Biology subjects

Luo, Y.

Publications and source records attributed to Luo, Y..

At least 415 records · Page 23Linked to original sources

bioRxiv2019

Deep learning for cancer type classification

Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. To address these limitations, we propose DeepCues, a deep learning model that utilizes convolutional neural networks to derive features from DNA sequencing data for disease classification and relevant gene discovery. Using whole-exome sequencing, germline variants and somatic mutations, including insertions and deletions, are interactively amalgamated as features. In this study, we applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p=8.8E-25). Using DeepCues, we found that the top 20 genes associated with breast cancer have a 40% overlap with the top 20 breast cancer genes in the COSMIC database. These data support DeepCues as a novel method to improve the representational resolution of both germline variants and somatic mutations interactively and their power in predicting cancer types, as well the genes involved in each cancer.

cancer biology↗

bioRxiv2019

CRISPR-GNL: an improved model for predicting CRISPR activity by machine learning and featurization

MotivationThe CRISPR/Cas9 system has been broadly used in genetic engineering. However, risks of potential off-targets and the variability of on-target activity among different targets are two limiting factors. Several bioinformatic tools have been developed for CRISPR on-target activity and off-target prediction. However, the general application of the current prediction models is hampered by the great variation among different algorithms.\n\nResultsIn this study, we thoroughly re-analyzed 13 published datasets with eight regression models. We proved that the current model gave very low cross-dataset and cross-species prediction outcome. To overcome these limitations, we have developed an improved model (a generalization score, GNL) based on normalized gene editing activity from 8,101 gRNAs and 2,488 features using Bayesian Ridge Regression model. Our results demonstrated that the GNL model is a better general algorithm for CRISPR on-target activity prediction\n\nAvailability and implementationThe prediction scorer is available on GitHub (https://github.com/TerminatorJ/GNL_Scorer).\n\nContactJ.W. (wangjun6@genomics.cn) or Y.L. (luoyonglun@genomics.cn)\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci

Understanding how genetic regulatory variation affects gene expression in different T cell states is essential to deciphering autoimmunity. We conducted a high-resolution RNA-seq time course analysis of stimulated memory CD4+ T cells from 24 healthy individuals. We identified 186 genes with dynamic allele-specific expression, where the balance of alleles changes over time. These genes were four fold enriched in autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes, particularly for a major autoimmune risk gene, HLA-DQB1. Each HLA-DQB1 allele had one of three distinct transcriptional regulatory programs. Using CRISPR/Cas9 genomic editing we demonstrated that a single nucleotide variant at the promoter is causal for T cell-specific control of HLA-DQB1 expression. Our study in CD4+ T cells shows that genetic variation in cis regulatory elements may affect gene expression in a lymphocyte activation status-dependent manner contributing to the inter-individual complexity of immune responses.

genomics↗

bioRxiv2019

Structural determinants of microtubule minus end preference in CAMSAP CKK domains

CAMSAP/Patronins regulate microtubule minus-end dynamics. Their end specificity is mediated by their CKK domains, which we proposed recognise specific tubulin conformations found at minus ends. To critically test this idea, we compared the human CAMSAP1 CKK domain (HsCKK) with a CKK domain from Naegleria gruberi (NgCKK), which has lost minus-end specificity. Near-atomic cryo-electron microscopy structures of HsCKK- and NgCKK-microtubule complexes show that these CKK domains share the same protein fold, bind at the intradimer interprotofilament tubulin junction, but exhibit subtly different footprints on microtubules. Whereas NgCKK binding does not alter the microtubule architecture, HsCKK remodels its microtubule interaction site and changes the underlying polymer structure because the tubulin lattice conformation is not optimal for its binding. NMR experiments show that HsCKK is remarkably rigid, supporting this remodelling ability. Thus, in contrast to many MAPs, CKK domains can differentiate subtly specific tubulin conformations to enable microtubule minus-end recognition.

