Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 865 records · Page 48Linked to original sources

bioRxiv2021

GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates

Biomedical entity normalization unifies the language across biomedical experiments and studies, and further enables us to obtain a holistic view of life sciences. Current approaches mainly study the normalization of more standardized entities such as diseases and drugs, while disregarding the more ambiguous but crucial entities such as pathways, functions and cell types, hindering their real-world applications. To achieve biomedical entity normalization on these under-explored entities, we first introduce an expert-curated dataset OBO-syn encompassing 70 different types of entities and 2 million curated entity-synonym pairs. To utilize the unique graph structure in this dataset, we propose GraphPrompt, a promptbased learning approach that creates prompt templates according to the graphs. Graph-Prompt obtained 41.0% and 29.9% improvement on zero-shot and few-shot settings respectively, indicating the effectiveness of these graph-based prompt templates. We envision that our method GraphPrompt and OBO-syn dataset can be broadly applied to graph-based NLP tasks, and serve as the basis for analyzing diverse and accumulating biomedical data.

bioinformatics↗

bioRxiv2021

Two Liberibacter Effectors Combine to Suppress Critical Innate Immune Defenses and Facilitate Huanglongbing Pathogenesis in Citrus

Genome sequence analyses predicted the presence of effectors in the gram-negative Candidatus Liberibacter asiaticus (CLas) even without the presence of a classical type III secretion system. Since CLas is not culturable, it is not possible to perform traditional gene knockout experiments to determine the role of various effectors in Huanglongbing (HLB) pathogenesis. Therefore, we followed an alternative functional genomics approach to examine the role of the CLas effectors in HLB pathogenesis in general and more specifically in suppressing citrus innate immune response. Here, we focused on the CLas effectors, P235 and Effector 3, to perform the following studies. First, proteomic studies by LC-MS/MS were conducted to screen the putative interacting citrus protein partners of P235 and Effector 3 from the healthy and CLas-infected Hamlin extracts and the most probable candidates were identified based upon their high protein scores from LC-MS/MS. Second, a transgenic tobacco split GFP system was designed for in planta detection of the most probable citrus interacting protein partners of P235 and Effector 3. Third, in vitro and in planta studies were performed to show that each of two effectors interacts with and inhibits the functions of multiple citrus proteins belonging to the innate immune pathways. These inhibitory interactions led to a high level of reactive oxygen species (ROS), blocking of bactericidal lipid binding protein (LTP), and induction of premature programmed cell death (PCD), thereby supporting CLas infection and HLB pathogenesis. Finally, an LTP mimic was designed to sequester and block the CLas effector and to rescue the bactericidal activity of LTP.

plant biology↗

bioRxiv2021

Zika virus-specific IgM elicited during pregnancy exhibits ultrapotent neutralization

Congenital Zika virus (ZIKV) infection results in neurodevelopmental deficits in up to 14% of infants born to ZIKV-infected mothers. Neutralizing antibodies are a critical component of protective immunity. Here, we demonstrate that plasma IgM responses contribute to ZIKV immunity in pregnancy, mediating neutralization up to three months post symptoms. From a ZIKV-infected pregnant woman, we established a B cell line secreting a pentameric ZIKV-specific IgM (DH1017.IgM) that exhibited ultrapotent ZIKV neutralization dependent on the IgM isotype. DH1017.IgM targets a novel envelope dimer epitope within Domain II. The arrangement of this epitope on the virion is compatible with concurrent engagement of all ten antigen-binding sites of DH1017.IgM, a solution not achievable by IgG antibodies. DH1017.IgM protected against lethal ZIKV challenge in mice. Our findings identify a unique role of antibodies of the IgM isotype in protection against ZIKV and posit DH1017.IgM as a safe and effective candidate immunoprophylactic, particularly during pregnancy. Key pointsO_LIPlasma IgM contributes to early ZIKV neutralization during pregnancy C_LIO_LIUltrapotent neutralization by pentameric DH1017.IgM mAb depends on isotype C_LIO_LIDH1017.IgM can engage all binding sites concurrently through different angles of approach C_LIO_LIDH1017.IgM protects mice against lethal ZIKV challenge C_LI

immunology↗

bioRxiv2021

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure must be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

bioinformatics↗

bioRxiv2021

Remodeling of dermal adipose tissue alleviates cutaneous toxicity induced by anti-EGFR therapy

