in endocytic capacity. In a recent work, we have shown that the endocytic capacity of a cell depends on its size and surface density of endocytic components (transporters). We also demonstrated that in MDA-MB-231 breast cancer cells, the cell-surface transporter density (n) may decay with cell radius (r) following the power rule n ∼ r^α, where α ≈ -1. In this work, we investigate how n and r may independently contribute to the endocytic heterogeneity of a cell population. Our analysis indicates that the smaller cells display more heterogeneity because of the higher stochastic variations in n. By contrast, the larger cells display a more uniform uptake, reflecting less-stochastic variations in n. We provide analyses of these dependencies by establishing a stochastic model. Our analysis reveals that the exponent α in the above relationship is not a constant; rather, it is a random variable whose distribution depends on cell size r. Using Bayesian analysis, we characterize the cell-size-dependent distributions of α that accurately capture the particle uptake heterogeneity of MDA-MB-231 cells.

Community engagement interventions for communicable disease control in low- and lower- middle-income countries: evidence from a review of systematic reviews

Background

Community engagement (CE) interventions include a range of approaches to involve communities in the improvement of their health and wellbeing. Working with communities defined by location or some other shared interest, these interventions may be important in assisting equity and reach of communicable disease control (CDC) in low and lower-middle income countries (LLMIC). We conducted an umbrella review to identify approaches to CE in communicable disease control, effectiveness of these approaches, mechanisms and factors influencing success.

Methods

We included systematic reviews that: i) focussed on CE interventions; ii) involved adult community members; iii) included outcomes relevant to communicable diseases in LLMIC; iv) were written in English. Quantitative results were extracted and synthesised narratively. A qualitative synthesis process enabled identification of mechanisms of effect and influencing factors. We followed guidance from the Joanna Briggs Institute, assessed quality with the DARE tool and reported according to standard systematic review methodology.

Results

Thirteen systematic reviews of medium-to-high quality were identified between June and July 2017. Reviews covered the following outcomes: HIV and STIs (6); malaria (2); TB (1); child and maternal health (3) and mixed (1). Approaches included: CE through peer education and community health workers, community empowerment interventions and more general community participation or mobilisation. Techniques included sensitisation with the community and involvement in the identification of resources, intervention development and delivery. Evidence of effectiveness of CE on health outcomes was mixed and quality of primary studies variable. We found: i) significantly reduced neonatal mortality following women’s participatory learning and action groups; ii) significant reductions in HIV and other STIs with empowerment and mobilisation interventions with marginalised groups; iii) significant reductions in malaria incidence or prevalence in a small number of primary studies; iv) significant reductions in infant diarrhoea following community health worker interventions. Mechanisms of impact commonly occurred through social and behavioural processes, particularly: changing social norms, increasing social cohesion and social capacity. Factors influencing effectiveness of CE interventions included extent of population coverage, shared leadership and community control over outcomes.

Conclusion

Community engagement interventions may be effective in supporting CDC in LLMIC. Careful design of CE interventions appropriate to context, disease and community is vital.

Analysis of Fn14-NF-κB signaling response dynamics using a mechanistic model

Fn14 is a transmembrane receptor protein belonging to the tumor necrosis factor receptor (TNFR) superfamily. Many experimental reports have shown that crosslinking of the receptor by its extracellular ligand TWEAK induces prolonged activation of transcription factor NF-κB. This behavior is distinct from TNF-α receptor, which is a more well-characterized member of the TNFR family. TNF-α receptor, despite sharing many similar molecular interactions with Fn14, only transiently activates NF-κB in response to TNF-α stimulation. Here, we investigate molecular mechanisms that enable Fn14 to display such distinctive behavior. In particular, we focus on two specific features of the Fn14 pathway that potentially give rise to a positive feedback regulation and differentiate it from the TNF-α receptor signaling. By developing a mechanistic model, we analyze how these features may determine the dynamics of an Fn14-NF-κB response. Our analysis reveals that stimulation of Fn14 by TWEAK may generate highly non-linear dynamics, including stable limit cycles and bistable responses. The type of response depends both on the strength and duration of a TWEAK signal. Our predictions and analyses also show that the molecular interactions underlying the positive feedback explain the prolonged activation of NF-κB under certain parameter regimes. In light of the model predictions, we propose possible deregulations of Fn14 leading to its overexpression in solid tumors and tissue injuries.