biophysics↗

bioRxiv2019

The invasion of de-differentiating cancer cells into hierarchical tissues

Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy and differentiating into a whole range of specialized cells. There is increasing evidence that tumors are structured in a very similar way, mirroring the hierarchical structure of the host tissue. In some tissues, differentiated cells can also revert to the stem cell phenotype, which increases the risk that cells that have already acquired mutations lead to long lasting clones in the tissue. Recently, the modelling community has paid special attention to the consequences of de-differentiation on cellular hierarchies. However, the adaptive significance of de-differentiation is still poorly understood and thus it is unclear under which circumstances de-differentiating cells will invade a tissue. To address this, we developed mathematical models to investigate how de-differentiation could be selected as an adaptive mechanism in the context of cellular hierarchies. We consider the cases of stepwise and jumpwise de-differentiation in this study. Our results show that the emergence of de-differentiation is driven by the combination of the properties of the cellular hierarchy and the de-differentiation pattern and derive thresholds for which de-differentiation is expected to emerge.

evolutionary biology↗

bioRxiv2019

Robust Determination of Protein Allosteric Signaling Pathways

To understand how protein function changes upon an allosteric perturbation, such as ligand binding and mutation, significant progress in characterizing allosteric network from molecular dynamics (MD) simulations has been made. However, determining which amino acid(s) play an essential role in the propagation of signals may prove challenging, even when the location of the source and sink is known for a protein or protein complex. This challenge is mainly due to the large fluctuations in protein dynamics that cause instability of the network topology within a single trajectory or between multiple replicas. To solve this problem, we introduce the current-flow betweenness scheme, originated from electrical network theory, to protein dynamical network analysis. To demonstrate the benefit of this new method, we chose a prototypic allosteric enzyme (IGPS or HisH-HisF dimer) as our benchmark system. Using multiple replicas of simulations and multiple network topology comparison metrics (edge ranking, path length, and node frequency), we show that the current-flow betweenness provides a significant improvement in the convergence of the allosteric networks. The improved stability of the network topology allows us to generate a delta-network between the apo and holo forms of the protein. We illustrated that the delta-network is a more rigorous way to capture the subtle changes in the networks that would otherwise be neglected by comparing node usage frequencies alone. We have also investigated the use of a linear smoothing function to improve the stability of the contact map. The methodology presented here is general and may be applied to other topology and weighting schemes. We thus conclude that, for determining protein signaling pathways between the pair(s) of source and sink, multiple MD simulation replicas are necessary and the current-flow betweenness scheme introduced here provides a more robust approach than the geodesic scheme based on correlation edge weighting. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/565457v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@54260corg.highwire.dtl.DTLVardef@8e1043org.highwire.dtl.DTLVardef@cda004org.highwire.dtl.DTLVardef@1c485cb_HPS_FORMAT_FIGEXP M_FIG For Table of Contents Only C_FIG

biophysics↗

bioRxiv2019

A positively selected, common, missense variant in FBN1 confers a 2.2 centimeter reduction of height in the Peruvian population

Peruvians are among the shortest people in the world. To understand the genetic basis of short stature in Peru, we examined an ethnically diverse group of Peruvians and identified a novel, population-specific, missense variant in FBN1 (E1297G) that is significantly associated with lower height in the Peruvian population. Each copy of the minor allele (frequency = 4.7%) reduces height by 2.2 cm (4.4 cm in homozygous individuals). This is the largest effect size known for a common height-associated variant. This variant shows strong evidence of positive selection within the Peruvian population and is significantly more frequent in Native American populations from coastal regions of Peru compared to populations from the Andes or the Amazon, suggesting that short stature in Peruvians is the result of adaptation to the coastal environment. One Sentence SummaryA mutation found in Peruvians has the largest known effect on height for a common variant. This variant is specific to Native American ancestry.

genomics↗

bioRxiv2019

The SUMO ligase Su(var)2-10 links piRNA-guided target recognition to chromatin silencing

