Kevin A. Janes

Polysaccharide colloidal particles as delivery systems for macromolecules.

Mucosal delivery of complex molecules such as peptides, proteins, oligonucleotides, and plasmids is one of the most intensively studied subjects. The use of colloidal carriers made of hydrophilic polysaccharides, i.e. chitosan, has arisen as a promising alternative for improving the transport of such macromolecules across biological surfaces. This article reviews the approaches which have aimed to associate macromolecules to chitosan in the form of colloidal structures and analyzes the evidence of their efficacy in improving the transport of the associated molecule through mucosae and epithelia. Chitosan has been shown to form colloidal particles and entrap macromolecules through a number of mechanisms, including ionic crosslinking, desolvation, or ionic complexation, though some of these systems have been realized only in conjunction with DNA molecules. An alternative involving the chemical modification of chitosan has also been useful for the association of macromolecules to self-assemblies and vesicles. To date, the in vivo efficacy of these chitosan-based colloidal carriers has been reported for two different applications: while DNA-chitosan hybrid nanospheres were found to be acceptable transfection carriers, ionically crosslinked chitosan nanoparticles appeared to be efficient vehicles for the transport of peptides across the nasal mucosa. The potential applications and future prospects of these new systems for mucosal delivery of macromolecules are highlighted at the end of the chapter.

Chitosan nanoparticles as delivery systems for doxorubicin

The aim of this paper was to evaluate the potential of chitosan nanoparticles as carriers for the anthracycline drug, doxorubicin (DOX). The challenge was to entrap a cationic, hydrophilic molecule into nanoparticles formed by ionic gelation of the positively charged polysaccharide chitosan. To achieve this objective, we attempted to mask the positive charge of DOX by complexing it with the polyanion, dextran sulfate. This modification doubled DOX encapsulation efficiency relative to controls and enabled real loadings up to 4.0 wt.% DOX. Separately, we investigated the possibility of forming a complex between chitosan and DOX prior to the formation of the particles. Despite the low complexation efficiency, no dissociation of the complex was observed upon formation of the nanoparticles. Fluorimetric analysis of the drug released in vitro showed an initial release phase, the intensity of which was dependent on the association mode, followed by a very slow release. The evaluation of the activity of DOX-loaded nanoparticles in cell cultures indicated that those containing dextran sulfate were able to maintain cytostatic activity relative to free DOX, while DOX complexed to chitosan before nanoparticle formation showed slightly decreased activity. Additionally, confocal studies showed that DOX was not released in the cell culture medium but entered the cells while remaining associated to the nanoparticles. In conclusion, these preliminary studies showed the feasibility of chitosan nanoparticles to entrap the basic drug DOX and to deliver it into the cells in its active form.

A Systems Model of Signaling Identifies a Molecular Basis Set for Cytokine-Induced Apoptosis

Signal transduction pathways control cellular responses to stimuli, but it is unclear how molecular information is processed as a network. We constructed a systems model of 7980 intracellular signaling events that directly links measurements to 1440 response outputs associated with apoptosis. The model accurately predicted multiple time-dependent apoptotic responses induced by a combination of the death-inducing cytokine tumor necrosis factor with the prosurvival factors epidermal growth factor and insulin. By capturing the role of unsuspected autocrine circuits activated by transforming growth factor–α and interleukin-1α, the model revealed new molecular mechanisms connecting signaling to apoptosis. The model derived two groupings of intracellular signals that constitute fundamental dimensions (molecular “basis axes”) within the apoptotic signaling network. Projection along these axes captures the entire measured apoptotic network, suggesting that cell survival is determined by signaling through this canonical basis set.

