Biology subjects

Hu, G.

Publications and source records attributed to Hu, G..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

Comparison analysis on transcriptomic of different human trophoblast development model

AimsMultiple models of trophoblastic cell development were developed. However, systematic comparisons of these cell models are lacking. Methods and ResultsIn this study, first-trimester chorionic villus and decidua tissues were collected. Transcriptome data was acquired by RNA-seq and the expression levels of trophoblast specific transcription factors were identified by immunofluorescence and RNA-seq data analysis. Differentially expressed genes between chorionic villus and decidua tissues and its related biological functions were identified. We identified genes that were relatively highly expressed and enriched transcription factors in trophoblast cells of different trophoblast cell models. ConclusionsThis analysis is of certain significance for further exploration of the development of placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

cell biology↗

bioRxiv2021

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data is a widely used method that can measure neural responses to naturalistic stimuli that are consistent across subjects. However, interdependent correlation values in ISC artificially inflated the degrees of freedom, which hinders the investigation of individual differences. Besides, the existing ISC model mainly focus on similarities between subjects but fails to distinguish neural responses to different stimuli features. To estimate large-scale brain networks evoked with naturalistic stimuli, we propose a novel analytic framework to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In the framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. Tensor component analysis (TCA) will then reveal spatially and temporally shared components, i.e., naturalistic stimuli evoked networks, their temporal courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with TCA, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of TCA algorithm. We demonstrate the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also apply the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are identified evoked by naturalistic movie viewing.

neuroscience↗

bioRxiv2020

Predicting placenta transcriptional regulatory interactions based on immunohistochemistry images data and convolutional neural network

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, immunohistochemistry images of different TFs in chorionic villus were obtained by a high-resolution scanner. Next, we used a convolutional neural network and machine learning method to infer gene interaction networks of human placenta from these images based on the transfer learning technique. The experimental results show that deep learning models reveals regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. Conclusions: We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

bioinformatics↗

bioRxiv2020

INO80 promotes H2A.Z occupancy to regulate 1 cell fate transition in pluripotent stem cells

The INO80 chromatin remodeler is involved in many chromatin-dependent cellular functions. However, its role in pluripotency and cell fate transition is not fully defined. We examined the impact of Ino80 deletion in the naive and primed pluripotent stem cells. We found that Ino80 deletion had minimal effect on self-renewal and gene expression in the naive state, but led to cellular differentiation and de-repression of developmental genes in the transition toward and maintenance of the primed state. Mechanistically, INO80 pre-marked gene promoters that would adopt the H3K4me3 and H3K27me3 bivalent histone modifications. It promoted H2A.Z occupancy at these future bivalent domains to facilitate H3K27me3 installation and maintenance as well as downstream gene repression. Thus, INO80-dependent H2A.Z occupancy is a critical a licensing step for bivalency and poised gene expression in pluripotent stem cells. Our results uncovered an unexpected function of INO80 in H2A.Z deposition and gene repression, and an epigenetic mechanism by which chromatin remodeling, histone variant and modification coordinately control cell fate.

developmental biology↗

bioRxiv2020

ORF3a mediated-incomplete autophagy facilitates SARS-CoV-2 replication

SARS-CoV-2 is the causative agent for the COVID-19 pandemic and there is an urgent need to understand the cellular response to SARS-CoV-2 infection. Beclin-1 is an essential scaffold autophagy protein that forms two distinct subcomplexes with modulators Atg14 and UVRAG, responsible for autophagosome formation and maturation, respectively. In the present study, we found that SARS-CoV-2 infection triggers an incomplete autophagy response, elevated autophagosome formation but impaired autophagosome maturation, and declined autophagy by genetic knockout of essential autophagic genes reduces SARS-CoV-2 replication efficiency. By screening 28 viral proteins of SARS-CoV-2, we demonstrated that expression of ORF3a alone is sufficient to induce incomplete autophagy. Mechanistically, SARS-CoV-2 ORF3a interacts with autophagy regulator UVRAG to facilitate Beclin-1-Vps34-Atg14 complex but selectively inhibit Beclin-1-Vps34-UVRAG complex. Interestingly, although SARS-CoV ORF3a shares 72.7% amino acid identity with the SARS-CoV-2 ORF3a, the former had no effect on cellular autophagy response. Thus, our findings provide the mechanistic evidence of possible takeover of host autophagy machinery by ORF3a to facilitate SARS-CoV-2 replication and raises the possibility of targeting the autophagic pathway for the treatment of COVID-19.