Regulation of transcription is the main mechanism responsible for precise control of gene expression. While the majority of transcriptional regulation is mediated by a multitude of DNA-binding transcription factors that bind to regulatory gene regions, an elegant alternative strategy employs small RNA guides, piwi-interacting RNAs (piRNAs) to identify targets of transcriptional repression. Here we show that in Drosophila the small ubiquitin-like protein SUMO and the SUMO E3 ligase Su(var)2-10 are required for piRNA-guided deposition of repressive chromatin marks and transcriptional silencing of piRNA targets. Su(var)2-10 links the piRNA-guided target recognition complex to the silencing effector by binding the piRNA/Piwi complex and inducing SUMO-dependent recruitment of the SetDB1/Wde histone methyltransferase effector. We propose that in Drosophila, the nuclear piRNA pathway has co-opted a conserved mechanism of SUMO-dependent recruitment of the SetDB1/Wde chromatin modifier to confer repression of genomic parasites. Highlights- piRNA-induced transposon silencing requires SUMO and the SUMO E3 ligase Su(var)2-10 - Su(var)2-10 links the target recognition complex to the silencing effector - Su(var)2-10 binds the piRNA-guided target recognition complex and deposits SUMO on target chromatin - Su(var)2-10 induces SUMO-dependent recruitment of the SetDB1/Wde histone methyltransferase to target loci

molecular biology↗

bioRxiv2019

The SUMO ligase Su(var)2-10 controls eu- and heterochromatic gene expression via establishment of H3K9 trimethylation and negative feedback regulation

Chromatin is critical for genome compaction and gene expression. On a coarse scale, the genome is divided into euchromatin, which harbors the majority of genes and is enriched in active chromatin marks, and heterochromatin, which is gene-poor but repeat-rich. The conserved molecular hallmark of heterochromatin is the H3K9me3 modification, which is associated with gene silencing. We found that in Drosophila deposition of most of the H3K9me3 mark depends on SUMO and the SUMO-ligase Su(var)2-10, which recruits the histone methyltransferase complex SetDB1/Wde. In addition to repressing repeats, H3K9me3 also influences expression of both hetero- and euchromatic host genes. High H3K9me3 levels in heterochromatin are required to suppress spurious non-canonical transcription and ensure proper gene expression. In euchromatin, a set of conserved genes is repressed by Su(var)2-10/SetDB1-induced H3K9 trimethylation ensuring tissue-specific gene expression. Several components of heterochromatin are themselves repressed by this pathway providing a negative feedback mechanism to ensure chromatin homeostasis. Highlights- Proper expression of host genes residing in heterochromatin requires Su(var)2-10-dependent installation of the H3K9me3 mark to suppress spurious non-canonical transcription. - A set of euchromatic host genes is repressed by transposon-independent installation of H3K9me3 in a process that depends on Su(var)2-10 and SUMO. - Installation of H3K9me3 via Su(var)2-10 ensures tissue-specific gene expression. - H3K9me3-dependent silencing of genes encoding proteins involved in heterochromatin formation provides negative feedback regulation to maintain heterochromatin homeostasis.

molecular biology↗

bioRxiv2019

Mitigating Data Scarcity in Protein Binding Prediction Using Meta-Learning

A plethora of biological functions are performed through various types of protein-peptide binding. Prime examples include the protein kinase phosphorylation on peptide substrates and the binding of major histocompatibility complex to neoantigens in the immune system. Understanding the specificity of protein-peptide interactions is critical for unraveling the architectures of functional pathways and the mechanisms of cellular processes in human cells. Despite mass-spectrometric techniques were developed for the identification of protein-peptide interactions, our understanding of the preferences of proteins on their binding peptides is still rudimentary. As a complementary direction, a line of computational prediction methods has been recently proposed to predict protein-peptide bindings which efficiently provide rich functional annotations on a large scale. To achieve a high prediction accuracy, these computational methods require a sufficient amount of data to build the prediction model. However, the number of experimentally verified protein-peptide bindings is often limited in real cases. For example, a majority of protein kinases have very few experimentally verified phosphorylation sites (e.g., less than 30 sites) in existing databases. These methods are thus limited to building accurate prediction models for only well-characterized proteins with a large volume of known binding peptides and cannot be extended to predict new binding peptides for less-studied proteins. In this paper, we introduce a generic framework to address this issue of data scarcity in protein binding prediction. We demonstrate the applicability of our framework in predicting kinase-specific phosphorylation sites. Our method uses an effective training strategy to build a prediction model with robust transferability. The model is able to predict the phosphorylation sites of a less-studied kinase, even if there is only a small number of phosphorylation sites known for this kinase. To achieve this, we train the model via a meta-learning phase followed by a few-shot learning phase. We demonstrate our framework has better transferability than state-of-the-art methods and is effective in utilizing limited data to accurately predict phosphorylation sites for less-characterized kinases. The implementation of our framework is available at https://github.com/luoyunan/MetaKinase.