Quantitative Analysis of the Correlation between Cell Size and Cellular Uptake of Particles

The size of a cell is central to many functions, including cellular communication and exchange of materials with the environment. This modeling and experimental study focused on understanding how the size of a cell determines its ability to uptake nanometer-scale extracellular materials from the environment. Several mechanisms in the cell plasma membrane mediate cellular uptake of nutrients, biomolecules, and particles. These mechanisms involve recognition and internalization of the extracellular molecules via endocytic components, such as clathrin-coated pits, vacuoles, and micropinocytic vesicles. Because the demand for an external resource could be different for cells of different sizes, the collective actions of these various endocytic routes should also vary based on the cell size. Here, using a reaction-diffusion model, we analyze single-cell data to interrogate the one/one mapping between the size of the MDA-MB 231 breast cancer cells and their ability to uptake nanoparticles. Our analysis indicates that under both reaction- and diffusion-controlled regimes, cellular uptake follows a linear relationship with the cell radius. Furthermore, this linear dependency is insensitive to particle size variation within 20-200 nm range. This result is counterintuitive because the general perception is that cellular uptake is proportional to the cell volume (mass) or surface area and hence follow a cubic or square relationship with the cell radius. A further analysis using our model reveals a potential mechanism underlying this linear relationship.

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or K_D value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals. However, it is unclear how information is processed to generate appropriate and specific responses to signals, especially given that these networks tend to share many components. For example, receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins. Here, to investigate the downstream signaling of a well-studied receptor tyrosine kinase (RTK), the insulin-like growth factor 1 (IGF1) receptor (IGF1R), we formulated and analyzed 45 cell line-specific mathematical models, which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R. The models were parameterized using available protein copy number and site-specific affinity measurements, and restructured to allow for network generation. We find that recruitment is influenced by the protein abundance profile of a cell, with different patterns of recruitment in different cell lines. Furthermore, in a given cell line, we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion. We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data, and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations. These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved.

A spatio-temporal model reveals self-limiting FcɛRI cross-linking by multivalent antigens

Aggregation of cell surface receptor proteins by multivalent antigens is an essential early step for immune cell signalling. A number of experimental and modelling studies in the past have investigated multivalent ligand-mediated aggregation of IgE receptors (FcɛRI) in the plasma membrane of mast cells. However, understanding of the mechanisms of FcɛRI aggregation remains incomplete. Experimental reports indicate that FcɛRI forms relatively small and finite-sized clusters when stimulated by a multivalent ligand. By contrast, modelling studies have shown that receptor cross-linking by a trivalent ligand may lead to the formation of large receptor superaggregates that may potentially give rise to hyperactive cellular responses. In this work, we have developed a Brownian dynamics-based spatio-temporal model to analyse FcɛRI aggregation by a trivalent antigen. Unlike the existing models, which implemented non-spatial simulation approaches, our model explicitly accounts for the coarse-grained site-specific features of the multivalent species (molecules and complexes). The model incorporates membrane diffusion, steric collisions and sub-nanometre-scale site-specific interaction of the time-evolving species of arbitrary structures. Using the model, we investigated temporal evolution of the species and their diffusivities. Consistent with a recent experimental report, our model predicted sharp decay in species mobility in the plasma membrane in response receptor cross-linking by a multivalent antigen. We show that, due to such decay in the species mobility, post-stimulation receptor aggregation may become self-limiting. Our analysis reveals a potential regulatory mechanism suppressing hyperactivation of immune cells in response to multivalent antigens.

A model-based analysis of tissue targeting efficacy of nanoparticles

Tissue targeting is a critical challenge for systemic delivery of drug nanocarriers. To overcome this challenge, major research efforts have been undertaken to design ligand-conjugated nanoparticles. However, limited work has been done to quantitatively assess the effectiveness of such approach. In this work, using a mechanistic spatio-temporal model, I investigate the effectiveness of ligand-directed tissue targeting. By applying an approach from the colloidal filtration theory, I develop a Brownian dynamics model of nanoparticle-cell interaction. The model incorporates a single cell and its surrounding flow field. It considers both specific (receptor-mediated) and non-specific (bare cell surface-mediated) recognition of nanoparticles subject to convective and diffusive motion. Using the model, I investigate how the specific and non-specific interactions compare in determining the overall targeting efficacy. My analysis provides some interesting findings that contradict the general notion that effective targeting is possible based upon the differential receptor expression in cancer and non-cancer cells. I show that such strategy may yield only a marginal gain in the targeting efficacy. Moreover, non-specific interaction may have an important influence on particle recognition by cells even at high receptor expression levels.

Abstract P5-07-14: miR-17-92 cluster, an oncogenic microRNA cluster acts as a context dependent tumour suppressor in breast cancer

Background: The miR-17-92 is an oncogenic miRNA cluster that generate six mature miRNAs: miR-17, miR-18a, miR-19a, miR-20a, miR-19b-1, and miR-92-1. Accumulating evidences indicate the oncogenic role of the miR-17-92 cluster in human cancers. Amplification of 13q31-q32, which is the locus of the miR-17-92 cluster, have been reported in haematopoietic malignancies, such as B cell lymphoma. In contrast, MIR17HG was deleted in 21.9% of breast cancers and loss of heterozygosity at 13q12-q13 was associated with poor prognosis in breast cancer. This oncogenic miRNA cluster is deferentially expressed in various cancer. Since, miR-17-92 cluster shows differential expression among the cancers types, it is hypothesized that biological function may also vary depending on the context. Indeed, this cluster has been shown to act a tumour suppressor in some cancers. However, the functional role of this cluster in the subtypes of breast cancer remains largely unknown.

