Biology subjects

Yang, Y.

Publications and source records attributed to Yang, Y..

At least 1,063 records · Page 59Linked to original sources

bioRxiv2021

SOX4 facilitates PGR protein stability and FOXO1 expression conducive for human endometrial decidualization

The establishment of receptive endometrium in human necessitates appropriate decidualization of stromal cells, which involves steroids regulated periodic transformation of endometrial stromal cells during menstrual cycle. Insufficient decidualization of endometrium contributes to not only the failure of embryo implantation and unexplained infertility, but also the occurrence of recurrent spontaneous abortion, intrauterine growth retardation, preeclampsia, and other clinical gynecological diseases. However, the potential molecular regulatory mechanism underlying the initiation and maintenance of decidualization in humans is yet to be fully elucidated. In this investigation, we document that SOX4 is a key regulator of human endometrial stromal cells (hESCs) decidualization by directly regulating PRL and FOXO1 expression as revealed by whole genomic binding of SOX4 assay and RNA-Seq. Besides, our immunoprecipitation and mass spectrometry results unravel that SOX4 modulates progesterone receptor (PGR) stability through repressing E3 ubiquitin ligase HERC4 mediated degradation. More importantly, we provide evidence that dysregulated SOX4-HERC4-PGR axis is a potential cause of defective decidualization and recurrent implantation failure (RIF) in IVF patients. In summary, this study evidences that SOX4 is a new and critical regulator for human endometrial decidualization, and provides insightful information for the pathology of decidualization-related infertility and will pave the way for pregnancy improvement.

physiology↗

bioRxiv2021

A multimodal AI system for out-of-distribution generalization of seizure detection

Epilepsy is one of the most common severe neurological disorders worldwide. The International League Against Epilepsy (ILAE) define epilepsy as a brain disorder that generates (1) two unprovoked seizures more than 24 hrs apart, or (2) one unprovoked seizure with at least 60% risk of recurrence over the next ten years. Complete remission has been defined as ten years seizure free with the last five years medication free. This requires a cost-effective ambulatory ultra-long term out-patient monitoring solution. The common practice of self-reporting is inaccurate. Applying artificial intelligence (AI) to scalp electroencephalogram (EEG) interpretation is becoming increasingly common, but other data modalities such as electrocardiograms (ECGs) are simpler to collect and often recorded simultaneously with EEG. Both recordings contain biomarkers in the detection of seizures. Here, we propose a state-of-the-art performing AI system that combines EEG and ECG for seizure detection, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted nonpatient-specific inference-only tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 patients shortlisted by neurologists and 30 randomly selected). Across all datasets, our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for prior state-of-the-art approaches using EEG and ECG alone, respectively. Our models state-of-the-art performance and robustness to out-ofdistribution datasets can improve the accuracy and efficiency of epilepsy diagnoses.

neuroscience↗

bioRxiv2021

Room-temperature Storage of Lyophilized Engineered Bacteria using Tardigrade Intrinsically Disordered Proteins

Tardigrades, which live in transiently wet environments such as moss, are well-known for their extreme resistance to desiccation. Tardigrade intrinsically disordered proteins (TDPs) have been reported to also protect bacteria and yeast under desiccation [4, 5, 32]. In this study, we utilized lyophilization to achieve room-temperature storage of engineered bacteria. By using TDPs, engineered bacteria are protected under lyophilization and their original functions are preserved [12, 17, 18]. This study shows that TDPs can be expressed in the Escherichia coli (E. coli) BL21 and DH5, and bacteria treated with Cytosolic-abundant heat soluble protein (CAHS) 106094 displayed the highest survival rate after lyophilization [16, 41, 44]. Moreover, this study shows that the co-expression of TDPs can improve the preservation of bacteria and maintain high survival rates after prolonged room temperature storage. Additionally, the TDPs can be expressed using different vectors, which means that they can be used in different types of engineered bacteria. This study offers a new storage method that not only improves the storage of biological material for industrial and daily usage, but also for future iGEM (International Genetically Engineered Machine Competition) teams to store and use their engineered bacteria in different applications.

synthetic biology↗

bioRxiv2021

A Robust and Scalable Graph Neural Network for Accurate Single Cell Classification