bioinformatics↗

bioRxiv2018

Estimating heritability of complex traits in admixed populations with summary statistics

The increasing size and diversity of genome-wide association studies provide an exciting opportunity to study how the genetics of complex traits vary among diverse populations. Here, we introduce covariate-adjusted LD score regression (cov-LDSC), a method to accurately estimate genetic heritability [Formula] and its enrichment in both homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the GWAS samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates [Formula] by 10% - 60% in admixed populations; in contrast, cov-LDSC is robust to all simulation parameters. We apply cov-LDSC to genotyping data from approximately 170,000 Latino, 47,000 African American and 135,000 European individuals. We estimate [Formula] and detect heritability enrichment in three quantitative and five dichotomous phenotypes respectively, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals. Our results show that most traits have high concordance of [Formula] and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of [Formula]. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size{tau} * is 0.16 {+/-} 0.04, 0.28 {+/-} 0.11 and 0.18 {+/-} 0.03 in Latino, African American and European populations respectively. Our results demonstrate that our approach is a powerful way to analyze genetic data for complex traits from underrepresented populations. Author summaryAdmixed populations such as African Americans and Hispanic Americans bear a disproportionately high burden of disease but remain underrepresented in current genetic studies. It is important to extend current methodological advancements for understanding the genetic basis of complex traits in homogeneous populations to individuals with admixed genetic backgrounds. Here, we develop a computationally efficient method to answer two specific questions. First, does genetic variation contribute to the same amount of phenotypic variation (heritability) across diverse populations? Second, are the genetic mechanisms shared among different populations? To answer these questions, we use our novel method to conduct the first comprehensive heritability-based analysis of a large number of admixed individuals. We show that there is a high degree of concordance in total heritability and tissue-specific enrichment between different ancestral groups. However, traits such as age at menarche show a noticeable differences among populations. Our work provides a powerful way to analyze genetic data in admixed populations and may contribute to the applicability of genomic medicine to admixed population groups.

genetics↗

bioRxiv2018

The long noncoding RNA Hand2as orchestrates heart development through regulation of precise expression of HAND2

Rigorous exploration and dissection of potential actions and effects of long noncoding RNA (lncRNA) in animals remain challenging. Here using multiple knockout mouse models and single-cell RNA sequencing, we demonstrate that the divergent lncRNA Hand2as has a key, complex modulatory effect on the expression of its neighboring gene HAND2 and subsequently on heart development and function, largely independent of Hand2as transcription and transcripts. Full-length deletion of Hand2as in mouse causes moderate yet prevalent upregulation of HAND2 in hundreds of cardiac cells, resulting in profound biological consequences, including dysregulated cardiac gene programs, congenital heart defects and perinatal lethality. We propose a cis-functional role for the Hand2as locus in dampening HAND2 expression to restrain cardiomyocyte proliferation, thereby orchestrating a balanced development of cardiac cell lineages. This study highlights the need for complementary genetic and single-cell approaches to delineate the function and primary molecular effects of an lncRNA in animals.\n\nImpact statementThe long noncoding RNA Hand2as critically controls the precise expression of its neighboring gene HAND2, thereby balancing cardiac lineages and expression programs that are essential for heart development and function.

developmental biology↗

bioRxiv2018

Noninvasive prenatal detection of fetal trisomy and single gene disease by shotgun sequencing of placenta originated exosome DNA: a proof-of-concept validation

