Sparse Large-Scale Nonlinear Dynamical Modeling of Human Hippocampus for Memory Prostheses

In order to build hippocampal prostheses for restoring memory functions, we build sparse multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from hippocampal CA3 and CA1 regions of epileptic patients performing a variety of memory-dependent delayed match-to-sample (DMS) tasks. Using CA3 and CA1 spike trains as inputs and outputs respectively, sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the CA3-CA1 spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains during multiple memory events, e.g., sample presentation, sample response, match presentation and match response, of the DMS task, and thus will serve as the computational basis of human hippocampal memory prostheses.

Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling

Characterization of long-term activity-dependent plasticity from behaviorally driven spiking activity is important for understanding the underlying mechanisms of learning and memory. In this letter, we present a computational framework for quantifying spike-timing-dependent plasticity (STDP) during behavior by identifying a functional plasticity rule solely from spiking activity. First, we formulate a flexible point-process spiking neuron model structure with STDP, which includes functions that characterize the stationary and plastic properties of the neuron. The STDP model includes a novel function for prolonged plasticity induction, as well as a more typical function for synaptic weight change based on the relative timing of input-output spike pairs. Consideration for system stability is incorporated with weight-dependent synaptic modification. Next, we formalize an estimation technique using a generalized multilinear model (GMLM) structure with basis function expansion. The weight-dependent synaptic modification adds a nonlinearity to the model, which is addressed with an iterative unconstrained optimization approach. Finally, we demonstrate successful model estimation on simulated spiking data and show that all model functions can be estimated accurately with this method across a variety of simulation parameters, such as number of inputs, output firing rate, input firing type, and simulation time. Since this approach requires only naturally generated spikes, it can be readily applied to behaving animal studies to characterize the underlying mechanisms of learning and memory.

Abstract LB-348: Developing a personalized anti-metastatic therapy to treat KRAS, LKB1-mutant lung adenocarcinoma

LKB1 is the 2rd most commonly mutated tumor suppressor gene in human lung adenocarcinoma, is commonly co-mutated with KRAS, and leads to more aggressive, treatment-resistant tumors in mouse models. The identification of druggable signaling molecules that result from specific alterations in LKB1 could result in a personalized clinical strategy to target this high-risk patient population. We have previously published that LKB1 acts to limit focal adhesion kinase (FAK) activity in human lung cancer cells to restrict cell adhesion and migration. Based on our prior published data we hypothesize that FAK pathway inhibition will suppress invasion and metastasis in LKB1-mutant tumors in vivo. To investigate our hypothesis, we have designed the first rolling-enrollment pre-clinical mouse trial to target invasion and metastasis using a small-molecule FAK inhibitor. To enroll mice with early-stage lung adenocarcinoma, we developed a novel lentiviral-Cre induced KrasG12D; Lkb1fl/fl genetically engineered mouse model (GEMM) (KLLLenti) that develops 100% adenocarcinomas, expresses a luciferase reporter gene, and has elevated levels of active FAK in late stage invasive tumors. Importantly, short-term treatment of KLLLenti mice with a pharmacologic FAK inhibitor potently suppresses the invasive progression of primary tumors. Moreover, long-term treatment results in improved progression-free survival, and delays metastatic spread to the lymph nodes. We further pursue mechanistic studies to investigate how LKB1-mutant tumor tissue gains a metastatic advantage in vivo, and using a combination of 3D tumor spheroid assays, and multiphoton microscopy, present results that LKB1-mutant tumors use a unique form of hybrid invasion that relies both on cell:cell and cell-matrix adhesion, and in doing so, are equipped to more efficiently invade into the collagen-dense microenvironment of the lung. We will also present data that similar molecular and cell biologic phenotypes can be found in a subset of KRAS, LKB1-mutant human clinical samples. Our studies suggest that when used early, FAK inhibitors may be a viable clinical strategy to prevent or delay metastasis in the KRAS, LKB1-mutant patient population, and begin to define alternate escape pathways by which this highly invasive cell population may escape first-line therapy.

