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He, B.

Publications and source records attributed to He, B..

At least 127 records · Page 7Linked to original sources

mTORC1 and JUN are activated after deletion of Prohibitin 1 in Schwann cells and may link mitochondrial dysfunction to demyelination

Schwann cell (SC) mitochondria are quickly emerging as an important regulator of myelin maintenance in the peripheral nervous system (PNS). However, the mechanisms underlying demyelination in the context of mitochondrial dysfunction in the PNS are incompletely understood. We recently showed that conditional ablation of the mitochondrial protein Prohibitin 1 (Phb1) in SCs causes a severe and fast progressing demyelinating peripheral neuropathy, but the mechanism that causes failure of myelin maintenance remained unknown. Here, we report that mTORC1 and JUN are continuously activated in the absence of Phb1, likely due to mitochondrial damage. Moreover, we demonstrate that these pathways are involved in the demyelination process, and that inhibition of mTORC1 using rapamycin partially rescues the demyelinating pathology. Therefore, we propose that mTORC1 and JUN may play a critical role as executioners of demyelination in the context of perturbations to SC mitochondria.

neuroscience↗

Duple extinguishment of COVID-19: single compound synergized inhibition of SARS-CoV-2 replication and direct suppression of inflammatory cytokines in vitro/vivo

We firstly disclose single compound yields better therapeutic outcome than Remdesivir in COVID-19 hamster treatments as it is armed with direct inhibition viral replication and intrinsic suppression inflammatory cytokines expression. Crystal data reveals that Au (I), released from Au22Glutathione18 (GA), covalently binds thiolate of Cys145 of SARS-CoV-2 Mpro. GA directly decreases SARS-CoV-2 viral replication (EC50: ~0.24 M) and intrinsically down-regulates NF{kappa}B pathway therefore significantly inhibiting expression of inflammatory cytokines in cells. The lung viral load and inflammatory cytokines in GA-treated COVID-19 transgenic mice are found to be significantly lower than that of control mice. When COVID-19 golden hamsters are treated by GA, the lung inflammatory cytokines levels are significantly lower than that of Remdesivir while their lung viral load are decreased to same level. The pathological results show that GA treatment significantly reduce lung inflammatory injuries when compared to that of Remdesivir-treated COVID-19 golden hamsters. One Sentence SummaryWe found that gold cluster molecule directly inhibits SARS-CoV-2 replication and intrinsically suppresses inflammatory cytokines expression in COVID-19 transgenic mouse and golden hamster model, gold cluster providing a better lung injury protection than Remdesivir in COVID-19 golden hamsters via intranasally dropping administration.

microbiology↗

Benefits of Deep Learning Classification of Continuous Noninvasive Brain Computer Interface Control

Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. While the performance of noninvasive BCI is hindered by long training times and variable user proficiency, it may be improved by deep learning methods, such as convolutional neural networks (CovNets). Prior work has suggested that the application of deep learning to EEG signals collected over the motor cortex during motor imagery based BCI increases classification accuracy in standard sensorimotor rhythm (SMR) BCI datasets. It remains to be seen whether these improvements can generalize to practical scenarios such as continuous control tasks (as opposed to prior work reporting one classification per trial), or whether valuable information remains latent outside of the motor cortex (as no prior work has compared full scalp coverage to motor only electrode montages). Here we report that deep learning methods significantly increase offline classification accuracy on an independent, large, and longitudinal online motor imagery BCI dataset with up to 4-classes and continuous 2-dimensional feedback. Improvements in classification accuracy were found to negatively correlate with initial online BCI performance, suggesting deep learning methods preferentially benefit BCI participants who need it most. The CovNets also significantly increased the information transfer rate (ITR) of the BCI system: They produced a two-fold increase in ITR without loss in classification accuracy when comparing CovNet models trained with full scalp EEG coverage to the traditional motor cortex specific decoding. Our results suggest that a variety of neural biomarkers useful for BCI, including those outside the motor cortex, can be detected through deep learning methods.

neuroscience↗

Effects of Long-Term Meditation Practices on Sensorimotor Rhythm Based BCI Learning

