Targeting alternative splicing by RNAi: from the differential impact on splice variants to triggering artificial pre-mRNA splicing.

Alternative splicing generates multiple transcript and protein isoforms from a single gene and controls transcript intracellular localization and stability by coupling to mRNA export and nonsense-mediated mRNA decay (NMD). RNA interference (RNAi) is a potent mechanism to modulate gene expression. However, its interactions with alternative splicing are poorly understood. We used artificial microRNAs (amiRNAs, also termed shRNAmiR) to knockdown all splice variants of selected target genes in Arabidopsis thaliana. We found that splice variants, which vary by their protein-coding capacity, subcellular localization and sensitivity to NMD, are affected differentially by an amiRNA, although all of them contain the target site. Particular transcript isoforms escape amiRNA-mediated degradation due to their nuclear localization. The nuclear and NMD-sensitive isoforms mask RNAi action in alternatively spliced genes. Interestingly, Arabidopsis SPL genes, which undergo alternative splicing and are targets of miR156, are regulated in the same manner. Moreover, similar results were obtained in mammalian cells using siRNAs, indicating cross-kingdom conservation of these interactions among RNAi and splicing isoforms. Furthermore, we report that amiRNA can trigger artificial alternative splicing, thus expanding the RNAi functional repertoire. Our findings unveil novel interactions between different post-transcriptional processes in defining transcript fates and regulating gene expression.

Coordination Dynamics: A Foundation for Understanding Social Behavior.

Humans' interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people…) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB's evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights.

The cyclin-dependent kinase G group defines a thermo-sensitive alternative splicing circuit modulating the expression of Arabidopsis ATU2AF65A.

The ability to adapt growth and development to temperature variations is crucial to generate plant varieties resilient to predicted temperature changes. However, the mechanisms underlying plant response to progressive increases in temperature have just started to be elucidated. Here, we report that the cyclin-dependent kinase G1 (CDKG1) is a central element in a thermo-sensitive mRNA splicing cascade that transduces changes in ambient temperature into differential expression of the fundamental spliceosome component, ATU2AF65A. CDKG1 is alternatively spliced in a temperature-dependent manner. We found that this process is partly dependent on both the cyclin-dependent kinase G2 (CDKG2) and the interacting co-factor CYCLIN L1 (CYCL1), resulting in two distinct messenger RNAs. The relative abundance of both CDKG1 transcripts correlates with ambient temperature and possibly with different expression levels of the associated protein isoforms. Both CDKG1 alternative transcripts are necessary to fully complement the expression of ATU2AF65A across the temperature range. Our data support a previously unidentified temperature-dependent mechanism based on the alternative splicing (AS) of CDKG1 and regulated by CDKG2 and CYCL1. We propose that changes in ambient temperature affect the relative abundance of CDKG1 transcripts, and this in turn translates into differential CDKG1 protein expression coordinating the AS of ATU2AF65A.

How Interpersonal Coordination Affects Individual Behavior (and Vice Versa): Experimental analysis and adaptive HKB model of social memory.

How one behaves after interacting with a friend may not be the same as before the interaction. The present study investigated which spontaneous coordination patterns formed between two persons and whether a remnant of the interaction remained ("social memory"). Pairs of people sat face-to-face and continuously flexed index fingers while vision between partners was manipulated to allow or prevent information exchange. Trials consisted of three successive twenty-second intervals: without vision, with vision, and again without vision. Steady, transient, or absent phase coupling was observed during vision. In support of social memory, participants tended to remain near each other's movement frequency after the interaction ended. Furthermore, the greater the stability of interpersonal coordination, the more similar partners' post-interactional frequencies became. Proposing that social memory resulted from prior frequency adaptation, a model based on Haken-Kelso-Bunz oscillators reproduced the experimental findings, even for patterns observed on individual trials. Parametric manipulations revealed multiple routes to social memory through the interplay of adaptation and other model parameters. The experimental results, model, and interpretation motivate potential future research and therapeutic applications.

The coordination dynamics of mobile conjugate reinforcement.