Citation Format: Melissa Gilbert-Ross, Jessica Konen, Junghui Koo, John Shupe, Gabriel L. Sica, Zhengjia Chen, Brian S. Robinson, Madhusmita Behera, Michael R. Rossi, Geoffrey H. Smith, Charles E. Hill, Suresh M. Ramalingam, Haian Fu, Fadlo R. Khuri, Wei Zhou, Adam Marcus. Developing a personalized anti-metastatic therapy to treat KRAS, LKB1-mutant lung adenocarcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-348.

Implementing spiking neuron model and spike-timing-dependent plasticity with generalized Laguerre-Volterra models

To perform large-scale simulations of the brain or build biologically-inspired cognitive architectures, it is essential to have a succinct and flexible model of spiking neurons. The model should be able to capture the nonlinear dynamical properties of various types of neurons and the nonstationary properties such as the spike-timing-dependent plasticity (STDP). In this paper, we propose a generalized Laguerre-Volterra modeling approach for such a task. Due to its built-in nonlinear dynamical terms, the generalized Laguerre-Volterra model (GLVM) can capture various biological processes/mechanisms. Using Laguerre expansion of Volterra kernel technique, the model is fully represented with a small set of coefficients. The calculation of the model variables can be expressed recursively based on only the current and the one-step-before values and thus can be performed efficiently. In addition, we show that, using the same methodology, STDP can be implemented as a specific form of second-order Volterra kernel describing the causal relationship between pairs of input-output spikes and the changes of the feedforward kernels in the GLVMs.

Estimation of a large-scale generalized Volterra model for neural ensembles with group lasso and local coordinate descent

Estimation of neural models based on observed spike timing faces challenges as the amount of recorded units increases, especially when identifying detailed model features. Given that neural regions are generally sparsely connected, input selection is a critical step in model estimation but oftentimes computationally and theoretically challenging. In this paper, we detail an efficient methodology for estimating a sparse, nonlinear dynamical multiple-input, single-output model (MISO) applicable to large-scale (n > 50) single-unit recorded activity. The main contribution of this paper is the complete implementation of a principled group-lasso and local coordinate descent (LCD) algorithm into a generalized Volterra model (GVM) framework to achieve efficient sparse model estimation. Input selection is achieved with group-lasso by simultaneously selecting groups of parameters that are associated with each input. LCD yields efficient computation as the amount of inputs and parameters increase. We investigate and validate the performance of this estimation procedure with the application to a 64 input simulated model.

Generalized Volterra kernel model identification of spike-timing-dependent plasticity from simulated spiking activity

This paper presents a methodology to estimate a learning rule that governs activity-dependent plasticity from behaviorally recorded spiking events. To demonstrate this framework, we simulate a probabilistic spiking neuron with spike-timing-dependent plasticity (STDP) and estimate all model parameters from the simulated spiking data. In the neuron model, output spiking activity is generated by the combination of noise, feedback from the output, and an input-feedforward component whose magnitude is modulated by synaptic weight. The synaptic weight is calculated with STDP with the following features: (1) weight change based on the relative timing of input-output spike pairs, (2) prolonged plasticity induction, and (3) considerations for system stability. Estimation of all model parameters is achieved iteratively by formulating the model as a generalized linear model with Volterra kernels and basis function expansion. Successful estimation of all model parameters in this study demonstrates the feasibility of this approach for in-vivo experimental studies. Furthermore, the consideration of system stability and prolonged plasticity induction enhances the ability to capture how STDP affects a neural population's signal transformation properties over a realistic time course. Plasticity characterization with this estimation method could yield insights into functional implications of STDP and be incorporated into a cortical prosthesis.