Sensorimotor rhythm (SMR) based brain-computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery (MI). Despite the non-invasiveness, ease of use and low cost, this kind of BCI has limitation due to long training times and BCI inefficiency-- where a subpopulation cannot generate decodable EEG signals to perform the control task. Meditation is a mental training method to improve mindfulness and awareness, and is reported to have a positive effect on ones mental state. Here we investigate the behavioral and electrophysiological differences between experienced meditators and meditation naive subjects in 1-dimensional and 2-dimensional cursor control tasks. We found that within subjects who have room for improvement, meditators outperformed control subjects in both tasks, and there were fewer BCI insufficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups, and showed that meditators had higher SMR predictor and were better able to generate decodable EEG signals to achieve SMR BCI control.

bioengineering↗

Mapping Connectional Differences between Humans and Macaques in the Nucleus Accumbens Shell-Core Architecture

Two nucleus accumbens subregions, the shell and core, differ in the patterns whereby they integrate signals from prefrontal and limbic areas of the brain. In this study, we investigated whether the disproportionate volumetric differences of these brain areas, particularly the prefrontal cortex, between humans and macaques are accompanied by unique modifications of their macroscopic integrative connections with the shell and core. More specifically, we characterized the tractographic connectivity profiles of the human and macaque shell-core architecture and compared them between the two species. To make the cross-species comparisons more viable, we used the same whole-brain voxel-wise tractography-defined shell-like and core-like divisions in the two species as seeds and delineated pairs of interspecies connectionally comparable (ICC) target regions based on the similarity of the resting-state functional connectivity profiles for the two species, and finally used these seeds and ICC targets to establish a fingerprint-based common space for cross-species comparisons. Our results revealed that dissimilar structural connectivity profiles were found in the prefrontal but not the subcortical target group. We further localized this difference to specific targets to infer possible functional modifications between the two species.

neuroscience↗

Transcranial Focused Ultrasound Neuromodulation of Voluntary Movement-related Cortical Activity in Humans

Transcranial focused ultrasound (tFUS) is an emerging non-invasive brain stimulation tool for safely and reversibly modulating brain circuits. The effectiveness of tFUS on human brain has been demonstrated, but how tFUS influences the human voluntary motor processing in the brain remains unclear. We apply low-intensity tFUS to modulate the movement-related cortical potential (MRCP) originating from human subjects practicing a voluntary foot tapping task. 64-channel electroencephalograph (EEG) is recorded concurrently and further used to reconstruct the brain source activity specifically at the primary leg motor cortical area using the electrophysiological source imaging (ESI). The ESI illustrates the ultrasound modulated MRCP source dynamics with high spatiotemporal resolutions. The MRCP source is imaged and its source profile is further evaluated for assessing the tFUS neuromodulatory effects on the voluntary MRCP. Moreover, the effect of ultrasound pulse repetition frequency (UPRF) is further assessed in modulating the MRCP. ESI results show that tFUS significantly increases the MRCP source profile amplitude (MSPA) comparing to a sham ultrasound condition, and further, a high UPRF enhances the MSPA more than a low UPRF. This work provides the first evidence of tFUS enhancing the human voluntary movement-related cortical activity through excitatory modulation.

neuroscience↗

SIFNet: Electromagnetic Source Imaging Framework Using Deep Neural Networks

Electroencephalography (EEG) and magnetoencephalography (MEG) are used to measure brain activity, noninvasively, and are useful tools for brain research and clinical management of brain disorders. Tremendous effort has been made in solving the inverse source imaging problem from EEG/MEG measurements. This is a challenging ill-posed problem, since the number of measurements is much smaller than the number of possible sources in the brain. Various methods have been developed to estimate underlying brain sources from noninvasive EEG/MEG as this can offer insight about the underlying brain electrical activity with significantly improved spatial resolution. In this work, we propose a novel data-driven Source Imaging Framework using deep learning neural networks (SIFNet), where (1) a simulation pipeline is designed to model realistic brain activation and EEG/MEG signals to train generalizable neural networks, (2) and a residual convolutional neural network is trained using the simulated data, capable of estimating source distributions from EEG/MEG recordings. The performance of our proposed SIFNet approach is evaluated in a series of computer simulations, which indicates the excellent performance of SIFNet outperforming conventional weighted minimum norm algorithms that were tested in this work. The SIFNet is further tested by analyzing interictal EEG data recorded in a clinical setting from a focal epilepsy patient. The results of this clinical data analysis indicate accurate localization of epileptogenic activity as validated by the epileptogenic zone clinically determined in this patient. In sum, the proposed SIFNet approach promises to offer an alternative solution to the EEG/MEG inverse source imaging problem, shows promising signs of being robust against measurement noise, and is easy to implement, therefore, being translatable to clinical practice.

bioengineering↗

Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band

Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in EEG signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals mindfulness practice as well as performance on a sustained attention task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

neuroscience↗

Full-length genome sequence of segmented RNA virus from ticks was obtained using small RNA sequencing data

In 2014, A novel tick-borne virus of the genus Flavivirus was first reported from the Mogiana region in Brazil. This virus was named the Mogiana tick virus (MGTV). Later, MGTV was also named as Jingmen tick virus (JMTV), Kindia tick virus (KDTV), Guangxi tick virus (GXTV) etc. In the present study, we used small RNA sequencing (sRNA-seq) to detect viruses in ticks and detected MGTV in Amblyomma testudinarium ticks, which had been captured in Yunnan province of China in the year of 2016. The full-length genome sequence of a new MGTV strain Yunnan2016 (GenBank: MT080097, MT080098, MT080099 and MT080100) was obtained and recommended to be included into the NCBI RefSeq database for the future studies of MGTV. Our phylogenetic analyses showed that viruses named MGTV, JMTV, KDTV and GXTV are monophyletic: the MGTV group (lineage) of viruses. We show, for the first time, that 5' and 3' sRNAs can be used to obtain full-length sequences of the 5 and 3 ends of, but not limited to genome sequences of RNA viruses. And we proved the feasibility of using the sRNA-seq based method for the detection of viruses in a sample containing miniscule RNA.

bioinformatics↗

Super-resolved spatial transcriptomics by deep data fusion

In situ RNA capturing has made it possible to record histology and spatial gene expression from the same tissue section. Here, we introduce a method that combines data from both modalities to infer super-resolved full-transcriptome expression maps. Our method unravels transcriptional heterogeneity in micrometer-scale anatomical features and enables image-based in silico spatial transcriptomics without hybridization or sequencing.

bioinformatics↗

Modulation of metabolic functions through Cas13d-mediated gene knockdown in liver

RNA knockdown in vivo carries significant potential for disease modelings and therapies. Despite the emerging approaches of CRISPR/Cas9-mediated permanent knock out of targeted genes, strategies targeting RNA for disruption are advantageous in the treatment of acquired metabolic disorders when permanent modification of the genome DNA is not appropriate, and RNA virus infection diseases when pathogenic DNA is not available (such as SARS-Cov-2 and MERS infections). Recently, Cas13d, a family of RNA-targeting CRISPR effectors, has been shown to accomplish robust down-regulation of cellular RNAs in mammalian cells in vitro. Among the various Cas13d subtypes, CasRx (RfxCas13d) showed the most potent RNA knockdown efficiency in HEK293T cells. However, the RNA-targeting activity of Cas13d still needs to be verified in vivo. In this study, the CasRx system was demonstrated to efficiently and functionally knock down genes related to metabolism functions, including Pten, Pcsk9 and lncLstr, in mouse hepatocytes. CasRx-mediated simultaneous knockdown of multiple genes was also achieved by sgRNA arrays, providing a useful strategy to modulate complex metabolism networks. Moreover, the AAV (adeno-associated virus)-mediated delivery of CasRx and Pcsk9 sgRNAs into mouse liver successfully decreased serum PCSK9, resulting in significant reduction of serum cholesterol levels. Importantly, CasRx-mediated knockdown of Pcsk9 is reversible and Pcsk9 could be repeatedly down-regulated, providing an effective strategy to reversibly modulate metabolic genes. The present work supplies a successful proof-of-concept trial that suggests efficient and regulatory knockdown of target metabolic genes for a designed metabolism modulation in the liver.

genomics↗

High-fidelity base editor with no detectable genome-wide off-target effects

Base editors hold promise for correcting pathogenic mutations, while substantial single nucleotide variations (SNVs) on both DNA and RNA were generated by cytosine base editors (CBEs). Here we examined possibilities to reduce off-target effects by engineering cytosine deaminases. By screening 24 CBEs harboring various rAPOBEC1 (BE3) or human APOBEC3A (BE3-hA3A) mutations on the ssDNA or RNA binding domain, we found 8 CBE variations could maintain high on-target editing efficiency. Using Genome-wide Off-target analysis by Two-cell embryo Injection (GOTI) method and RNA sequencing analysis, we found DNA off-target SNVs induced by BE3 could be completely eliminated in BE3R126E but the off-target RNA SNVs was only slightly reduced. By contrast, BE3-hA3AY130F abolished the RNA off-target effects while could not reduce the DNA off-target effects. Notably, BE3R132E, BE3W90Y+R126E and BE3W90F+R126E achieved the elimination of off-target SNVs on both DNA and RNA, suggesting the feasibility of engineering base editors for high fidelity deaminases.

genomics↗

Transcriptional regulatory network controlling the ontogeny of hematopoietic stem cells

Hematopoietic stem cell (HSC) ontogeny is accompanied by dynamic changes in gene regulatory networks. We performed RNA-Seq and histone mark ChIP-Seq to define the transcriptomes and epigenomes of cells representing key developmental stages of HSC ontogeny in the mouse. The five populations analyzed were embryonic day 10.5 (E10.5) endothelium and hemogenic endothelium from the major arteries (dorsal aorta, umbilical and vitelline), an enriched population of pre-hematopoietic stem cells (pre-HSCs), fetal liver HSCs, and adult bone marrow HSCs. We observed dynamic and combinatorial epigenetic changes that mark regulatory DNA sequences including gene promoters and enhancers. Using epigenetic signatures, we identified enhancers for each developmental stage. Only 12% of enhancers are primed, and 78% are active, suggesting the vast majority of enhancers are established de novo at the developmental stages where they are required to control their target genes, without prior priming in earlier stages. We constructed developmental-stage-specific transcriptional regulatory networks during HSC ontogeny by linking enhancers and predicted bound transcription factors to their target promoters using a novel computational algorithm. Our computational analyses predicted known transcriptional regulators for the endothelial-to-hematopoietic transition, validating our overall approach, and identified putative novel transcription factors whose regulon activities correlate with the emergence of pre-HSCs. We validated roles for the broadly expressed transcription factors SP3 and MAZ in arterial hemogenic endothelium. Our data and computational analyses provide a useful resource for uncovering regulators of HSC formation.

developmental biology↗

Developmental trajectory of pre-hematopoietic stem cell formation fromendothelium

Hematopoietic stem and progenitor cells (HSPCs) differentiate from hemogenic endothelial (HE) cells through an endothelial to hematopoietic cell transition (EHT). Newly formed HSPCs accumulate in intra-arterial clusters (IACs) before colonizing the fetal liver. To examine the cell and molecular transitions during the EHT, and the heterogeneity of HSPCs within IACs, we profiled [~]37,000 cells from the caudal arteries of embryonic day 9.5 (E9.5) to E11.5 mouse embryos by single-cell transcriptome and chromatin accessibility sequencing. We identified an intermediate developmental stage prior to HE that we termed pre-HE, characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD motifs. A developmental bottleneck separates pre-HE from HE, with RUNX1 dosage regulating the efficiency of the pre-HE to HE transition. Distinct developmental trajectories within IAC cells result in two populations of CD45+ HSPCs; an initial wave of lympho-myeloid-biased progenitors, followed by precursors of hematopoietic stem cells (pre-HSCs).

developmental biology↗

Diverse noncoding mutations contribute to deregulation of cis-regulatory landscape in pediatric cancers

Interpreting the function of noncoding mutations in cancer genomes remains a major challenge. Here we developed a computational framework to identify risk noncoding mutations of all classes by joint analysis of mutation and gene expression data. We identified thousands of SNVs/small indels and structural variants as candidate risk mutations in five major pediatric cancers. We experimentally validated the oncogenic role of CHD4 overexpression via enhancer hijacking in B-ALL. We observed a general exclusivity of coding and noncoding mutations affecting the same genes and pathways. We showed that integrated mutation signatures can help define novel patient subtypes with different clinical outcomes. Our study introduces a general strategy to systematically identify and characterize the full spectrum of noncoding mutations in cancers.