What we know about infant learning and memory is founded largely on systematic studies by the late Carolyn Rovee-Collier (1942-2014) and her associates of a phenomenon called mobile conjugate reinforcement. Experiments show that when a ribbon is attached from a 3-month-old infant's foot to a mobile suspended overhead the baby quickly realizes it can make the mobile move. The mobile, which offers interesting sights and sounds, responds conjugately to the baby's vigorous kicks which increase in rate by a factor of 3-4. In this paper, using the concepts, methods and tools of coordination dynamics, we present a theoretical model which reproduces the experimental observations of Rovee-Collier and others and predicts a number of additional features that can be experimentally tested. The model is a dynamical system consisting of three equations, one for the baby's leg movements, one for the jiggling motion of the mobile and one for the functional coupling between the two. A key mechanism in the model is positive feedback which is shown to depend sensitively on bifurcation parameters related to the infant's level of attention and inertial properties of the mobile. The implications of our model for the dynamical (and developmental) origins of agency are discussed.

Temporal changes of diffusion patterns in mild traumatic brain injury via group-based semi-blind source separation.

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies. In this paper, we introduce an approach based on spatial group independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process, thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.

A chloroplast retrograde signal regulates nuclear alternative splicing.

Light is a source of energy and also a regulator of plant physiological adaptations. We show here that light/dark conditions affect alternative splicing of a subset of Arabidopsis genes preferentially encoding proteins involved in RNA processing. The effect requires functional chloroplasts and is also observed in roots when the communication with the photosynthetic tissues is not interrupted, suggesting that a signaling molecule travels through the plant. Using photosynthetic electron transfer inhibitors with different mechanisms of action, we deduce that the reduced pool of plastoquinones initiates a chloroplast retrograde signaling that regulates nuclear alternative splicing and is necessary for proper plant responses to varying light conditions.

Detection of correlated sources in EEG using combination of beamforming and surface Laplacian methods.

Beamforming offers a way to estimate the solution to the inverse problem in EEG and MEG but is also known to perform poorly in the presence of highly correlated sources, e.g. during binaural auditory stimulation, when both left and right primary auditory cortices are activated simultaneously. Surface Laplacian, or the second spatial derivative calculated from the electric potential, allows for deblurring of EEG potential recordings reducing the effects of low skull conductivity and is independent of the reference electrode location. We show that anatomically constrained beamforming in conjunction with the surface Laplacian allows for detection of both locations and dynamics of temporally correlated sources in EEG. Whole-head 122 channel binaural stimulus EEG data were simulated using a boundary element method (BEM) and realistic geometry forward model. We demonstrate that in contrast to conventional potential-based EEG beamforming, Laplacian beamforming allows to determine locations of correlated source dipoles without any a priori assumption about the number of sources. We also show (by providing simulations of auditory evoked potentials) that the dynamics at the detected source locations can be derived from subsets of electrodes. Deblurring auditory evoked potential maps subdivides EEG signals from each hemisphere and allows for the beamformer to be applied separately for left and right hemispheres.

Beyond the blank slate: routes to learning new coordination patterns depend on the intrinsic dynamics of the learner-experimental evidence and theoretical model.

Using an approach that combines experimental studies of bimanual movements to visual stimuli and theoretical modeling, the present paper develops a dynamical account of sensorimotor learning, that is, how new skills are acquired and old ones modified. A significant aspect of our approach is the focus on the individual learner as the basic unit of analysis, in particular the quantification of predispositions and capabilities that the individual learner brings to the learning environment. Such predispositions constitute the learner's behavioral repertoire, captured here theoretically as a dynamical landscape ("intrinsic dynamics"). The learning process is demonstrated to not only lead to a relatively permanent improvement of performance in the required task-the usual outcome-but also to alter the individual's entire repertoire. Changes in the dynamical landscape due to learning are shown to result from two basic mechanisms or "routes": bifurcation and shift. Which mechanism is selected depends the initial individual repertoire before new learning begins. Both bifurcation and shift mechanisms are accommodated by a dynamical model, a relatively straightforward development of the well-established HKB model of movement coordination. Model simulations show that although environmental or task demands may be met equally well using either mechanism, the bifurcation route results in greater stabilization of the to-be-learned behavior. Thus, stability not (or not only) error is demonstrated to be the basis of selection, both of a new pattern of behavior and the path (smooth shift versus abrupt qualitative change) that learning takes. In line with these results, recent neurophysiological evidence indicates that stability is a relevant feature around which brain activity is organized while an individual performs a coordination task. Finally, we explore the consequences of the dynamical approach to learning for theories of biological change.

Enhancement of fiber orientation distribution reconstruction in diffusion-weighted imaging by single channel blind source separation.

