Phosphoproteomic investigation of targets of protein phosphatases in EGFR signaling.

Receptor tyrosine kinases (RTKs) initiate cellular signaling pathways, which are regulated through a delicate balance of phosphorylation and dephosphorylation events. While many studies of RTKs have focused on downstream-activated kinases catalyzing the site-specific phosphorylation, few studies have focused on the phosphatases carrying out the dephosphorylation. In this study, we analyzed six protein phosphatase networks using chemical inhibitors in context of epidermal growth factor receptor (EGFR) signaling by mass spectrometry-based phosphoproteomics. Specifically, we focused on protein phosphatase 2C (PP2C), involved in attenuating p38-dependent signaling pathways in various cellular responses, and confirmed its effect in regulating p38 activity in EGFR signaling. Furthermore, utilizing a p38 inhibitor, we classified phosphosites whose phosphorylation status depends on PP2C inhibition into p38-dependent and p38-independent sites. This study provides a large-scale dataset of phosphatase-regulation of EGF-responsive phosphorylation sites, which serves as a useful resource to deepen our understanding of EGFR signaling.

One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes.

Mass spectrometry (MS)-based proteomics workflows typically involve complex, multi-step processes, presenting challenges with sample losses, reproducibility, requiring substantial time and financial investments, and specialized skills. Here we introduce One-Tip, a proteomics methodology that seamlessly integrates efficient, one-pot sample preparation with precise, narrow-window data-independent acquisition (nDIA) analysis. One-Tip substantially simplifies sample processing, enabling the reproducible identification of >9000 proteins from ~1000 HeLa cells. The versatility of One-Tip is highlighted by nDIA identification of ~6000 proteins in single cells from early mouse embryos. Additionally, the study incorporates the Uno Single Cell Dispenser™, demonstrating the capability of One-Tip in single-cell proteomics with >3000 proteins identified per HeLa cell. We also extend One-Tip workflow to analysis of extracellular vesicles (EVs) extracted from blood plasma, demonstrating its high sensitivity by identifying >3000 proteins from 16 ng EV preparation. One-Tip expands capabilities of proteomics, offering greater depth and throughput across a range of sample types.

An Engineered Synthetic Receptor-Aptamer Pair for an Artificial Signal Transduction System.

Cell membrane receptors regulate cellular responses through sensing extracellular environmental signals and subsequently transducing them. Receptor engineering provides a means of directing cells to react to a designated external cue and exert programmed functions. However, rational design and precise modulation of receptor signaling activity remain challenging. Here, we report an aptamer-based signal transduction system and its applications in controlling and customizing the functions of engineered receptors. A previously reported membrane receptor-aptamer pair was used to design a synthetic receptor system that transduces cell signaling depending on exogenous aptamer input. To eliminate the cross-reactivity of the receptor with its native ligand, the extracellular domain of the receptor was engineered to ensure that the receptor was solely activated by the DNA aptamer. The present system features tunability in the signaling output level using aptamer ligands with different receptor dimerization propensities. In addition, the functional programmability of DNA aptamers enables the modular sensing of extracellular molecules without the need for genetic engineering of the receptor.

Phosphoproteomics of primary AML patient samples reveals rationale for AKT combination therapy and p53 context to overcome selinexor resistance.

Acute myeloid leukemia (AML) is a heterogeneous disease with variable patient responses to therapy. Selinexor, an inhibitor of nuclear export, has shown promising clinical activity for AML. To identify the molecular context for monotherapy sensitivity as well as rational drug combinations, we profile selinexor signaling responses using phosphoproteomics in primary AML patient samples and cell lines. Functional phosphosite scoring reveals that p53 function is required for selinexor sensitivity consistent with enhanced efficacy of selinexor in combination with the MDM2 inhibitor nutlin-3a. Moreover, combining selinexor with the AKT inhibitor MK-2206 overcomes dysregulated AKT-FOXO3 signaling in resistant cells, resulting in synergistic anti-proliferative effects. Using high-throughput spatial proteomics to profile subcellular compartments, we measure global proteome and phospho-proteome dynamics, providing direct evidence of nuclear translocation of FOXO3 upon combination treatment. Our data demonstrate the potential of phosphoproteomics and functional phosphorylation site scoring to successfully pinpoint key targetable signaling hubs for rational drug combinations.

Structure-guided design enables development of a hyperpolarized molecular probe for the detection of aminopeptidase N activity in vivo.

