A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement.

Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and execution are each associated with distinct patterns of motor cortex activity. Key questions are how these dynamic activity patterns are generated and how they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" and the transition from planning to execution of directional licking. Ascending glutamatergic neurons in the midbrain reticular and pedunculopontine nuclei show short latency and phasic changes in spike rate that are selective for the Go cue. This signal is transmitted via the thalamus to the motor cortex, where it triggers a rapid reorganization of motor cortex state from planning-related activity to a motor command, which in turn drives appropriate movement. Our studies show how midbrain can control cortical dynamics via the thalamus for rapid and precise motor behavior.

Modularity and robustness of frontal cortical networks.

Neural activity underlying short-term memory is maintained by interconnected networks of brain regions. It remains unknown how brain regions interact to maintain persistent activity while exhibiting robustness to corrupt information in parts of the network. We simultaneously measured activity in large neuronal populations across mouse frontal hemispheres to probe interactions between brain regions. Activity across hemispheres was coordinated to maintain coherent short-term memory. Across mice, we uncovered individual variability in the organization of frontal cortical networks. A modular organization was required for the robustness of persistent activity to perturbations: each hemisphere retained persistent activity during perturbations of the other hemisphere, thus preventing local perturbations from spreading. A dynamic gating mechanism allowed hemispheres to coordinate coherent information while gating out corrupt information. Our results show that robust short-term memory is mediated by redundant modular representations across brain regions. Redundant modular representations naturally emerge in neural network models that learned robust dynamics.

The Serotonergic System Tracks the Outcomes of Actions to Mediate Short-Term Motor Learning.

To execute accurate movements, animals must continuously adapt their behavior to changes in their bodies and environments. Animals can learn changes in the relationship between their locomotor commands and the resulting distance moved, then adjust command strength to achieve a desired travel distance. It is largely unknown which circuits implement this form of motor learning, or how. Using whole-brain neuronal imaging and circuit manipulations in larval zebrafish, we discovered that the serotonergic dorsal raphe nucleus (DRN) mediates short-term locomotor learning. Serotonergic DRN neurons respond phasically to swim-induced visual motion, but little to motion that is not self-generated. During prolonged exposure to a given motosensory gain, persistent DRN activity emerges that stores the learned efficacy of motor commands and adapts future locomotor drive for tens of seconds. The DRN's ability to track the effectiveness of motor intent may constitute a computational building block for the broader functions of the serotonergic system. VIDEO ABSTRACT.

Diet and immune response: how today's plate shapes tomorrow's health.

Nutrition is emerging as a promising therapeutic tool to modulate the immune system in health and disease. We propose that the timing of dietary interventions is probably what determines their success. In this context, we explore recent research that identifies the early phases of dietary intervention as critical time windows for modulating immunity and optimizing cancer therapy. Furthermore, we highlight how the timing of intervention can yield different outcomes. The data suggest that nutrient availability and absorption over a short period can significantly impact mammalian immune and even non-immune landscapes. This, in turn, can lead to changes in mucosal and systemic immunity, potentially exacerbating or ameliorating inflammation, and perhaps influencing tumor cells and their response to cancer therapies.

Top-down modulation in canonical cortical circuits with short-term plasticity.

Cortical dynamics and computations are strongly influenced by diverse GABAergic interneurons, including those expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP). Together with excitatory (E) neurons, they form a canonical microcircuit and exhibit counterintuitive nonlinear phenomena. One instance of such phenomena is response reversal, whereby SST neurons show opposite responses to top-down modulation via VIP depending on the presence of bottom-up sensory input, indicating that the network may function in different regimes under different stimulation conditions. Combining analytical and computational approaches, we demonstrate that model networks with multiple interneuron subtypes and experimentally identified short-term plasticity mechanisms can implement response reversal. Surprisingly, despite not directly affecting SST and VIP activity, PV-to-E short-term depression has a decisive impact on SST response reversal. We show how response reversal relates to inhibition stabilization and the paradoxical effect in the presence of several short-term plasticity mechanisms demonstrating that response reversal coincides with a change in the indispensability of SST for network stabilization. In summary, our work suggests a role of short-term plasticity mechanisms in generating nonlinear phenomena in networks with multiple interneuron subtypes and makes several experimentally testable predictions.

Different states of synaptic vesicle priming explain target cell type-dependent differences in neurotransmitter release.

Pronounced differences in neurotransmitter release from a given presynaptic neuron, depending on the synaptic target, are among the most intriguing features of cortical networks. Hippocampal pyramidal cells (PCs) release glutamate with low probability to somatostatin expressing oriens-lacunosum-moleculare (O-LM) interneurons (INs), and the postsynaptic responses show robust short-term facilitation, whereas the release from the same presynaptic axons onto fast-spiking INs (FSINs) is ~10-fold higher and the excitatory postsynaptic currents (EPSCs) display depression. The mechanisms underlying these vastly different synaptic behaviors have not been conclusively identified. Here, we applied a combined functional, pharmacological, and modeling approach to address whether the main difference lies in the action potential-evoked fusion or else in upstream priming processes of synaptic vesicles (SVs). A sequential two-step SV priming model was fitted to the peak amplitudes of unitary EPSCs recorded in response to complex trains of presynaptic stimuli in acute hippocampal slices of adult mice. At PC-FSIN connections, the fusion probability (Pfusion) of well-primed SVs is 0.6, and 44% of docked SVs are in a fusion-competent state. At PC-O-LM synapses, Pfusion is only 40% lower (0.36), whereas the fraction of well-primed SVs is 6.5-fold smaller. Pharmacological enhancement of fusion by 4-AP and priming by PDBU was recaptured by the model with a selective increase of Pfusion and the fraction of well-primed SVs, respectively. Our results demonstrate that the low fidelity of transmission at PC-O-LM synapses can be explained by a low occupancy of the release sites by well-primed SVs.

Complexin has a dual synaptic function as checkpoint protein in vesicle priming and as a promoter of vesicle fusion.

The presynaptic SNARE-complex regulator complexin (Cplx) enhances the fusogenicity of primed synaptic vesicles (SVs). Consequently, Cplx deletion impairs action potential-evoked transmitter release. Conversely, though, Cplx loss enhances spontaneous and delayed asynchronous release at certain synapse types. Using electrophysiology and kinetic modeling, we show that such seemingly contradictory transmitter release phenotypes seen upon Cplx deletion can be explained by an additional of Cplx in the control of SV priming, where its ablation facilitates the generation of a "faulty" SV fusion apparatus. Supporting this notion, a sequential two-step priming scheme, featuring reduced vesicle fusogenicity and increased transition rates into the faulty primed state, reproduces all aberrations of transmitter release modes and short-term synaptic plasticity seen upon Cplx loss. Accordingly, we propose a dual presynaptic function for the SNARE-complex interactor Cplx, one as a "checkpoint" protein that guarantees the proper assembly of the fusion machinery during vesicle priming, and one in boosting vesicle fusogenicity.

Improved prediction of DNA and RNA binding proteins with deep learning models.

Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play important roles in essential biological processes. To facilitate functional annotation and accurate prediction of different types of NABPs, many machine learning-based computational approaches have been developed. However, the datasets used for training and testing as well as the prediction scopes in these studies have limited their applications. In this paper, we developed new strategies to overcome these limitations by generating more accurate and robust datasets and developing deep learning-based methods including both hierarchical and multi-class approaches to predict the types of NABPs for any given protein. The deep learning models employ two layers of convolutional neural network and one layer of long short-term memory. Our approaches outperform existing DBP and RBP predictors with a balanced prediction between DBPs and RBPs, and are more practically useful in identifying novel NABPs. The multi-class approach greatly improves the prediction accuracy of DBPs and RBPs, especially for the DBPs with ~12% improvement. Moreover, we explored the prediction accuracy of single-stranded DNA binding proteins and their effect on the overall prediction accuracy of NABP predictions.

Tuning synaptic strength by regulation of AMPA glutamate receptor localization.

Long-term potentiation (LTP) of excitatory synapses is a leading model to explain the concept of information storage in the brain. Multiple mechanisms contribute to LTP, but central amongst them is an increased sensitivity of the postsynaptic membrane to neurotransmitter release. This sensitivity is predominantly determined by the abundance and localization of AMPA-type glutamate receptors (AMPARs). A combination of AMPAR structural data, super-resolution imaging of excitatory synapses, and an abundance of electrophysiological studies are providing an ever-clearer picture of how AMPARs are recruited and organized at synaptic junctions. Here, we review the latest insights into this process, and discuss how both cytoplasmic and extracellular receptor elements cooperate to tune the AMPAR response at the hippocampal CA1 synapse.

Proteomic Profiling of Muscular Adaptations to Short-Term Concentric Versus Eccentric Exercise Training in Humans.