Methods: The oncomine datasets were analysed for expression of MIR17HG in breast cancer at parameters p-value threshold of 0.01 with minimum 2-fold change. We generated stable sub-clones of MCF7, T47D, SKBR3 and MDA-MB231 cells overexpressing miR-17-92 cluster by transducing with lentivirus expressing miR-17-92. Proliferation was assessed by MTS assay and colony forming assay. Cell migration was tested using scratch method. Invasion potentiality was monitored by using matrigel Boyden chamber invasion assay. For drug response analysis, control and microRNA overexpressing sub-clones were exposed to different chemotherapeutic agents at different concentrations followed by MTS assay at different time intervals. Association of miRNAs belonging to miR-17-92 with outcome in breast cancer was determined by Kaplan-Meier analysis on METABRIC dataset.

Results: We observed that expression of MIR17HG was increased in tissues and cell lines from triple negative breast cancer (TNBC) but decreased in the tissues and cell lines from the estrogen receptor (ER)-positive breast cancer. Our results showed that ectopic expression of miR-17-92 cluster significantly suppressed cell proliferation, colony formation, migration and invasion of ER-positive (MCF7, T47D) and HER2-enriched (SKBR3) cells whereas it enhanced cell proliferation, colony formation, migration and invasion in (TNBC) MDA-MB 231 cells. Further, we found that expression of miR-17-92 cluster sensitized MCF7 cells whereas it rendered SKBR3 cells resistant to chemotherapeutic compounds. Higher expression of five miRNAs of this cluster was associated with better relapse free survival (RFS) in (miR-17, miR-19a, miR-20a, miR-19b and miR-92) Luminal A subtype whereas three miRNAs of this cluster were associated with poor RFS in (miR-17, miR-18a and miR-92) HER2-enriched and (miR-17, miR-19b and miR-92) TNBC subtypes.

Conclusions: Taken together our results suggest that miR-17-92 cluster acts as a tumour suppressor in ER-positive and HER2-enriched breast cancer cells but shows oncogenic role in TNBC. Our observations underscore the functional complexity of miR-17-92 in a context-dependent and cell type-dependent manner, and more investigations are warranted to fully explore the functional complexity of miR-17-92 in subtypes of breast cancer.

Citation Format: Hossain MM, Arabkari V, Barua D, Gupta A, Islam MN, Gupta S. miR-17-92 cluster, an oncogenic microRNA cluster acts as a context dependent tumour suppressor in breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-07-14.

A multiscale modeling study of particle size effects on the tissue penetration efficacy of drug-delivery nanoparticles

BACKGROUND

Particle size is a key parameter for drug-delivery nanoparticle design. It is believed that the size of a nanoparticle may have important effects on its ability to overcome the transport barriers in biological tissues. Nonetheless, such effects remain poorly understood. Using a multiscale model, this work investigates particle size effects on the tissue distribution and penetration efficacy of drug-delivery nanoparticles.

RESULTS

We have developed a multiscale spatiotemporal model of nanoparticle transport in biological tissues. The model implements a time-adaptive Brownian Dynamics algorithm that links microscale particle-cell interactions and adhesion dynamics to tissue-scale particle dispersion and penetration. The model accounts for the advection, diffusion, and cellular uptakes of particles. Using the model, we have analyzed how particle size affects the intra-tissue dispersion and penetration of drug delivery nanoparticles. We focused on two published experimental works that investigated particle size effects in in vitro and in vivo tissue conditions. By analyzing experimental data reported in these two studies, we show that particle size effects may appear pronounced in an in vitro cell-free tissue system, such as collagen matrix. In an in vivo tissue system, the effects of particle size could be relatively modest. We provide a detailed analysis on how particle-cell interactions may determine distribution and penetration of nanoparticles in a biological tissue.

CONCLUSION

Our work suggests that the size of a nanoparticle may play a less significant role in its ability to overcome the intra-tissue transport barriers. We show that experiments involving cell-free tissue systems may yield misleading observations of particle size effects due to the absence of advective transport and particle-cell interactions.