BackgroundDuring human pregnancy, Placental trophectoderm cells can release exosomes into maternal circulation. Trophoblast cells also give rise to cell-free DNA (cfDNA) and has been used for noninvasive prenatal screening for chromosomal aneuploidy. We intended to prove the existence of exosomal DNA (exoDNA) in the exosomes of maternal blood and compared exoDNA with plasma cfDNA in terms of genome distribution, fragment length, and the possibility of detecting genetic diseases.\n\nMethodsMaternal blood from 20 euploid pregnancies, 9 T21 pregnancies, 3 T18 pregnancies, 1 T13 pregnancy and 2 pregnancies with FGFR3 mutations were obtained. Exosomes enriched from maternal plasma were confirmed by transmission electronic microscopy (TEM), western blotting and flow cytometry. ExoDNA was extracted and its fetal origin was confirmed by realtime fluorescence quantitative PCR(Q-PCR). Besides, exoDNA content was uncovered by Q-PCR. To characterize exoDNA and compare with cfDNA, pair-end whole genome sequencing was performed. Lastly, the fetal risk of genetic diseases was analyzed using the exoDNA sequencing data.\n\nResultsExoDNA span on all 23 pairs of chromosomes and mitochondria, sharing a similar distribution pattern and higher GC content comparing with cfDNA. ExoDNA showed shorter fragments yet lower fetal fraction than cfDNA. ExoDNA could be used to determine fetal gender correctly, and all trisomies as well as de novo FGFR3 mutations.\n\nConclusionsWe proved that fetal exoDNA could be identified in the exosomes extracted from maternal plasma. ExoDNA shared some similar features to cfDNA and could potentially be used to detect genetic diseases in fetus.

genetics↗

bioRxiv2018

The research progress of p53 tumour suppressor activity controlled by Numb in triple-negative breast cancer

Numb is known as a cell fate determinant as it determines the direction of cell differentiation by asymmetrically partitioning at mitosis. It has been shown to be a tumor suppressor, and there is frequent loss of Numb expression in breast cancer. Numb enters in a tricomplex with p53 and the E3 ubiquitin ligase HDM2 (also known as MDM2), thereby preventing ubiquitination and degradation of p53. The aim of this study was to investigate the expression and migration of Numb, HDM2 and p53 proteins in the membrane, cytoplasmic and nuclear fractions of MCF-10A cells and MDA-MB-231 cells. We extracted the cell fractions to detect changes of these three protein levels after re-expression of NUMB in the basal-like triple-negative cell line MDA-MB-231 and knocking down NUMB in the normal mammary epithelial cell line MCF-10A. Our results show that Numb protein can migrate from cytoplasm to nucleus of the MDA-MB-231 cells. Numb protein regulates p53 levels in nucleus of MCF-10A cells and MDA-MB-231 cells, and the Numb levels is positively correlated with p53 levels. We have a finding on HDM2 protein that after knocking down NUMB in MCF-10A cells, it was remarkably reduced in the membrane fraction of NUMB knockdown cells, but its mRNA levels was significantly increased. We also examined the Numb expression in 125 patients with triple-negative breast cancer, 61(48.8%) displayed deficient or reduced expression of Numb. The percent of Ki67>14% in retained Numb group was significantly lower than that in the reduced and deficient Numb group (86.00% vs. 98.40%, P=0.0171).

molecular biology↗

bioRxiv2018

Multiplexed Profiling of Single-cell Extracellular Vesicles Secretion

Extracellular vesicles (EVs) are important intercellular mediators regulating health and disease. Conventional EVs surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EVs secretion. Herein, by using spatially patterned antibodies barcode, we realized multiplexed profiling of single-cell EVs secretion from more than 1000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to deep understanding of previously undifferentiated single cell heterogeneity underlying EVs secretion. Notably, we observed the decrement of certain EV phenotypes (e.g. CD63+EVs) were associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EVs secretion and cytokines secretion simultaneously from the same single cells to investigate multidimensional spectrum of intercellular communications, from which we resolved three functional subgroups with distinct secretion profiles by visualized clustering. In particular, we found EVs secretion and cytokines secretion were generally dominated by different cell subgroups. The technology introduced here enables comprehensive evaluation of EVs secretion heterogeneity at single cell level, which may become an indispensable tool to complement current single cell analysis and EV research.\n\nSignificanceExtracellular vesicles (EVs) are cell derived nano-sized particles medicating cell-cell communication and transferring biology information molecules like nucleic acids to regulate human health and disease. Conventional methods for EV surface markers profiling cant tell the differences in the quantity and phenotypes of EVs secretion between cells. To address this need, we developed a platform for profiling an array of surface markers on EVs from large numbers of single cells, enabling more comprehensive monitoring of cellular communications. Single cell EVs secretion assay led to previously unobserved cell heterogeneity underlying EVs secretion, which might open up new avenues for studying cell communication and cell microenvironment in both basic and clinical research.