MotivationSingle-cell RNA sequencing (scRNA-seq) techniques provide high-resolution data on cellular heterogeneity in diverse tissues, and a critical step for the data analysis is cell type identification. Traditional methods usually cluster the cells and manually identify cell clusters through marker genes, which is time-consuming and subjective. With the launch of several large-scale single-cell projects, millions of sequenced cells have been annotated and it is promising to transfer labels from the annotated datasets to newly generated datasets. One powerful way for the transferring is to learn cell relations through the graph neural network (GNN), while vanilla GNN is difficult to process millions of cells due to the expensive costs of the message-passing procedure at each training epoch. ResultsHere, we have developed a robust and scalable GNN-based method for accurate single cell classification (GraphCS), where the graph is constructed to connect similar cells within and between labelled and unlabelled scRNA-seq datasets for propagation of shared information. To overcome the slow information propagation of GNN at each training epoch, the diffused information is pre-calculated via the approximate Generalized PageRank algorithm, enabling sublinear complexity for a high speed and scalability on millions of cells. Compared with existing methods, GraphCS demonstrates better performance on simulated, cross-platform, and cross-species scRNA-seq datasets. More importantly, our model can achieve superior performance on a large dataset with one million cells within 50 minutes.

bioinformatics↗

bioRxiv2021

Analysis of rhizosphere bacterial diversity of Angelica dahurica var. formosana based on different experimental sites and varieties (strains)

Only with good producing areas and good germplasm can good medicinal materials be produced. In this study, in order to explore the effects of soil environment and germplasm on yield and quality of Angelica dahurica var. formosana from microorganism level, Illumina MiSeq was used to study the rhizosphere bacterial diversity of A. dahurica var. formosana based on different origin and varieties (strains). The results showed that the bacterial community of A. dahurica var. formosana was stable and conserved to a certain extent, and the bacteria of Proteobacteria and Firmicutes might play an important role in improving the yield and quality of A. dahurica var. formosana. Soil variables were the key factors affecting the rhizosphere bacterial community composition of A. dahurica var. formosana. These results are of great significance for further understanding the growth promotion mechanism of beneficial rhizosphere bacteria, reducing fertilizer application, expanding planting area, improving soil environment, and improving yield and quality. ImportanceRhizosphere microorganisms play an important role in plant growth and accumulation of secondary metabolites. In this study, it was found for the first time that the rhizosphere bacterial community of A. dahurica var. formosana was relatively stable and conserved which could adapt to different environmental changes and resist the interference of external factors to a certain extent, providing a theoretical basis for distant planting and promotion of planting. The bacterial taxa of Proteobacteria and Firmicutes might play an important role in improving the yield and quality of A. dahurica var. formosana. This provided a theoretical basis for the exploitation and utilization of rhizosphere microbial resources of A. dahurica var. formosana to artificially control the soil environment and provided a new idea for increasing yield and quality of A. dahurica var. formosana.

microbiology↗

bioRxiv2021

The challenge of structural heterogeneity in the native mass spectrometry studies of the SARS-CoV-2 spike protein interactions with its host cell-surface receptor

Native mass spectrometry (MS) enjoyed tremendous success in the past two decades in a wide range of studies aiming at understanding the molecular mechanisms of physiological processes underlying a variety of pathologies and accelerating the drug discovery process. However, the success record of native MS has been surprisingly modest with respect to the most recent challenge facing the biomedical community - the novel coronavirus infection (COVID-19). The major reason for the paucity of successful studies that use native MS to target various aspects of SARS-CoV-2 interaction with its host is the extreme degree of structural heterogeneity of the viral protein playing a key role in the host cell invasion. Indeed, the SARS-CoV-2 spike protein (S-protein) is extensively glycosylated, presenting a formidable challenge for native mass spectrometry (MS) as a means of characterizing its interactions with both the host cell-surface receptor ACE2 and the drug candidates capable of disrupting this interaction. In this work we evaluate the utility of native MS complemented with the experimental methods using gas-phase chemistry (limited charge reduction) to obtain meaningful information on the association of the S1 domain of the S-protein with the ACE2 ectodomain, and the influence of a small synthetic heparinoid on this interaction. Native MS reveals the presence of several different S1 oligomers in solution and allows the stoichiometry of the most prominent S1/ACE2 complexes to be determined. This enables meaningful interpretation of the changes in native MS that are observed upon addition of a small synthetic heparinoid (the pentasaccharide fondaparinux) to the S1/ACE2 solution, confirming that the small polyanion destabilizes the protein/receptor binding.

biochemistry↗

bioRxiv2021

X-DPI: A structure-aware multi-modal deep learning model for drug-protein interactions prediction