Laguerre-Volterra identification of spike-timing-dependent plasticity from spiking activity: a simulation study

This paper presents a Laguerre-Volterra methodology for identifying a plasticity learning rule from spiking neural data with four components: 1) By analyzing input-output spiking data, the effective contribution of an input on the output firing probability can be quantified with weighted Volterra kernels. 2) The weight of these Volterra kernels can be tracked over time using the stochastic state point processing filtering algorithm (SSPPF) 3) Plasticity system Volterra kernels can be estimated by treating the tracked change in weight over time as the plasticity system output and the spike timing data as the input. 4) Laguerre expansion of all Volterra kernels allows for minimization of open parameters during estimation steps. A single input spiking neuron with Spike-timing-dependent plasticity (STDP) and prolonged STDP induction is simulated. Using the spiking data from this simulation, the amplitude of the STDP learning rule and the time course of the induction is accurately estimated. This framework can be applied to identify plasticity for more complicated plasticity paradigms and is applicable to in vivo data.

The ecdysone receptor coactivator Taiman links Yorkie to transcriptional control of germline stem cell factors in somatic tissue

The Hippo pathway is a conserved signaling cascade that modulates tissue growth. Although its core elements are well defined, factors modulating Hippo transcriptional outputs remain elusive. Here we show that components of the steroid-responsive ecdysone (Ec) pathway modulate Hippo transcriptional effects in imaginal disc cells. The Ec receptor coactivator Taiman (Tai) interacts with the Hippo transcriptional coactivator Yorkie (Yki) and promotes expression of canonical Yki-responsive genes. Tai enhances Yki-driven growth, while Tai loss, or a form of Tai unable to bind Yki, suppresses Yki-driven tissue growth. This growth suppression is not correlated with impaired induction of canonical Hippo-responsive genes but with suppression of a distinct pro-growth program of Yki-induced/Tai-dependent genes, including the germline stem cell factors nanos and piwi. These data reveal Hippo/Ec pathway crosstalk in the form a Yki-Tai complex that collaboratively induces germline genes as part of a transcriptional program that is normally repressed in developing somatic epithelia.

Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses

In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed match-to-sample task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling

This paper presents a systems identification approach for studying the long-term synaptic plasticity using natural spiking activities. This approach consists of three modeling steps. First, a multi-input, single-output (MISO), nonlinear dynamical spiking neuron model is formulated to estimate and represent the synaptic strength in means of functional connectivity between input and output neurons. Second, this MISO model is extended to a nonstationary form to track the time-varying properties of the synaptic strength. Finally, a Volterra modeling method is used to extract the synaptic learning rule, e.g., spike-timing-dependent plasticity, for the explanation of the input-output nonstationarity as the consequence of the past input-output spiking patterns. This framework is developed to study the underlying mechanisms of learning and memory formation in behaving animals, and may serve as the computational basis for building the next-generation adaptive cortical prostheses.

The contribution of relative activation levels between populations of cells to network activity in a large-scale biologically realistic model of the hippocampus

In previously published work, we showed the progress we've made towards creating a large-scale, biologically realistic model of the rat hippocampus, starting with the projection from entorhinal cortex (EC) to the dentate gyrus (DG). We created the model to help us study how the common components of neurobiological systems in mammals - large numbers of neurons with intricate, branching morphologies; active, non-linear membrane properties; nonuniform distributions throughout membrane surface of these non-linear conductances; non-uniform and topographic connectivity between pre- and post-synaptic neurons; and activity-dependent changes in synaptic function - combine and contribute to give a particular brain region its "neural processing" properties. In this work, we report on the results of a series of simulations we ran to test the role of feed-forward and feedback inhibition in the dentate gyrus. We find that a) the system shows rhythmic bands of activity only in the presence of feedback inhibition, b) that the frequency of rhythmicity increases with increasing amounts of feed-forward inhibition, c) that it decreases with increasing amounts of feedback inhibition, and d) that strong excitatory inputs appear to enhance and prolong the amount of rhythmicity in the system.