A Multiscale Algorithm for Spatiotemporal Modeling of Multivalent Protein-Protein Interaction

This article introduces a multiscale framework for spatiotemporal modeling of protein-protein interaction. Cellular protein molecules represent multivalent species that contain modular features, such as binding domains and phosphorylation motifs. The binding and transformations of these features occur at a small time and spatial scale. On the other hand, space and time involved in protein diffusion, colocalization, and formation of complexes could be relatively large. Here, we present an agent-based framework integrated with a multiscale Brownian Dynamics (BD) simulation algorithm. The framework employs spatial graphs to describe multivalent molecules and complexes with their site-specific details. By implementing a time-adaptive feature, the BD algorithm enables efficient computation while capturing the site-specific interactions of the diffusing species at the sub-nanometer scale. We demonstrate these capabilities by modeling two multivalent molecules, one representing a ligand and the other a receptor, in a two-dimensional plane (cell membrane). Using the model, we show that the algorithm can accelerate computation by orders of magnitudes in both concentrated and dilute regimes. We also show that the algorithm enables robust model predictions against a wide range of selection of time step sizes.

Bioresponsive polymer coated drug nanorods for breast cancer treatment

Ineffective drug release at the target site is among the top challenges for cancer treatment. This reflects the facts that interaction with the physiological condition can denature active ingredients of drugs, and low delivery to the disease microenvironment leads to poor therapeutic outcomes. We hypothesize that depositing a thin layer of bioresponsive polymer on the surface of drug nanoparticles would not only protect drugs from degradation but also allow the release of drugs at the target site. Here, we report a one-step process to prepare bioresponsive polymer coated drug nanorods (NRs) from liquid precursors using the solvent diffusion method. A thin layer (10.3 ± 1.4 nm) of poly(ε-caprolactone) (PCL) polymer coating was deposited on the surface of camptothecin (CPT) anti-cancer drug NRs. The mean size of PCL-coated CPT NRs was 500.9 ± 91.3 nm length × 122.7 ± 10.1 nm width. The PCL polymer coating was biodegradable at acidic pH 6 as determined by Fourier transform infrared spectroscopy. CPT drugs were released up to 51.5% when PCL coating dissolved into non-toxic carboxyl and hydroxyl groups. Trastuzumab (TTZ), a humanized IgG monoclonal antibody, was conjugated to the NR surface for breast cancer cell targeting. Combination treatments using CPT and TTZ decreased the HER-2 positive BT-474 breast cancer cell growth by 66.9 ± 5.3% in vitro. These results suggest effective combination treatments of breast cancer cells using bioresponsive polymer coated drug delivery.

BioNetGen 2.2: advances in rule-based modeling

: BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation and analysis of larger and more complex models than previously possible.

AVAILABILITY AND IMPLEMENTATION

Stable BioNetGen releases (Linux, Mac OS/X and Windows), with documentation, are available at http://bionetgen.org Source code is available at http://github.com/RuleWorld/bionetgen CONTACT: bionetgen.help@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

Overexpression of sonic hedgehog in the triple negative breast cancer: clinicopathological characteristics of high burden breast cancer patients from Bangladesh

Dysregulation of Hedgehog (Hh) signaling pathway has been documented in mammary gland development and breast cancer (BC) progression. Despite the remarkable progress in therapeutic interventions, BC related mortality in Bangladesh increased in the last decade. Triple negative breast cancer (TNBC) still presents a critical therapeutic challenge. Thus effective targeted therapy is urgently needed. In this study, we report the clinicopathological characteristics and prognosis of BC patients from Bangladesh. Routine immunohistochemical analysis and high throughput RNA-Seq data from the TCGA library were used to analyze the expression pattern and association of high and low level of Shh expression in a collection of BC patients with a long-term follow-up. High levels of Shh were observed in a subset of BC tumors with poor prognostic pathological features. Higher level of Shh expression correlated with a significantly poorer overall survival of patients compared with patients whose tumors expressed a low level of Shh. These data support the contention that Shh could be a novel biomarker for breast cancer that is involved in mediating the aggressive phenotype of BC. We propose that BC patients exhibiting a higher level of Shh expression, representing a subset of BC patients, would be amenable to Shh targeted therapy.

Optimal aggregation of FcεRI with a structurally defined trivalent ligand overrides negative regulation driven by phosphatases

To investigate why responses of mast cells to antigen-induced IgE receptor (FcεRI) aggregation depend nonlinearly on antigen dose, we characterized a new artificial ligand, DF3, through complementary modeling and experimentation. This ligand is a stable trimer of peptides derived from bacteriophage T4 fibritin, each conjugated to a hapten (DNP). We found low and high doses of DF3 at which degranulation of mast cells sensitized with DNP-specific IgE is minimal, but ligand-induced receptor aggregation is comparable to aggregation at an intermediate dose, optimal for degranulation. This finding makes DF3 an ideal reagent for studying the balance of negative and positive signaling in the FcεRI pathway. We find that the lipid phosphatase SHIP and the protein tyrosine phosphatase SHP-1 negatively regulate mast cell degranulation over all doses considered. In contrast, SHP-2 promotes degranulation. With high DF3 doses, relatively rapid recruitment of SHIP to the plasma membrane may explain the reduced degranulation response. Our results demonstrate that optimal secretory responses of mast cells depend on the formation of receptor aggregates that promote sufficient positive signaling by Syk to override phosphatase-mediated negative regulatory signals.