MotivationIdentifying the drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. Existing methods usually use unrealistic datasets with hidden bias, which will limit the accuracy of virtual screening methods. Meanwhile, most DPIs prediction methods pay more attention to molecular representation but lack effective research on protein representation and high-level associations between different instances. To this end, we presented here a novel structure-aware multi-modal DPIs prediction model, X-DPI, performing on a curated industry-scale benchmark dataset. ResultsWe built a high-quality benchmark dataset named GalaxyDB for DPIs prediction. This industry-scale dataset along with an unbiased training procedure resulted in a more robust benchmark study. For informative protein representation, we constructed a structure-aware graph neural network method from the protein sequence by combining predicted contact maps and graph neural networks. Through further integration of structure-based representation and high-level pre-trained embeddings for molecules and proteins, our model captured more effectively the feature representation of the interactions between them. As a result, X-DPI outperformed state-of-the-art DPIs prediction methods and obtained 5.30% Mean Square Error (MSE) improved in the DAVIS dataset and 8.89% area under the curve (AUC) improved in GalaxyDB dataset. Moreover, our model is an interpretable model with the transformer-based interaction mechanism, which can accurately reveal the binding sites between molecule and protein.

bioinformatics↗

bioRxiv2021

Exploration of molecular mechanism underlying the self-flocculation of Zymomonas mobilis through comparative omics analyses and experimental validations for developing robust production systems

Zymomonas mobilis metabolizes sugar through the Entner-Doudoroff pathway with less ATP generated for lower biomass accumulation and more substrate to product formation with improved yield, since ATP is dissipated predominately through growth for intracellular energy homeostasis, making it a platform to be engineered as microbial cell factories, particularly for producing bulk commodities with major cost from feedstock consumption. ZM401, a self-flocculating mutant, presents advantages for production including cost-effective biomass recovery through gravity sedimentation, self-immobilization within bioreactors for high cell density to improve productivity and enhanced tolerance to environmental stresses for high product titers, but molecular mechanism underlying this phenotype is largely unknown. In this work, we sequenced and assembled the genome of ZM401 to explore genetic basis for the self-flocculation of the bacterial cells through comparative genomic and transcriptomic analyses, molecular docking simulations for enzymes encoded by functional genes and their substrates/activators, and experimental validations. Our results demonstrated that the single nucleotide deletion in ZMO1082 disrupted its stop codon for the putative gene being fused with ZMO1083, which created an exciting gene encoding the subunit A of the bacterial cellulose synthase with unique function for synthesizing cellulose microfibrils to flocculate the bacterial cells, and the single nucleotide mutation in ZMO1055 compromised the function of bifunctional diguanylate cyclase/phosphodiesterase encoded by the gene on the degradation of c-di-GMP for its intracellular accumulation to activate the cellulose biosynthesis. These discoveries are significant not only for optimizing the self-flocculation of Z. mobilis, but also engineering other bacteria with the self-flocculating phenotype for robust production.

microbiology↗

bioRxiv2021

Heparan Sulfate-dependent RAGE oligomerization is indispensable for pathophysiological functions of RAGE

RAGE, a druggable inflammatory receptor, is known to function as an oligomer but the exact oligomerization mechanism remains poorly understood. Previously we have shown that heparan sulfate (HS) plays an active role in RAGE oligomerization. To understand the physiological significance of HS-induced RAGE oligomerization in vivo, we generated RAGE knock-in mice (RageAHA/AHA) by introducing point mutations to specifically disrupt HS-RAGE interaction. The RAGE mutant demonstrated normal ligand-binding but impaired capacity of HS-binding and oligomerization. Remarkably, RageAHA/AHA mice phenocopied Rage-/- mice in two different pathophysiological processes, namely bone remodeling and neutrophil-mediated liver injury, which demonstrates that HS-induced RAGE oligomerization is essential for RAGE signaling. Our findings suggest that it should be possible to block RAGE signaling by inhibiting HS-RAGE interaction. To test this, we generated a monoclonal antibody that targets the HS-binding site of RAGE. This antibody blocks RAGE signaling in vitro and in vivo, recapitulating the phenotype of RageAHA/AHA mice. By inhibiting HS-RAGE interaction genetically and pharmacologically, our work validated an alternative strategy to antagonize RAGE. Finally, we have performed RNA-seq analysis of neutrophils and lungs and found that while Rage-/- mice had a broad alteration of transcriptome in both tissues compared to wild-type mice, the changes of transcriptome in RageAHA/AHA mice were much more restricted. This unexpected finding suggests that by preserving the expression of RAGE protein (in a dominant-negative form), RageAHA/AHA mouse might represent a cleaner genetic model to study physiological roles of RAGE in vivo compared to Rage-/- mice.

