Biology subjects

Xu, J.

Publications and source records attributed to Xu, J..

At least 703 records · Page 39Linked to original sources

bioRxiv2020

Discovering a less-is-more effect to select transcription factor binding sites informative for motif inference

Many statistical methods have been developed to infer the binding motifs of a transcription factor (TF) from a subset of its numerous binding regions in the genome. We refer to such regions, e.g. detected by ChIP-seq, as binding sites. The sites with strong binding signals are selected for motif inference. However, binding signals do not necessarily indicate the existence of target motifs. Moreover, even strong binding signals can be spurious due to experimental artifacts. Here, we observe that such uninformative sites without target motifs tend to be "crowded" -- i.e. have many other TF binding sites present nearby. In addition, we find that even if a crowded site contains recognizable target motifs, it can still be uninformative for motif inference due to the presence of interfering motifs from other TFs. We propose using less crowded and shorter binding sites in motif interference and develop specific recommendations for carrying this out. We find our recommendations substantially improve the resulting motifs in various contexts by 30%-70%, implying a "less-is-more" effect.

bioinformatics↗

bioRxiv2020

A novel high-accuracy genome assembly method utilizing a high-throughput workflow

Across domains of biological research using genome sequence data, high-quality reference genome sequences are essential for characterizing genetic variation and understanding the genetic basis of phenotypes. However, the construction of genome assemblies for various species is often hampered by complexities of genome organization, especially repetitive and complex sequences, leading to mis-assembly and missing regions. Here, we describe a high-throughput gold standard genome assembly workflow using a large-scale bacterial artificial chromosome (BAC) library with a refined two-step pooling strategy and the Lamp assembler algorithm. This strategy minimizes the laborious processes of physical map construction and clone-by-clone sequencing, enabling inexpensive sequencing of several thousand BAC clones. By applying this strategy with a minimum tiling path BAC clone library for the short arm of chromosome 2D (2DS) of bread wheat, 98% of BAC sequences, covering 92.7% of the 2DS chromosome, were assembled correctly for this species with a highly complex and repetitive genome. We also identified 48 large mis-assemblies in the reference wheat genome assembly (IWGSC RefSeq v1.0) and corrected these large mis-assemblies in addition to filling 92.2% of the gaps in RefSeq v1.0. Our 2DS assembly represents a new benchmark for the assembly of complex genomes with both high accuracy and efficiency.

genomics↗

bioRxiv2020

Study of Real-Valued Distance Prediction For Protein Structure Prediction with Deep Learning

Inter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then employ a novel method to fold a protein by the predicted mean and deviation. Our findings include: 1) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); 2) our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; 3) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling; and 4) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models.

bioinformatics↗

bioRxiv2020

Dynamic integration and segregation of amygdala subregional functional circuits linking to physiological arousal

The dynamical organization of brain networks is essential to support human cognition and emotion for rapid adaption to ever-changing environment. As the core nodes of emotion-related brain circuitry, the basolateral amygdala (BLA) and centromedial amygdala (CMA) as two major amygdalar nuclei, are recognized to play distinct roles in affective functions and internal states, via their unique connections with cortical and subcortical structures in rodents. However, little is known how the dynamical organization of emotion-related brain circuitry reflects internal autonomic responses in humans. Using resting-state functional magnetic resonance imaging (fMRI) with K-means clustering approach in a total of 79 young healthy individuals (cohort 1: 42; cohort 2: 37), we identified two distinct states of BLA- and CMA-based intrinsic connectivity patterns, with one state (integration) showing generally stronger BLA- and CMA-based intrinsic connectivity with multiple brain networks, while the other (segregation) exhibiting weaker yet dissociable connectivity patterns. In an independent cohort 2 of fMRI data with concurrent recording of skin conductance, we replicated two similar dynamic states and further found higher skin conductance level in the integration than segregation state. Moreover, machine learning-based Elastic-net regression analyses revealed that time-varying BLA and CMA intrinsic connectivity with distinct network configurations yield higher predictive values for spontaneous fluctuations of skin conductance level in the integration than segregation state. Our findings highlight dynamic functional organization of emotion-related amygdala nuclei circuits and networks and its links to spontaneous autonomic arousal in humans.

neuroscience↗

bioRxiv2020

HTT silencing delays onset and slows progression of Huntington disease like phenotype: Monitoring with a novel neurovascular biomarker