The role of topography in the transformation of spatiotemporal patterns by a large-scale, biologically realistic model of the rat dentate gyrus

A large-scale, biologically realistic, computational model of the rat hippocampus is being constructed to study the input-output transformation that the hippocampus performs. In the initial implementation, the layer II entorhinal cortex neurons, which provide the major input to the hippocampus, and the granule cells of the dentate gyrus, which receive the majority of the input, are modeled. In a previous work, the topography, or the wiring diagram, connecting these two populations had been derived and implemented. This paper explores the consequences of two features of the topography, the distribution of the axons and the size of the neurons' axon terminal fields. The topography converts streams of independently generated random Poisson trains into structured spatiotemporal patterns through spatiotemporal convergence achievable by overlapping axon terminal fields. Increasing the axon terminal field lengths allowed input to converge over larger regions of space resulting in granule activation across a greater area but did not increase the total activity as a function of time as the number of targets per input remained constant. Additional simulations demonstrated that the total distribution of spikes in space depends not on the distribution of the presynaptic axons but the distribution of the postsynaptic population. Analyzing spike counts emphasizes the importance of the postsynaptic distribution, but it ignores the fact that each individual input may be carrying unique information. Therefore, a metric should be created that relates and tracks individual inputs as they are propagated and integrated through hippocampus.

Towards a large-scale biologically realistic model of the hippocampus

Real neurobiological systems in the mammalian brain have a complicated and detailed structure, being composed of 1) large numbers of neurons with intricate, branching morphologies--complex morphology brings with it complex passive membrane properties; 2) active membrane properties--nonlinear sodium, potassium, calcium, etc. conductances; 3) non-uniform distributions throughout the dendritic and somal membrane surface of these non-linear conductances; 4) non-uniform and topographic connectivity between pre- and post-synaptic neurons; and 5) activity-dependent changes in synaptic function. One of the essential, and as yet unanswered questions in neuroscience is the role of these fundamental structural and functional features in determining "neural processing" properties of a given brain system. To help answer that question, we're creating a large-scale biologically realistic model of the intrinsic pathway of the hippocampus, which consists of the projection from layer II entorhinal cortex (EC) to dentate gyrus (DG), EC to CA3, DG to CA3, and CA3 to CA1. We describe the computational hardware and software tools the model runs on, and demonstrate its viability as a modeling platform with an EC-to-DG model.

Implementation of topographically constrained connectivity for a large-scale biologically realistic model of the hippocampus

In order to understand how memory works in the brain, the hippocampus is highly studied because of its role in the encoding of long-term memories. We have identified four characteristics that would contribute to the encoding process: the morphology of the neurons, their biophysics, synaptic plasticity, and the topography connecting the input to and the neurons within the hippocampus. To investigate how long-term memory is encoded, we are constructing a large-scale biologically realistic model of the rat hippocampus. This work focuses on how topography contributes to the output of the hippocampus. Generally, the brain is structured with topography such that the synaptic connections formed by an input neuron population are organized spatially across the receiving population. The first step in our model was to construct how entorhinal cortex inputs connect to the dentate gyrus of the hippocampus. We have derived realistic constraints from topographical data to connect the two cell populations. The details on how these constraints were applied are presented. We demonstrate that the spatial connectivity has a major impact on the output of the simulation, and the results emphasize the importance of carefully defining spatial connectivity in neural network models of the brain in order to generate relevant spatiotemporal patterns.

Implementation of activity-dependent synaptic plasticity rules for a large-scale biologically realistic model of the hippocampus

A large-scale computational model of the hippocampus should consider plasticity at different time scales in order to capture the non-stationary information processing behavior of the hippocampus more accurately. This paper presents a computational model that describes hippocampal long-term potentiation/depression (LTP/LTD) and short-term plasticity implemented in the NEURON simulation environment. The LTP/LTD component is based on spike-timing-dependent plasticity (STDP). The short-term plasticity component modifies a previously defined deterministic model at a population synapse level to a probabilistic model that can be implemented at a single synapse level. The plasticity mechanisms are validated and incorporated into a large-scale model of the entorhinal cortex projection to the dentate gyrus. Computational expense of the added plasticity was also evaluated and shown to increase simulation time by less than a factor of two. This model can be easily included in future large-scale hippocampal simulations to investigate the effects of LTP/LTD and short-term plasticity in conjunction with other biological considerations on system function.