microbiology↗

bioRxiv2020

A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell Transcriptomics

Recent development of single-cell RNA-seq (scRNA-seq) technologies has led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effect, which is inevitable in studies involving human tissues. Most existing methods remove batch effect in a low-dimensional embedding space. Although useful for clustering, batch effect is still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effect. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Popular methods such as Seurat3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effect in the gene expression space, but MNN can only analyze two batches at a time and it becomes computationally infeasible when the number of batches is large. Here we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data, while correcting batch effect both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC consistently outperforms scVI, DCA, and MNN. With CarDEC denoising, those non-highly variable genes offer as much signal for clustering as the highly variable genes, suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDECs denoised and batch corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effect. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.

bioinformatics↗

bioRxiv2020

SingleChannelNet: A model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Most of the existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, few studies are able to obtain high accuracy sleep staging using raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-convolution blocks and several max-average pooling layers to learn different scales of feature representations. To demonstrate the efficiency of the proposed model, we evaluate our model using different raw single-channel EEGs (C4/A1 and Fpz-Cz) on two different datasets (CC-SHS and Sleep-EDF datasets). Experimental results show that the proposed architecture can achieve better over-all accuracy and Cohens kappa (CCSHS: 90.2%-86.5%, Sleep-EDF: 86.1%-80.5%) compared with state-of-the-art approaches. Additionally, the proposed model can learn features automatically for sleep stage classification using different single-channel EEGs with distinct sampling rates from different datasets without using any hand-engineered features.

bioengineering↗

bioRxiv2020

Comparative analysis reveals the species-specific genetic determinants of ACE2 required for SARS-CoV-2 entry

Coronavirus interaction with its viral receptor is a primary genetic determinant of host range and tissue tropism. SARS-CoV-2 utilizes ACE2 as the receptor to enter host cell in a species-specific manner. We and others have previously shown that ACE2 orthologs from New World monkey, koala and mouse cannot interact with SARS-CoV-2 to mediate viral entry, and this defect can be restored by humanization of the restrictive residues in New World monkey ACE2. To better understand the genetic determinants behind the ability of ACE2 orthologs to support viral entry, we compared koala and mouse ACE2 sequences with that of human and identified the key residues in koala and mouse ACE2 that restrict viral receptor activity. Humanization of these critical residues rendered both koala and mouse ACE2 capable of binding the spike protein and facilitating viral entry. The single mutation that allowed for mouse ACE2 to serve as a viral receptor provides a potential avenue for the development of SARS-CoV-2 mouse model.

microbiology↗

bioRxiv2020

Burkholderia collagen-like protein 8, Bucl8, is a unique outer membrane component of a tetrapartite efflux pump in Burkholderia pseudomallei and Burkholderia mallei