Anti-epidermal growth factor receptor (EGFR) therapy-associated cutaneous toxicity is a syndrome characterized by papulopustular rash, local inflammation, folliculitis and microbial infection, resulting in a decrease in quality of life and dose interruption. However, no effective clinical intervention is available for this adverse effect. Here, we report the atrophy of dermal white adipose tissue (dWAT), a highly plastic adipose tissue with various skin-specific functions, correlates with rash occurrence and exacerbation in a murine model of EGFR inhibitor-induced rash. The reduction in dWAT is due to the inhibition of adipogenic differentiation by defects in peroxisome proliferator-activated receptor {gamma} (PPAR{gamma}) signaling, and increased lipolysis by the induced expression of the lipolytic cytokine IL6. The activation of PPAR{gamma} by rosiglitazone maintains adipogenic differentiation and represses the transcription of IL6, eventually improving skin functions and ameliorating the severity of rash without altering the antitumor effects. Thus, activation of PPAR{gamma} represents a promising approach to ameliorate cutaneous toxicity in patients with cancer who receive anti-EGFR therapy. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

pharmacology and toxicology↗

bioRxiv2021

Deep-BGCpred: A unified deep learning genome-mining framework for biosynthetic gene cluster prediction

Natural products produced by microorganisms constitute an important source of essential pharmaceuticals, including antimicrobial and anti-tumor drugs. These bioactive molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The rapid increase of microbial genomics resources, due to the availability of high-throughput sequencing technologies, has spurred the development of computational methods for microbial genome mining for BGC discovery. Current machine learning methods, however, have limited successes in uncovering novel BGCs due to an excessive number of false positives in their predictions. To this end, we propose Deep-BGCpred, a framework that effectively addresses the aforementioned issue by improving a deep learning model termed DeepBGC. The new model embeds multi-source protein family domains and employs a stacked Bidirectional Long Short-Term Memory model to boost accuracy for BGC identifications. In particular, it integrates two customized strategies, sliding window strategy and dual-model serial screening, to improve the models performance stability and reduce the number of false positive in BGC predictions. We compare the proposed model against other well-established methods on common benchmarks and achieve new state-of-the-art results with convincing evidences. We expect that researchers working on genome mining for natural products may be greatly benefited from our newly proposed method, Deep-BGCpred.

bioinformatics↗

bioRxiv2021

Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8

TLR7 and TLR8 are key members of the Toll-like receptor family, playing crucial roles in the signaling pathways of innate immunity, and thus become attractive therapeutic targets of many diseases including infections and cancer. Although TLR7 and TLR8 show a highly degree of sequence homology, their biological response to small molecule binding is very different. Aiming to understand the mechanism of selective profiles of small molecule modulators against TLR7 and TLR8, we carried out molecular dynamic simulations on three imidazoquinoline derivatives bound to the receptors separately. They are Resiquimod (R), Hybrid-2 (H), and Gardiquimod (G), selective agonists of TLR7 and TLR8. Our MD trajectories indicated that in the complex of TLR7-R and TLR7-G, the two chains forming the TLR7 dimer tended to remain "open" conformation, while the rest systems maintained in the closed format. The agonists R, H, and G developed conformational deviation mainly on the aliphatic tail. Furthermore, we attempted to quantify the selectivity between TLR7 and TLR8 by binding free energies via MM-GBSA method. It showed that the three selected modulators were more favorable for TLR7 than TLR8, and the ranking from the strongest to the weakest was H, R and G, aligning well with experiment data. In the TLR7, the flexible and hydrophobic aliphatic side chain of H has stronger van der Waals interactions with Val381 and Phe351 but only pick up interaction with one amino acid residue i.e. Tyr353 of TLR8. Unsurprisingly, the positively charged side chain of G has less favor interaction with Ile585 of TLR7 and Val573 of TLR8 explaining G is weak agonist in both TLR7 and TLR8. All three imidazoquinolines can form stable hydrogen bonds with Asp555 of TLR7 and the corresponding Asp543 of TLR8. In brief, the set of total 400ns MD studies sheds light on the potential selective mechanisms of agonists towards TLR7 and TLR8, indicating the van der Waals interaction as the driving force for the agonists binding, thus provides us insights for more potent and selective modulators to cooperate with the hydrophobic nature of the binding pocket.