The molecular mechanisms underlying muscular adaptations to concentric (CON) and eccentric (ECC) exercise training have been extensively explored. However, most previous studies have focused on specifically selected proteins, thus, unable to provide a comprehensive protein profile and potentially missing the crucial mechanisms underlying muscular adaptation to exercise training. We herein aimed to investigate proteomic profiles of human skeletal muscle in response to short-term resistance training. Twenty young males were randomly and evenly assigned to two groups to complete a 4-week either ECC or CON training program. Measurements of body composition and physiological function of the quadriceps femoris were conducted both before and after the training. Muscle biopsies from the vastus lateralis of randomly selected participants (five in ECC and four in CON) of both before and after the training were analyzed using the liquid-chromatography tandem mass spectrometry in combination with bioinformatics analysis. Neither group presented a significant difference in body composition or leg muscle mass; however, muscle peak torque, total work, and maximal voluntary contraction were significantly increased after the training in both groups. Proteomics analysis revealed 122 differentially abundant proteins (DAPs; p value < 0.05 & fold change >1.5 or <0.67) in ECC, of which the increased DAPs were mainly related to skeletal muscle contraction and cytoskeleton and enriched specifically in the pentose phosphate pathway, extracellular matrix-receptor interaction, and PI3K-Akt signaling pathway, whereas the decreased DAPs were associated with the mitochondrial respiratory chain. One hundred one DAPs were identified in CON, of which the increased DAPs were primarily involved in translation/protein synthesis and the mitochondria respiratory, whereas the decreased DAPs were related to metabolic processes, cytoskeleton, and de-ubiquitination. In conclusion, the 4-week CON and ECC training resulted in distinctly different proteomic profiles, especially in proteins related to muscular structure and metabolism.

Mechanisms of Persistent Activity in Cortical Circuits: Possible Neural Substrates for Working Memory.

A commonly observed neural correlate of working memory is firing that persists after the triggering stimulus disappears. Substantial effort has been devoted to understanding the many potential mechanisms that may underlie memory-associated persistent activity. These rely either on the intrinsic properties of individual neurons or on the connectivity within neural circuits to maintain the persistent activity. Nevertheless, it remains unclear which mechanisms are at play in the many brain areas involved in working memory. Herein, we first summarize the palette of different mechanisms that can generate persistent activity. We then discuss recent work that asks which mechanisms underlie persistent activity in different brain areas. Finally, we discuss future studies that might tackle this question further. Our goal is to bridge between the communities of researchers who study either single-neuron biophysical, or neural circuit, mechanisms that can generate the persistent activity that underlies working memory.

Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction.

MOTIVATION: Exploring the potential long noncoding RNA (lncRNA)-disease associations (LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the high cost of biological experiments, developing a computational method is a practical necessity to effectively accelerate experimental screening process of candidate LDAs. However, under the high sparsity of LDA dataset, many computational models hardly exploit enough knowledge to learn comprehensive patterns of node representations. Moreover, although the metapath-based GNN has been recently introduced into LDA prediction, it discards intermediate nodes along the meta-path and results in information loss. RESULTS: This paper presents a new multi-view contrastive heterogeneous graph attention network (GAT) for lncRNA-disease association prediction, MCHNLDA for brevity. Specifically, MCHNLDA firstly leverages rich biological data sources of lncRNA, gene and disease to construct two-view graphs, feature structural graph of feature schema view and lncRNA-gene-disease heterogeneous graph of network topology view. Then, we design a cross-contrastive learning task to collaboratively guide graph embeddings of the two views without relying on any labels. In this way, we can pull closer the nodes of similar features and network topology, and push other nodes away. Furthermore, we propose a heterogeneous contextual GAT, where long short-term memory network is incorporated into attention mechanism to effectively capture sequential structure information along the meta-path. Extensive experimental comparisons against several state-of-the-art methods show the effectiveness of proposed framework.The code and data of proposed framework is freely available at https://github.com/zhaoxs686/MCHNLDA.

TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning.

Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies.

HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM.

The Hi-C experiments have been extensively used for the studies of genomic structures. In the last few years, spatiotemporal Hi-C has largely contributed to the investigation of genome dynamic reorganization. However, computationally modeling and forecasting spatiotemporal Hi-C data still have not been seen in the literature. We present HiC4D for dealing with the problem of forecasting spatiotemporal Hi-C data. We designed and benchmarked a novel network and named it residual ConvLSTM (ResConvLSTM), which is a combination of residual network and convolutional long short-term memory (ConvLSTM). We evaluated our new ResConvLSTM networks and compared them with the other five methods, including a naïve network (NaiveNet) that we designed as a baseline method and four outstanding video-prediction methods from the literature: ConvLSTM, spatiotemporal LSTM (ST-LSTM), self-attention LSTM (SA-LSTM) and simple video prediction (SimVP). We used eight different spatiotemporal Hi-C datasets for the blind test, including two from mouse embryogenesis, one from somatic cell nuclear transfer (SCNT) embryos, three embryogenesis datasets from different species and two non-embryogenesis datasets. Our evaluation results indicate that our ResConvLSTM networks almost always outperform the other methods on the eight blind-test datasets in terms of accurately predicting the Hi-C contact matrices at future time-steps. Our benchmarks also indicate that all of the methods that we benchmarked can successfully recover the boundaries of topologically associating domains called on the experimental Hi-C contact matrices. Taken together, our benchmarks suggest that HiC4D is an effective tool for predicting spatiotemporal Hi-C data. HiC4D is publicly available at both http://dna.cs.miami.edu/HiC4D/ and https://github.com/zwang-bioinformatics/HiC4D/.