Common effector processing mediates cell-specific responses to stimuli

The fundamental components of many signalling pathways are common to all cells. However, stimulating or perturbing the intracellular network often causes distinct phenotypes that are specific to a given cell type. This ‘cell specificity’ presents a challenge in understanding how intracellular networks regulate cell behaviour and an obstacle to developing drugs that treat signalling dysfunctions. Here we apply a systems-modelling approach to investigate how cell-specific signalling events are integrated through effector proteins to cause cell-specific outcomes. We focus on the synergy between tumour necrosis factor and an adenoviral vector as a therapeutically relevant stimulus that induces cell-specific responses. By constructing models that estimate how kinase-signalling events are processed into phenotypes through effector substrates, we find that accurate predictions of cell specificity are possible when different cell types share a common ‘effector-processing’ mechanism. Partial-least-squares regression models based on common effector processing accurately predict cell-specific apoptosis, chemokine release, gene induction, and drug sensitivity across divergent epithelial cell lines. We conclude that cell specificity originates from the differential activation of kinases and other upstream transducers, which together enable different cell types to use common effectors to generate diverse outcomes. The common processing of network signals by downstream effectors points towards an important cell biological principle, which can be applied to the understanding of cell-specific responses to targeted drug therapies.

A multiplexed homogeneous fluorescence-based assay for protein kinase activity in cell lysates

New methods to quantify protein kinase activities directly from complex cellular mixtures are critical for understanding biological regulatory pathways. Herein, a fluorescence-based chemosensor strategy for the direct measurement of kinase activities in crude mammalian cell lysates is described. We first designed a new fluorescent peptide reporter substrate for each target kinase. These kinase chemosensors were readily phosphorylated by recombinant target enzyme and underwent a several-fold fluorescence increase upon phosphorylation. Then, using unfractionated cell lysates, a homogeneous kinase assay was developed that was reproducible, linear and highly preferential for monitoring changes in cellular activity of the target kinase. The general protocol was developed for the kinase Akt and then easily extended to measure protein kinase A (PKA) and mitogen-activated protein kinase–associated protein kinase 2 (MK2) activities. This assay platform is immediately useful for studying protein kinase signaling in crude cellular extracts.

A Compendium of Signals and Responses Triggered by Prodeath and Prosurvival Cytokines

Cell-signaling networks consist of proteins with a variety of functions (receptors, adaptor proteins, GTPases, kinases, proteases, and transcription factors) working together to control cell fate. Although much is known about the identities and biochemical activities of these signaling proteins, the ways in which they are combined into networks to process and transduce signals are poorly understood. Network-level understanding of signaling requires data on a wide variety of biochemical processes such as posttranslational modification, assembly of macromolecular complexes, enzymatic activity, and localization. No single method can gather such heterogeneous data in high throughput, and most studies of signal transduction therefore rely on series of small, discrete experiments. Inspired by the power of systematic datasets in genomics, we set out to build a systematic signaling dataset that would enable the construction of predictive models of cell-signaling networks. Here we describe the compilation and fusion of ∼10,000 signal and response measurements acquired from HT-29 cells treated with tumor necrosis factor-α, a proapoptotic cytokine, in combination with epidermal growth factor or insulin, two prosurvival growth factors. Nineteen protein signals were measured over a 24-h period using kinase activity assays, quantitative immunoblotting, and antibody microarrays. Four different measurements of apoptotic response were also collected by flow cytometry for each time course. Partial least squares regression models that relate signaling data to apoptotic response data reveal which aspects of compendium construction and analysis were important for the reproducibility, internal consistency, and accuracy of the fused set of signaling measurements. We conclude that it is possible to build self-consistent compendia of cell-signaling data that can be mined computationally to yield important insights into the control of mammalian cell responses.

Cue-Signal-Response Analysis of TNF-Induced Apoptosis by Partial Least Squares Regression of Dynamic Multivariate Data

Biological signaling networks process extracellular cues to control important cell decisions such as death-survival, growth-quiescence, and proliferation-differentiation. After receptor activation, intracellular signaling proteins change in abundance, modification state, and enzymatic activity. Many of the proteins in signaling networks have been identified, but it is not known how signaling molecules work together to control cell decisions. To begin to address this issue, we report the use of partial least squares regression as an analytical method to glean signal-response relationships from heterogeneous multivariate signaling data collected from HT-29 human colon carcinoma cells stimulated to undergo programmed cell death. By partial least squares modeling, we relate dynamic and quantitative measurements of 20-30 intracellular signals to cell survival after treatment with tumor necrosis factor alpha (a death factor) and insulin (a survival factor). We find that partial least squares models can distinguish highly informative signals from redundant uninformative signals to generate a reduced model that retains key signaling features and signal-response relationships. In these models, measurements of biochemical characteristics, based on very different techniques (Western blots, kinase assays, etc.), are grouped together as covariates, showing that heterogenous data have been effectively fused. Importantly, informative protein predictors of cell responses are always multivariate, demonstrating the multicomponent nature of the decision process.

