Cancer genes disfavoring T cell immunity identified via integrated systems approach.

Adoptive T cell therapies (ACT) have been curative for a limited number of cancer patients. The sensitization of cancer cells to T cell killing may expand the benefit of these therapies for more patients. To this end, we use a three-step approach to identify cancer genes that disfavor T cell immunity. First, we profile gene transcripts upregulated by cancer under selection pressure from T cell killing. Second, we identify potential tumor gene targets and pathways that disfavor T cell killing using signaling pathway activation libraries and genome-wide loss-of-function CRISPR-Cas9 screens. Finally, we implement pharmacological perturbation screens to validate these targets and identify BIRC2, ITGAV, DNPEP, BCL2, and ERRα as potential ACT-drug combination candidates. Here, we establish that BIRC2 limits antigen presentation and T cell recognition of tumor cells by suppressing IRF1 activity and provide evidence that BIRC2 inhibition in combination with ACT is an effective strategy to increase efficacy.

Muscarinic acetylcholine receptors control baseline activity and Hebbian stimulus timing-dependent plasticity in fusiform cells of the dorsal cochlear nucleus.

Cholinergic modulation contributes to adaptive sensory processing by controlling spontaneous and stimulus-evoked neural activity and long-term synaptic plasticity. In the dorsal cochlear nucleus (DCN), in vitro activation of muscarinic acetylcholine receptors (mAChRs) alters the spontaneous activity of DCN neurons and interacts with N-methyl-d-aspartate (NMDA) and endocannabinoid receptors to modulate the plasticity of parallel fiber synapses onto fusiform cells by converting Hebbian long-term potentiation to anti-Hebbian long-term depression. Because noise exposure and tinnitus are known to increase spontaneous activity in fusiform cells as well as alter stimulus timing-dependent plasticity (StTDP), it is important to understand the contribution of mAChRs to in vivo spontaneous activity and plasticity in fusiform cells. In the present study, we blocked mAChRs actions by infusing atropine, a mAChR antagonist, into the DCN fusiform cell layer in normal hearing guinea pigs. Atropine delivery leads to decreased spontaneous firing rates and increased synchronization of fusiform cell spiking activity. Consistent with StTDP alterations observed in tinnitus animals, atropine infusion induced a dominant pattern of inversion of StTDP mean population learning rule from a Hebbian to an anti-Hebbian profile. Units preserving their initial Hebbian learning rules shifted toward more excitatory changes in StTDP, whereas units with initial suppressive learning rules transitioned toward a Hebbian profile. Together, these results implicate muscarinic cholinergic modulation as a factor in controlling in vivo fusiform cell baseline activity and plasticity, suggesting a central role in the maladaptive plasticity associated with tinnitus pathology.NEW & NOTEWORTHY This study is the first to use a novel method of atropine infusion directly into the fusiform cell layer of the dorsal cochlear nucleus coupled with simultaneous recordings of neural activity to clarify the contribution of muscarinic acetylcholine receptors (mAChRs) to in vivo fusiform cell baseline activity and auditory-somatosensory plasticity. We have determined that blocking the mAChRs increases the synchronization of spiking activity across the fusiform cell population and induces a dominant pattern of inversion in their stimulus timing-dependent plasticity. These modifications are consistent with similar changes established in previous tinnitus studies, suggesting that mAChRs might have a critical contribution in mediating the maladaptive alterations associated with tinnitus pathology. Blocking mAChRs also resulted in decreased fusiform cell spontaneous firing rates, which is in contrast with their tinnitus hyperactivity, suggesting that changes in the interactions between the cholinergic and GABAergic systems might also be an underlying factor in tinnitus pathology.

Tinnitus: Maladaptive auditory-somatosensory plasticity.

Tinnitus, the phantom perception of sound, is physiologically characterized by an increase in spontaneous neural activity in the central auditory system. However, as tinnitus is often associated with hearing impairment, it is unclear how a decrease of afferent drive can result in central hyperactivity. In this review, we first assess methods for tinnitus induction and objective measures of the tinnitus percept in animal models. From animal studies, we discuss evidence that tinnitus originates in the cochlear nucleus (CN), and hypothesize mechanisms whereby hyperactivity may develop in the CN after peripheral auditory nerve damage. We elaborate how this process is likely mediated by plasticity of auditory-somatosensory integration in the CN: the circuitry in normal circumstances maintains a balance of auditory and somatosensory activities, and loss of auditory inputs alters the balance of auditory somatosensory integration in a stimulus timing dependent manner, which propels the circuit towards hyperactivity. Understanding the mechanisms underlying tinnitus generation is essential for its prevention and treatment. This article is part of a Special Issue entitled .