In diffusion-weighted imaging (DWI), reliable fiber tracking results rely on the accurate reconstruction of the fiber orientation distribution function (fODF) in each individual voxel. For high angular resolution diffusion imaging (HARDI), deconvolution-based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fiber populations. However, HARDI-based methods usually require relatively high b-values and a large number of gradient directions to produce good results. Such requirements are not always easy to meet in common clinical studies due to limitations in MRI facilities. Moreover, most of these approaches are sensitive to noise. In this study, we propose a new framework to enhance the performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and two phantom datasets demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results show that the proposed method has the potential to be applied to low angular resolution DWI which is commonly used in clinical studies.

Anatomically constrained minimum variance beamforming applied to EEG.

Neural activity as measured non-invasively using electroencephalography (EEG) or magnetoencephalography (MEG) originates in the cortical gray matter. In the cortex, pyramidal cells are organized in columns and activated coherently, leading to current flow perpendicular to the cortical surface. In recent years, beamforming algorithms have been developed, which use this property as an anatomical constraint for the locations and directions of potential sources in MEG data analysis. Here, we extend this work to EEG recordings, which require a more sophisticated forward model due to the blurring of the electric current at tissue boundaries where the conductivity changes. Using CT scans, we create a realistic three-layer head model consisting of tessellated surfaces that represent the cerebrospinal fluid-skull, skull-scalp, and scalp-air boundaries. The cortical gray matter surface, the anatomical constraint for the source dipoles, is extracted from MRI scans. EEG beamforming is implemented on simulated sets of EEG data for three different head models: single spherical, multi-shell spherical, and multi-shell realistic. Using the same conditions for simulated EEG and MEG data, it is shown (and quantified by receiver operating characteristic analysis) that EEG beamforming detects radially oriented sources, to which MEG lacks sensitivity. By merging several techniques, such as linearly constrained minimum variance beamforming, realistic geometry forward solutions, and cortical constraints, we demonstrate it is possible to localize and estimate the dynamics of dipolar and spatially extended (distributed) sources of neural activity.

Neuromagnetic motor fields accompanying self-paced rhythmic finger movement at different rates.

We have studied the effect of movement rate on MEG activity associated with self-paced finger movement in four subjects to determine whether the amplitude or latency of motor-evoked activity changes across a range of rates. Subjects performed a continuation paradigm at 21 distinct rates (range: 0.5-2.5 Hz) chosen because of their relevance for many types of sensorimotor coordination (e.g. musical performance). Results revealed a pair of field patterns whose topography and temporal dynamics were similar across all subjects. The strongest pattern was a movement-evoked field (MEF) that emerged during the response and exhibited one or two polarity reversals in time depending on the subject. The MEF complex was tightly coupled to the biphasic response profile but neither latency nor peak amplitude of each MEF component had significant dependence on the temporal duration between successive responses, i.e. movement rate. In contrast, the maximal amplitude of a second, weaker pattern decreased by over 50% when movement rates exceeded 1.1 Hz (inter-response interval <1 s). This pattern was characterized by a change in field line direction over the midline of the scalp and a gradual accumulation of amplitude prior to movement onset. Both characteristics are suggestive of a readiness field. The observed rate-dependent changes in this field may contribute to known transitions in sensorimotor coordination that emerge when the frequency of coordination is increased.

Cortical and cerebellar activity of the human brain during imagined and executed unimanual and bimanual action sequences: a functional MRI study.

The neural (blood oxygenation level dependent) correlates of executed and imagined finger sequences, both unimanual and bimanual, were studied in adult right-handed volunteers using functional magnetic resonance imaging (fMRI) of the entire brain. The finger to thumb opposition tasks each consisted of three conditions, two unimanual and one bimanual. Each experimental condition consisted of overt movement of the fingers in a prescribed sequence and imagery of the same task. An intricate network consisting of sensorimotor cortex, supplementary motor area (SMA), superior parietal lobule and cerebellum was identified when the tasks involved both planning and execution. During imagery alone, however, cerebellar activity was largely absent. This apparent decoupling of sensorimotor cortical and cerebellar areas during imagined movement sequences, suggests that cortico-cerebellar loops are engaged only when action sequences are both intended and realized. In line with recent models of motor control, the cerebellum may monitor cortical output and feed back corrective information to the motor cortex primarily during actual, not imagined, movements. Although parietal cortex activation occurred during both execution and imagery tasks, it was most consistently present during bimanual action sequences. The engagement of the superior parietal lobule appears to be related to the increased attention and memory resources associated, in the present instance, with coordinating difficult bimanual sequences.