A High-throughput Quantitative Multiplex Kinase Assay for Monitoring Information Flow in Signaling Networks Application to Sepsis-Apoptosis

To treat complex human diseases effectively, a systems-level approach is needed to understand the interplay of environmental cues, intracellular signals, and cellular behaviors that underlie disease states. This approach requires high-throughput, multiplex techniques that measure quantitative temporal variations of multiple protein activities in the intracellular signaling network. Here, we describe a single microtiter-based format that simultaneously quantifies protein kinase activities in the phosphatidylinositol 3-kinase pathway (Akt), nuclear factor-κB pathway (IKK), and three core mitogen-activated protein kinase pathways (ERK, JNK1, MK2). These parallel high-throughput assays are stringently linear, redundantly specific, reproducible, and sensitive compared with classical low-throughput techniques. When applied to a model of sepsis-induced colon epithelial apoptosis, this approach identified a late phase of Akt activity as a critical mediator of cell survival that quantitatively contributed to the efficacy of insulin as an anti-apoptotic cue. Thus, sampling parallel nodes in the intracellular signaling network identified part of the molecular mechanism underlying the efficacy of insulin in the treatment of human sepsis.

Intersection of FOXO- and RUNX1-mediated gene expression programs in single breast epithelial cells during morphogenesis and tumor progression

Gene expression networks are complicated by the assortment of regulatory factors that bind DNA and modulate transcription combinatorially. Single-cell measurements can reveal biological mechanisms hidden by population averages, but their value has not been fully explored in the context of mRNA regulation. Here, we adapted a single-cell expression profiling technique to examine the gene expression program downstream of Forkhead box O (FOXO) transcription factors during 3D breast epithelial acinar morphogenesis. By analyzing patterns of mRNA fluctuations among individual matrix-attached epithelial cells, we found that a subset of FOXO target genes was jointly regulated by the transcription factor Runt-related transcription factor 1 (RUNX1). Knockdown of RUNX1 causes hyperproliferation and abnormal morphogenesis, both of which require normal FOXO function. Down-regulating RUNX1 and FOXOs simultaneously causes widespread oxidative stress, which arrests proliferation and restores normal acinar morphology. In hormone-negative breast cancers lacking human epidermal growth factor receptor 2 (HER2) amplification, we find that RUNX1 down-regulation is strongly associated with up-regulation of FOXO1, which may be required to support growth of RUNX1-negative tumors. The coordinate function of these two tumor suppressors may provide a failsafe mechanism that inhibits cancer progression.

An analysis of critical factors for quantitative immunoblotting

Sample preparation, detection scheme, and normalization approach affect the quantification of protein abundance and modification state by immunoblotting. Best practices for immunoblotting Immunoblotting, also known as Western blotting, is a common technique used by cell and molecular biologists to detect the presence of proteins, changes in protein abundance, and the presence and changes in abundance of posttranslational modifications. Unfortunately, many factors influence reproducibility and the ability to produce quantitative results with this technique. Janes provides a detailed analysis of how variations in sample preparation, detection scheme, and normalization approach affect the reliability, reproducibility, and quantitative value of proteins evaluated by immunoblotting. Immunoblotting (also known as Western blotting) combined with digital image analysis can be a reliable method for analyzing the abundance of proteins and protein modifications, but not every immunoblot-analysis combination produces an accurate result. I illustrate how sample preparation, protocol implementation, detection scheme, and normalization approach profoundly affect the quantitative performance of immunoblotting. This study implemented diagnostic experiments that assess an immunoblot-analysis workflow for accuracy and precision. The results showed that ignoring such diagnostics can lead to pseudoquantitative immunoblot data that markedly overestimate or underestimate true differences in protein abundance.