NMDA Receptors Mediate Stimulus-Timing-Dependent Plasticity and Neural Synchrony in the Dorsal Cochlear Nucleus.

Auditory information relayed by auditory nerve fibers and somatosensory information relayed by granule cell parallel fibers converge on the fusiform cells (FCs) of the dorsal cochlear nucleus, the first brain station of the auditory pathway. In vitro, parallel fiber synapses on FCs exhibit spike-timing-dependent plasticity with Hebbian learning rules, partially mediated by the NMDA receptor (NMDAr). Well-timed bimodal auditory-somatosensory stimulation, in vivo equivalent of spike-timing-dependent plasticity, can induce stimulus-timing-dependent plasticity (StTDP) of the FCs spontaneous and tone-evoked firing rates. In healthy guinea pigs, the resulting distribution of StTDP learning rules across a FC neural population is dominated by a Hebbian profile while anti-Hebbian, suppressive and enhancing LRs are less frequent. In this study, we investigate in vivo, the NMDAr contribution to FC baseline activity and long term plasticity. We find that blocking the NMDAr decreases the synchronization of FC- spontaneous activity and mediates differential modulation of FC rate-level functions such that low, and high threshold units are more likely to increase, and decrease, respectively, their maximum amplitudes. Three significant alterations in mean learning-rule profiles were identified: transitions from an initial Hebbian profile towards (1) an anti-Hebbian; (2) a suppressive profile; and (3) transitions from an anti-Hebbian to a Hebbian profile. FC units preserving their learning rules showed instead, NMDAr-dependent plasticity to unimodal acoustic stimulation, with persistent depression of tone-evoked responses changing to persistent enhancement following the NMDAr antagonist. These results reveal a crucial role of the NMDAr in mediating FC baseline activity and long-term plasticity which have important implications for signal processing and auditory pathologies related to maladaptive plasticity of dorsal cochlear nucleus circuitry.

Stimulus-timing-dependent modifications of rate-level functions in animals with and without tinnitus.

Tinnitus has been associated with enhanced central gain manifested by increased spontaneous activity and sound-evoked firing rates of principal neurons at various stations of the auditory pathway. Yet, the mechanisms leading to these modifications are not well understood. In a recent in vivo study, we demonstrated that stimulus-timing-dependent bimodal plasticity mediates modifications of spontaneous and tone-evoked responses of fusiform cells in the dorsal cochlear nucleus (DCN) of the guinea pig. Fusiform cells from sham animals showed primarily Hebbian learning rules while noise-exposed animals showed primarily anti-Hebbian rules, with broadened profiles for the animals with behaviorally verified tinnitus (Koehler SD, Shore SE. J Neurosci 33: 19647-19656, 2013a). In the present study we show that well-timed bimodal stimulation induces alterations in the rate-level functions (RLFs) of fusiform cells. The RLF gains and maximum amplitudes show Hebbian modifications in sham and no-tinnitus animals but anti-Hebbian modifications in noise-exposed animals with evidence for tinnitus. These findings suggest that stimulus-timing bimodal plasticity produced by the DCN circuitry is a contributing mechanism to enhanced central gain associated with tinnitus.

Listening to another sense: somatosensory integration in the auditory system.

Conventionally, sensory systems are viewed as separate entities, each with its own physiological process serving a different purpose. However, many functions require integrative inputs from multiple sensory systems and sensory intersection and convergence occur throughout the central nervous system. The neural processes for hearing perception undergo significant modulation by the two other major sensory systems, vision and somatosensation. This synthesis occurs at every level of the ascending auditory pathway: the cochlear nucleus, inferior colliculus, medial geniculate body and the auditory cortex. In this review, we explore the process of multisensory integration from (1) anatomical (inputs and connections), (2) physiological (cellular responses), (3) functional and (4) pathological aspects. We focus on the convergence between auditory and somatosensory inputs in each ascending auditory station. This review highlights the intricacy of sensory processing and offers a multisensory perspective regarding the understanding of sensory disorders.

Genesis of interictal spikes in the CA1: a computational investigation.