Bacterial efflux pumps are an important pathogenicity trait because they extrude a variety of xenobiotics. Our laboratory previously identified in silico Burkholderia collagen-like protein 8 (Bucl8) in the Tier one select agents Burkholderia pseudomallei and Burkholderia mallei. We hypothesize that Bucl8, which contains two predicted tandem outer membrane efflux pump domains, is a component of a putative efflux pump. Unique to Bucl8, as compared to other outer membrane proteins, is the presence of an extended extracellular region containing a collagen-like (CL) domain and a non-collagenous C-terminus (Ct). Molecular modeling and circular dichroism spectroscopy with a recombinant protein, corresponding to this extracellular CL-Ct portion of Bucl8, demonstrated that it adopts a collagen triple helix, whereas functional assays screening for Bucl8 ligands identified binding to fibrinogen. Bioinformatic analysis of the bucl8 gene locus revealed it resembles a classical efflux-pump operon. The bucl8 gene is co-localized with downstream fusCDE genes encoding fusaric acid (FA) resistance, and with an upstream gene, designated as fusR, encoding a LysR-type transcriptional regulator. Using RT-qPCR, we defined the boundaries and transcriptional organization of the fusR-bucl8-fusCDE operon. We found exogenous FA induced bucl8 transcription over 80-fold in B. pseudomallei, while deletion of the entire bucl8 locus decreased the MIC of FA 4-fold in its isogenic mutant. We furthermore showed that the Bucl8 pump expressed in the heterologous Escherichia coli host confers FA resistance. On the contrary, the Bucl8 pump did not confer resistance to a panel of clinically-relevant antimicrobials in Burkholderia and E. coli. We finally demonstrated that deletion of the bucl8-locus drastically affects the growth of the mutant in L-broth. We determined that Bucl8 is a component of a novel tetrapartite efflux pump, which confers FA resistance, fibrinogen binding, and optimal growth. Author SummaryBurkholderia pseudomallei and Burkholderia mallei are highly infectious and multidrug resistant bacteria that are classified by the National Institute of Allergy and Infectious Diseases as Tier one select agents partly due to the intrinsic multidrug resistance associated with expression of the efflux pumps. To date, only few efflux pumps predicted in Burkholderia spp. have been studied in detail. In the current study we introduce Bucl8, an outer membrane component of an unreported putative efflux pump with a unique extended extracellular portion that forms a collagen triple helix and binds fibrinogen. We demonstrate Bucl8s role in fusaric acid resistance by defining its operon via bioinformatic and transcriptional analyses, as well as by employing loss-of-function and gain-of-function genetic approaches. Our studies also implicate the Bucl8-associated pump in metabolic and physiologic homeostasis. Understanding how Bucl8 efflux pump contributes to Burkholderia pathology will foster development of pump inhibitors targeting transport mechanism or identifying potential surface-exposed vaccine targets.

microbiology↗

bioRxiv2020

A Look-ahead Monte Carlo Simulation Method for Improving Parental Selection in Trait Introgression

Multiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches.

bioinformatics↗

bioRxiv2020

The S1/S2 boundary of SARS-CoV-2 spike protein modulates cell entry pathways and transmission

The global spread of SARS-CoV-2 is posing major public health challenges. One unique feature of SARS-CoV-2 spike protein is the insertion of multi-basic residues at the S1/S2 subunit cleavage site, the function of which remains uncertain. We found that the virus with intact spike (Sfull) preferentially enters cells via fusion at the plasma membrane, whereas a clone (Sdel) with deletion disrupting the multi-basic S1/S2 site instead utilizes a less efficient endosomal entry pathway. This idea was supported by the identification of a suite of endosomal entry factors specific to Sdel virus by a genome-wide CRISPR-Cas9 screen. A panel of host factors regulating the surface expression of ACE2 was identified for both viruses. Using a hamster model, animal-to-animal transmission with the Sdel virus was almost completely abrogated, unlike with Sfull. These findings highlight the critical role of the S1/S2 boundary of the SARS-CoV-2 spike protein in modulating virus entry and transmission.

microbiology↗

bioRxiv2020

Genetic signatures for lineage/sublineage classification of HPV16, 18, 52 and 58 variants

Increasing evidences indicate that high-risk HPV variants are heterogeneous in carcinogenicity and ethnic dispersion. In this work, we identified genetic signatures for convenient determination of lineage/sublineage of HPV16, 18, 52 and 58 variants. Using publicly available genomes, we found that E2 of HPV16, L2 of HPV18, L1 and LCR of HPV52, and L2, LCR and E1 of HPV58 contain the proper genetic signature for lineage/sublineage classification. Sets of hierarchical signature nucleotide positions (SNPs) were further confirmed for high accuracy (>98%) by classifying HPV genomes obtained from Chinese females, which included 117 HPV16 variants, 48 HPV18 variants 117 HPV52 variants and 89 HPV58 variants. The circulation of HPV variants posing higher cancer risk in Eastern China, such as HPV16 A4 and HPV58 A3, calls for continuous surveillance in this region. The marker genes and signature nucleotide positions may facilitate cost-effective diagnostic detections of HPV variants in clinical settings.

evolutionary biology↗

bioRxiv2020

Growth, metabolism, and morphology of Akkermansia muciniphila grown in different nutrient media

As a kind of potential probiotic, Akkermansia muciniphila abundance in human body is directly causally related to obesity, diabetes, inflammation and abnormal metabolism. In this study, A. muciniphila dynamic cultures using five different media were implemented in an in vitro bionic intestinal reactor for the first time instead of the traditional static culture using brain heart infusion broth (BHI) or BHI + porcine mucin (BPM). The biomass under dynamic culture using BPM reached 1.92 g/L, which improved 44.36% compared with the value under static culture using BPM. The biomass under dynamic culture using human mucin (HM) further increased to the highest level of 2.89 g/L. Under dynamic culture using porcine mucin (PM) and HM, the main metabolites were short-chain fatty acids (acetic acid and butyric acid), while using other media, a considerable amount of branched-chain fatty acids (isobutyric and isovaleric acids) were produced. Under dynamic culture Using HM, the cell diameters reached 999 nm, and the outer membrane protein concentration reached the highest level of 26.26 g/mg. This study provided a preliminary theoretical basis for the development of A. muciniphila as the next generation probiotic.

microbiology↗

bioRxiv2020

Insight into vaccine development for Alpha-coronaviruses based on structural and immunological analyses of spike proteins

Coronaviruses that infect humans belong to the Alpha-coronavirus (including HCoV-229E) and Beta-coronavirus (including SARS-CoV and SARS-CoV-2) genera. In particular, SARS-CoV-2 is currently a major threat to public health worldwide. However, no commercial vaccines against the coronaviruses that can infect humans are available. The spike (S) homotrimers bind to their receptors through the receptor-binding domain (RBD), which is believed to be a major target to block viral entry. In this study, we selected Alpha-coronavirus (HCoV-229E) and Beta-coronavirus (SARS-CoV and SARS-CoV-2) as models. Their RBDs were observed to adopt two different conformational states (lying or standing). Then, structural and immunological analyses were used to explore differences in the immune response with RBDs among these coronaviruses. Our results showed that more RBD-specific antibodies were induced by the S trimer with the RBD in the "standing" state (SARS-CoV and SARS-CoV-2) than the S trimer with the RBD in the "lying" state (HCoV-229E), and the affinity between the RBD-specific antibodies and S trimer was also higher in the SARS-CoV and SARS-CoV-2. In addition, we found that the ability of the HCoV-229E RBD to induce neutralizing antibodies was much lower and the intact and stable S1 subunit was essential for producing efficient neutralizing antibodies against HCoV-229E. Importantly, our results reveal different vaccine strategies for coronaviruses, and S-trimer is better than RBD as a target for vaccine development in Alpha-coronavirus. Our findings will provide important implications for future development of coronavirus vaccines. ImportanceOutbreak of coronaviruses, especially SARS-CoV-2, poses a serious threat to global public health. Development of vaccines to prevent the coronaviruses that can infect humans has always been a top priority. Coronavirus spike (S) protein is considered as a major target for vaccine development. Currently, structural studies have shown that Alpha-coronavirus (HCoV-229E) and Beta-coronavirus (SARS-CoV and SARS-CoV-2) RBDs are in lying and standing state, respectively. Here, we tested the ability of S-trimer and RBD to induce neutralizing antibodies among these coronaviruses. Our results showed that Beta-CoVs RBDs are in a standing state, and their S proteins can induce more neutralizing antibodies targeting RBD. However, HCoV-229E RBD is in a lying state, and its S protein induces a low level of neutralizing antibody targeting RBD. Our results indicate that Alpha-coronavirus is more conducive to escape host immune recognition, and also provide novel ideas for the development of vaccines targeting S protein.

microbiology↗

bioRxiv2020

Activation of the myeloid checkpoint inhibitor LILRB3 (ILT5) elicits profound immununomodulation

Despite advances in identifying the key immunoregulatory roles of many of the human leukocyte immunoglobulin (Ig)-like receptor (LILR) family members, the function of the inhibitory molecule LILRB3 (ILT5, CD85a, LIR3) remains unclear. Studies indicate a predominant myeloid expression; however, high homology within the LILR family and a relative paucity of reagents have hindered progress for this receptor. To investigate its function and potential immunomodulatory capacity, a panel of LILRB3-specific monoclonal antibodies (mAb) was generated. LILBR3-specific mAb bound to discrete epitopes in either Ig-like domain two or four. LILRB3 ligation on primary human monocytes by agonistic mAb resulted in phenotypic and functional changes, leading to potent inhibition of immune responses in vitro, including significant reduction in T cell proliferation. Importantly, agonizing LILRB3 in humanized mice induced tolerance and permitted efficient engraftment of allogeneic cells. Our findings reveal powerful immunosuppresive functions of LILRB3 and identify it as an important myeloid checkpoint receptor.