Dynamic nuclear polarization (DNP) is a cutting-edge technique that markedly enhances the detection sensitivity of molecules using nuclear magnetic resonance (NMR)/magnetic resonance imaging (MRI). This methodology enables real-time imaging of dynamic metabolic status in vivo using MRI. To expand the targetable metabolic reactions, there is a demand for developing exogenous, i.e., artificially designed, DNP-NMR molecular probes; however, complying with the requirements of practical DNP-NMR molecular probes is challenging because of the lack of established design guidelines. Here, we report Ala-[1-13C]Gly-d2-NMe2 as a DNP-NMR molecular probe for in vivo detection of aminopeptidase N activity. We developed this probe rationally through precise structural investigation, calculation, biochemical assessment, and advanced molecular design to achieve rapid and detectable responses to enzyme activity in vivo. With the fabricated probe, we successfully detected enzymatic activity in vivo. This report presents a comprehensive approach for the development of artificially derived, practical DNP-NMR molecular probes through structure-guided molecular design.

A DNA Aptamer That Inhibits the Aberrant Signaling of Fibroblast Growth Factor Receptor in Cancer Cells.

Growth factor receptors are activated through dimerization by the binding of their ligands and play pivotal roles in normal cell function. However, the aberrant activity of the receptors has been associated with cancer malignancy. One of the main causes of the aberrant receptor activation is the overexpression of receptors and the resultant formation of unliganded receptor dimers, which can be activated in the absence of external ligand molecules. Thus, the unliganded receptor dimer is a promising target to inhibit aberrant signaling in cancer. Here, we report an aptamer that specifically binds to fibroblast growth factor receptor 2b and inhibits the aberrant receptor activation and signaling. Our investigation suggests that this aptamer inhibits the formation of the receptor dimer occurring in the absence of external ligand molecules. This work presents a new inhibitory function of aptamers and the possibility of oligonucleotide-based therapeutics for cancer.

Using Engineered Mammalian Cells for an Epitope-Directed Antibody Affinity Maturation System.

Antibodies have been attracting attention as therapeutic tools owing to their high affinity and specificity. To develop potent antibodies, affinity maturation, epitope regulation, and using target antigens in native form are pivotal requirements. Here we describe a method to conduct epitope-directed affinity maturation of antibodies using engineered mammalian cells. This method utilizes protein chimeras that transduce cell death signaling in response to antibody binding. As the competition of antibody binding inhibits the cell death signaling, only affinity-matured antibodies retaining the same epitope as an original one can be selected using cell survival as readout.

An Information Theoretic Approach to Reveal the Formation of Shared Representations.

Modality-invariant categorical representations, i.e., shared representation, is thought to play a key role in learning to categorize multi-modal information. We have investigated how a bimodal autoencoder can form a shared representation in an unsupervised manner with multi-modal data. We explored whether altering the depth of the network and mixing the multi-modal inputs at the input layer affect the development of the shared representations. Based on the activation of units in the hidden layers, we classified them into four different types: visual cells, auditory cells, inconsistent visual and auditory cells, and consistent visual and auditory cells. Our results show that the number and quality of the last type (i.e., shared representation) significantly differ depending on the depth of the network and are enhanced when the network receives mixed inputs as opposed to separate inputs for each modality, as occurs in typical two-stage frameworks. In the present work, we present a way to utilize information theory to understand the abstract representations formed in the hidden layers of the network. We believe that such an information theoretic approach could potentially provide insights into the development of more efficient and cost-effective ways to train neural networks using qualitative measures of the representations that cannot be captured by analyzing only the final outputs of the networks.

An epitope-directed antibody affinity maturation system utilizing mammalian cell survival as readout.

Upon developing therapeutically potent antibodies, there are significant requirements, such as increasing their affinity, regulating their epitope, and using native target antigens. Many antibody selection systems, such as a phage display method, have been developed, but it is still difficult to fulfill these requirements at the same time. Here, we propose a novel epitope-directed antibody affinity maturation system utilizing mammalian cell survival as readout. This system is based on the competition of antibody binding, and can target membrane proteins expressed in a native form on a mammalian cell surface. Using this system, we successfully selected an affinity-matured anti-ErbB2 single-chain variable fragment variant, which had the same epitope as the original one. In addition, the affinity was increased mainly due to the decrease in the dissociation rate. This novel cell-based antibody affinity maturation system could contribute to directly obtaining therapeutically potent antibodies that are functional on the cell surface.

A new approach to solving the feature-binding problem in primate vision.