Interictal spikes (IISs) are spontaneous high amplitude, short time duration <400 ms events often observed in electroencephalographs (EEG) of epileptic patients. In vitro analysis of resected mesial temporal lobe tissue from patients with refractory temporal lobe epilepsy has revealed the presence of IIS in the CA1 subfield. In this paper, we develop a biophysically relevant network model of the CA1 subfield and investigate how changes in the network properties influence the susceptibility of CA1 to exhibit an IIS. We present a novel template based approach to identify conditions under which synchronization of paroxysmal depolarization shift (PDS) events evoked in CA1 pyramidal (Py) cells can trigger an IIS. The results from this analysis are used to identify the synaptic parameters of a minimal network model that is capable of generating PDS in response to afferent synaptic input. The minimal network model parameters are then incorporated into a detailed network model of the CA1 subfield in order to address the following questions: (1) How does the formation of an IIS in the CA1 depend on the degree of sprouting (recurrent connections) between the CA1 Py cells and the fraction of CA3 Shaffer collateral (SC) connections onto the CA1 Py cells? and (2) Is synchronous afferent input from the SC essential for the CA1 to exhibit IIS? Our results suggest that the CA1 subfield with low recurrent connectivity (absence of sprouting), mimicking the topology of a normal brain, has a very low probability of producing an IIS except when a large fraction of CA1 neurons (>80%) receives a barrage of quasi-synchronous afferent input (input occurring within a temporal window of ≤24 ms) via the SC. However, as we increase the recurrent connectivity of the CA1 (P sprout > 40); mimicking sprouting in a pathological CA1 network, the CA1 can exhibit IIS even in the absence of a barrage of quasi-synchronous afferents from the SC (input occurring within temporal window >80 ms) and a low fraction of CA1 Py cells (≈30%) receiving SC input. Furthermore, we find that in the presence of Poisson distributed random input via SC, the CA1 network is able to generate spontaneous periodic IISs (≈3 Hz) for high degrees of recurrent Py connectivity (P sprout > 70). We investigate the conditions necessary for this phenomenon and find that spontaneous IISs closely depend on the degree of the network's intrinsic excitability.

Recognition memory and theta-gamma interactions in the hippocampus.

Neuronal oscillations and cross-frequency interactions in the rat hippocampus relate in important ways to memory processes and serve as a model for studying oscillatory activity in cognition more broadly. We report here that hippocampal synchrony (CA3-CA1 coherence) increased markedly in the low gamma range as rats were exploring novel objects, particularly those for which the rat subsequently showed good memory. The gamma synchrony varied across phases of the theta rhythm such that coherence was highest at the falling slope and trough of the theta wave. Further, the shape of the theta wave was more asymmetric and elongated at the falling slope during exploration of objects for which the rat subsequently showed good memory as compared with objects for which the rat subsequently showed poor memory. The results showed a strong association between event-related gamma synchrony in rat hippocampus and memory encoding for novel objects. In addition, a novel potential mechanism of cross-frequency interactions was observed whereby dynamic alterations in the shape of theta wave related to memory in correspondence with the strength of gamma synchrony. These findings add to our understanding of how theta and gamma oscillations interact in the hippocampus in the service of memory.

Computational modeling of channelrhodopsin-2 photocurrent characteristics in relation to neural signaling.

Channelrhodopsins-2 (ChR2) are a class of light sensitive proteins that offer the ability to use light stimulation to regulate neural activity with millisecond precision. In order to address the limitations in the efficacy of the wild-type ChR2 (ChRwt) to achieve this objective, new variants of ChR2 that exhibit fast mon-exponential photocurrent decay characteristics have been recently developed and validated. In this paper, we investigate whether the framework of transition rate model with 4 states, primarily developed to mimic the biexponential photocurrent decay kinetics of ChRwt, as opposed to the low complexity 3 state model, is warranted to mimic the mono-exponential photocurrent decay kinetics of the newly developed fast ChR2 variants: ChETA (Gunaydin et al., Nature Neurosci. 13:387-392, 2010) and ChRET/TC (Berndt et al., Proc. Natl. Acad. Sci. 108:7595-7600, 2011). We begin by estimating the parameters of the 3-state and 4-state models from experimental data on the photocurrent kinetics of ChRwt, ChETA, and ChRET/TC. We then incorporate these models into a fast-spiking interneuron model (Wang and Buzsaki, J. Neurosci. 16:6402-6413, 1996) and a hippocampal pyramidal cell model (Golomb et al., J. Neurophysiol. 96:1912-1926, 2006) and investigate the extent to which the experimentally observed neural response to various optostimulation protocols can be captured by these models. We demonstrate that for all ChR2 variants investigated, the 4 state model implementation is better able to capture neural response consistent with experiments across wide range of optostimulation protocol. We conclude by analytically investigating the conditions under which the characteristic specific to the 3-state model, namely the monoexponential photocurrent decay of the newly developed variants of ChR2, can occur in the framework of the 4-state model.