Recruitment of the adaptor protein Grb2 to EGFR tetramers

Adaptor protein Grb2 binds phosphotyrosines in the epidermal growth factor (EGF) receptor (EGFR) and thereby links receptor activation to intracellular signaling cascades. Here, we investigated how recruitment of Grb2 to EGFR is affected by the spatial organization and quaternary state of activated EGFR. We used the techniques of image correlation spectroscopy (ICS) and lifetime-detected Förster resonance energy transfer (also known as FLIM-based FRET or FLIM–FRET) to measure ligand-induced receptor clustering and Grb2 binding to activated EGFR in BaF/3 cells. BaF/3 cells were stably transfected with fluorescently labeled forms of Grb2 (Grb2–mRFP) and EGFR (EGFR–eGFP). Following stimulation of the cells with EGF, we detected nanometer-scale association of Grb2–mRFP with EGFR–eGFP clusters, which contained, on average, 4 ± 1 copies of EGFR–eGFP per cluster. In contrast, the pool of EGFR–eGFP without Grb2–mRFP had an average cluster size of 1 ± 0.3 EGFR molecules per punctum. In the absence of EGF, there was no association between EGFR–eGFP and Grb2–mRFP. To interpret these data, we extended our recently developed model for EGFR activation, which considers EGFR oligomerization up to tetramers, to include recruitment of Grb2 to phosphorylated EGFR. The extended model, with adjustment of one new parameter (the ratio of the Grb2 and EGFR copy numbers), is consistent with a cluster size distribution where 2% of EGFR monomers, 5% of EGFR dimers, <1% of EGFR trimers, and 94% of EGFR tetramers are associated with Grb2. Together, our experimental and modeling results further implicate tetrameric EGFR as the key signaling unit and call into question the widely held view that dimeric EGFR is the predominant signaling unit.

Exploring higher-order EGFR oligomerisation and phosphorylation--a combined experimental and theoretical approach

The epidermal growth factor receptor (EGFR) kinase is generally considered to be activated by either ligand-induced dimerisation or a ligand-induced conformational change within pre-formed dimers. Ligand-induced higher-order EGFR oligomerisation or clustering has been reported but it is not clear how EGFR oligomers, as distinct from EGFR dimers, influence signaling outputs. To address this question, we combined measures of receptor clustering (microscopy; image correlation spectroscopy) and phosphorylation (Western blots) with modelling of mass-action chemical kinetics. A stable BaF/3 cell-line that contains a high proportion (>90%) of inactive dimers of EGFR-eGFP but no secreted ligand and no other detectable ErbB receptors was used as the model cell system. EGF at concentrations of greater than 1 nM was found to cluster EGFR-eGFP dimers into higher-order complexes and cause parallel increases in EGFR phosphorylation. The kinetics of EGFR clustering and phosphorylation were both rapid, plateauing within 2 minutes after stimulation with 30 nM EGF. A rule-based model was formulated to interpret the data. This model took into account ligand binding, ligand-induced conformational changes in the cytosolic tail, monomer-dimer-trimer-tetramer transitions via ectodomain- and kinase-mediated interactions, and phosphorylation. The model predicts that cyclic EGFR tetramers are the predominant phosphorylated species, in which activated receptor dimers adopt a cyclic side-by-side orientation, and that receptor kinase activation is stabilised by the intramolecular interactions responsible for cyclic tetramerization.

Single-cell measurements of IgE-mediated FcεRI signaling using an integrated microfluidic platform

Heterogeneity in responses of cells to a stimulus, such as a pathogen or allergen, can potentially play an important role in deciding the fate of the responding cell population and the overall systemic response. Measuring heterogeneous responses requires tools capable of interrogating individual cells. Cell signaling studies commonly do not have single-cell resolution because of the limitations of techniques used such as Westerns, ELISAs, mass spectrometry, and DNA microarrays. Microfluidics devices are increasingly being used to overcome these limitations. Here, we report on a microfluidic platform for cell signaling analysis that combines two orthogonal single-cell measurement technologies: on-chip flow cytometry and optical imaging. The device seamlessly integrates cell culture, stimulation, and preparation with downstream measurements permitting hands-free, automated analysis to minimize experimental variability. The platform was used to interrogate IgE receptor (FcεRI) signaling, which is responsible for triggering allergic reactions, in RBL-2H3 cells. Following on-chip crosslinking of IgE-FcεRI complexes by multivalent antigen, we monitored signaling events including protein phosphorylation, calcium mobilization and the release of inflammatory mediators. The results demonstrate the ability of our platform to produce quantitative measurements on a cell-by-cell basis from just a few hundred cells. Model-based analysis of the Syk phosphorylation data suggests that heterogeneity in Syk phosphorylation can be attributed to protein copy number variations, with the level of Syk phosphorylation being particularly sensitive to the copy number of Lyn.

