High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines.

Hundreds of genetically characterized cell lines are available for the discovery of genotype-specific cancer vulnerabilities. However, screening large numbers of compounds against large numbers of cell lines is currently impractical, and such experiments are often difficult to control. Here we report a method called PRISM that allows pooled screening of mixtures of cancer cell lines by labeling each cell line with 24-nucleotide barcodes. PRISM revealed the expected patterns of cell killing seen in conventional (unpooled) assays. In a screen of 102 cell lines across 8,400 compounds, PRISM led to the identification of BRD-7880 as a potent and highly specific inhibitor of aurora kinases B and C. Cell line pools also efficiently formed tumors as xenografts, and PRISM recapitulated the expected pattern of erlotinib sensitivity in vivo

HIPK2 and extrachromosomal histone H2B are separately recruited by Aurora-B for cytokinesis

Cytokinesis, the final phase of cell division, is necessary to form two distinct daughter cells with correct distribution of genomic and cytoplasmic materials. Its failure provokes genetically unstable states, such as tetraploidization and polyploidization, which can contribute to tumorigenesis. Aurora-B kinase controls multiple cytokinetic events, from chromosome condensation to abscission when the midbody is severed. We have previously shown that HIPK2, a kinase involved in DNA damage response and development, localizes at the midbody and contributes to abscission by phosphorylating extrachromosomal histone H2B at Ser14. Of relevance, HIPK2-defective cells do not phosphorylate H2B and do not successfully complete cytokinesis leading to accumulation of binucleated cells, chromosomal instability, and increased tumorigenicity. However, how HIPK2 and H2B are recruited to the midbody during cytokinesis is still unknown. Here, we show that regardless of their direct (H2B) and indirect (HIPK2) binding of chromosomal DNA, both H2B and HIPK2 localize at the midbody independently of nucleic acids. Instead, by using mitotic kinase-specific inhibitors in a spatio-temporal regulated manner, we found that Aurora-B kinase activity is required to recruit both HIPK2 and H2B to the midbody. Molecular characterization showed that Aurora-B directly binds and phosphorylates H2B at Ser32 while indirectly recruits HIPK2 through the central spindle components MgcRacGAP and PRC1. Thus, among different cytokinetic functions, Aurora-B separately recruits HIPK2 and H2B to the midbody and these activities contribute to faithful cytokinesis

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces

Background: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. Methods: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. Results: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. Conclusions: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions

A Symbiotic Brain-Machine Interface through Value-Based Decision Making

BACKGROUND: In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). METHODOLOGY: The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. CONCLUSIONS: Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain

Volitional modulation of optically recorded calcium signals during neuroprosthetic learning

Brain-machine interfaces are not only promising for neurological applications, but also powerful for investigating neuronal ensemble dynamics during learning. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with 2-photon imaging in motor and somatosensory cortex. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across- and within-sessions. Learning was accompanied by striking modifications of firing correlations within spatially localized networks at fine scales

A brain-computer interface with vibrotactile biofeedback for haptic information

<p>Abstract</p> <p>Background</p> <p>It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only <it>vibrotactile feedback</it>, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.</p> <p>Methods</p> <p>A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance.</p> <p>Results and Conclusion</p> <p>Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.</p

HURP Expression-Assisted Risk Scores Identify Prognosis Distinguishable Subgroups in Early Stage Liver Cancer