immunology↗

bioRxiv2021

Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ

AO_SCPLOWBSTRACTC_SCPLOWFluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame are either computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson-Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to the conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high performance (> 8 dB) denoising in less time than other fluorescence microscopy denoising methods.

neuroscience↗

bioRxiv2021

Scaling between DNA and cell size governs bacterial growth homeostasis and resource allocation

Bacteria maintain a stable cell size and a certain DNA content through proliferation as described by classic growth laws. How cells behave when this inherent scaling is broken, however, has rarely been interrogated. Here we engineered Escherichia coli cells with extremely low DNA contents using a tunable synthetic tool CRISPRori that temporarily inhibited chromosome replication initiation. A detailed mechanistic model coupling DNA replication, cell growth, and division revealed a fundamental DNA-centric growth law, which was validated by two observations. First, lineage dynamics were robust to large CRISPRori perturbations with division cycles rapidly restoring through a timer mechanism rather than the adder rule. Second, cellular growth transitioned into a linear regime at low DNA-cytoplasm ratios. Experiments and theory showed that in this regime, cellular resource was redirected to plasmid-borne gene expression. Together with the ability of CRISPRori to bi-directionally modulate plasmid copy numbers, these findings suggest a novel strategy for bio-production enhancement.

systems biology↗

bioRxiv2021

Cold protection allows local cryotherapy in a clinical-relevant model of traumatic optic neuropathy

Therapeutic hypothermia (TH) is potentially an important therapy for central nervous system (CNS) trauma. However, its clinical application remains controversial, hampered by two major factors: 1) Many of the CNS injury sites, such as the optic nerve (ON), are deeply buried, preventing access for local TH. The alternative is to apply TH systemically, which significantly limits the applicable temperature range. 2) Even with possible access for "local refrigeration", cold-induced cellular damage offsets the benefit of TH. Here we present a clinically translatable model of traumatic optic neuropathy (TON) by applying clinical trans-nasal endoscopic surgery to goats and non-human primates. This model faithfully recapitulates clinical features of TON such as the injury site (pre-chiasmatic ON), the spatiotemporal pattern of neural degeneration, and the accessibility of local treatments with large operating space. We also developed a computer program to simplify the endoscopic procedure and expand this model to other large animal species. Moreover, applying a cold-protective treatment, inspired by our previous hibernation research, enables us to deliver deep hypothermia (4{degrees}C) locally to mitigate inflammation and metabolic stress (indicated by the transcriptomic changes after injury) without cold-induced cellular damage, and confers prominent neuroprotection both structurally and functionally. Intriguingly, neither treatment alone was effective, demonstrating that in situ deep hypothermia combined with hibernation-mimicking cold protection constitutes a breakthrough for TH as a therapy for TON and other CNS traumas.

neuroscience↗

bioRxiv2021

De novo assembly of 20 chickens reveals the undetectable phenomenon for thousands of core genes on sub-telomeric regions

The gene numbers and evolutionary rates of birds were assumed to be much lower than that of mammals, which in sharp contrast to the huge species number and morphological diversity of birds. It is very necessary to construct a complete avian genome and analyze its evolution.We constructed a chicken pan-genome from 20 de novo genome assemblies with high sequencing depth, newly identified 1,335 protein-coding genes and 3,011 long noncoding RNAs. The majority of these novel genes were detected across most individuals of the examined transcriptomes but were accidentally measured in each of the DNA sequencing data regardless of Illumina or PacBio technology. Furthermore, different from previous pan-genome models, most of these novel genes were overrepresented on chromosomal sub-telomeric regions, surrounded with extremely high proportions of tandem repeats, and strongly blocked DNA sequencing. These hidden genes were proved to be shared by all chicken genomes, included many housekeeping genes, and enriched in immune pathways. Comparative genomics revealed the novel genes had three-fold elevated substitution rates than known ones, updating the evolutionary rates of birds. Our study provides a framework for constructing a better chicken genome, which will contribute towards the understanding of avian evolution and improvement of poultry breeding.

genomics↗

bioRxiv2021

Diversity, function and evolution of aquatic vertebrate genomes

Aquatic vertebrates consist of jawed fish (cartilaginous fish and bony fish), aquatic mammals, reptiles and amphibians. Here, we present a comprehensive analysis of 630 aquatic vertebrate genomes to generate a standardized compendium of genomic data. We demonstrate its value by assessing their genome features as well as illuminating gene families related to the transition from water to land, such as Hox genes and olfactory receptor genes. We found that LINEs are the major transposable element (TE) type in cartilaginous fish and aquatic mammals, while DNA transposons are the dominate type in bony fish. To our surprise, TE types are not fixed in amphibians, the first group that transitioned to living on land. These results illustrate the value of a unified resource for comparative genomic analyses of aquatic vertebrates. Our data and strategy are likely to support all evolutionary and ecological research on vertebrates.

genomics↗

bioRxiv2021

Distance-dependent inhibition of translation initiation by downstream out-of-frame AUGs reveals that ribosome scans in a Brownian ratchet process

While eukaryotic ribosomes are widely presumed to scan mRNA for the AUG codon to initiate translation in a strictly 5'-3' movement (strictly unidirectional scanning model), other evidence has suggested that the ribosome uses small-amplitude 5'-3' and 3'-5' oscillations with a net 5'-3' movement to recognize the AUG codon (Brownian ratchet scanning model). Here, we generated 13,437 yeast variants, each with an ATG triplet placed downstream (dATGs) of the annotated ATG (aATG) codon of green fluorescent protein. We found that out-of-frame dATGs could inhibit translation at the aATG, but with diminishing strength over increasing distance between aATG and dATG, undetectable beyond [~]17 nt. Computational simulations revealed that each triplet is scanned back and forth approximately [~]10 times until an AUG codon is recognized. Collectively, our findings uncover the basic process by which eukaryotic ribosomes scan for initiation codons, and how this process could shape eukaryotic genome evolution and influence cancer development.

molecular biology↗

bioRxiv2021

CTCF blocks anti-sense transcription initiation at divergent gene promoters

Transcription at most promoters is divergent, initiating at closely spaced oppositely oriented core promoters to produce sense transcripts along with often unstable upstream antisense (uasTrx). How antisense transcription is regulated and to what extent it is coordinated with sense transcription is largely unknown. Here by combining acute degradation of the multi-functional transcription factor CTCF and nascent transcription measurements, we find that CTCF specifically suppresses antisense but not sense transcription at hundreds of divergent promoters, the great majority of which bear proximal CTCF binding sites. Genome editing, chromatin conformation studies, and high-resolution transcript mapping revealed that precisely positioned CTCF directly suppresses the initiation of uasTrx, in a manner independent of its chromatin architectural function. Primary transcript RNA FISH revealed co-bursting of sense and anti-sense transcripts is disfavored, suggesting CTCF-regulated competition for transcription initiation. In sum, CTCF shapes the transcriptional landscape in part by suppressing upstream antisense transcription.

molecular biology↗

bioRxiv2021

LungCancer3D: A Comprehensive Database for Integrating Lung Cancer Chromatin Architecture with Other Multi-omics

With the breakthrough of chromatin conformation capture technologies in recent years, the importance of three-dimensional (3D) genome structure in gene expression, cell function regulation, disease occurrence, and development has been gradually recognized. To provide a comprehensive visualization of chromatin architecture and other multi-omics data for lung cancer research, we have constructed a comprehensive database, LungCancer3D (http://www.lungcancer3d.net). This web-based tool focuses on displaying human lung cancer-related HiC data along with a variety of other publicly available data, such as RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, DNA methylation, DNA mutation, and copy number variations. Researchers can visualize these diverse multi-omics data directly through the genome browser and discover how the genes expression is regulated at diverse levels. For example, we have demonstrated that the high expression level of C-MYC in lung cancer may be caused by the distant enhancer introduced by the de novo chromatin loops in lung cancer cells to bind the C-MYC promoter. The integrated multi-omics analyses through the LungCancer3D website can reveal the mechanisms underlying lung cancer development and provide potential targets for lung cancer therapy.