Huntingtons disease (HD) is a dominantly inherited, fatal neurodegenerative disorder caused by a CAG expansion in the Huntingtin (HTT) gene, coding for pathologic mutant HTT protein (mHTT). Because of its gain-of-function mechanism and monogenic etiology, strategies to lower HTT are being actively investigated as disease-modifying therapies. Most approaches are currently targeted at the manifest HD stage, when clinical outcomes are used to evaluate the effectiveness of therapy. However, as almost 50% of striatal volume has been lost at the time of onset of manifest HD it would be preferable to begin therapy in the premanifest period. An unmet challenge is how to evaluate therapeutic efficacy before the presence of clinical symptoms as outcome measures. To address this, we have been developing more sensitive biomarkers such as functional neuroimaging with the goal of identifying noninvasive biomarkers that provide insight into the best time to introduce HTT-lowering treatment. In this study, we mapped the temporal trajectories of arteriolar cerebral blood volumes (CBVa) using inflow-based vascular-space-occupancy (iVASO) MRI technique in an HD mouse model. Significantly elevated CBVa was evident in premanifest zQ175 HD mice prior to motor deficits and striatal atrophy, recapitulating altered CBVa in human premanifest HD. CRISPR/Cas9-mediated non-allele-specific HTT silencing in striatal neurons restored altered CBVa in premanifest zQ175 mice, delayed onset of striatal atrophy, and slowed the progression of motor phenotype and brain pathology. This study showed the potential of CBVa as a noninvasive fMRI biomarker for premanifest HD clinical trials and demonstrates long-term benefits of introducing an HTT lowering treatment in the premanifest HD.

neuroscience↗

bioRxiv2020

Comparative efficacy of the novel diarylquinoline TBAJ-587 and bedaquiline against a resistant Rv0678 mutant in a mouse model of tuberculosis

Since its conditional approval in 2012, bedaquiline (BDQ) has been a valuable tool for treatment of drug-resistant tuberculosis. More recently, a novel short-course regimen combining BDQ with pretomanid and linezolid won approval to treat highly drug-resistant tuberculosis. Clinical reports of emerging BDQ resistance have identified mutations in Rv0678 that de-repress the expression of the MmpL5/MmpS5 efflux transporter as the most common cause. Because the effect of these mutations on bacterial susceptibility to BDQ is relatively small (e.g., 2-8x MIC shift), increasing the BDQ dose would increase antibacterial activity but also pose potential safety concerns, including QTc prolongation. Substitution of BDQ with another diarylquinoline with superior potency and/or safety has the potential to overcome these limitations. TBAJ-587 has greater in vitro potency than BDQ, including against Rv0678 mutants, and may offer a larger safety margin. Using a mouse model of tuberculosis and different doses of BDQ and TBAJ-587, we found that against wild-type M. tuberculosis H37Rv and an isogenic Rv0678 mutant, TBAJ-587 has greater efficacy against both strains than BDQ, whether alone or in combination with pretomanid and either linezolid or moxifloxacin and pyrazinamide. TBAJ-587 also reduced the emergence of resistance to diarylquinolines and pretomanid.

microbiology↗

bioRxiv2020

A scale-free, fully connected global transition network underlies known microbiome diversity

Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here we propose as solution a search-based microbiome transition network. By traversing a composition-similarity based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiome at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes.

microbiology↗

bioRxiv2020

Human Identical Sequences of SARS-CoV-2 Promote Clinical Progression of COVID-19 by Upregulating Hyaluronan via NamiRNA-Enhancer Network

The COVID-19 pandemic is a widespread and deadly public health crisis. The pathogen SARS-CoV-2 replicates in the lower respiratory tract and causes fatal pneumonia. Although tremendous efforts have been put into investigating the pathogeny of SARS-CoV-2, the underlying mechanism of how SARS-CoV-2 interacts with its host is largely unexplored. Here, by comparing the genomic sequences of SARS-CoV-2 and human, we identified five fully conserved elements in SARS-CoV-2 genome, which were termed as "human identical sequences (HIS)". HIS are also recognized in both SARS-CoV and MERS-CoV genome. Meanwhile, HIS-SARS-CoV-2 are highly conserved in the primate. Mechanically, HIS-SARS-CoV-2, behaving as virus-derived miRNAs, directly target to the human genomic loci and further interact with host enhancers to activate the expression of adjacent and distant genes, including cytokines gene and angiotensin converting enzyme II (ACE2), a well-known cell entry receptor of SARS-CoV-2, and hyaluronan synthase 2 (HAS2), which further increases hyaluronan formation. Noteworthily, hyaluronan level in plasma of COVID-19 patients is tightly correlated with severity and high risk for acute respiratory distress syndrome (ARDS) and may act as a predictor for the progression of COVID-19. HIS antagomirs, which downregulate hyaluronan level effectively, and 4-Methylumbelliferone (MU), an inhibitor of hyaluronan synthesis, are potential drugs to relieve the ARDS related ground-glass pattern in lung for COVID-19 treatment. Our results revealed that unprecedented HIS elements of SARS-CoV-2 contribute to the cytokine storm and ARDS in COVID-19 patients. Thus, blocking HIS-involved activating processes or hyaluronan synthesis directly by 4-MU may be effective strategies to alleviate COVID-19 progression.