Hepatoma up-regulated protein (HURP) is a component of the chromatin-dependent pathway for spindle assembly. We examined the prognostic predictive value of HURP in human hepatocellular carcinoma (HCC).HURP expression was evaluated by immunocytochemistry of fine needle aspirated hepatoma cells in 97 HCC patients with Barcelona Clinic Liver Cancer (BCLC) stage A. Subsequently, these patients underwent partial hepatectomy (n = 18) or radiofrequency ablation (n = 79) and were followed for 2 to 35 months. The clinicopathological parameters were submitted for survival analysis.HURP expression in aspirated HCC cells was detected in 19.6% patients. Kaplan-Meier survival analysis showed that positive HURP expression (P = 0.023), cytological grading ≥3 (P = 0.008), AFP ≥35 ng/mL (P = 0.039), bilirubin ≥1.3 mg/dL (P = 0.010), AST ≥50 U/L (P = 0.003) and ALT ≥35 U/L (P = 0.005) were all associated with a shorter disease-free survival. A stepwise multivariate Cox proportional hazard model revealed that positive HURP expression (HR, 2.334; 95% CI, 1.165-4.679, P = 0.017), AST ≥50 U/L (HR, 3.697; 95% CI, 1.868-7.319, p<0.001), cytological grade ≥3 (HR, 4.249; 95% CI, 2.061-8.759, P<0.001) and tumor number >1 (HR, 2.633; 95% CI, 1.212-5.722, P = 0.014) were independent predictors for disease-free survival. By combining the 4 independent predictors, patients with different risk scores (RS) showed distinguishable disease-free survival (RS≤1 vs. RS = 2, P = 0.001; RS = 2 vs. RS = 3, P<0.001). In contrast, the patients cannot be separated into prognosis distinguishable subgroups by using AJCC/UICC TNM staging system.HCC patients with BCLC stage A can be separated into three prognosis-distinguishable groups by use of a risk score that is based upon HURP expression in aspirated HCC cells, ALT, cytological grade and tumor number

A Translational Regulator, PUM2, Promotes Both Protein Stability and Kinase Activity of Aurora-A

Aurora-A, a centrosomal serine-threonine kinase, orchestrates several key aspects of cell division. However, the regulatory pathways for the protein stability and kinase activity of Aurora-A are still not completely understood. In this study, PUM2, an RNA-binding protein, is identified as a novel substrate and interacting protein of Aurora-A. Overexpression of the PUM2 mutant which fails to interact with Aurora-A, and depletion of PUM2 result in a decrease in the amount of Aurora-A. PUM2 physically binds to the D-box of Aurora-A, which is recognized by APC/CCdh1. Overexpression of PUM2 prevents ubiquitination and enhances the protein stability of Aurora-A, suggesting that PUM2 protects Aurora-A from APC/CCdh1-mediated degradation. Moreover, association of PUM2 with Aurora-A not only makes Aurora-A more stable but also enhances the kinase activity of Aurora-A. Our study suggests that PUM2 plays two different but important roles during cell cycle progression. In interphase, PUM2 localizes in cytoplasm and plays as translational repressor through its RNA binding domain. However, in mitosis, PUM2 physically associates with Aurora-A to ensure enough active Aurora-A at centrosomes for mitotic entry. This is the first time to reveal the moonlight role of PUM2 in mitosis

Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD

Constitutive Phosphorylation of Aurora-A on Ser51 Induces Its Stabilization and Consequent Overexpression in Cancer

The serine/threonine kinase Aurora-A (Aur-A) is a proto-oncoprotein overexpressed in a wide range of human cancers. Overexpression of Aur-A is thought to be caused by gene amplification or mRNA overexpression. However, recent evidence revealed that the discrepancies between amplification of Aur-A and overexpression rates of Aur-A mRNA were observed in breast cancer, gastric cancer, hepatocellular carcinoma, and ovarian cancer. We found that aggressive head and neck cancers exhibited overexpression and stabilization of Aur-A protein without gene amplification or mRNA overexpression. Here we tested the hypothesis that aberration of the protein destruction system induces accumulation and consequently overexpression of Aur-A in cancer.Aur-A protein was ubiquitinylated by APC(Cdh1) and consequently degraded when cells exited mitosis, and phosphorylation of Aur-A on Ser51 was observed during mitosis. Phosphorylation of Aur-A on Ser51 inhibited its APC(Cdh1)-mediated ubiquitylation and consequent degradation. Interestingly, constitutive phosphorylation on Ser51 was observed in head and neck cancer cells with protein overexpression and stabilization. Indeed, phosphorylation on Ser51 was observed in head and neck cancer tissues with Aur-A protein overexpression. Moreover, an Aur-A Ser51 phospho-mimetic mutant displayed stabilization of protein during cell cycle progression and enhanced ability to cell transformation.Broadly, this study identifies a new mode of Aur-A overexpression in cancer through phosphorylation-dependent inhibition of its proteolysis in addition to gene amplification and mRNA overexpression. We suggest that the inhibition of Aur-A phosphorylation can represent a novel way to decrease Aur-A levels in cancer therapy