We discuss a recently proposed approach to solve the classic feature-binding problem in primate vision that uses neural dynamics known to be present within the visual cortex. Broadly, the feature-binding problem in the visual context concerns not only how a hierarchy of features such as edges and objects within a scene are represented, but also the hierarchical relationships between these features at every spatial scale across the visual field. This is necessary for the visual brain to be able to make sense of its visuospatial world. Solving this problem is an important step towards the development of artificial general intelligence. In neural network simulation studies, it has been found that neurons encoding the binding relations between visual features, known as binding neurons, emerge during visual training when key properties of the visual cortex are incorporated into the models. These biological network properties include (i) bottom-up, lateral and top-down synaptic connections, (ii) spiking neuronal dynamics, (iii) spike timing-dependent plasticity, and (iv) a random distribution of axonal transmission delays (of the order of several milliseconds) in the propagation of spikes between neurons. After training the network on a set of visual stimuli, modelling studies have reported observing the gradual emergence of polychronization through successive layers of the network, in which subpopulations of neurons have learned to emit their spikes in regularly repeating spatio-temporal patterns in response to specific visual stimuli. Such a subpopulation of neurons is known as a polychronous neuronal group (PNG). Some neurons embedded within these PNGs receive convergent inputs from neurons representing lower- and higher-level visual features, and thus appear to encode the hierarchical binding relationship between features. Neural activity with this kind of spatio-temporal structure robustly emerges in the higher network layers even when neurons in the input layer represent visual stimuli with spike timings that are randomized according to a Poisson distribution. The resulting hierarchical representation of visual scenes in such models, including the representation of hierarchical binding relations between lower- and higher-level visual features, is consistent with the hierarchical phenomenology or subjective experience of primate vision and is distinct from approaches interested in segmenting a visual scene into a finite set of objects.

The emergence of polychronization and feature binding in a spiking neural network model of the primate ventral visual system.

We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the input training images. The performance is improved by including top-down and lateral synaptic connections, as well as introducing multiple synaptic contacts between each pair of pre- and postsynaptic neurons, with different synaptic contacts having different axonal delays. Spike-timing-dependent plasticity thus allows the model to select the most effective axonal transmission delay between neurons. Furthermore, neurons representing the binding relationship between low-level and high-level visual features emerge through visually guided learning. This begins to provide a way forward to solving the classic feature binding problem in visual neuroscience and leads to a new hypothesis concerning how information about visual features at every spatial scale may be projected upward through successive neuronal layers. We name this hypothetical upward projection of information the "holographic principle." (PsycINFO Database Record

The neural representation of the gender of faces in the primate visual system: A computer modeling study.

We use an established neural network model of the primate visual system to show how neurons might learn to encode the gender of faces. The model consists of a hierarchy of 4 competitive neuronal layers with associatively modifiable feedforward synaptic connections between successive layers. During training, the network was presented with many realistic images of male and female faces, during which the synaptic connections are modified using biologically plausible local associative learning rules. After training, we found that different subsets of output neurons have learned to respond exclusively to either male or female faces. With the inclusion of short range excitation within each neuronal layer to implement a self-organizing map architecture, neurons representing either male or female faces were clustered together in the output layer. This learning process is entirely unsupervised, as the gender of the face images is not explicitly labeled and provided to the network as a supervisory training signal. These simulations are extended to training the network on rotating faces. It is found that by using a trace learning rule incorporating a temporal memory trace of recent neuronal activity, neurons responding selectively to either male or female faces were also able to learn to respond invariantly over different views of the faces. This kind of trace learning has been previously shown to operate within the primate visual system by neurophysiological and psychophysical studies. The computer simulations described here predict that similar neurons encoding the gender of faces will be present within the primate visual system. (PsycINFO Database Record

Understanding the neural basis of cognitive bias modification as a clinical treatment for depression.