immunology↗

bioRxiv2020

Functional and Genetic Analysis of Viral Receptor ACE2 Orthologs Reveals Broad Potential Host Range of SARS-CoV-2

The pandemic of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major global health threat. Epidemiological studies suggest that bats are the natural zoonotic reservoir for SARS-CoV-2. However, the host range of SARS-CoV-2 and intermediate hosts that facilitate its transmission to humans remain unknown. The interaction of coronavirus with its host receptor is a key genetic determinant of host range and cross-species transmission. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) as the receptor to enter host cells in a species-dependent manner. It has been shown that human, palm civet, pig and bat ACE2 can support virus entry, while the murine ortholog cannot. In this study, we characterized the ability of ACE2 from diverse species to support viral entry. We found that ACE2 is expressed in a wide range of species, with especially high conservation in mammals. By analyzing amino acid residues of ACE2 critical for virus entry, based on structure of SARS-CoV spike protein interaction with human, bat, palm civet, pig and ferret ACE2, we identified approximately eighty ACE2 proteins from mammals that could potentially mediate SARS-CoV-2 entry. We chose 48 representative ACE2 orthologs among eighty orthologs for functional analysis and it showed that 44 of these mammalian ACE2 orthologs, including those of domestic animals, pets, livestock, and animals commonly found in zoos and aquaria, could bind SARS-CoV-2 spike protein and support viral entry. In contrast, New World monkey ACE2 orthologs could not bind SARS-CoV-2 spike protein and support viral entry. We further identified the genetic determinant of New World monkey ACE2 that restricts viral entry using genetic and functional analyses. In summary, our study demonstrates that ACE2 from a remarkably broad range of species can facilitate SARS-CoV-2 entry. These findings highlight a potentially broad host tropism of SARS-CoV-2 and suggest that SARS-CoV-2 might be distributed much more widely than previously recognized, underscoring the necessity to monitor susceptible hosts to prevent future outbreaks.

microbiology↗

bioRxiv2020

Plagues of Desert Locust: No invasion risk to China

Recently, the most serious upsurge of desert locust (Schistocerca gregaria) in the last 25 years is spreading across eastern Africa and southwestern Asia. Parts of the desert locust invasion area, namely the northern border areas of Pakistan and India are very close to China, and whether locust swarms will invade China is of wide concern. To answer this question, we identified areas of potentially suitable habitat for the desert locust within China based on historical precipitation and temperature data, and found that parts of Xinjiang and Inner Mongolia provinces could provide ephemeral habitat in summer, but these places are remote from any other desert locust breeding area. Presently, the desert locust populations in Pakistan and India are mature and have laid eggs, and are less likely to spread long distances. The next generation of adults will appear in April and May, and so we examined twenty years historical wind data (2000-2019) for this period. Our results showed that winds at the height of locust swarm flight blew eastward during April and May, but the wind speeds were quite slow and would not facilitate desert locust eastward migration over large distances. Furthermore, simulated trajectories of desert locust swarms with 10 days migration mostly ended within India. The most easterly point of these trajectories just reached eastern India, close to the border between India and Myanmar, and this is very close to the eastern border of the invasion area of desert locust described in previous studies. In conclusion, the risk that the desert locust will invade China is very low.

ecology↗

bioRxiv2020

Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis

An important step in single-cell RNA-seq (scRNA-seq) analysis is to cluster cells into different populations or types. Here we describe ItClust, an Iterative Transfer learning algorithm with neural network for scRNA-seq Clustering. ItClust learns cell type knowledge from well-annotated source data, but also leverages information in the target data to make it less dependent on the source data quality. Through extensive evaluations using datasets from different species and tissues generated with diverse scRNA-seq protocols, we show that ItClust significantly improves clustering and cell type classification accuracy compared to popular unsupervised clustering and supervised cell type classification algorithms.

genomics↗