Biology subjects

Zhou, X.

Publications and source records attributed to Zhou, X..

At least 667 records · Page 37Linked to original sources

bioRxiv2019

Discovery of a Selective, State-Independent Inhibitor of NaV1.7 by Modification of Guanidinium Toxins

The voltage-gated sodium channel isoform NaV1.7 is highly expressed in small diameter dorsal root ganglion neurons and is obligatory for nociceptive signal transmission. Genetic gain-of-function and loss-of-function NaV1.7 mutations have been identified in select individuals, and are associated with episodic extreme pain disorders and insensitivity to pain, respectively. These findings implicate NaV1.7 as a key pharmacotherapeutic target for the treatment of pain. While several small molecules targeting NaV1.7 have been advanced to clinical development, no NaV1.7-selective compound has shown convincing efficacy in clinical pain applications. Here we describe the discovery and characterization of ST-2262, a NaV1.7 inhibitor that blocks the extracellular vestibule of the channel with an IC50 of 72 nM and greater than 200-fold selectivity over off-target sodium channel isoforms, NaV1.1-1.6 and NaV1.8. In contrast to other NaV1.7 inhibitors that preferentially inhibit the inactivated state of the channel, ST-2262 is equipotent against resting and inactivated protein conformers. In a non-human primate model, animals treated with ST-2262 exhibit markedly reduced sensitivity to noxious heat. These findings establish the extracellular vestibule of the sodium channel as a viable receptor site for selective ligand design and provide insight into the pharmacology of state-independent inhibition of NaV1.7. Significance StatementPain is among the most common reasons for seeking medical care, yet many frequently prescribed drugs, particularly the opioids, cause problematic side effects and carry a risk of addiction. Voltage-gated sodium ion channels (NaVs) have emerged as promising targets for the development of non-opioid pain medicines. NaVs are involved in the propagation of electrical signals along neurons throughout the body. Humans born without a functional copy of one sodium channel subtype, NaV1.7, are unable to experience most types of pain. In the present work, we disclose the discovery and characterization of a selective inhibitor of NaV1.7 that reduces sensitivity to a painful thermal stimulus in non-human primates. Findings from this work may help guide the development of novel, non-addictive drug candidates as alternatives to opioids.

neuroscience↗

bioRxiv2019

Conserved and specific genomic features of endogenous polydnaviruses revealed by whole genome sequencing of two ichneumonid wasps

Polydnaviruses (PDVs) are mutualistic endogenous viruses associated with some lineages of parasitoid wasps that allow successful development of the wasps within their hosts. PDVs include two taxa resulting from independent virus acquisitions in braconid (bracoviruses) and ichneumonid wasps (ichnoviruses). PDV genomes are fully incorporated into the wasp genomes and comprise (1) virulence genes located on proviral segments that are packaged into the viral particle, and (2) genes involved in the production of the viral particles, which are not encapsidated. Whereas the genomic organization of bracoviruses within the wasp genome is relatively well known, the architecture of endogenous ichnoviruses remains poorly understood. We sequenced the genome of two ichnovirus-carrying wasp species, Hyposoter didymator and Campoletis sonorensis. Complete assemblies with long scaffold sizes allowed identification of the integrated ichnovirus, highlighting an extreme dispersion within the wasp genomes of the viral loci, i.e. isolated proviral segments and clusters of replication genes. Comparing the two wasp species, proviral segments harbor distinct gene content and variable genomic environment, whereas viral machinery clusters show conserved gene content and order, and can be inserted in collinear wasp genomic regions. This distinct architecture is consistent with the biological properties of the two viral elements: proviral segments producing virulence proteins allowing parasitism success are fine-tuned to the host physiology, while an ancestral viral architecture was likely maintained for the genes involved in virus particle production. Finding a distinct genomic architecture of ichnoviruses and bracoviruses highlights different evolutionary trajectories leading to virus domestication in the two wasp lineages.

genomics↗

bioRxiv2019

Single cell analysis of the effects of developmental lead (Pb) exposure on the hippocampus.

