Biology subjects

He, Y.

Publications and source records attributed to He, Y..

At least 469 records · Page 26Linked to original sources

bioRxiv2022

Reversal of obesogenic feeding and hypometabolism by a bifurcating GABAergic neural circuit

Diet-induced obesity is characterized by unsatiated consumption of energy-dense diets and impaired metabolism, whereby anti-obesity effect of the high-level of circulating leptin is unknowingly blunted. Emerging evidence suggests that the leptin receptor (LepR) signaling system, residing within the agouti-related protein (AgRP) neurons of the hypothalamus, critically contributes to obesogenic feeding, nutrient partitioning, and energy metabolism. However, the neural circuit mechanism underlying the leptin-dependent control of obesogenic feeding and energy balance remains largely elusive. Here, we show that two distinct subgroups of LepR-expressing AgRP neurons send non-collateral, GABAergic projections to the dorsomedial hypothalamic nucleus (DMH) and to the medial part of the medial preoptic nucleus (MPO) for the differential control of metabolic homeostasis and obesogenic feeding, respectively. We found that the AgRPLepR-DMH neural circuit plays a significant role in leptin-dependent control of metabolic homeostasis through the 3-containing GABAA receptor signaling on the melanocortin 4 receptor neurons within the DMH (MC4RDMH). In contrast, the AgRPLepR-MPO neural circuit elicits dominant effects on the appetitive response to high-fat diet through the 2-containing GABAA receptors on the MC4RMPO neurons. Consistent with these behavioral results, the post-synaptic GABAA neurons located within the DMH and MPO displayed differentiated firing responses under various feeding and nutrient conditions. Our results demonstrate that these novel GABAergic neural circuits exert differentiated control of metabolic hemostasis and obesogenic feeding via distinct post-synaptic targets of leptin-responsive AgRP neurons. The findings of two genetically and anatomically distinct GABAA receptor signaling pathways within the DMH and MPO would undoubtedly accelerate the development of targeted, individualized, anti-obesity therapy.

neuroscience↗

bioRxiv2022

Comprehensive Evaluation of ACE2-Fc Combination with Neutralization Antibody on Broad Protection against SARS-CoV-2 and Its Variants

Emerging SARS-CoV-2 variants are threatening the efficacy of antibody therapies. Combination treatments including ACE2-Fc have been developed to overcome the evasion of neutralizing antibodies (NAbs) in individual cases. Here we conducted a comprehensive evaluation of this strategy by combining ACE2-Fc with NAbs of diverse epitopes on the RBD. NAb+ACE2-Fc combinations efficiently neutralized HIV-based pseudovirus carrying the spike protein of the Delta or Omicron variants, achieving a balance between efficacy and breadth. In an antibody escape assay using replication-competent VSV-SARS-CoV-2-S, all the combinations had no escape after fifteen passages. By comparison, all the NAbs without combo with ACE2-Fc had escaped within six passages. Further, the VSV-S variants escaped from NAbs were neutralized by ACE2-Fc, revealing the mechanism of NAb+ACE2-Fc combinations survived after fifteen passages. We finally examined ACE2-Fc neutralization against pseudovirus variants that were resistant to the therapeutic antibodies currently in clinic. Our results suggest ACE2-Fc is a universal combination partner to combat SARS-CoV-2 variants including Delta and Omicron.

cell biology↗

bioRxiv2022

Quantitative proteomics of the CDK9 interactome reveals a novel function of the HSP90-CDC37-P-TEFb complex for BETi-induced HIV-1 latency reactivation

Brd4 has been intensively investigated as a promising drug target because of its implicated functions in oncogenesis, inflammation and HIV-1 transcription. The formation of the Brd4-P-TEFb (CDK9/Cyclin T1) complex and its regulation of transcriptional elongation is critical for HIV latency reactivation and expression of many oncogenes. To further investigate the mechanism of the Brd4-P-TEFb complex in controlling elongation, mass spectrometry-based quantitative proteomics of the CDK9 interactome was performed. Upon treatment with the selective BET bromodomain inhibitor (BETi) JQ1, 535 proteins were successfully identified with high confidence as CDK9-interacting proteins. Among them, that increased bindings of HSP90 and CDC37 to CDK9 were particularly striking, and our data suggest that the HSP90-CDC37-P-TEFb complex is involved in controlling P-TEFbs dynamic equilibrium during BETi-induced HIV-1 latency reactivation. Furthermore, the HSP90-CDC37-P-TEFb complex directly regulates HIV-1 transcription and relies on the recruitment by heat shock factor 1 (HSF1) for binding to the HIV-1 promoter. These results advance the understanding of HSP90-CDC37-P-TEFb in HIV-1 latency reversal and enlighten the development of potential strategies to eradicate HIV-1 using a combination of targeted drugs.

molecular biology↗

bioRxiv2022

Computed tomographic analysis of the dental system of three Jurassic ceratopsians: implications for the evolution of the tooth replacement pattern and diet in early-diverging ceratopsians

The dental system of ceratopsids is among the most specialized structure in Dinosauria, and includes high angled wear surfaces, split tooth roots, and multiple teeth in each tooth family. However, the early evolution of this unique dental system is generally poorly understood due to a lack of knowledge of the dental morphology and development in early-diverging ceratopsians. Here we study the dental system of three of the earliest-diverging Chinese ceratopsians: Yinlong and Hualianceratops from the early Late Jurassic of Xinjiang, and Chaoyangsaurus from the Late Jurassic of Liaoning. By using micro-computed tomographic analyses, our study has revealed significant new information regarding the dental system of these early ceratopsians, including no more than five replacement teeth in each jaw quadrant; at most one generation of replacement teeth in each alveolus; nearly full resorption of the functional tooth root during tooth replacement; and occlusion with low-angled, concave wear facets that differs significantly from the shearing occlusal system seen in ceratopsids. Yinlong displays an increase in the number of maxillary tooth alveoli and a decrease in the number of replacement teeth during ontogeny as well as the retention of remnants of functional teeth in the largest individual. Early-diverging ceratopsians thus display a relatively slow tooth replacement rate compared to late-diverging ceratopsians. Combined with paleobotany and palaeoenvironment data, Yinlong likely uses gastroliths to triturate foodstuffs, and the difference in diet strategy might have influenced the pattern of tooth replacement in later-diverging ceratopsians.

paleontology↗

bioRxiv2022

Determinants of trafficking, conduction, and disease within a K+ channel revealed through multiparametric deep mutational scanning

A longstanding goal in protein science and clinical genetics is to develop quantitative models of sequence, structure, and function relationships and delineate the mechanisms by which mutations cause disease. Deep Mutational Scanning (DMS) is a promising strategy to map how amino acids contribute to protein structure and function and to advance clinical variant interpretation. Here, we introduce 7,429 single residue missense mutation into the Inward Rectifier K+ channel Kir2.1 and determine how this affects folding, assembly, and trafficking, as well as regulation by allosteric ligands and ion conduction. Our data provides high-resolution information on a cotranslationally{-}folded biogenic unit, trafficking and quality control signals, and segregated roles of different structural elements in fold-stability and function. We show that Kir2.1 trafficking mutants are underrepresented in variant effect databases, which has implications for clinical practice. By comparing fitness scores with expert-reviewed variant effects, we can predict the pathogenicity of variants of unknown significance and disease mechanisms of know pathogenic mutations. Our study in Kir2.1 provides a blueprint for how multiparametric DMS can help us understand the mechanistic basis of genetic disorders and the structure-function relationships of proteins.

biochemistry↗

bioRxiv2022

Core Microbiota Drive the Prevalence of Extracellular Antibiotic Resistome in the Water Compartments

Unlike intracellular chromosome, extracellular DNA (eDNA) may accelerate the spreading of antibiotic resistance genes (ARGs) through natural transformation, but one of the core issues regarding to the bacterial taxonomic characterization of eDNA in the complex water environments is largely unknown. Hence, Illumina Miseq sequencing was used to identify the genotype of eDNA from wastewater (WW), river water (RW) and stormwater (SW) runoff. High-throughput qPCR targeting 384 genes was implemented to detect extracellular ARGs (eARGs) and mobile genetic elements (eMGEs). We obtained 2,708,291 high quality sequences from 66 eDNA samples. The SW exhibited the significant higher Shannon Index. Subsequently, we identified 34 core bacteria sources of eDNA widely distributed in the three water compartments. Among which, Pseudomonas, Flavobacterium, Limnohabitans, Burkholderiaceae_unclassified, Methylotenera and Acinetobacter were the most prevalent. A total of 302 eARGs and eMGEs were detected, suggesting that eDNA is an important antibiotic resistance reservoir. Among the 127 shared genes of the three groups, 15 core resistance genes were filtered, including IS6100, sul1 NEW, intI1, ISPps1-pseud, aac3-Via, qacH_351 and ISSm2-Xanthob. The Procrustes analysis and Variance Partitioning Analysis (VPA) demonstrated that core bacteria and MGEs were significantly correlated with eARGs. These results suggested that the occurrence and changes of eARGs in the water compartments may be largely attributed to the core microbiota and eMGEs. SynopsisThe identification of bacterial taxa of extracellular DNA is the preliminary issue for the natural transformation of extracellular antibiotic resistome.

microbiology↗

bioRxiv2021

Structure of telomerase-bound CST with Polymerase α-Primase

Telomeres are the physical ends of linear chromosomes, composed of short repeating sequences (e.g. TTGGGG in Tetrahymena for the G-strand) of double-stranded DNA with a single-strand 3-overhang of the G-strand and a group of proteins called shelterin1,2. Among these, TPP1 and POT1 associate with the 3-overhang, with POT1 binding the G-strand3 and TPP1 recruiting telomerase via interaction with telomerase reverse transcriptase (TERT)4. The ends of the telomeric DNA are replicated and maintained by telomerase5, for the G-strand, and subsequently DNA Polymerase -Primase6,7 (PolPrim), for the C-strand8. PolPrim is stimulated by CTC1-STN1-TEN1 (CST)9-12, but the structural basis of both PolPrim and CST recruitment to telomere ends remains unknown. Here we report cryo-EM structures of Tetrahymena CST in the context of telomerase holoenzyme, both in the absence and presence of PolPrim, as well as of PolPrim alone. Ctc1 binds telomerase subunit p50, a TPP1 ortholog, on a flexible Ctc1 binding motif unveiled jointly by cryo-EM and NMR spectroscopy. PolPrim subunits are arranged in a catalytically competent conformation, in contrast to previously reported autoinhibited conformation. Polymerase POLA1 binds Ctc1 and Stn1, and its interface with Ctc1 forms an entry port for G-strand DNA to the POLA1 active site. Together, we obtained a snapshot of four key players required for telomeric DNA synthesis in a single complex--telomerase core RNP, p50/TPP1, CST and PolPrim--that provides unprecedented insights into CST and PolPrim recruitment and handoff between G-strand and C-strand synthesis.

molecular biology↗

bioRxiv2021

A deep learning-based multisite neuroimage harmonization framework established with traveling-subject dataset

The accumulation of multisite large-sample MRI datasets collected by large brain research projects in the last decade has provided a critical resource for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects, observed in the imaging data and their derived structural and functional features, has prevented the derivation of consistent findings across different studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we proposed a deep learning-based framework to harmonize imaging data from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling-subject dataset from SRPBS and harmonized the gray matter volume maps from eight source sites to a target site. The proposed framework significantly eliminated inter-site differences in gray matter volume. The embedded encoders successfully captured both the abstract texture of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in site effect removal, data distribution homogenization, and intra-subject similarity improvement. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that could enhance reliability and reproducibility in multisite studies for brain development and brain disorders.

neuroscience↗

bioRxiv2021

Mapping Consistent, Reproducible, and Transcriptionally Relevant Connectome Hubs of the Human Brain

Human brain connectomes include sets of densely connected regions, known as connectome hubs, which play a vital role in understanding global brain communication, cognitive processing, and brain disorders. However, the consistency and reproducibility of functional connectome hubs anatomical localization have not been established to date and the genetic signatures underlying robust connectome hubs remain unknown. Here, we conduct the first worldwide, harmonized meta-connectomic analysis by pooling resting-state functional MRI data of 5,212 healthy young adults across 61 independent cohorts. We identify highly consistent and reproducible functional connectome hubs in heteromodal and unimodal regions both across cohorts and across individuals. These connectome hubs show heterogeneous connectivity profiles and are critical for both intra- and inter-network communications. Using post-mortem gene expression data, we show that these connectome hubs have a spatiotemporally distinctive transcriptomic pattern dominated by genes involved in the neuropeptide signaling pathway, neurodevelopmental processes, and metabolic processes. These results highlight the robustness of macroscopic connectome hubs and their potential cellular and molecular underpinnings.

neuroscience↗

bioRxiv2021

High-throughput tuning of ovarian cancer spheroids for on-chip invasion assays

We developed an invasion assay by using microfabricated culture devices. First, ovarian tumor spheroids were generated with a culture patch device consisting of an agarose membrane formed with a honeycomb microframe - the patch - and gelatin nanofiber backbone. By changing the dimensions of the honeycomb compartments we were able to control the number of cells and size of the spheroids. When the spheroids were placed on a patch coated with a thin membrane of fibrillary type I collagen, spheroid disruption was observed due to substrate induced cell migration. This process is straightforward and should be applicable to other cancer types, as well as assays under microfluidic conditions, thereby holding the potential for use in tumor modeling and anti-cancer drug development.

bioengineering↗

bioRxiv2021

The Flowering Repressor SVP recruits the TOPLESS co-repressor to control flowering in chrysanthemum and Arabidopsis

Plant flowering time is a consequence of the perception of environmental and endogenous signals. The MCM1-AGAMOUSDEFICIENS-SRF-box (MADS-box) gene SHORT VEGETATIVE PHASE (SVP) is a pivotal repressor that negatively regulates the floral transition during the vegetative phase. The transcriptional corepressor TOPLESS (TPL) plays critical roles in many aspects of plant life. An interaction first identified between the second LXLXLX motif (LRLGLP) of CmSVP with CmTPL1-2, which can repress the expression of a key flowering factor CmFTL3 by binding its promotor CArG element in chrysanthemum. Genetic analysis suggested that the CmSVP-CmTPL1-2 transcriptional complex is a prerequisite for SVP to act as a floral repressor, which reduces CmFTL3 transcriptional activity. CmSVP rescued the phenotype of the svp-31 mutant in Arabidopsis, and overexpression of AtSVP or CmSVP in the Arabidopsis dominant negative mutation tpl-1 led to a loss-of-function in late flowering, which confirmed the highly conserved function of SVP in the two completely different species. Thus, we have validated a conserved machinery wherein SVP relies on TPL to inhibit flowering through the direct regulation of FT, which is more meaningful for the evolution of species and could be translated to high-quality cultivation and breeding of crops.

plant biology↗

bioRxiv2021

Identification of a promiscuous conserved CTL epitope within the SARS-CoV-2 spike protein

The COVID-19 disease caused by infection with SARS-CoV-2 and its variants is devastating to the global public health and economy. To date, over a hundred COVID-19 vaccines are known to be under development and the few that have been approved to fight the disease are using the spike protein as the primary target antigen. Although virus neutralizing epitopes are mainly located within the RBD of the spike protein, the presence of T cell epitopes, particularly the CTL epitopes that are likely to be needed for killing infected cells, has received comparatively little attention. In this study, we predicted several potential T cell epitopes with web-based analytic tools, and narrowed them down from several potential MHC-I and MHC-II epitopes by ELIspot and cytolytic assays to a conserved MHC-I epitope. The epitope is highly conserved in current viral variants including the most recent Omicron and compatible with presentation by most HLA alleles worldwide. In conclusion, we identified a CTL epitope suitable for evaluating the CD8+ T cell-mediated cellular response and potentially for addition into future COVID-19 vaccine candidates to maximize CTL responses against SARS-CoV-2.

immunology↗

bioRxiv2021

Word structure tunes electrophysiological and hemodynamic responses in the frontal cortex

To date, it is still unclear how word structure might impact lexical processing in the brain for morphological impoverished language like Chinese. In this study, concurrent EEG and fNIRS recordings were performed to inspect the temporal and spatial brain activity that are related to the morphological priming effect (compound/derivation constitute priming vs. non-morphological priming) and word structure (compound vs. derivation) modulation. Interestingly, it was discovered that the morphological priming effect was mainly detected by the behavioral performance and spatial brain activation in the left prefrontal cortex, while word structure effect was revealed by the behavioral data as well as the temporal and spatial brain activation patterns. In addition, Chinese derivations exhibited significantly enhanced brain activation in the frontal cortex and involved more brain networks as compared to lexicalized compounds. The results were interpreted by the differing connection patterns between constitute morphemes within a given word structure from spreading activation perspective. More importantly, we demonstrated that Chinese word structure effect showed a distinct brain activation pattern as compared to that from the dual-route mechanism in alphabetic languages. Therefore, this pilot work paves a new avenue for comprehensively understanding the underlying cognitive neural mechanism associated with Chinese derivations and coordinate compounds.

neuroscience↗

bioRxiv2021

Cell cycle stage classification using phase imaging with computational specificity

Traditional methods for cell cycle stage classification rely heavily on fluorescence microscopy to monitor nuclear dynamics. These methods inevitably face the typical phototoxicity and photobleaching limitations of fluorescence imaging. Here, we present a cell cycle detection workflow using the principle of phase imaging with computational specificity (PICS). The proposed method uses neural networks to extract cell cycle-dependent features from quantitative phase imaging (QPI) measurements directly. Our results indicate that this approach attains very good accuracy in classifying live cells into G1, S, and G2/M stages, respectively. We also demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as cell population distribution across different stages of the cell cycle. We envision that the proposed method can become a nondestructive tool to analyze cell cycle progression in fields ranging from cell biology to biopharma applications. TeaserWe present a non-destructive, high-throughput method for cell cycle detection combining label-free imaging and deep learning.

bioengineering↗

bioRxiv2021

AlphaFold Models Illuminate Half of Dark Human Proteins

We investigate the use of confidence scores to predict the accuracy of a given AlphaFold model for drug discovery. Predicted accuracy is improved by eliminating confidence scores below 80, due to effects of disorder. 95% of models corresponding to a set of recent crystal structures are accurate at the fold level. Conformational discordance in the training set has a more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening, by which AlphaFold provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.

pharmacology and toxicology↗

bioRxiv2021

Base-resolution prediction of transcription factor binding signals by a deep learning framework

Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framework leveraging existing fully convolutional neural networks (FCN) to predict TF-DNA binding signals at the base-resolution level, called FCNsignal. The proposed FCNsignal can simultaneously achieve the following tasks: (i) modeling the base-resolution signals of binding regions; (ii) discriminating binding or non-binding regions; (iii) locating TF-DNA binding regions; (iv) predicting binding motifs. The experimental results on 53 TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets show that our proposed framework outperforms some existing state-of-the-art methods. In addition, we explored to use the trained FCNsignal to locate all potential TF-DNA binding regions on a whole chromosome and predict DNA sequences of arbitrary length, and the results show that our framework can find most of the known binding regions and accept sequences of arbitrary length. Furthermore, we demonstrated the potential ability of our framework in discovering causal disease-associated single-nucleotide polymorphisms (SNPs) through a series of experiments. Author summaryIdentification of transcription factor binding sites (TFBSs) is fundamental to study gene regulatory networks in biological systems, as TFs activate or suppress the transcription of genes by binding to specific TFBSs. With the development of high-throughput sequencing technologies and deep learning (DL), several DL-based approaches have been developed for systematically studying TFBSs, achieving impressive performance. Nevertheless, these methods either excessively focus on discriminating binding or non-binding sequences or individually accomplish multiple TFBSs-associated tasks. In this work, we provide an integrated framework, which utilizes the FCN architecture to predict TF-DNA binding signals at the base-resolution level, to simultaneously study multiple TFBSs-associated tasks. More importantly, we also demonstrate that our proposed framework has the ability to locate all potential TF-DNA binding regions from DNA sequences of arbitrary length. We hope that our framework can provide a new perspective on studying the mechanism of TF-DNA binding and its related tasks.

bioinformatics↗

bioRxiv2021

Understanding gene regulatory mechanisms based on gene classification

The CpG dinucleotide and its methylation play vital roles in gene regulation as well as 3D genome organization. Previous studies have divided genes into several categories based on the CpG intensity around transcription starting sites (TSS) and found that housekeeping genes tend to possess high CpG density while tissue-specific genes are generally characterized by low CpG density. In this study, we investigated how the CpG density distribution of a gene affects its transcription and regulation pattern. Based on the CpG density distribution around TSS, the human genes are clearly divided into different categories. Not only sequence properties, these different clusters exhibited distinctly different structural features, regulatory mechanisms, and correlation patterns between expression level and CpG/TpG density. These results emphasized that the usage of epigenetic marks in gene regulation is partially rooted in the sequence property of genes, such as their CpG density distribution.

genomics↗

bioRxiv2021

Structure Insight into Photosystem I Octamer from Cyanobacteria

Diversity of photosystem oligomers is essential to understand how photosynthetic organisms adopted to light conditions. Given by the structural and physiological significance, the assemblies of PSI supercomplex is of great interest in both chloroplast and cyanobacteria recently. In this study, two novel photosystem I supercomplexes were isolated for the first time from the low light incubated culture of filamentous cyanobacterium Anabaena sp. PCC 7120. These complexes were defined as PSI hexamers and octamers through biochemical and biophysical characterization. Their 77K emission spectra indicated that the red forms of chlorophylls seemed not to be affected during oligomerization. By cryo-EM single particle analysis, a near-atomic (7.0 [A]) resolution structure of PSI octamer was resolved, and the molecular assemblies of stable PSI octamer was revealed.

biophysics↗