Drosophila endocytic neoplastic tumor suppressor genes regulate Sav/Wts/Hpo signaling and the c-Jun N-terminal kinase pathway

Genetic screens in the fruit fly Drosophila melanogaster have identified a class of neoplastic tumor suppressor genes (endocytic nTSGs), which encode proteins that localize to endosomes and facilitate the trafficking of membrane-bound receptors and adhesion molecules into the degradative lysosome. Loss of endocytic nTSGs transforms imaginal disc epithelia into highly proliferative, invasive tissues that fail to differentiate and display defects in cellular apicobasal polarity, adhesion and tissue architecture. As vertebrate homologs of some Drosophila nTSGs are linked to tumor formation, identifying molecular changes in signaling associated with nTSG loss could inform understanding of neoplastic transformation in vertebrates. Here we show that mutations in genes that act at multiple steps of the endolysosomal pathway lead to autonomous activation of the Sav/Wts/Hpo (SWH) transcriptional effector Yki (YAP/TAZ in vertebrates) and the Jun N-terminal kinase (JNK), which is known to promote Yki activity in cells with disrupted polarity. Yki and JNK activity are elevated by mutations at multiple steps in the endolysosomal pathway including mutations in the AP-2σ gene, which encodes a component of the AP-2 adaptor complex that recruits cargoes into clathrin-coated pits for subsequent internalization. Moreover, reduction of JNK activity can decrease elevated Yki-signaling caused by altered endocytosis. These studies reveal a broad requirement for components of the endocytic pathway in regulating SWH and JNK outputs, and place Drosophila endocytic nTSGs into a network that involving two major signaling pathways implicated in oncogenesis.

Crumbs regulates Salvador/Warts/Hippo signaling in Drosophila via the FERM-domain protein Expanded

BACKGROUND

Altered expression of apicobasal polarity factors is associated with cancer in vertebrates and tissue overgrowth in invertebrates, yet the mechanisms by which these factors affect growth-regulatory pathways are not well defined. We have tested the basis of an overgrowth phenotype driven by the Drosophila protein Crumbs (Crb), which nucleates an apical membrane complex that functionally interacts with the Par6/Par3/aPKC and Scrib/Dlg/Lgl apicobasal polarity complexes.

RESULTS

We find that Crb-driven growth is dependent upon the Salvador/Warts/Hippo (SWH) pathway and its transcriptional effector Yorkie (Yki). Expression of the Crb intracellular domain elevates Yki activity, and this correlates in tissues and cultured cells with loss of Expanded (Ex), an apically localized SWH component that inhibits Yki. Reciprocally, loss of crb elevates Ex levels, although this excess Ex does not concentrate to its normal location at apical junctions. The Ex-regulatory domain of Crb maps to the juxtamembrane FERM-binding motif (JM), a cytoskeletal interaction domain distinct from the PDZ-binding motif (PBM) through which Crb binds polarity factors. Expression of Crb-JM drives Yki activity and organ growth with little effect on tissue architecture, while Crb-PBM reciprocally produces tissue architectural defects without significant effect on Yki activity.

CONCLUSIONS

These studies identify Crb as a novel SWH regulator via JM-dependent effects on Ex levels and localization and suggest that discrete domains within Crb may allow it to integrate junctional polarity signals with a conserved growth pathway.