[Correction Notice: An Erratum for this article was reported in Vol 85(3) of Journal of Consulting and Clinical Psychology (see record 2017-07144-002). In the article, there was an error in the Discussion section's first paragraph for Implications and Future Work. The in-text reference citation for Penton-Voak et al. (2013) was incorrectly listed as "Blumenfeld, Preminger, Sagi, and Tsodyks (2006)". All versions of this article have been corrected.] Objective: Cognitive bias modification (CBM) eliminates cognitive biases toward negative information and is efficacious in reducing depression recurrence, but the mechanisms behind the bias elimination are not fully understood. The present study investigated, through computer simulation of neural network models, the neural dynamics underlying the use of CBM in eliminating the negative biases in the way that depressed patients evaluate facial expressions. METHOD: We investigated 2 new CBM methodologies using biologically plausible synaptic learning mechanisms-continuous transformation learning and trace learning-which guide learning by exploiting either the spatial or temporal continuity between visual stimuli presented during training. We first describe simulations with a simplified 1-layer neural network, and then we describe simulations in a biologically detailed multilayer neural network model of the ventral visual pathway. RESULTS: After training with either the continuous transformation learning rule or the trace learning rule, the 1-layer neural network eliminated biases in interpreting neutral stimuli as sad. The multilayer neural network trained with realistic face stimuli was also shown to be able to use continuous transformation learning or trace learning to reduce biases in the interpretation of neutral stimuli. CONCLUSIONS: The simulation results suggest 2 biologically plausible synaptic learning mechanisms, continuous transformation learning and trace learning, that may subserve CBM. The results are highly informative for the development of experimental protocols to produce optimal CBM training methodologies with human participants. (PsycINFO Database Record

The visually guided development of facial representations in the primate ventral visual pathway: A computer modeling study.

Experimental studies have shown that neurons at an intermediate stage of the primate ventral visual pathway, occipital face area, encode individual facial parts such as eyes and nose while neurons in the later stages, middle face patches, are selective to the full face by encoding the spatial relations between facial features. We have performed a computer modeling study to investigate how these cell firing properties may develop through unsupervised visually guided learning. A hierarchical neural network model of the primate's ventral visual pathway is trained by presenting many randomly generated faces to the network while a local learning rule modifies the strengths of the synaptic connections between neurons in successive layers. After training, the model is found to have developed the experimentally observed cell firing properties. In particular, we have shown how the visual system forms separate representations of facial features such as the eyes, nose, and mouth as well as monotonically tuned representations of the spatial relationships between these facial features. We also demonstrated how the primate brain learns to represent facial expression independently of facial identity. Furthermore, based on the simulation results, we propose that neurons encoding different global attributes simply represent different spatial relationships between local features with monotonic tuning curves or particular combinations of these spatial relations. (PsycINFO Database Record

Neural network model develops border ownership representation through visually guided learning.

As Rubin's famous vase demonstrates, our visual perception tends to assign luminance contrast borders to one or other of the adjacent image regions. Experimental evidence for the neuronal coding of such border-ownership in the primate visual system has been reported in neurophysiology. We have investigated exactly how such neural circuits may develop through visually-guided learning. More specifically, we have investigated through computer simulation how top-down connections may play a fundamental role in the development of border ownership representations in the early cortical visual layers V1/V2. Our model consists of a hierarchy of competitive neuronal layers, with both bottom-up and top-down synaptic connections between successive layers, and the synaptic connections are self-organised by a biologically plausible, temporal trace learning rule during training on differently shaped visual objects. The simulations reported in this paper have demonstrated that top-down connections may help to guide competitive learning in lower layers, thus driving the formation of lower level (border ownership) visual representations in V1/V2 that are modulated by higher level (object boundary element) representations in V4. Lastly we investigate the limitations of our model in the more general situation where multiple objects are presented to the network simultaneously.

A computational exploration of complementary learning mechanisms in the primate ventral visual pathway.

In order to develop transformation invariant representations of objects, the visual system must make use of constraints placed upon object transformation by the environment. For example, objects transform continuously from one point to another in both space and time. These two constraints have been exploited separately in order to develop translation and view invariance in a hierarchical multilayer model of the primate ventral visual pathway in the form of continuous transformation learning and temporal trace learning. We show for the first time that these two learning rules can work cooperatively in the model. Using these two learning rules together can support the development of invariance in cells and help maintain object selectivity when stimuli are presented over a large number of locations or when trained separately over a large number of viewing angles.

Computational modeling of the neural representation of object shape in the primate ventral visual system.

Neurons in successive stages of the primate ventral visual pathway encode the spatial structure of visual objects. In this paper, we investigate through computer simulation how these cell firing properties may develop through unsupervised visually-guided learning. Individual neurons in the model are shown to exploit statistical regularity and temporal continuity of the visual inputs during training to learn firing properties that are similar to neurons in V4 and TEO. Neurons in V4 encode the conformation of boundary contour elements at a particular position within an object regardless of the location of the object on the retina, while neurons in TEO integrate information from multiple boundary contour elements. This representation goes beyond mere object recognition, in which neurons simply respond to the presence of a whole object, but provides an essential foundation from which the brain is subsequently able to recognize the whole object.

Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity.

Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red-green and blue-yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell).