Biology subjects

Ma, R.

Publications and source records attributed to Ma, R..

At least 73 records · Page 4Linked to original sources

bioRxiv2023

Beyond conflict: kinship theory of intragenomic conflict predicts individual variation in altruistic behavior

Studies of the genetic basis of behavioral variation have emphasized gene cooperation within networks, often overlooking gene conflicts. The Kinship Theory of Intragenomic Conflict (KTIC) proposes that conflicts can occur within genes when parent-specific alleles have different strategies for maximizing reproductive fitness. Here, we test a prediction of the KTIC - that selection should favor alleles which promote "altruistic" behaviors that support the reproductive fitness of kin. In honey bee (Apis mellifera) colonies, workers act altruistically when tending to the queen by performing a "retinue" behavior, distributing the queens mandibular pheromone (QMP) throughout the hive. Workers exposed to QMP do not activate their ovaries, ensuring they care for the queens brood instead of competing to lay unfertilized eggs. Thus, the KTIC predicts that response to QMP should be favored by the maternal genome. Using a reciprocal cross design, we tested for parent-of-origin effects on the workers 1) responsiveness to QMP, 2) ovary activation, and 3) brain transcriptome. We hypothesized that QMP-responsive workers have smaller and less active ovaries, influenced by the workers parent-of-origin. With an allele-specific transcriptomic analysis, we tested whether QMP-responsive workers show enriched maternal allele-biased gene expression compared to QMP-unresponsive workers. Finally, we explored how parent-of-origin gene expression patterns are associated with overall gene expression patterns and regulatory networks. We report evidence in support of the KTIC for the retinue behavior and associated conflicts within gene networks. Our study provides new insights into the genetic basis of behavior and the potential for behavioral variation influenced by intragenomic conflict.

animal behavior and cognition↗

bioRxiv2023

TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses

Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present TISSUE (Transcript Imputation with Spatial Single-cell Uncertainty Estimation) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Across eleven benchmark datasets, TISSUE provides well-calibrated prediction intervals for predicted expression values. Moreover it consistently reduces false discovery rates for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics, and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers. TISSUE is publicly available as a flexible wrapper method for existing spatial gene expression prediction methods to assist researchers with implementing uncertainty-aware analyses of spatial transcriptomics data.

bioinformatics↗

bioRxiv2023

Reinstating targeted protein degradation with DCAF1 PROTACs in CRBN PROTAC resistant settings

Targeted protein degradation (TPD) of neo-substrates with proteolysis targeting chimeras (PROTACs) or molecular glues has emerged as a key modality in exploring new biology as well as designing new drug candidates where catalytic inhibition is neither efficacious nor an option. TPD is mediated through harnessing E3 ligases and redirecting them to ubiquitinate de novo target proteins for subsequent proteasomal degradation. Until recently, E3 ligase chemical matter available for mediating TPD has been limited to a relatively low number of ligases, considering that over 600 E3 ligases are encoded by the human genome. In addition, the most utilized ligase for TPD approaches, CRBN, has been observed to be downregulated in settings of acquired resistance to immunomodulatory inhibitory drugs (IMiDs). IMiDs are molecular glues that target IKZF transcription factors to CRBN for degradation. Resistance is potentially accelerated by non-essentiality of CRBN for cell viability. Here we investigated if the essential E3 ligase receptor DCAF1 can be harnessed for TPD utilizing a potent, non-covalent DCAF1 binder. We show that this binder, selective for the CRL4DCAF1 E3 ligase complex, can be functionalized into an efficient DCAF1-BRD9 PROTAC. Chemical and genetic rescue experiments confirm specific degradation via the CRL4DCAF1 E3 ligase. We further highlight the versatility of DCAF1 for TPD by developing a DCAF1-dasatininb PROTAC targeting multiple cytosolic and membrane bound tyrosine kinases. We expand these findings towards Brutons tyrosine kinase (BTK) selective PROTACs and through extensive optimization and characterization efforts share key observations that led to a potent and selective DCAF1-BTK PROTAC (DBt-10). Finally, with this PROTAC DBt-10, we show rescue of BTK degradation in a BTK-dependent, CRBN-degradation-resistant cell line and provide a rationale for E3 ligase swap to overcome CRBN mediated resistance.

biochemistry↗

bioRxiv2023

Single-cell RNA-sequencing identifies a novel subset of CD9+ reparative macrophages modulated by CCN2 derived from Nlrx1 deficient cardiomyocytes in the infarcted heart

RationaleNlrx1, a pattern recognition receptor belonging to the NLR family, is highly expressed in the heart and is significantly increased after myocardial infarction in vivo, however, the role of Nlrx1 in post-infarction inflammation and healing is poorly understood. Although the effects of infiltrating macrophages on survival of cardiomyocytes and healing of injured hearts have been intensely studied, whether or not and how surviving cardiomyocytes might influence the phenotype and function of macrophages in the infarcted hearts was largely unexplored. ObjectiveTo investigate the role of Nlrx1 in post-infarction inflammation and healing and to dissect the underlying mechanism. Methods and resultsSingle cell RNA-sequencing of 23,805 cells isolated from infarcted hearts revealed 7 clusters of infarct macrophages including CCR2+, Isg+, Mki67+ proliferative, and LYVE1+ cardiac resident macrophages, and identified a novel subset of CD9+Il1rn+ macrophages, which were characterized by high expression of Cd9, Il1rn, Gpnmb and Spp1, and downregulation of proinflammatory cytokines and chemokines. CD9+Il1rn+ macrophages were largely found in infarcted hearts of Nlrx1KO mice, and were transcriptionally unique with reparative phenotype. Loss of Nlrx1 attenuated ventricular dysfunction and adverse remodeling, and decreased post-infarction mortality. Flow cytometry analysis showed markedly increased number of CD9+ macrophages and higher mean fluorescence intensity of CD9 in Nlrx1KO mice. RNA-seq analysis revealed that CD9+ macrophages of Nlrx1KO infarcted hearts exhibited a reparative and anti-inflammatory phenotype as featured by upregulation of wound healing-associated genes including CD9, Spp1 and Fn1, and downregulation of proinflammatory genes. Conditioned medium from Nlrx1 deficient cardiomyocytes upon hypoxia stimulation skewed macrophage polarization towards a reparative and inflammation-suppressed phenotype. Gain-and loss-of function of Nlrx1 combined with RNA-seq analysis identified CCN2, whose expression and secretion were significantly increased in Nlrx1 deficient cardiomyocytes upon hypoxia, as a cardiomyocytes-derived mediator responsible, at least partly, for reprograming macrophage towards a reparative phenotype. In vivo, CCN2 expression was markedly elevated in Nlrx1KO infarcted hearts. Moreover, exogenous CCN2 partially mediated macrophage transition towards a reparative phenotype. ConclusionsCD9+ IL1rn+ macrophages modulated by Nlrx1 deficient cardiomyocytes-derived CCN2, is a novel subset of reparative macrophages contributing to infarct healing. Inhibition of myocardial Nlrx1 and/or activation of CCN2 to boost CD9+ IL1rn+ reparative macrophages may represent new therapeutics for infarct healing following ischemic injury.

immunology↗

bioRxiv2023

Centrifuge+: improving metagenomic analysis upon Centrifuge

SummaryAccurate abundance estimation of species is essential for metagenomic analysis. Although many methods have been developed for classification of metagenomic data and abundance estimation of species, the abundance estimation of species remains challenging due to the ambiguous reads that align equally well to more than one genome. Here, we present Centrifuge+, which introduces unique mapping rate to describe the influence of similarities among species in the reference database when analyzing ambiguous reads. In contrast to the popular Centrifuge, Centrifuge+ improved the accuracy of abundance estimation on simulated reads from 4278 complete prokaryotic genomes. Availability and implementationThe source code is available at https://github.com/mNGSmethods/Centrifugep. Contacth.guo@foxmail.com or jlsljf0101@126.com Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

Molecular Mechanocytometry Using Tension-activated Cell Tagging (TaCT)

Flow cytometry is routinely used to measure single-cell gene expression by staining cells with fluorescent antibodies and nucleic acids. Here we present Tension-activated Cell Tagging (TaCT) to fluorescently label cells based on the magnitude of molecular force transmitted through cell adhesion receptors. As a proof-of-concept, we analyzed fibroblasts and mouse platelets after TaCT using conventional flow cytometry.

bioengineering↗

bioRxiv2022

A Spectral Method for Assessing and Combining Multiple Data Visualizations

Dimension reduction and data visualization aim to project a high-dimensional dataset to a low-dimensional space while capturing the intrinsic structures in the data. It is an indispensable part of modern data science, and many dimensional reduction and visualization algorithms have been developed. However, different algorithms have their own strengths and weaknesses, making it critically important to evaluate their relative performance for a given dataset, and to leverage and combine their individual strengths. In this paper, we propose an efficient spectral method for assessing and combining multiple visualizations of a given dataset produced by diverse algorithms. The proposed method provides a quantitative measure - the visualization eigenscore - of the relative performance of the visualizations for preserving the structure around each data point. Then it leverages the eigenscores to obtain a consensus visualization, which has much improved quality over the individual visualizations in capturing the underlying true data structure. Our approach is flexible and works as a wrapper around any visualizations. We analyze multiple simulated and real-world datasets from diverse applications to demonstrate the effectiveness of the eigenscores for evaluating visualizations and the superiority of the proposed consensus visualization. Furthermore, we establish rigorous theoretical justification of our method based on a general statistical framework, yielding fundamental principles behind the empirical success of consensus visualization along with practical guidance.

bioinformatics↗

bioRxiv2022

Development of an affinity-enhanced clinical candidate TCR targeting NY-ESO-1 with optimal potency and high specificity

The clinical success of T-cell receptor (TCR)-based immunotherapy depends on the efficacy and specificity of TCRs. Naturally occurring TCRs have limited anti-tumor potency due to their low affinity for tumor antigens. Affinity enhancement is a promising strategy to generate highly potent TCRs. However, it is concerned that affinity-enhanced TCRs are prone to lose specificity. We isolated low affinity TCRs specific for NY-ESO-1157-165/HLA-A*02:01 from peripheral blood mononuclear cells of healthy donors. An affinity-enhanced TCR candidate with optimal affinity and specificity was generated using phage display and an extensive set of in vitro and in vivo assays. Alanine scanning mutagenesis showed that the TCR candidate retained specificity by making extensive contacts to the side chains of NY-ESO-1157-165 peptide. Adoptive transfer of T cells engineered with this candidate (termed TAEST16001) significantly inhibited tumor growth in subcutaneous, metastatic, and patient-derived xenograft (PDX) mouse tumor models. This study demonstrates that sophisticated engineering and screening techniques can be utilized to generate a clinical candidate TCR with potent anti-tumor activity without losing specificity. TAEST16001 was approved by the Center for Drug Evaluation (CDE) as the first TCR-based immunotherapy clinical trial in China (ClinicalTrials.gov Identifier: NCT03159585).

immunology↗

bioRxiv2022

DUnet: A deep learning guided protein-ligand binding pocket prediction

Investigating protein-ligand binding sites is the key step in engineering protein/enzyme activity and selectivity. In this study, we developed a 3D convolutional neural network DUnet that derived from DenseNet and UNet for predicting the protein-ligand binding sites. To train DUnet, the features of protein 3D structure were extracted by describing the atomic physical characters, and the ligand binding sites were used as training labels. DUnet was trained using three dataset, the scPDB dataset (collecting of protein-ligand complexes from Protein Data Bank), scPDB and SC6K (collecting of protein-ligand complexes deposited after January 1st, 2018 from Protein Data Bank) datasets, and scPDB and its derived dataset by rotating the samples in the dataset. DUnet displayed better performance than the current state-of-art methods during the benchmark test using independent validation sets, and enlarging the training set contributed to better accuracy. We developed a small dataset contains commonly used industrial enzymes for testing DUnet and found that it was also accurate in predicting the substrate binding sites. We experimentally characterized the substrate binding sites of microbial transglutaminase according to the prediction and showed the significance of these sites. Finally, DUnet was used to predict the ligand binding sites of Swiss-Prot annotated proteins.

bioinformatics↗

bioRxiv2022

Massively Increasing the number of Antibody-Virus Interactions across Studies

A central challenge in every field of biology is to use existing measurements to predict the outcomes of future experiments. In this work, we consider the wealth of antibody inhibition data against variants of the influenza virus. Due to this viruss genetic diversity and evolvability, the variants examined in one study will often have little-to-no overlap with other studies, making it difficult to discern common patterns or unify datasets for further analysis. To that end, we develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We use this framework to greatly expand seven influenza datasets utilizing hemagglutination inhibition, validating our method upon 200,000 existing measurements and predicting 2,000,000 new values {+/-} uncertainties. With these new values, we quantify the transferability between seven vaccination and infection studies in humans and ferrets, show that the serum potency is negatively correlated with breadth, and present a tool for pandemic preparedness. This data-driven approach does not require any information beyond each viruss name and measurements, and even datasets with as few as 5 viruses can be expanded, making this approach widely applicable. Future influenza studies using hemagglutination inhibition can directly utilize our curated datasets to predict newly measured antibody responses against {approx}80 H3N2 influenza viruses from 1968-2011, whereas immunological studies utilizing other viruses or a different assay only need a single partially-overlapping dataset to extend their work. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."

immunology↗

bioRxiv2022

Dynamic visualization of high-dimensional data

Dimensionality reduction (DR) is commonly used to project highdimensional data into lower dimensions for visualization, which could then generate new insights and hypotheses. However, DR algorithms necessarily introduce distortions in the visualization and cannot faithfully represent all relations in the data. As such, there is a need for methods to assess the reliability of DR visualizations. Here we present DynamicViz, a framework for generating dynamic visualizations that capture the sensitivity of DR visualizations to perturbations in the data. DynamicVic can be applied to all commonly used DR methods. We show the utility of dynamic visualizations in diagnosing common interpretative pitfalls of static visualizations and extending existing single-cell analyses. We introduce the variance score to quantify the dynamic variability of observations in these visualizations. The variance score characterizes natural variability in the data and can be used to optimize DR algorithm implementations. We have made DynamicViz freely available to assist with the evaluation of DR visualizations.

bioinformatics↗

bioRxiv2022

The Genetic and Evolutionary Basis of Gene Expression Variation in East Africans

BackgroundMapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases. How the genetic architecture of molecular traits varies across human populations, however, has been less explored. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations. ResultsWe find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine mapping of causal variation. Integrating our QTL scans with signatures of selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation, TMEM216, with evidence of population-specific selection in Nilo-Saharan speaking pastoralists. ConclusionExtending QTL-mapping studies beyond groups of European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.

genomics↗

bioRxiv2022

The endoplasmic reticulum membrane protein complex (EMC) negatively regulates intestinal homeostasis through the Hippo signaling pathway

Disruption of tissue homeostasis often results in many diseases. Balanced control of stem cell proliferation and differentiation underlines tissue homeostasis. However, how endogenous factors influence the proliferation and differentiation of intestinal stem cells (ISCs) under physiological conditions remains not fully understood. Here, we find that the endoplasmic reticulum membrane protein complex (EMC) negatively regulates ISC proliferation in adult Drosophila midgut. Compromising EMC function in progenitors leads to excessive ISC proliferation and intestinal homeostasis disruption. Mechanistically, the EMC complex associates with and stabilizes Hippo (Hpo), the key component of the Hpo signaling pathway. In the absence of the EMC complex, Yki (Yorkie) is activated to promote ISC proliferation. Furthermore, the role of the EMC complex in stem cell proliferation control is evolutionarily conserved. Thus, our study uncovers the molecular mechanism of the EMC complex in controlling stem cell proliferation. Our results provide new insight into the underlying mechanisms of how stem cell proliferation is properly controlled under physiological conditions.

cell biology↗

bioRxiv2021

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

The purpose of this work is to enhance KinasePhos, a machine-learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProt, GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models were observed to be more effective than other prediction tools. For example, the prediction of sites phosphorylated by the Akt, CKT, and PKA families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the Shapley additive explanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned with the goal of providing comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/index.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

bioinformatics↗

bioRxiv2021

Genetic Analysis of Bphse: a Novel Gene Complementing Resistance to Bordetella pertussis-Induced Histamine Sensitization

AO_SCPLOWBSTRACTC_SCPLOWHistamine is a bioactive amine associated with a plethora of normal and pathophysiological processes, with the latter being dependent on both genetic and environmental factors including infectious agents. Previously, we showed in mice that susceptibility to Bordetella pertussis and pertussis toxin (PTX) induced histamine sensitization (Bphs) is controlled by histamine receptor H1 (Hrh1/HRH1) alleles. Bphs susceptible and resistant alleles (Bphss/Bphsr) encode for two-conserved protein haplotypes. Given the importance of HRH1 signaling in health and disease, we sequenced Hrh1 across an extended panel of laboratory and wild-derived inbred strains and phenotyped them for Bphs. Unexpectedly, eight strains homozygous for the Bphsr allele phenotyped as Bphss, suggesting the existence of a modifying locus segregating among the strains capable of complementing Bphsr. Genetic analyses mapped this modifier locus to mouse chromosome 6; designated Bphs-enhancer (Bphse), within a functional linkage disequilibrium domain encoding multiple loci controlling responsiveness to histamine (Bphs/Hrh1 and Histh1-4). Interval-specific single-nucleotide polymorphism (SNP) based association testing across 50 laboratory and wild-derived inbred mouse strains and functional prioritization analyses resulted in the identification of candidate genes for Bphse within a [~]5.5 Mb interval (Chr6:111.0-116.4 Mb), including Atg7, Plxnd1, Tmcc1, Mkrn2, Il17re, Pparg, Lhfpl4, Vgll4, Rho and Syn2. Taken together, these results demonstrate the power of combining network-based computational methods with the evolutionarily significant diversity of wild-derived inbred mice to identify novel genetic mechanisms controlling susceptibility and resistance to histamine shock.

genetics↗

bioRxiv2021

Dual roles of a novel oncolytic viral vector-based SARS-CoV-2 vaccine: preventing COVID-19 and treating tumor progression

The ongoing coronavirus disease 2019 (COVID-19) pandemic is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Cancer patients are usually immunocompromised and thus are particularly susceptible to SARS-CoV-2 infection resulting in COVID-19. Although many vaccines against COVID-19 are being preclinically or clinically tested or approved, none have yet been specifically developed for cancer patients or reported as having potential dual functions to prevent COVID-19 and treat cancer. Here, we confirmed that COVID-19 patients with cancer have low levels of antibodies against the spike (S) protein, a viral surface protein mediating the entry of SARS-CoV-2 into host cells, compared with COVID-19 patients without cancer. We developed an oncolytic herpes simplex virus-1 vector-based vaccine named oncolytic virus (OV)-spike. OV-spike induced abundant anti-S protein neutralization antibodies in both tumor-free and tumor-bearing mice, which inhibit infection of VSV-SARS-CoV-2 and wild-type (WT) live SARS-CoV-2 as well as the B.1.1.7 variant in vitro. In the tumor-bearing mice, OV-spike also inhibited tumor growth, leading to better survival in multiple preclinical tumor models than the untreated control. Furthermore, OV-spike induced anti-tumor immune response and SARS-CoV-2-specific T cell response without causing serious adverse events. Thus, OV-spike is a promising vaccine candidate for both preventing COVID-19 and enhancing the anti-tumor response. One Sentence SummaryA herpes oncolytic viral vector-based vaccine is a promising vaccine with dual roles in preventing COVID-19 and treating tumor progression

microbiology↗

bioRxiv2021

Alpha oscillatory activity causally linked to working memory retention: insights from online phase-locking closed-loop transcranial alternating current stimulation (tACS)

Although previous studies have reported correlations between alpha oscillations and the "retention" sub-process of working memory (WM), causal evidence has been limited in human neuroscience due to the lack of delicate modulation of human brain. Conventional tACS is not suitable for demonstrating the causal evidence for parietal alpha oscillations in WM retention because of its inability to modulate brain oscillations within a short period (i.e., the retention sub-process). Here, we developed an online phase-corrected closed-loop transcranial alternating current stimulation (tACS) system capable of precisely correcting for the phase differences between tACS and concurrent endogenous oscillations. This system permits both up- and down-regulation of brain oscillations at the target stimulation frequency within a short stimulation period, and is here applied to empirically demonstrate that parietal alpha oscillations causally relate to WM retention. Our experimental design included both in-phase and anti-phase alpha-tACS applied to 39 participants during the retention sub-processes of a modified Sternberg paradigm. Compared to in-phase alpha-tACS, anti-phase alpha-tACS decreased both WM performance and alpha activity. Moreover, the in-phase tACS-induced changes in WM performance were positively correlated with alpha oscillatory activity. These findings strongly support a causal link between alpha oscillations and WM retention, and illustrate the broad application prospects of phase-corrected tACS.

neuroscience↗

bioRxiv2021

High Gamma and Beta Temporal Interference Stimulation in the Human Motor Cortex Improves Motor Functions

BackgroundTemporal interference (TI) stimulation is a new technique of noninvasive brain stimulation. Envelope-modulated waveforms with two high-frequency carriers can activate neurons in target brain regions without stimulating the overlying cortex, which has been validated in mouse brains. However, whether TI stimulation can work on the human brain has not been elucidate. ObjectiveTo assess the effectiveness and safety aspect of the envelope-modulated waveform of TI stimulation on human primary motor cortex (M1). MethodsParticipants attended three sessions of 30-min TI stimulation at 2 mA during a random reaction time task (RRTT) or a serial reaction time task (SRTT). Motor cortex excitability was measured before and after TI stimulation. ResultsIn the RRTT experiment, only 70 Hz TI stimulation had a promoting effect on the reaction time (RT) performance and excitability of the motor cortex compared to sham stimulation. Meanwhile, compared with the sham condition, only 20 Hz TI stimulation significantly facilitated motor learning in the SRTT experiment, which was significantly positively correlated with the increase in motor evoked potential. ConclusionThese results indicate that the envelope-modulated waveform of TI stimulation has a significant promoting effect on human motor functions, experimentally suggesting the effectiveness of TI stimulation in humans for the first time and pave the way for further explorations.

neuroscience↗