molecular biology↗

bioRxiv2020

Transcription factors Aryl hydrocarbon receptor and TGF-inducible early gene are involved in an axis modulating immune response in mosquitoes

Immune homeostasis ensures effective pathogen defense and avoids overactivity, which is achieved through an orchestrated transcriptional network. Here we demonstrate that mosquito AhR and TIEG mediate a transcriptional axis to modulate the immune response. The AhR agonist compromised the immunity with reduced survival upon the challenge with bacterium Serratia fonticola, while the AhR antagonists enhanced the immunity with increased survival. The phenotype of pharmacological immune enhancement was corroborated genetically by the AhR gene silencing. The transcriptome comparison following AhR manipulations highlighted a set of AhR regulated genes, from which transcription factor TIEG, the ortholog of Kruppel-like factor 10, was chosen for further study. TIEG was required for the AhR mediated immune modulation. Silencing TIEG increased survival and reversed the immune suppression mediated by agonist-activated AhR. Among the transcriptomes, there were genes sharing co-expression patterns in the cohorts with AhR manipulation pharmacologically or genetically. Moreover, the mosquitoes with silenced TIEG and AhR shared ~68% altered genes upon infection. Together, the data suggest TIEG is downstream of AhR, acting as a major transcription factor mediating the immune modulation. The TIEG targets include genes involved in sugar metabolism and circadian rhythms, both processes are critical for immune homeostasis. In the naive mosquitoes, the AhR-TIEG axis prevents the adverse effect of the overactivated IMD pathway created by silencing the inhibitor Caspar. In summary, AhR and TIEG constitute a transcriptional axis that mediates a gene network critical for maintaining immune homeostasis. SignificanceImmune homeostasis is sustained by various parameters involving different transcriptional regulatory networks. Such knowledge in mosquitoes remains scarce. Here, using AhR manipulation and transcriptome interrogation, we demonstrate that AhR and TIEG (a KLF10 ortholog) constitute a transcriptional axis to mediate immune modulation using an antibacterial immune model in the malaria vector Anopheles gambiae. AhR is a ligand-activated transcription factor that senses environmental signals and transcribes relevant genes to modulate immune responses. TIEG/KLF10, conserved from invertebrates to mammals, mediates various transcriptional networks. Our data show that the AhR-TIEG axis controls the genes involving in sugar sensing and circadian rhythms in the infection context. This finding warrants further study to elucidate the transcriptional control of metabolic and circadian behind immune homeostasis.

immunology↗

bioRxiv2020

Transferrin receptor is another receptor for SARS-CoV-2 entry

Angiotensin-converting enzyme 2 (ACE2) has been suggested as a receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry to cause coronavirus disease 2019 (COVID-19). However, no ACE2 inhibitors have shown definite beneficiaries for COVID-19 patients, applying the presence of another receptor for SARS-CoV-2 entry. Here we show that ACE2 knockout dose not completely block virus entry, while TfR directly interacts with virus Spike protein to mediate virus entry and SARS-CoV-2 can infect mice with over-expressed humanized transferrin receptor (TfR) and without humanized ACE2. TfR-virus co-localization is found both on the membranes and in the cytoplasma, suggesting SARS-CoV-2 transporting by TfR, the iron-transporting receptor shuttling between cell membranes and cytoplasma. Interfering TfR-Spike interaction blocks virus entry to exert significant anti-viral effects. Anti-TfR antibody (EC50 ~16.6 nM) shows promising anti-viral effects in mouse model. Collectively, this report indicates that TfR is another receptor for SARS-CoV-2 entry and a promising anti-COVID-19 target.

microbiology↗

bioRxiv2020

PEPATAC: An optimized ATAC-seq pipeline with serial alignments

MotivationAs chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects. ResultsPEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project. AvailabilityBSD2-licensed code and documentation at https://pepatac.databio.org.

genomics↗

bioRxiv2020

Improved protein structure prediction by deep learning irrespective of co-evolution information