cell biology↗

bioRxiv2021

Oncomodulin Delays Age-Related Hearing Loss in C57 and CBA Mice

Ca2+ signaling is a major contributor to sensory hair cell function in the cochlea. Oncomodulin (OCM) is a Ca2+ binding protein preferentially expressed in outer hair cells of the cochlea and few other specialized cell types. Here, we expand on our previous reports and show that OCM prevents early progressive hearing loss in mice of two different genetic backgrounds: CBA/CaJ and C57BI/6J. In both backgrounds, genetic disruption of Ocm leads to early progressive hearing loss as measured by auditory brainstem response (ABR) and distortion product otoacoustic emission (DPOAE). In both strains, loss of Ocm reduced hearing across lifetime (hearing span) by more than 50% relative to wild type (WT). Even though the two WT strains have very different hearing spans, OCM plays a considerable and similar role within their genetic environment to regulate hearing function. The accelerated ARHL of the Ocm KO illustrates the importance of Ca2+ signaling in maintaining hearing health.

neuroscience↗

bioRxiv2021

Detection of Xanthomonas translucens pv. undulosa from wheat by quantitative PCR

A probe-based quantitative PCR (qPCR) protocol was developed for detection and evaluation of the wheat bacterial leaf streak pathogen Xanthomonas translucens pv. undulosa (Xtu). The protocol can also detect X. translucens pv. translucens (Xtt), but cant differentiate the two pathovars. When tested on DNA from plant, non-target bacteria and culture of microorganisms from wheat seeds, the qPCR showed a high specificity. On purified Xtu DNA, the qPCR was more sensitive than a loop-mediated isothermal amplification (LAMP) assay. When DNA samples from a set of serial dilutions of Xtu cells were tested, the qPCR method could repeatedly generate quantification cycle (Cq) values from the dilutions containing [≥]1,000 cells. Since 2 {micro}L of the total of 50 {micro}L DNA was used in one reaction, one qPCR reaction could detect the presence of the bacteria in samples containing as few as 40 bacterial cells. The qPCR could detect the bacteria from both infected seed and leaf tissues. For seed testing, a protocol for template preparation was standardized, which allowed one qPCR reaction to test DNA from the surface of one seed. Thus, the qPCR system could theoretically detect Xtu and/or Xtt in samples where the bacteria had an average concentration [≥]40 cells per seed.

microbiology↗

bioRxiv2021

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large sample sizes. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.

genomics↗

bioRxiv2021

Comprehensive analysis of RNA-seq and whole genome sequencing data reveals no evidence for SARS-CoV-2 integrating into host genome

SARS-CoV-2, as the causation of severe epidemic of COVID-19, is one kind of positive single-stranded RNA virus with high transmissibility. However, whether or not SARS-CoV-2 can integrate into host genome needs thorough investigation. Here, we performed both RNA sequencing (RNA-seq) and whole genome sequencing on SARS-CoV-2 infected human and monkey cells, and investigated the presence of host-virus chimeric events. Through RNA-seq, we did detect the chimeric host-virus reads in the infected cells. But further analysis using mixed libraries of infected cells and uninfected zebrafish embryos demonstrated that these reads are falsely generated during library construction. In support, whole genome sequencing also didnt identify the existence of chimeric reads in their corresponding regions. Therefore, the evidence for SARS-CoV-2s integration into host genome is lacking. One-Sentence SummarySARS-CoV-2 does not integrate into host genome through whole genome sequencing.

genomics↗

bioRxiv2021

Identifying developing interneurons as a potential target for multiple genetic autism risk factors in human and rodent forebrain.

Autism spectrum condition or autism is associated with numerous monogenic and polygenic genetic risk factors including the polygenic 16p11.2 microdeletion. A central question is what neural cells are affected. To systematically investigate we analysed single cell transcriptomes from gestational week (GW) 8-26 human foetal prefrontal cortex and identified a subset of interneurons (INs) first appearing at GW23 with enriched expression of a disproportionately large fraction of risk factor transcripts. This suggests the hypothesis that these INs are disproportionately vulnerable to mutations causing autism. We investigated this in a rat model of the 16p11.2 microdeletion. We found no change in the numbers or position of either excitatory or inhibitory neurons in the somatosensory cortex or CA1 of 16p11.2+/- rats but found that CA1 Sst INs were hyperexcitable with an enlarged axon initial segment, which was not the case for CA1 pyramidal cells. This study prompts deeper investigation of IN development as a convergent target for autism genetic risk factors.

neuroscience↗

bioRxiv2021

The Copper Responsive Transcription Factor SPL7 Represses Key Abscisic Acid Biosynthetic Genes to Balance Growth and Drought Tolerance