Functional interactions between the erupted/tsg101 growth suppressor gene and the DaPKC and rbf1 genes in Drosophila imaginal disc tumors

BACKGROUND

The Drosophila gene erupted (ept) encodes the fly homolog of human Tumor Susceptibility Gene-101 (TSG101), which functions as part of the conserved ESCRT-1 complex to facilitate the movement of cargoes through the endolysosomal pathway. Loss of ept or other genes that encode components of the endocytic machinery (e.g. synatxin7/avalanche, rab5, and vps25) produces disorganized overgrowth of imaginal disc tissue. Excess cell division is postulated to be a primary cause of these 'neoplastic' phenotypes, but the autonomous effect of these mutations on cell cycle control has not been examined.

PRINCIPAL FINDINGS

Here we show that disc cells lacking ept function display an altered cell cycle profile indicative of deregulated progression through the G1-to-S phase transition and express reduced levels of the tumor suppressor ortholog and G1/S inhibitor Rbf1. Genetic reductions of the Drosophila aPKC kinase (DaPKC), which has been shown to promote tumor growth in other fly tumor models, prevent both the ept neoplastic phenotype and the reduction in Rbf1 levels that otherwise occurs in clones of ept mutant cells; this effect is coincident with changes in localization of Notch and Crumbs, two proteins whose sorting is altered in ept mutant cells. The effect on Rbf1 can also be blocked by removal of the gamma-secretase component presenilin, suggesting that cleavage of a gamma-secretase target influences Rbf1 levels in ept mutant cells. Expression of exogenous rbf1 completely ablates ept mutant eye tissues but only mildly affects the development of discs composed of cells with wild type ept.

CONCLUSIONS

Together, these data show that loss of ept alters nuclear cell cycle control in developing imaginal discs and identify the DaPKC, presenilin, and rbf1 genes as modifiers of molecular and cellular phenotypes that result from loss of ept.

Genetic interactions between the Drosophila tumor suppressor gene ept and the stat92E transcription factor

BACKGROUND

Tumor Susceptibility Gene-101 (TSG101) promotes the endocytic degradation of transmembrane proteins and is implicated as a mutational target in cancer, yet the effect of TSG101 loss on cell proliferation in vertebrates is uncertain. By contrast, Drosophila epithelial tissues lacking the TSG101 ortholog erupted (ept) develop as enlarged undifferentiated tumors, indicating that the gene can have anti-growth properties in a simple metazoan. A full understanding of pathways deregulated by loss of Drosophila ept will aid in understanding potential links between mammalian TSG101 and growth control.

PRINCIPAL FINDINGS

We have taken a genetic approach to the identification of pathways required for excess growth of Drosophila eye-antennal imaginal discs lacking ept. We find that this phenotype is very sensitive to the genetic dose of stat92E, the transcriptional effector of the Jak-Stat signaling pathway, and that this pathway undergoes strong activation in ept mutant cells. Genetic evidence indicates that stat92E contributes to cell cycle deregulation and excess cell size phenotypes that are observed among ept mutant cells. In addition, autonomous Stat92E hyper-activation is associated with altered tissue architecture in ept tumors and an effect on expression of the apical polarity determinant crumbs.

CONCLUSIONS

These findings identify ept as a cell-autonomous inhibitor of the Jak-Stat pathway and suggest that excess Jak-Stat signaling makes a significant contribution to proliferative and tissue architectural phenotypes that occur in ept mutant tissues.

Optical homodyne PSK demonstration of 1.5 photons per bit at 156 Mbps with rate-(1/2) turbo coding

We report the first demonstration of a near quantum-limited optical homodyne PSK receiver combined with powerful forward-error-correction coding, achieving 1.5 photons/bit sensitivity, within 4.5 dB of the Shannon limit. Phase-locking was achieved at 1.55 microm using an analog dither-based optical phase-locked loop with an external phase modulator. Analysis for this configuration with arbitrary loop damping is given showing a performance advantage for the overdamped case.