We describe our latest study of the deep convolutional residual neural networks (ResNet) for protein structure prediction, including deeper and wider ResNets, the efficacy of different input features, and improved 3D model building methods. Our ResNet can predict correct folds (TMscore>0.5) for 26 out of 32 CASP13 FM (template-free-modeling) targets and L/5 long-range contacts for these targets with precision over 80%, a significant improvement over the CASP13 results. Although co-evolution analysis plays an important role in the most successful structure prediction methods, we show that when co-evolution is not used, our ResNet can still predict correct folds for 18 of the 32 CASP13 FM targets including several large ones. This marks a significant improvement over the top co-evolution-based, non-deep learning methods at CASP13, and other non-coevolution-based deep learning models, such as the popular recurrent geometric network (RGN). With only primary sequence, our ResNet can also predict correct folds for all 21 human-designed proteins we tested. In contrast, RGN predicts correct folds for only 3 human-designed proteins and zero CASP13 FM target. In addition, we find that ResNet may fare better for the human-designed proteins when trained without co-evolution information than with co-evolution. These results suggest that ResNet does not simply denoise co-evolution signals, but instead is able to learn important sequence-structure relationship from experimental structures. This has important implications on protein design and engineering especially when evolutionary information is not available. Availability: http://raptorx.uchicago.edu/ and https://github.com/j3xugit/RaptorX-3DModeling/

bioinformatics↗

bioRxiv2020

Genome engineering of Nannochloropsis with large deletions for constructing microalgal minigenomes

Industrial microalgae are promising photosynthetic cell factories, yet tools for targeted genome engineering are limited. Here for the model industrial oleaginous microalga Nannochloropsis oceanica we established a method to precisely and serially delete large genome fragments of ~100 kb from its 30.01-Mb nuclear genome. We started by identifying the "non-essential" chromosomal regions (i.e., low-expression region or LER) based on minimal gene expression under N-replete and N-depleted conditions. The largest such LER (LER1) is ~98 kb in size, located near the telomere of the 502.09 kb-long Chromosome 30 (Chr 30). We deleted 81 kb and further distal and proximal deletions of up to 110 kb (21.9% of Chr 30) in LER1 by dual targeting the boundaries with the episome-based CRISPR/Cas9 system. The telomere-deletion mutants showed normal telomeres consisting of CCCTAA repeats, revealing telomere regeneration capability after losing distal part of Chr 30. Interestingly, the deletions caused no significant alteration in growth, lipid production or photosynthesis (transcript-abundance change for < 3% genes under N depletion). We also performed double-deletion of both LER1 and LER2 (from Chr 9) that totals ~214 kb, and phenotypes are essentially normal. Therefore, loss of the large yet "non-essential" regions does not necessarily sacrifice important traits. Such serial targeted deletions of large genomic regions have not been reported in plants or microalgae, and will accelerate crafting minimal genomes as chassis for photosynthetic production.

biochemistry↗

bioRxiv2020

Improved protein model quality assessment by integrating sequential and pairwise features using deep learning

MotivationAccurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection, but also useful for model refinement. Progress has been steadily made by introducing new features and algorithms (especially deep neural networks), but accuracy of quality assessment (QA) is still not very satisfactory, especially local QA on hard protein targets. ResultsWe propose a new single-model-based QA method ResNetQA for both local and global quality assessment. Our method predicts model quality by integrating sequential and pairwise features using a deep neural network composed of both 1D and 2D convolutional residual neural networks (ResNet). The 2D ResNet module extracts useful information from pairwise features such as model-derived distance maps, co-evolution information and predicted distance potential. The 1D ResNet is used to predict local (global) model quality from sequential features and pooled pairwise information generated by 2D ResNet. Tested on the CASP12 and CASP13 datasets, our experimental results show that our method greatly outperforms existing state-of-the-art methods. Our ablation studies indicate that the 2D ResNet module and pairwise features play an important role in improving model quality assessment. Availability and Implementationhttps://github.com/AndersJing/ResNetQA Contactjinboxu@gmail.com

bioinformatics↗

bioRxiv2020

PD-L1 lncRNA splice promotes lung adenocarcinoma progression via enhancing c-Myc activity