Spatiotemporal analysis of the neuromagnetic response to rhythmic auditory stimulation: rate dependence and transient to steady-state transition.

OBJECTIVE: Whole head magnetoencephalography was used to investigate the spatiotemporal dynamics of neuromagnetic brain activity associated with rhythmic auditory stimulation. METHODS: In order to characterize the evolution of the auditory responses we applied a Karhunen-Loève decomposition and k-means cluster analysis to globally compare spatial patterns of brain activity at different latencies and stimulation rates. Tones were presented binaurally at 27 different stimulation rates within a perceptually and behaviorally relevant range from 0.6 to 8.1 Hz. RESULTS: Over this range, we observed a linear increase of the amplitude of the main auditory response at 100 ms latency (N1m) with increasing inter-stimulus interval, and qualitative changes of the overall spatiotemporal dynamics of the auditory response. In particular, a transition occurred between a transient evoked response at low frequencies, and a continuous steady-state response at high frequencies. CONCLUSIONS: We show the onset of temporal overlap between responses to successive tones that leads to this transition. Response overlap begins to occur near 2 Hz, marking the onset of a continuous perceptual representation.

Cortical and subcortical networks underlying syncopated and synchronized coordination revealed using fMRI. Functional magnetic resonance imaging.

Inherent differences in difficulty between on the beat (synchronization) and off the beat (syncopation) coordination modes are well known. Synchronization is typically quite easy and, once begun, may be carried out with little apparent attention demand. Syncopation tends to be difficult, even though it has been described as a simple, phase-shifted version of a synchronized pattern. We hypothesize that syncopation, unlike synchronization, is organized on a cycle-by-cycle basis, thereby imposing much greater preparatory and attentional demands on the central nervous system. To test this hypothesis we used fMRI to measure the BOLD response during syncopation and synchronization to an auditory stimulus. We found that the distribution of cortical and subcortical areas involved in intentionally coordinating movement with an external metronome depends on the timing pattern employed. Both synchronized and syncopated patterns require activation of contralateral sensorimotor and caudal supplementary motor cortices as well as the (primarily ipsilateral) cerebellum. Moving off the beat, however, requires not only additional activation of the cerebellum but also the recruitment of another network comprised of the basal ganglia, dorsolateral premotor, rostral supplementary motor, prefrontal, and temporal association cortices. No areas were found to be more active during synchronization than syncopation. The functional role of the cortical and subcortical regions areas involved in syncopation supports the hypothesis that whereas synchronization requires little preparation and monitoring, syncopated movements are planned and executed individually on each perception-action cycle.

Spatiotemporal forward solution of the EEG and MEG using network modeling.

Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dynamics of the brain signals is highly ordered and also accessible by dynamic systems theory. However, models of EEG and MEG dynamics have typically been formulated only in terms of phenomenological modeling such as fixed-current dipoles or spatial EEG and MEG patterns. In this paper, it is our goal to connect three levels of organization, that is the level of coordination behavior, the level of patterns observed in the EEG and MEG and the level of neuronal network dynamics. To do so, we develop a methodological framework, which defines the spatiotemporal dynamics of neural ensembles, the neural field, on a sphere in three dimensions. Using magnetic resonance imaging we map the neural field dynamics from the sphere onto the folded cortical surface of a hemisphere. The neural field represents the current flow perpendicular to the cortex and, thus, allows for the calculation of the electric potentials on the surface of the skull and the magnetic fields outside the skull to be measured by EEG and MEG, respectively. For demonstration of the dynamics, we present the propagation of activation at a single cortical site resulting from a transient input. Finally, a mapping between finger movement profile and EEG/MEG patterns is obtained using Volterra integrals.

Self-organizing dynamics of the human brain: Critical instabilities and Sil'nikov chaos.

Using a sensorimotor coordination task in conjunction with an array of SQUIDs (Superconducting QUantum Interference Devices) we demonstrate critical instabilities in human brain activity patterns. Analysis of the dominant spatial pattern of the brain and its time-varying amplitude displays a task-dependent geometry characteristic of Sil'nikov-like chaos, which changes qualitatively at the transition. (c) 1995 American Institute of Physics.