BackgroundLead (Pb) exposure is ubiquitous and has permanent developmental effects on childhood intelligence and behavior and adulthood risk of dementia. The hippocampus is a key brain region involved in learning and memory, and its cellular composition is highly heterogeneous. Pb acts on the hippocampus by altering gene expression, but the cell type-specific responses are unknown. ObjectiveExamine the effects of perinatal Pb treatment on adult hippocampus gene expression, at the level of individual cells, in mice. MethodsIn mice perinatally exposed to control water (n=4) or a human physiologically-relevant level (32 ppm in maternal drinking water) of Pb (n=4), two weeks prior to mating through weaning, we tested for gene expression and cellular differences in the hippocampus at 5-months of age. Analysis was performed using single cell RNA-sequencing of 5,258 cells from the hippocampus by 10x Genomics Chromium to 1) test for gene expression differences averaged across all cells by treatment; 2) compare cell cluster composition by treatment; and 3) test for gene expression and pathway differences within cell clusters by treatment. ResultsGene expression patterns revealed 12 cell clusters in the hippocampus, mapping to major expected cell types (e.g. microglia, astrocytes, neurons, oligodendrocytes). Perinatal Pb treatment was associated with 12.4% more oligodendrocytes (P=4.4x10-21) in adult mice. Across all cells, differential gene expression analysis by Pb treatment revealed cluster marker genes. Within cell clusters, differential gene expression with Pb treatment (q<0.05) was observed in endothelial, microglial, pericyte, and astrocyte cells. Pathways up-regulated with Pb treatment were protein folding in microglia (P=3.4x10-9) and stress response in oligodendrocytes (P=3.2x10-5). ConclusionBulk tissue analysis may be confounded by changes in cell type composition and may obscure effects within vulnerable cell types. This study serves as a biological reference for future single cell studies of toxicant or neuronal complications, to ultimately characterize the molecular basis by which Pb influences cognition and behavior.

pharmacology and toxicology↗

bioRxiv2019

Dimensional neuroimaging: (Internet) Gaming Disorder symptoms according to the WHO and APA frameworks associate with lower striatal volume

Growing concerns about the addictive nature of internet and computer games led to the endorsement of Internet Gaming Disorder (IGD) as an emerging disorder by the American Psychological Association (APA) in 2013 and the recognition of Gaming Disorder (GD) as a new diagnosis by the World Health Organization (WHO) in 2019. While the definition of clear diagnostic criteria for IGD is beneficial for clinicians and those in need of treatment, it may also stigmatize normal behavior. Furthermore, the neurobiological mechanisms underlying IGD symptoms, and whether they resemble those of other addictive disorders remains highly debated. To this end the present study employed a dimensional imaging approach to determine associations between IGD symptom-load according to the APA and WHO diagnostic frameworks and brain structure in a comparably large sample of healthy subjects. It was found that higher symptom-load on psychometric tests assessing the APA and WHO diagnostic frameworks convergently associated with lower volumes of the striatum. The present results suggest for the first time a neurobiological foundation of the proposed diagnostic criteria for IGD according to both diagnostic classification systems, further indicating that the transition from non-disordered to disordered gaming is accompanied by progressive neuroplastic changes in the striatum, thus resembling progressive changes in other addictive disorders.

neuroscience↗

bioRxiv2019

CORE GREML: Estimating covariance between random effects in linear mixed models for genomic analyses of complex traits

Linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) are a key variance partitioning tool, where effects of multiple sources, such as different functional genomic regions, on phenotypes are treated as random. Classic LMMs assume independence between random effects, which can cause biased estimation of variance components. Here, we relax this independence assumption by introducing a generalised GREML, called CORE GREML, that can explicitly estimate the covariance between random effects. Using extensive simulations, we show that CORE GREML outperforms the conventional GREML, providing unbiased estimates of variance and covariance components. Using data from the UK biobank, we demonstrate that CORE GREML is useful for genomic partitioning analyses and for genome-transcriptome partitioning of phenotypic variance. For example, we found that the transcriptome, imputed using genotype data, explained a significant proportion of phenotypic variance for height (0.15, se = 5.4e-3, p-value = 1.5e-283), and that these transcriptomic effects on phenotypes correlated with effects of the genome (r = 0.35, se = 4.6e-2, p-value = 1.2e-14). We conclude that the covariance between random effects is a key parameter that needs to be estimated, especially when partitioning phenotypic variance by omic layer.

genetics↗

bioRxiv2019

Molecular mechanisms behind global distribution of earthworm revealed by the genome

Earthworms (Annelida: Crassiclitellata), are widely distributed around the world due to their great adaptability. However, lack of a high-quality genome sequence prevents gaining the many insights into physiology, phylogeny, and genome evolution that could come from a good earthworm genome. Herein, we report a complete genome assembly of the earthworm Amynthas corticis of about 1.2 Gb, based on a strategy combining third-generation long-read sequencing and Hi-C mapping. A total of 29,256 protein-coding genes are annotated in this genome. Analysis of resequencing data indicates that this earthworm is a triploid species. Furthermore, gene family evolution analysis shows that comprehensive expansion of gene families in the earthworm genome has produced more defensive functions compared with other species in Annelida. Quantitative proteomic iTRAQ analysis shows 97 immune related proteins and 16S rDNA sequences shows 88 microbes with significantly response to pathogenic Escherichia coli O157:H7. Our genome assembly provides abundant and valuable resources for the earthworm research community, serving as a first step toward uncovering the mysteries of this species, may explain its powerful defensive functions adapt to complex environment and invasion from molecular level.

genomics↗

bioRxiv2019

A generalizable multivariate brain pattern for interpersonal guilt

Feeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed Guilt-Related Brain Signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social affective processes, further indicating the specificity of the brain state it represents. These findings provide a step towards developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

neuroscience↗

bioRxiv2019

The natural compound Notopterol targets JAK2/3 to ameliorate macrophage-induced inflammation and arthritis

AbstractNotopterol (NOT) is one of the main constituents of the traditional Chinese medicinal herb Notopterygium incisum Ting ex H.T. Chang has anti-rheumatism activity, but the target of NOT remains unknown. Here we have demonstrated that orally or intraperitoneal administration of NOT exhibits significant therapeutic effects on the collagen-induced arthritis (CIA) model in both DBA/1J and C57/BL6 mice. NOT treatment in vivo and in vitro reduces production of inflammatory cytokines and chemokines in TNF- or LPS/IFN{gamma}-stimulated macrophages via blocking the JAK2/3-STAT3/5 activation. Mechanistically, NOT directly binds JAK2 to inhibit its activity via Arg980, Asn981, and Leu932 in the JH1 domain. Importantly, expression of the L938A/R980A/N981A mutant in zebrafish significantly inhibited the in vivo inflammatory response after LPS injection, which showed no further inhibitory effect upon NOT treatment. Combination of NOT and an anti-TNF antibodies could achieve a better therapeutic effect than anti-TNF alone in the CIA model. We therefore suggest that as a specific JAK2/3 inhibitor, the natural compound NOT ameliorates pathology of RA, which might be useful to treat other JAK2/3-related diseases.

immunology↗

bioRxiv2019

Statistical Analysis of Spatial Expression Pattern for Spatially Resolved Transcriptomic Studies