Cyclin E Both Regulates and Is Regulated by Calpain 2, a Protease Associated with Metastatic Breast Cancer Phenotype

Overexpression of cyclin E in breast tumors is associated with a poor response to tamoxifen therapy, greater genomic instability, more aggressive behavior, and a poor clinical prognosis. These tumors also express low molecular weight isoforms of cyclin E that are associated with higher kinase activity and increased metastatic potential. In the current study, we show that cyclin E overexpression in MCF7 cells transactivates the expression of calpain 2, leading to proteolysis of cyclin E as well as several known calpain substrates including focal adhesion kinase (FAK), calpastatin, pp60src, and p53. In vivo inhibition of calpain activity in MCF7-cyclin E cells impedes cyclin E proteolysis, whereas in vivo induction of calpain activity promotes cyclin E proteolysis. An analysis of human breast tumors shows that high levels of cyclin E are coincident with the expression of the low molecular weight isoforms, high levels of calpain 2 protein, and proteolysis of FAK. Lastly, studies using a mouse model of metastasis reveal that highly metastatic tumors express proteolyzed cyclin E and FAK when compared to tumors with a low metastatic potential. Our results suggest that cyclin E–dependent deregulation of calpain may be pivotal in modifying multiple cellular processes that are instrumental in the etiology and progression of breast cancer.

Substrate capacity considerations in developing kinase assays

In developing a screening assay for a serine/threonine kinase, we evaluated various formats of an in-plate enzyme-linked immunosorbent assay (ELISA), as well as solution-phase kinase assays using either ELISA or AlphaScreen detection. Substrate was available both as a biotinylated 15-residue peptide and as a 25-residue peptide containing the same sequence expressed as a glutathione S-transferase fusion protein. When increasing concentrations of either of these substrates were coated directly onto ELISA plates, the rates of the kinase reactions progressively increased. In contrast, when the biotin-peptide was captured onto NeutrAvidin-coated plates, the finite peptide binding capacity of the plates limited the amount of substrate that could be incorporated into the assay system and thereby limited the rate of the reaction at a given kinase concentration. Solution-phase kinase reactions can tolerate high substrate concentrations; however, analysis of kinase reaction samples containing biotin-peptide concentrations higher than the binding capacity of NeutrAvidin-coated plates resulted in an inability to detect differences between reactions run at different substrate concentrations. For AlphaScreen detection following solution-phase kinase reactions, limitations in the binding capacity of the donor and acceptor beads caused loss of signal for substrate concentrations above the maximum binding capacity. Overall, the solution-phase assays required significantly more kinase than the in-plate assays (1-4 microg/ml versus <100 ng/ml, respectively). These studies demonstrate that the amount of substrate that can be incorporated into an assay system substantially affects the rate of the kinase reaction and therefore the amount of kinase required for the assay.

A novel long chain polyunsaturated fatty acid, beta-Oxa 21:3n-3, inhibits T lymphocyte proliferation, cytokine production, delayed-type hypersensitivity, and carrageenan-induced paw reaction and selectively targets intracellular signals

A novel polyunsaturated fatty acid (PUFA), beta-oxa 21:3n-3, containing an oxygen atom in the beta position, was chemically synthesized, and found to have more selective biological activity than the n-3 PUFA, docosahexaenoic acid (22:6n-3) on cells of the immune system. Although beta-oxa 21:3n-3 was very poor compared with 22:6n-3 at stimulating oxygen radical production in neutrophils, it was more effective at inhibiting human T lymphocyte proliferation (IC(50) of 1.9 vs 5.2 microM, respectively). beta-Oxa 21:3n-3 also inhibited the production of TNF-beta, IFN-gamma, and IL-2 by purified human T lymphocytes stimulated with PHA plus PMA, anti-CD3 plus anti-CD28 mAbs, or PMA plus A23187. Metabolism of beta-oxa 21:3n-3 via the cyclooxygenase and lipoxygenase pathways was not required for its inhibitory effects. Consistent with its ability to suppress T lymphocyte function, beta-oxa 21:3n-3 significantly inhibited the delayed-type hypersensitivity response and carrageenan-induced paw edema in mice. In T lymphocytes, beta-oxa 21:3n-3 inhibited the agonist-stimulated translocation of protein kinase C-betaI and -epsilon, but not -alpha, -betaII, or -theta to a particulate fraction, and also inhibited the activation of the extracellular signal-regulated protein kinase, but not c-Jun NH(2)-terminal kinase and p38. In contrast, 22:6n-3 had no effects on these protein kinase C isozymes. The increase in antiinflammatory activity and loss of unwanted bioaction through the generation of a novel synthetic 22:6n-3 analogue provides evidence for a novel strategy in the development of anti-inflammatory agents by chemically engineering PUFA.

The amoeba-to-flagellate transformation test is not reliable for the diagnosis of the genus Naegleria. Description of three new Naegleria spp

Trophozoites of several isolates from one location in Australia have failed consistently to transform into flagellates, although they display all other characteristics of the genus Naegleria. When changing the standard transformation test, flagellates were produced. In phylogenetic trees derived from partial small subunit ribosomal DNA (SSUrDNA) sequences, one of these strains branches close to a cluster comprising N. clarki, N. australiensis, N. italica and N. jadini. It is proposed that these Australian isolates represent a new species, named N. fultoni (strain NG885). Failing to form flagellates since their isolation, even when different transformation procedures are used, are two Naegleria strains from Chile and Indonesia. In SSUrDNA-based phylogenetic trees the Chilean strain clusters with N. pussardi and the Indonesian strain clusters with N. galeacystis, but the degree of sequence difference from these described species (3.5% and 2.2%, respectively) is sufficient to propose that both of the strains represent new species, named N. chilensis (strain NG946) and N. indonesiensis (strain NG945), respectively. The close relationships between each of the new species and the Naegleria species with which they cluster in SSUrDNA-based trees were confirmed by ribosomal internal transcribed spacer region (ITS) sequence comparisons. In France, several non-flagellating N. fowleri strains were isolated from one location. ITS rDNA sequence comparisons indicated that they correspond to a 'type' of N. fowleri found in both Europe and the USA. A redefinition of the genus Naegleria is proposed as a consequence of these and previous findings.

In vitro resistance profile of the human immunodeficiency virus type 1 protease inhibitor BMS-232632

BMS-232632 is an azapeptide human immunodeficiency virus (HIV) type 1 (HIV-1) protease inhibitor that displays potent anti-HIV-1 activity (50% effective concentration [EC(50)], 2.6 to 5.3 nM; EC(90), 9 to 15 nM). In vitro passage of HIV-1 RF in the presence of inhibitors showed that BMS-232632 selected for resistant variants more slowly than nelfinavir or ritonavir did. Genotypic and phenotypic analysis of three different HIV strains resistant to BMS-232632 indicated that an N88S substitution in the viral protease appeared first during the selection process in two of the three strains. An I84V change appeared to be an important substitution in the third strain used. Mutations were also observed at the protease cleavage sites following drug selection. The evolution to resistance seemed distinct for each of the three strains used, suggesting multiple pathways to resistance and the importance of the viral genetic background. A cross-resistance study involving five other protease inhibitors indicated that BMS-232632-resistant virus remained sensitive to saquinavir, while it showed various levels (0. 1- to 71-fold decrease in sensitivity)-of cross-resistance to nelfinavir, indinavir, ritonavir, and amprenavir. In reciprocal experiments, the BMS-232632 susceptibility of HIV-1 variants selected in the presence of each of the other HIV-1 protease inhibitors showed that the nelfinavir-, saquinavir-, and amprenavir-resistant strains of HIV-1 remained sensitive to BMS-232632, while indinavir- and ritonavir-resistant viruses displayed six- to ninefold changes in BMS-232632 sensitivity. Taken together, our data suggest that BMS-232632 may be a valuable protease inhibitor for use in combination therapy.