bioengineering↗

bioRxiv2018

SV40 polyomavirus activates the Ras-MAPK signaling pathway for vacuolization, cell death, and virus release

Polyomaviruses are a family of small, non-enveloped DNA viruses that can cause severe disease in immunosuppressed individuals. Studies with SV40, a well-studied model polyomavirus, have revealed the role of host proteins in polyomavirus entry and trafficking to the nucleus, viral transcription and DNA replication, and cell transformation. In contrast, little is known about host factors or cellular signaling pathways involved in the late steps of productive infection leading to polyomavirus release. We previously showed that cytoplasmic vacuolization, a characteristic late cytopathic effect of SV40, depends on the specific interaction between the major viral capsid protein VP1 and its cell surface ganglioside receptor GM1. Here we show that late during infection, SV40 activates a signaling cascade in permissive CV-1 monkey cells involving Ras, Rac1, MKK4 and JNK to induce SV40-specific cytoplasmic vacuolization and subsequent cell lysis and virus release. Inhibition of individual components of this signaling pathway inhibits vacuolization, lysis and virus release, even though high-level intracellular virus replication occurs. The identification of this pathway for SV40-induced vacuolization and virus release provides new insights into the late steps of non-enveloped virus infection and reveals potential drug targets for the treatment of diseases caused by these viruses.\n\nIMPORTANCEThe polyomaviruses are small DNA viruses that include important model viruses and human pathogens that can cause fatal disease, including cancer, in immunosuppressed individuals. There are no vaccines or specific antiviral agents for any polyomavirus. Here, we show that late during infection, SV40 activates a signaling cascade involving Ras, Rac, and JNK that is required for cytoplasmic vacuolization and efficient virus release. This pathway may represent a new point of intervention to control infection by these viruses.

microbiology↗

bioRxiv2018

Genome structure and evolution of Antirrhnum majus L.

Snapdragon (Antirrhinum majus L.), a member of Plantaginaceae, is an important model for plant genetics and molecular studies on plant growth and development, transposon biology and self-incompatibility. Here we report a high-quality genome assembly of A. majus cultivated JI7 (A. majus cv.JI7) of a 510 Mb with 37,714 annotated protein-coding genes. The scaffolds covering 97.12% of the assembled genome were anchored on 8 chromosomes. Comparative and evolutionary analyses revealed that Plantaginaceae and Solanaceae diverged from their most recent ancestor around 62 million years ago (MYA). We also revealed the genetic architectures associated with complex traits such as flower asymmetry and self-incompatibility including a unique TCP duplication around 46-49 MYA and a near complete{psi} S-locus of ca.2 Mb. The genome sequence obtained in this study not only provides the first genome sequenced from Plantaginaceae but also bring the popular plant model system of Antirrhinum into a genomic age.

genomics↗

bioRxiv2018

Tibetan antelope rests like a Puppet

AbstractRest contributes to a large part of animals daily life, and animals usually rest in two ways, standing or in recumbence. For small or medium sized ungulates, they bed to rest in most cases, and standing rest is very rare and hardly seen. Here we described a standing rest behaviour of medium sized Tibetan antelopes (Pantholops hodgsonii) living on the roof of the world, Tibet Plateau, which has not been reported before. We named the standing rest behaviour here as Puppet behaviour, since the antelope can stand still for a certain time just like a Puppet. Of the total 304 focal individuals, 48.3% (98/203) of adult and sub-adult males expressed the Puppet behaviour, whereas only 6.3% (6/96) of females did, indicating an obvious sexual difference. Puppet behaviour occurred more frequently at noon and in the afternoon on sunny and cloudy days, meaning that day time and weather were both influential factors. Puppet behaviour was usually accompanied with rumination and sometimes ended with leg-shaking. Our results suggest that Puppet behaviour is probably an adaptive form of rest, which serves a thermoregulatory and anti-predation function, and is much simpler and safer than recumbent rest.

animal behavior and cognition↗