Recent development of various spatially resolved transcriptomic techniques has enabled gene expression profiling on complex tissues with spatial localization information. Identifying genes that display spatial expression pattern in these studies is an important first step towards characterizing the spatial transcriptomic landscape. Detecting spatially expressed genes requires the development of statistical methods that can properly model spatial count data, provide effective type I error control, have sufficient statistical power, and are computationally efficient. Here, we developed such a method, SPARK. SPARK directly models count data generated from various spatial resolved transcriptomic techniques through generalized linear spatial models. With a new efficient penalized quasi-likelihood based algorithm, SPARK is scalable to data sets with tens of thousands of genes measured on tens of thousands of samples. Importantly, SPARK relies on newly developed statistical formulas for hypothesis testing, producing well-calibrated p-values and yielding high statistical power. We illustrate the benefits of SPARK through extensive simulations and in-depth analysis of four published spatially resolved transcriptomic data sets. In the real data applications, SPARK is up to ten times more powerful than existing approaches. The high power of SPARK allows us to identify new genes and pathways that reveal new biology in the data that otherwise cannot be revealed by existing approaches.

genomics↗

bioRxiv2019

Efficient chromosome-scale haplotype-resolved assembly of human genomes

Haplotype-resolved or phased sequence assembly provides a complete picture of genomes and complex genetic variations. However, current phased assembly algorithms either fail to generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method that leverages long accurate reads and long-range conformation data for single individuals to generate chromosome-scale phased assembly within a day. Applied to three public human genomes, PGP1, HG002 and NA12878, our method produced haplotype-resolved assemblies with contig NG50 up to 25 Mb and phased [~]99.5% of heterozygous sites to 98-99% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies to discover structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as HLA and KIR. Our improved method will enable high-quality precision medicine and facilitate new studies of individual haplotype variation and population diversity.

bioinformatics↗

bioRxiv2019

Integrating multi-omics data with deep learning for predicting cancer prognosis

BackgroundGenomic information is nowadays widely used for precise cancer treatments. Since the individual type of omics data only represents a single view that suffers from data noise and bias, multiple types of omics data are required for accurate cancer prognosis prediction. However, it is challenging to effectively integrate multi-omics data due to the large number of redundant variables but relatively small sample size. With the recent progress in deep learning techniques, Autoencoder was used to integrate multi-omics data for extracting representative features. Nevertheless, the generated model is fragile from data noises. Additionally, previous studies usually focused on individual cancer types without making comprehensive tests on pan-cancer. Here, we employed the denoising Autoencoder to get a robust representation of the multi-omics data, and then used the learned representative features to estimate patients risks. ResultsBy applying to 15 cancers from The Cancer Genome Atlas (TCGA), our method was shown to improve the C-index values over previous methods by 6.5% on average. Considering the difficulty to obtain multi-omics data in practice, we further used only mRNA data to fit the estimated risks by training XGboost models, and found the models could achieve an average C-index value of 0.627. As a case study, the breast cancer prognosis prediction model was independently tested on three datasets from the Gene Expression Omnibus (GEO), and shown able to significantly separate high-risk patients from low-risk ones (C-index>0.6, p-values<0.05). Based on the risk subgroups divided by our method, we identified nine prognostic markers highly associated with breast cancer, among which seven genes have been proved by literature review. ConclusionOur comprehensive tests indicated that we have constructed an accurate and robust framework to integrate multi-omics data for cancer prognosis prediction. Moreover, it is an effective way to discover cancer prognosis-related genes.

bioinformatics↗

bioRxiv2019

Imputing missing RNA-seq data from DNA methylation by using transfer learning based-deep neural network

BackgroundGene expression plays a key intermediate role in linking molecular features at DNA level and phenotype. However, due to various limitations in experiments, the RNA-seq data is missing in many samples while there exists high-quality of DNA methylation data. As DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on single cancer dataset, and do not fully utilize information from large pan-cancer dataset. ResultsHere, we have developed a novel method to impute missing gene expression data from DNA methylation data through transfer learning-based neural network, namely TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-of-the-art methods in imputation accuracy with 7%-11% increase under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylation-driving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purpose by the independent test on the Wilms tumor dataset from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) project. ConclusionsTDimpute is an effective method for RNA-seq imputation with limited training samples.