Although blockade of programmed death-ligand 1 (PD-L1) to enhance T cell immune responses shows great promise in tumor immunotherapy, the efficacy of such immune-checkpoint inhibition strategy is limited for patients with solid tumors. The mechanism underlying the limited efficacy of PD-L1 inhibitors remains unclear. Here, we show that human lung adenocarcinoma, regardless of PD-L1 protein positive or negative, all produce a long non-coding RNA isoform of PD-L1 (PD-L1-lnc) via alternative splicing, which promotes lung adenocarcinoma proliferation and metastasis. PD-L1-lnc in various lung adenocarcinoma cells is significantly upregulated by IFN{gamma} in a manner similar to PD-L1 mRNA. Both in vitro and in vivo studies demonstrate that PD-L1-lnc increases proliferation and invasion but decreases apoptosis of lung adenocarcinoma cells. Mechanistically, PD-L1-lnc directly binds to c-Myc and enhances c-Myc transcriptional activity downstream in lung adenocarcinoma cells. Our results provide targeting PD-L1-lnc-c-Myc axis as a novel strategy for lung cancer therapy.

cancer biology↗

bioRxiv2020

Longitudinal multi-omics along gingivitis development reveal a suboptimal-health gum state with periodontitis-like microbiome

Most adults experience episodes of gingivitis, which can progress to the irreversible, chronic state of periodontitis. However the mechanistic roles of plaque in gingivitis onset and progression to periodontitis remain elusive. Here, we integrated the longitudinal multi-omics data from plaque metagenome, metabolome and salivary cytokines in 40 adults who transit from naturally-occurring gingivitis (NG), to healthy gingivae (baseline) and then to experimental gingivitis (EG). During EG, rapid and consistent alterations in plaque microbiota, metabolites and salivary cytokines emerged as early as 24-72 hours after pause of oral hygiene, defining an asymptomatic sub-optimal health (SoH) stage. SoH also features a steep and synergetic decrease of plaque-derived betaine and Rothia spp., suggesting an anti-gum-inflammation mechanism by health-promoting microbial residents. Global, cross-cohort meta-analysis revealed a high Microbiome-based Periodontitis Index at SoH state, due to its convergent taxonomical and functional profiles towards those of periodontitis. In contrast, caries SoH features a microbial signature very distinct from caries. Thus SoH is a universal state of polymicrobial inflammations with disease-specific features, which is key to maintaining a disease-preventive plaque.

microbiology↗

bioRxiv2020

Multimodal single-cell/nucleus RNA-sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease

Systematic identification of molecular networks in disease relevant immune cells of the nervous system is critical for elucidating the underlying pathophysiology of Alzheimers disease (AD). Two key immune cell types, disease-associated microglia (DAM) and disease-associated astrocytes (DAA), are biologically involved in AD pathobiology. Therefore, uncovering molecular determinants of DAM and DAA will enhance our understanding of AD biology, potentially identifying novel therapeutic targets for AD treatment. Here, we present an integrative, network-based methodology to uncover conserved molecular networks between DAM and DAA. Specifically, we leverage single-cell and single-nucleus RNA sequencing data from both AD transgenic mouse models and AD patient brains, drug-target networks, metabolite-enzyme associations, and the human protein-protein interactome, along with large-scale patient data validation from the MarketScan Medicare Supplemental Database. We find that common and unique molecular network regulators between DAM (i.e, PAK1, MAPK14, and SYK) and DAA (i.e., NFKB1, FOS, and JUN) are significantly enriched by multiple neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1) from genome-wide association studies. Further network analysis reveal shared immune pathways between DAM and DAA, including Fc gamma R-mediated phagocytosis, Th17 cell differentiation, and chemokine signaling. Furthermore, integrative metabolite-enzyme network analyses imply that fatty acids (i.e., elaidic acid) and amino acids (i.e., glutamate, serine, and phenylalanine) may trigger molecular alterations between DAM and DAA. Finally, we prioritize repurposed drug candidates for potential treatment of AD by agents that specifically reverse dysregulated gene expression of DAM or DAA, including an antithrombotic anticoagulant triflusal, a beta2-adrenergic receptor agonist salbutamol, and the steroid medications (fluticasone and mometasone). Individuals taking fluticasone (an approved anti-inflammatory and inhaled corticosteroid) displayed a significantly decreased incidence of AD (hazard ratio (HR) = 0.858, 95% confidence interval [CI] 0.829-0.888, P < 0.0001) in retrospective case-control validation. Furthermore, propensity score matching cohort studies also confirmed an association of mometasone with reduced incidence of AD in comparison to fluticasone (HR =0.921, 95% CI 0.862-0.984, P < 0.0001).

neuroscience↗

bioRxiv2020

A Bioelectric Neural Interface Towards Intuitive Prosthetic Control For Amputees

ObjectiveWhile prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful. ApproachHere we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subjects motor intention. Main resultsA pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patients true intention. SignificanceOur study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways. Clinical trialDExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

bioengineering↗