Computational model for autophagic vesicle dynamics in single cells

Macroautophagy (autophagy) is a cellular recycling program essential for homeostasis and survival during cytotoxic stress. This process, which has an emerging role in disease etiology and treatment, is executed in four stages through the coordinated action of more than 30 proteins. An effective strategy for studying complicated cellular processes, such as autophagy, involves the construction and analysis of mathematical or computational models. When developed and refined from experimental knowledge, these models can be used to interrogate signaling pathways, formulate novel hypotheses about systems, and make predictions about cell signaling changes induced by specific interventions. Here, we present the development of a computational model describing autophagic vesicle dynamics in a mammalian system. We used time-resolved, live-cell microscopy to measure the synthesis and turnover of autophagic vesicles in single cells. The stochastically simulated model was consistent with data acquired during conditions of both basal and chemically-induced autophagy. The model was tested by genetic modulation of autophagic machinery and found to accurately predict vesicle dynamics observed experimentally. Furthermore, the model generated an unforeseen prediction about vesicle size that is consistent with both published findings and our experimental observations. Taken together, this model is accurate and useful and can serve as the foundation for future efforts aimed at quantitative characterization of autophagy.

A mechanistic model of early FcεRI signaling: lipid rafts and the question of protection from dephosphorylation

We present a model of the early events in mast cell signaling mediated by FcεRI where the plasma membrane is composed of many small ordered lipid domains (rafts), surrounded by a non-order region of lipids consisting of the remaining plasma membrane. The model treats the rafts as transient structures that constantly form and breakup, but that maintain a fixed average number per cell. The rafts have a high propensity for harboring Lyn kinase, aggregated, but not unaggregated receptors, and the linker for the activation of T cells (LAT). Phosphatase activity in the rafts is substantially reduced compared to the nonraft region. We use the model to analyze published experiments on the rat basophilic leukemia (RBL)-2H3 cell line that seem to contradict the notion that rafts offer protection. In these experiments IgE was cross-linked with a multivalent antigen and then excess monovalent hapten was added to break-up cross-links. The dephosphorylation of the unaggregated receptor (nonraft associated) and of LAT (raft associated) were then monitored in time and found to decay at similar rates, leading to the conclusion that rafts offer no protection from dephosphorylation. In the model, because the rafts are transient, a protein that is protected while in a raft will be subject to dephosphorylation when the raft breaks up and the protein finds itself in the nonraft region of the membrane. We show that the model is consistent with the receptor and LAT dephosphorylation experiments while still allowing rafts to enhance signaling by providing substantial protection from phosphatases.

A computational model for early events in B cell antigen receptor signaling: analysis of the roles of Lyn and Fyn

BCR signaling regulates the activities and fates of B cells. BCR signaling encompasses two feedback loops emanating from Lyn and Fyn, which are Src family protein tyrosine kinases (SFKs). Positive feedback arises from SFK-mediated trans phosphorylation of BCR and receptor-bound Lyn and Fyn, which increases the kinase activities of Lyn and Fyn. Negative feedback arises from SFK-mediated cis phosphorylation of the transmembrane adapter protein PAG1, which recruits the cytosolic protein tyrosine kinase Csk to the plasma membrane, where it acts to decrease the kinase activities of Lyn and Fyn. To study the effects of the positive and negative feedback loops on the dynamical stability of BCR signaling and the relative contributions of Lyn and Fyn to BCR signaling, we consider in this study a rule-based model for early events in BCR signaling that encompasses membrane-proximal interactions of six proteins, as follows: BCR, Lyn, Fyn, Csk, PAG1, and Syk, a cytosolic protein tyrosine kinase that is activated as a result of SFK-mediated phosphorylation of BCR. The model is consistent with known effects of Lyn and Fyn deletions. We find that BCR signaling can generate a single pulse or oscillations of Syk activation depending on the strength of Ag signal and the relative levels of Lyn and Fyn. We also show that bistability can arise in Lyn- or Csk-deficient cells.

An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor—gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions.