bioinformatics↗

bioRxiv2019

Sub-species niche specialization in the oral microbiome is associated with nasopharyngeal carcinoma risk in an endemic area of southern China

Oral health and changes in the oral microbiome have been associated with both local and systemic cancer. Poor oral hygiene is a known risk factor for Nasopharyngeal Carcinoma (NPC), a virally-associated head and neck cancer endemic to southern China. We explored the relationship between NPC and the oral microbiome using 16s rRNA sequencing in a study of 499 NPC patients and 495 population-based age and sex frequency-matched controls from an endemic area of Southern China. We found a significant reduction in community richness in cases compared to controls. Differences in the overall microbial community structure between cases and controls could not be explained by other potential confounders; disease status explained 5 times more variation in the Unweighted UniFrac distance than the next most explanatory variable. In feature-based analyses, we identified a pair of co-excluding Granulicatella adiacens (G. adicans) amplicon sequence variants (ASVs) which were strongly associated with NPC status and differed by a single nucleotide. The G. adicans variant an individual carried was also associated with the overall microbial community based on beta diversity. Co-occurrence analysis suggested the two G. adicans ASVs sit at the center of two co-excluding clusters of closely related organisms. Our results suggest there are differences in the oral microbiome between NPC patients and healthy controls and these may be associated with both a loss of microbial diversity and niche specialization among closely related commensals. Importance The relationship between oral health and the risk of Nasopharyngeal Carcinoma has been previously established. However, the role of oral microbiome has not been evaluated in the disease in a large epidemiological study. This paper clearly establishes a difference in the oral microbiome between NPC patients and healthy controls which cannot be explained by other confounding factors. It furthermore identifies a pair of closely related co-excluding organisms associated with the disease, highlighting the importance of modern methods for single nucleotide resolution in 16s rRNA sequence characterization. To the best of our knowledge, this is one of the first examples of cancer-associated niche-specialization of the oral microbiome.

microbiology↗

bioRxiv2019

Molecular Networks and Key Regulators of the Dysregulated Neuronal System in Alzheimer’s Disease

To study the molecular mechanisms driving the pathogenesis and identify novel therapeutic targets of late onset Alzheimers Disease (LOAD), we performed an integrative network analysis of whole-genome DNA and RNA sequencing profiling of four cortical areas, including the parahippocampal gyrus, across 364 donors spanning the full spectrum of LOAD-related cognitive and neuropathological disease severities. Our analyses revealed thousands of molecular changes and uncovered for the first-time multiple neuron specific gene subnetworks most dysregulated in LOAD. ATP6V1A, a critical subunit of vacuolar-type H+-ATPase (v-ATPase), was predicted to be a key regulator of one neuronal subnetwork and its role in disease-related processes was evaluated through CRISPR-based manipulation of human induced pluripotent stem cell derived neurons and RNAi-based knockdown in transgenic Drosophila models. This study advances our understanding of LOAD pathogenesis by providing the global landscape and detailed circuits of complex molecular interactions and regulations in several key brain regions affected by LOAD and the resulting network models provide a blueprint for developing next generation therapeutics against LOAD.

neuroscience↗

bioRxiv2019

CRISPR/Cas13a signal amplification linked immunosorbent assay (CLISA)

The enzyme-linked immunosorbent assay (ELISA) is a basic technique used in analytical and clinical investigations. However, conventional ELISA is still not sensitive enough to detect ultra-low concentrations of biomarkers for the early diagnosis of cancer, cardiovascular risk, neurological disorders, and infectious diseases. Herein we show a mechanism utilizing the CRISPR/Cas13a-based signal export amplification strategy, which double-amplifies the output signal by T7 RNA polymerase transcription and CRISPR/Cas13a collateral cleavage activity. This process is termed the CRISPR/Cas13a signal amplification linked immunosorbent assay (CLISA). The proposed method was validated by detecting an inflammatory factor, human interleukin-6 (human IL-6), and a tumor marker, human vascular endothelial growth factor (human VEGF), which achieved limit of detection (LOD) values of 45.81 fg/mL (2.29 fM) and 32.27 fg/m (0.81 fM), respectively, demonstrating that CLISA is at least 102-fold more sensitive than conventional ELISA.

molecular biology↗

bioRxiv2019

Extraction of genome-wide reads distribution pattern through whole genome sequencing of cell-free DNA for tracking tissue-of-origin in cancer patients

Somatic mosaicism is widespread among tissues and could indicate distinct tissue origins of circulating cell-free DNA (cfDNA), DNA fragments released by lytic cells into the blood. By investigating the alignment patterns of whole genome sequencing reads with the genomic DNA of different tissues, we found that the read distributions formed type-specific patterns in some regions as a result of somatic mosaicism. We then utilized this information to construct a tissue-of-origin mapping model and evaluated its predictive performance on whole genome sequencing data from tissue and cfDNA samples. In total, 1,545 tissue samples associated with 13 cancer types were included, and identification of the tissue of origin achieved a specificity of 82% and a sensitivity of 80%. Furthermore, a total of 30 cfDNA samples from lung cancer and liver cancer patients and healthy controls were analyzed to predict their tissues of origin with a specificity of 87% and a sensitivity of 87%. Our results show that read distribution patterns from whole genome sequencing could be used to identify cfDNA tissues of origin with high accuracy, suggesting the potential application of our model to early cancer detection and diagnosis.

bioinformatics↗

bioRxiv2019

A new GABAergic projection from the BNST onto accumbal parvalbumin neurons controls anxiety

The prevailing view is that parvalbumin (PV) interneurons play modulatory roles in emotional response through local medium spiny projection neurons (MSNs). Here, we show that PV activity within the nucleus accumbens shell (sNAc) is required for producing anxiety-related avoidance when mice are under anxiogenic situations; sNAcPV neurons exhibited high excitability in chronically stressed mice model, which generated excessive maladaptive avoidance behavior in an anxiogenic context. We also discovered a novel GABAergic projections from the anterior dorsal bed nuclei of stria terminalis (adBNST) to sNAcPV neurons; optogenetic activation of these afferent terminals in sNAc produced an anxiolytic effect via GABA transmission. Next, we further demonstrated that chronic stressors attenuated the inhibitory synaptic transmission at adBNSTGABA [Formula] sNAcPV synapses, which in turn explains the hyperexcitability of sNAcPV neurons on stressed models; therefore, activation of these GABAergic afferents in sNAc rescued the excessive avoidance behavior related to anxious state.\n\nOur findings reveal the coordination between BNST and NAc through an inhibitory architecture in controlling of anxiety-related response and provide a neurobiological basis for therapeutic interventions in pathological anxiety.

neuroscience↗

bioRxiv2019

High-throughput sequencing analysis of bacterial diversity in raw and pasteurized goat milk

The aim of this study was to evaluate the microbial composition of both raw and pasteurized goat milk using high-throughput DNA sequencing. This analysis revealed that the dominant phylum found in the raw milk was Proteobacteria, and the dominant genus was Kluyvera; Proteobacteria and Kluyvera constituted up to 67.66% and 28.85% of the total bacteria population, respectively. The microorganisms in goat milk predominantly consist of Gram-negative bacteria. Notably, Akkermansia and Faecalibacterium were identified in goat milk for the first time. In addition, the results also indicate that some bacteria in pasteurized goat milk may exist in a viable but nonculturable (VBNC) state. This study provides a theoretical basis that may aid the community in better understanding bacterial diversity in goat milk. The results of this study will help us to improve the quality and safety of goat milk. ImportanceThe microbial diversity in goat milk and pasteurized goat milk at different refrigeration stages was described. Several bacterial species that have not previously been reported in goat milk were identified, including many VBNC bacteria. The findings provided the necessary microbial information for quality and safety of goat milk and dairy products.

microbiology↗