Plants adapt to adverse environments by turning on defense against abiotic stresses, which is mainly orchestrated by the phytohormone abscisic acid (ABA). But how ABA homeostasis is modulated to balance growth and stress responses is still largely unknown. Here we report that prior treatment of Arabidopsis seedling with high copper retardates growth but enhances draught tolerance at later stages by modulating ABA accumulation. Subsequent genetic, physiological, transcriptomic, and molecular investigations revealed that the copper responsive transcription factor SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 7 (SPL7) is a strong regulator of ABA accumulation. We showed that SPL7 is destabilized by high copper and consistently suppresses genes encoding three key oxygenases in the ABA biosynthetic pathway of land plants via binding to the GTAC copper response motifs in their promoters. These results revealed a new mechanism whereby copper availability, inversely reflected by SPL7 abundance, modulates de novo ABA biosynthesis to balance growth and drought tolerance. One-sentence summaryHigh copper availability represses SPL7, releasing its suppression on key ABA biosynthetic genes and leading to increased ABA accumulation that inhibits growth but enhances drought tolerance.

plant biology↗

bioRxiv2021

Efficacy of ancestral receptor-binding domain, S1 and trimeric spike protein vaccines against SARS-CoV-2 variants B.1.1.7, B.1.351, and B.1.617.1

The ongoing coronavirus disease 2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current SARS-CoV-2 vaccines are based on spike (S) protein, S1 subunit, or receptor-binding domain (RBD) of prototype strain. Emergence of several novel SARS-CoV-2 variants has raised concern about potential immune escape. In this study, we performed an immunogenicity comparison of ancestral RBD, S1, and S ectodomain trimer (S-trimer) antigens and tested the efficacy of these prototype vaccines against the circulating variants, especially B.1.617 that has been linked to Indias current COVID-19 surge. We found that RBD and S-trimer proteins could induce significantly higher neutralizing antibody titers than S1 protein. For the three vaccines, the neutralizing titers decreased over time, but still remained high for at least five months after immunization. Importantly, the three prototype vaccines were still effective in neutralizing the variants of concern, although B.1.351 and B.1.617.1 lineages showed varying degrees of reduction in neutralization by the immune sera. The vaccines-induced sera were shown to block receptor binding and inhibit S protein-mediated membrane fusion. In addition, the immune sera did not promote antibody-dependent enhancement (ADE) in vitro. Our work provides valuable information for development of SARS-CoV-2 subunit vaccines and also supports the continued use of ancestral RBD or S-based vaccines to fight the COVID-19 epidemic.

immunology↗

bioRxiv2021

Three-dimensional tracking of tethered particles for probing nanometer-scale single-molecule dynamics using plasmonic microscope

Three-dimensional (3D) tracking of surface-tethered single-particle reveals the dynamics of the molecular tether. However, most 3D tracking techniques lack precision, especially in axial direction, for measuring the dynamics of biomolecules with spatial scale of several nanometers. Here we present a plasmonic imaging technique that can track the motion of [~]100 tethered particles in 3D simultaneously with sub-nanometer axial precision at millisecond time resolution. By tracking the 3D coordinates of tethered particle with high spatial resolution, we are able to determine the dynamics of single short DNA and study its interaction with enzyme. We further show that the particle motion pattern can be used to identify specific and non-specific interactions in immunoassays. We anticipate that our 3D tracking technique can contribute to the understanding of molecular dynamics and interactions at the single-molecule level.

biophysics↗

bioRxiv2021

Unbiased screen of human transcriptome reveals an unexpected role of 3'UTRs in translation initiation

Although most eukaryotic mRNAs require a 5'-cap for translation initiation, some can also be translated through a poorly studied cap-independent pathway. Here we developed a circRNA-based system and unbiasedly identified more than 10,000 sequences in the human transcriptome that contain Cap-independent Translation Initiators (CiTIs). Surprisingly, most of the identified CiTIs are located in 3'UTRs, which mainly promote translation initiation in mRNAs bearing highly structured 5'UTR. Mechanistically, CiTI recruits several translation initiation factors including eIF3 and DHX29, which in turn unwind 5'UTR structures and facilitate ribosome scanning. Functionally, we showed that the translation of HIF1A mRNA, an endogenous DHX29 target, is antagonistically regulated by its 5'UTR structure and a new 3'-CiTI in response to hypoxia. Therefore, deletion of 3'-CiTI suppresses cell growth in hypoxia and tumor progression in vivo. Collectively, our study uncovers a new regulatory mode for translation where the 3'UTR actively participate in the translation initiation.

molecular biology↗