Grain protein function prediction based on self-attention mechanism and bidirectional LSTM.

With the development of genome sequencing technology, using computing technology to predict grain protein function has become one of the important tasks of bioinformatics. The protein data of four grains, soybean, maize, indica and japonica are selected in this experimental dataset. In this paper, a novel neural network algorithm Chemical-SA-BiLSTM is proposed for grain protein function prediction. The Chemical-SA-BiLSTM algorithm fuses the chemical properties of proteins on the basis of amino acid sequences, and combines the self-attention mechanism with the bidirectional Long Short-Term Memory network. The experimental results show that the Chemical-SA-BiLSTM algorithm is superior to other classical neural network algorithms, and can more accurately predict the protein function, which proves the effectiveness of the Chemical-SA-BiLSTM algorithm in the prediction of grain protein function. The source code of our method is available at https://github.com/HwaTong/Chemical-SA-BiLSTM.

Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.

Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.

DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the models' intrinsic mechanisms and prediction results are difficult to explain. There is still room for improvement in prediction performance. We present DeepSTF, a unique deep-learning architecture for predicting TFBSs by integrating DNA sequence and shape profiles. We use the improved transformer encoder structure for the first time in the TFBSs prediction approach. DeepSTF extracts DNA higher-order sequence features using stacked convolutional neural networks (CNNs), whereas rich DNA shape profiles are extracted by combining improved transformer encoder structure and bidirectional long short-term memory (Bi-LSTM), and, finally, the derived higher-order sequence features and representative shape profiles are integrated into the channel dimension to achieve accurate TFBSs prediction. Experiments on 165 ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) datasets show that DeepSTF considerably outperforms several state-of-the-art algorithms in predicting TFBSs, and we explain the usefulness of the transformer encoder structure and the combined strategy using sequence features and shape profiles in capturing multiple dependencies and learning essential features. In addition, this paper examines the significance of DNA shape features predicting TFBSs. The source code of DeepSTF is available at https://github.com/YuBinLab-QUST/DeepSTF/.

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

A novel deep learning identifier for promoters and their strength using heterogeneous features.

Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.

Differential involvement of mitochondria in post-tetanic potentiation at intracortical excitatory synapses of the medial prefrontal cortex.

Post-tetanic Ca2+ release from mitochondria produces presynaptic residual calcium, which contributes to post-tetanic potentiation. The loss of mitochondria-dependent post-tetanic potentiation is one of the earliest signs of Alzheimer's model mice. Post-tetanic potentiation at intracortical synapses of medial prefrontal cortex has been implicated in working memory. Although mitochondrial contribution to post-tetanic potentiation differs depending on synapse types, it is unknown which synapse types express mitochondria-dependent post-tetanic potentiation in the medial prefrontal cortex. We studied expression of mitochondria-dependent post-tetanic potentiation at different intracortical synapses of the rat medial prefrontal cortex. Post-tetanic potentiation occurred only at intracortical synapses onto layer 5 corticopontine cells from commissural cells and L2/3 pyramidal neurons. Among post-tetanic potentiation-expressing synapses, L2/3-corticopontine synapses in the prelimbic cortex were unique in that post-tetanic potentiation depends on mitochondria because post-tetanic potentiation at corresponding synapse types in other cortical areas was independent of mitochondria. Supporting mitochondria-dependent post-tetanic potentiation at L2/3-to-corticopontine synapses, mitochondria-dependent residual calcium at the axon terminals of L2/3 pyramidal neurons was significantly larger than that at commissural and corticopontine cells. Moreover, post-tetanic potentiation at L2/3-corticopontine synapses, but not at commissural-corticopontine synapses, was impaired in the young adult Alzheimer's model mice. These results would provide a knowledge base for comprehending synaptic mechanisms that underlies the initial clinical signs of neurodegenerative disorders.