Computational models of epilepsy.

PURPOSE: Approximately 30% of epilepsy patients suffer from medically refractory epilepsy, in which seizures can not controlled by the use of anti-epileptic drugs (AEDs). Understanding the mechanisms underlying these forms of drug-resistant epileptic seizures and the development of alternative effective treatment strategies are fundamental challenges for modern epilepsy research. In this context, computational modeling has gained prominence as an important tool for tackling the complexity of the epileptic phenomenon. In this review article, we present a survey of computational models of epilepsy from the point of view that epilepsy is a dynamical brain disease that is primarily characterized by unprovoked spontaneous epileptic seizures. METHOD: We introduce key concepts from the mathematical theory of dynamical systems, such as multi-stability and bifurcations, and explain how these concepts aid in our understanding of the brain mechanisms involved in the emergence of epileptic seizures. RESULTS: We present a literature survey of the different computational modeling approaches that are used in the study of epilepsy. Special emphasis is placed on highlighting the fine balance between the degree of model simplification and the extent of biological realism that modelers seek in order to address relevant questions. In this context, we discuss three specific examples from published literature, which exemplify different approaches used for developing computational models of epilepsy. We further explore the potential of recently developed optogenetics tools to provide novel avenue for seizure control. CONCLUSION: We conclude with a discussion on the utility of computational models for the development of new epilepsy treatment protocols.

Reduced representations of heterogeneous mixed neural networks with synaptic coupling.

In the human brain, large-scale neural networks are considered to instantiate the integrative mechanisms underlying higher cognitive, motor, and sensory functions. Computational models of such large-scale networks typically lump thousands of neurons into a functional unit, which serves as the "atom" for the network integration. These atoms display a low dimensional dynamics corresponding to the only type of behavior available for the neurons within the unit, namely, the synchronized regime. Other dynamical features are not part of the unit's repertoire. With this limitation in mind, here we have studied the dynamical behavior of a neural network comprising "all-to-all" synaptically connected excitatory and inhibitory nonidentical neurons. We found that the network exhibits various dynamical characteristics, synchronization being only a particular case. Then we construct a low-dimensional representation of the network dynamics, and we show that this reduced system captures well the main dynamical features of the entire population. Our approach provides an alternate model for a neurocomputational unit of a large-scale network that can account for rich dynamical features of the network at low computational costs.

Neural population modes capture biologically realistic large scale network dynamics.

Large scale brain networks are understood nowadays to underlie the emergence of cognitive functions, though the detailed mechanisms are hitherto unknown. The challenges in the study of large scale brain networks are amongst others their high dimensionality requiring significant computational efforts, the complex connectivity across brain areas and the associated transmission delays, as well as the stochastic nature of neuronal processes. To decrease the computational effort, neurons are clustered into neural masses, which then are approximated by reduced descriptions of population dynamics. Here, we implement a neural population mode approach (Assisi et al. in Phys. Rev. Lett. 94(1):018106, 2005; Stefanescu and Jirsa in PLoS Comput. Biol. 4(11):e1000219, 2008), which parsimoniously captures various types of population behavior. We numerically demonstrate that the reduced population mode system favorably captures the high-dimensional dynamics of neuron networks with an architecture involving homogeneous local connectivity and a large-scale, fiber-like connection with time delay.

A low dimensional description of globally coupled heterogeneous neural networks of excitatory and inhibitory neurons.

Neural networks consisting of globally coupled excitatory and inhibitory nonidentical neurons may exhibit a complex dynamic behavior including synchronization, multiclustered solutions in phase space, and oscillator death. We investigate the conditions under which these behaviors occur in a multidimensional parametric space defined by the connectivity strengths and dispersion of the neuronal membrane excitability. Using mode decomposition techniques, we further derive analytically a low dimensional description of the neural population dynamics and show that the various dynamic behaviors of the entire network can be well reproduced by this reduced system. Examples of networks of FitzHugh-Nagumo and Hindmarsh-Rose neurons are discussed in detail.