cancer biology↗

Molecular architecture of the DNA-binding sites of the P-loop ATPases MipZ and ParA from Caulobacter crescentus

Two related P-loop ATPases, ParA and MipZ, mediate the spatiotemporal regulation of chromosome segregation and cell division in Caulobacter crescentus. Both of these proteins share the ability to form dynamic concentration gradients that control the positioning of regulatory targets within the cell. Their proper localization relies on their nucleotide-dependent cycling between a monomeric and a dimeric state, driven by interaction with the chromosome partitioning protein ParB, and on the ability of the dimeric species to associate non-specifically with the nucleoid. In this study, we use a combination of genetic screening, biochemical analysis and hydrogen/deuterium exchange mass spectrometry to identify the residues mediating the interaction of MipZ with DNA. Our results show that the DNA-binding activity of MipZ relies on a series of positively charged and hydrophobic residues lining both sides of the dimer interface. MipZ thus appears to associate with DNA in a sequence-independent manner through electrostatic interactions with the DNA phosphate backbone. In support of this hypothesis, chromatin immunoprecipitation analyses did not reveal any specific target sites in vivo. When extending our analysis to ParA, we found that the architectures of the MipZ and ParA DNA-binding sites are markedly different, although their relative positions on the dimer surface and their mode of DNA binding are conserved. Importantly, bioinformatic analysis suggests that the same principles apply to other members of the P-loop ATPase family. ParA-like ATPases thus share common mechanistic features, although their modes of action have diverged considerably during the course of evolution.\n\nSIGNIFICANCEParA-like P-loop ATPases are involved in a variety of cellular processes in bacteria, including chromosome and plasmid segregation, chemoreceptor and carboxysome positioning, and division site placement. Many members of this large protein family depend on the ability to bind non-specific DNA for proper function. Although previous studies have yielded insights in the DNA-binding properties of some ParA-like ATPases, a comprehensive view of the underlying mechanisms is still lacking. Here, we combine state-of-the-art cell biological, biochemical and biophysical approaches to localize the DNA-binding regions of the ParA-like ATPases MipZ and ParA from Caulobacter crescentus. We show that the two proteins use the same interface and mode of action to associate with DNA, suggesting that the mechanistic basis of DNA binding may be conserved in the ParA-like ATPase family.

microbiology↗

Visualizing in vitro-in vivo correlation of miktoarm copolymer nanomicelles in cancer cellular uptake and trafficking

In this study, a clear correlation between the in vitro and in vivo cellular uptake and trafficking was discovered by delivering miktoarm copolymer nanomicelles (MCNs) to cancer cells and tumor tissues. To monitor this process, two different FRET pairs, DiO and DiI, DiD and DiR, were loaded into MCNs to monitor the Forster resonance energy transfer (FRET) efficiency. The change in FRET efficiency in vitro and in vivo demonstrated a similar sequence of events for the transport of MCNs: hyperbranched block PCL inserted into cytomembrane, while the loaded hydrophobic fluorescence probes were released and followed by time-dependent intracellular clustering within endocytic vesicles. Additionally, uptake of loaded fluorescence probes with successively increasing ratios of copolymers suggested that with the increase of mass ratio of copolymer to fluorescence probes, cellular uptake of probes significantly decreased. This result was also consistent with the uptake behavior in cancer tissues. Collectively, the interaction between MCNs and cellular membrane dictated the uptake and trafficking of core-loaded hydrophobic probes. This concept paves a new way to analyze in vitro-in vivo correlation of other nanocarriers for endocytosis mechanism studies as well as further novel copolymers design in biomedical applications.

pharmacology and toxicology↗

Deep Learning Interpretation of Echocardiograms

Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.85), normal left ventricular wall thickness (AUC = 0.75), left ventricular end systolic and diastolic volumes(R2 = 0.73 and R2 = 0.68), and ejection fraction (R2 = 0.48) as well as predicted systemic phenotypes of age (R2 = 0.46), sex (AUC = 0.88), weight (R2 = 0.56), and height (R2 = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlight hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, standardize interpretation in areas with insufficient qualified cardiologists, and more consistently produce echocardiographic measurements.

bioengineering↗