bioinformatics↗

bioRxiv2021

Learning the statistics of pain: computational and neural mechanisms

Pain invariably changes over time, and these temporal fluctuations are riddled with uncertainty about body safety. In theory, statistical regularities of pain through time contain useful information that can be learned, allowing the brain to generate expectations and inform behaviour. To investigate this, we exposed healthy participants to probabilistic sequences of low and high-intensity electrical stimuli to the left hand, containing sudden changes in stimulus frequencies. We demonstrate that humans can learn to extract these regularities, and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian models with dynamic update of beliefs. We studied brain activity using functional MRI whilst subjects performed the task, which allowed us to dissect the underlying neural correlates of these statistical inferences from their uncertainty and update. We found that the inferred frequency (posterior probability) of high intensity pain correlated with activity in bilateral sensorimotor cortex, secondary somatosensory cortex and right caudate. The uncertainty of statistical inferences of pain was encoded in the right superior parietal cortex. An intrinsic part of this hierarchical Bayesian model is the way that unexpected changes in frequency lead to shift beliefs and update the internal model. This is reflected by the KL divergence between consecutive posterior distributions and associated with brain responses in the premotor cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. In conclusion, this study extends what is conventionally considered a sensory pain pathway dedicated to process pain intensity, to include the generation of Bayesian internal models of temporal statistics of pain intensity levels in sensorimotor regions, which are updated dynamically through the engagement of premotor, prefrontal and parietal regions.

neuroscience↗

bioRxiv2021

High-resolution spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae

Drosophila has long been a successful model organism in multiple fields such as genetics and developmental biology. Drosophila genome is relatively smaller and less redundant, yet largely conserved with mammals, making it a productive model in studies of embryogenesis, cell signaling, disease mechanisms, etc. Spatial gene expression pattern is critical for understanding of complex signaling pathways and cell-cell interactions, whereas temporal gene expression changes need to be tracked during highly dynamic activities such as tissue development and disease progression. Systematic studies in Drosophila as a whole are still impeded by lack of these spatiotemporal transcriptomic information. Drosophila embryos and tissues are of relatively small size, limiting the application of current technologies to comprehensively resolve their spatiotemporal gene expression patterns. Here, utilizing SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. Our data recapitulated the spatial transcriptomes of embryonic and larval development in Drosophila. With these data, we identified known and previously undetected subregions in several tissues during development, and revealed known and potential gene regulatory networks of transcription factors within their topographic background. We further demonstrated that Stereo-seq data can be used for 3D reconstruction of Drosophila embryo spatial transcriptomes. Our data provides Drosophila research community with useful resources of spatiotemporally resolved transcriptomic information across developmental stages.

developmental biology↗

bioRxiv2021

High Resolution Nanostructure with Two-stages of Exponential Energy Dissipation at the Ultrathin Osteochondral Interface Tissue of Human Knee Joint

Cartilage adheres to subchondral bone via a specific osteochondral interface tissue where forces are transferred from soft cartilage to hard bone without fatigue damage over a lifetime of load cycles. However, the fine structure and mechanical properties of osteochondral interface tissue remain unclear. Here, we identified an ultrathin [~]20-30 m calcified region with two-layered micro-nano structures of osteochondral interface tissue in human knee joint, which exhibited characteristic biomolecular compositions and complex nanocrystals assembly. Within this region, an exponential increase of modulus (3 orders of magnitude) was conducive to the force transmission which was verified by finite element simulations. The nanoscale heterogeneity of hydroxyapatite, along with enrichment of elastic-responsive protein-titin which is usually present in muscle, endowed the osteochondral tissue with excellent energy dissipation and fatigue resistance properties. Our results provide potential design for high-performance interface materials for osteochondral interface regeneration and functional coatings.

bioengineering↗