Computational models of biological networks are useful for explaining experimental observations and predicting phenotypic behaviors. The construction of genome-scale metabolic and regulatory models is still a labor-intensive process, even with the availability of genome sequences and high-throughput datasets. Since our knowledge about biological systems is incomplete, these models are iteratively refined and validated as we discover new connections in biological networks, and eliminate inconsistencies between model predictions and experimental observations. To enable researchers to quickly determine what causes discrepancies between observed phenotypes and model predictions, we developed a new approach (GeneForce) that automatically corrects integrated metabolic and transcriptional regulatory network models. To illustrate the utility of the approach, we applied the developed method to well-curated models of E. coli metabolism and regulation. We found that the approach significantly improved the accuracy of phenotype predictions and suggested changes needed to the metabolic and/or regulatory models. We also used the approach to identify rescue non-growth phenotypes and to evaluate the conservation of transcriptional regulatory interactions between E. coli and S. typhimurium. The developed approach helps reconcile discrepancies between model predictions and experimental data by hypothesizing required network changes, and helps facilitate the development of new genome-scale models.

A bipolar clamp mechanism for activation of Jak-family protein tyrosine kinases

Most cell surface receptors for growth factors and cytokines dimerize in order to mediate signal transduction. For many such receptors, the Janus kinase (Jak) family of non-receptor protein tyrosine kinases are recruited in pairs and juxtaposed by dimerized receptor complexes in order to activate one another by trans-phosphorylation. An alternative mechanism for Jak trans-phosphorylation has been proposed in which the phosphorylated kinase interacts with the Src homology 2 (SH2) domain of SH2-B, a unique adaptor protein with the capacity to homo-dimerize. Building on a rule-based kinetic modeling approach that considers the concerted nature and combinatorial complexity of modular protein domain interactions, we examine these mechanisms in detail, focusing on the growth hormone (GH) receptor/Jak2/SH2-Bβ system. The modeling results suggest that, whereas Jak2-(SH2-Bβ)₂-Jak2 heterotetramers are scarcely expected to affect Jak2 phosphorylation, SH2-Bβ and dimerized receptors synergistically promote Jak2 trans-activation in the context of intracellular signaling. Analysis of the results revealed a unique mechanism whereby SH2-B and receptor dimers constitute a bipolar ‘clamp’ that stabilizes the active configuration of two Jak2 molecules in the same macro-complex.

Janus kinases (Jaks) interact with and activate receptors on the cell surface that mediate changes in gene expression. How these interactions are promoted and regulated is of central interest in fields such as cellular endocrinology and immunology. Here, detailed computational models of Jak activation are offered at the level of protein modification states and interaction domains, wherein the specification of only a handful of binding/reaction rules can produce networks comprised of thousands of differential equations. Specifically, we investigated the role of an adaptor protein, SH2-B, revealing a novel mechanism whereby it cooperates with receptors to form a stable complex that juxtaposes two Jak molecules for efficient activation. We refer to this mode of molecular assembly as the bipolar clamp mechanism.

Computational models of tandem SRC homology 2 domain interactions and application to phosphoinositide 3-kinase

Intracellular signal transduction proteins typically utilize multiple interaction domains for proper targeting, and thus a broad diversity of distinct signaling complexes may be assembled. Considering the coordination of only two such domains, as in tandem Src homology 2 (SH2) domain constructs, gives rise to a kinetic scheme that is not adequately described by simple models used routinely to interpret in vitro binding measurements. To analyze the interactions between tandem SH2 domains and bisphosphorylated peptides, we formulated detailed kinetic models and applied them to the phosphoinositide 3-kinase p85 regulatory subunit/platelet-derived growth factor beta-receptor system. Data for this system from different in vitro assay platforms, including surface plasmon resonance, competition binding, and isothermal titration calorimetry, were reconciled to estimate the magnitude of the cooperativity characterizing the sequential binding of the high and low affinity SH2 domains (C-SH2 and N-SH2, respectively). Compared with values based on an effective volume approximation, the estimated cooperativity is 3 orders of magnitude lower, indicative of significant structural constraints. Homodimerization of full-length p85 was found to be an alternative mechanism for high avidity binding to phosphorylated platelet-derived growth factor receptors, which would render the N-SH2 domain dispensable for receptor binding.

Structure-based kinetic models of modular signaling protein function: focus on Shp2

We present here a computational, rule-based model to study the function of the SH2 domain-containing protein tyrosine phosphatase, Shp2, in intracellular signal transduction. The two SH2 domains of Shp2 differentially regulate the enzymatic activity by a well-characterized mechanism, but they also affect the targeting of Shp2 to signaling receptors in cells. Our kinetic model integrates these potentially competing effects by considering the intra- and intermolecular interactions of the Shp2 SH2 domains and catalytic site as well as the effect of Shp2 phosphorylation. Even for the isolated Shp2/receptor system, which may seem simple by certain standards, we find that the network of possible binding and phosphorylation states is composed of over 1000 members. To our knowledge, this is the first kinetic model to fully consider the modular, multifunctional structure of a signaling protein, and the computational approach should be generally applicable to other complex intermolecular interactions.

Local and systemic antibody responses to naturally acquired enterotoxigenic Escherichia coli diarrhea in an endemic area

Fifteen patients hospitalized with acute, watery diarrhea and with enterotoxigenic Escherichia coli (ETEC) detected from stool samples were studied to evaluate the extent to which natural ETEC diarrhea induces local and systemic antibody responses to E. coli heat-labile toxin (LT), homologous lipopolysaccharide (LPS), and colonization factors (CFA/I and CFA/II). Specific IgA and IgG antibodies to LT, CFA I and II, and each patient's homologous LPS were determined by ELISA in serum, saliva, breastmilk, and intestinal lavage fluid. The majority of patients had greater than a twofold rise in local levels of IgA antibodies in the intestine: 80% of LT+ patients responded to LT, 63% of CFA+ patients responded to CFA, and 78% of all toxin-positive patients responded to the LPS of their infecting strain. Local antibody responses in the intestine were associated with responses in breastmilk and saliva, but relationships were not clear-cut, and the usefulness of these secretions as proxy measures of local intestinal antibody production remains unclear. Antibody responses in serum also occurred in most patients and were significantly more frequent in cases than in controls. This study demonstrates that natural ETEC disease results in local IgA responses to LT, CFA, and LPS in the gut and also in immune responses in breastmilk, saliva, and serum.

Predisposition for cholera of individuals with O blood group. Possible evolutionary significance

At the Matlab Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh, the authors examined the blood groups of patients hospitalized between January and September 1979 for diarrheal disease due to a variety of bacterial and viral agents. A significant association was identified only for cholera, in which cholera patients were twice as likely to have blood group O and one-ninth as likely to have blood group AB as community controls. A follow-up study of family contacts of cholera patients, carried out between September 1980 and July 1982, indicated that blood group did not affect an individual's risk of having a culture-proven infection with V. cholerae 01 but was directly related to the severity of disease. Individuals with the most severe diarrhea compared with those with asymptomatic infection were more often of blood group O (68% versus 36%, p less than 0.01) and less often of AB (0% versus 7%, p less than 0.01). It was not possible to identify the molecular basis for this genetically related protection using biologic models of cholera that are currently available. The constant selective pressure of cholera against people of O blood group may account in part for the extremely low prevalence of O group genes and the high prevalence of B group genes found among the people living in the Gangetic Delta.

Genetic susceptibility to cholera

In the course of studies of immunity to experimental cholera in man, 10(5) or 10(6) Vibrio cholerae were given to 66 college students and other community volunteers under quarantine in an isolation ward. HLA antigen and blood group determinations were carried out to test the hypothesis that severity of clinical cholera is dependent in part upon genetically-determined host susceptibility. Fifty-five volunteers developed diarrhoea; 38 had mild illness and 17 had severe cholera (stool volume greater than or equal to 5.0 litres). HLA antigens were found in similar frequency in volunteers with severe, mild or no diarrhoea; antigen A1, A2, A3 and B7 were most common. Blood group O, however, was found in 64% of persons with severe cholera versus 36-38% of volunteers with mild or absent illness. Thus, while no correlation was found between HLA type and severity of cholera, these results do support the claims of other investigators that blood group O is found more frequently in patients with severe cholera than in the normal population.

ABO blood groups and cholera

The ABO blood group of 87 bacteriologically proven cases of cholera and 33 bacteriologically negative cholera-like diarrhoea cases was determined. When compared with the normal blood group distribution in the population of the Philippines of the same racial group to which the patients belonged, a relatively larger number of cases were found to occur in individuals of group O and a lesser number in those of group A. A plea is made for further studies on these lines to explain the host susceptibility in cholera and other acute enterotoxigenic diarrhoeas.

Vibriocidal antibodies induced by Yersinia enterocolitica serotype IX

Yersinia enterocolitica serotype IX has been found to stimulate the production of vibriocidal antibody against Vibrio cholerae, particularly in Inaba serotype, in experimental rabbits and infected man to a significant degree. This activity could be absorbed by both Inaba and Yersinia antigens from anti-yersinia sera, but Yersinia antigen could not absorb vibriocidal activity from the anti-cholera sera, indicating a unilateral relation.Vibrio agglutinating antibody, particularly against Ogawa, in anti-yersinia rabbit or human sera was found to be less liable to non-specific stimulation.V. cholerae, while removing vibriocidal antibody, could not absorb the Yersinia agglutinin from anti-yersinia sera, suggesting that there is a different determinant for these two types of antibody activities.The non-specific production of vibriocidal antibody by Y. enterocolitica type IX indicates the need for caution in the interpretation of the results of vibriocidal tests where such organisms are prevalent.