Biology subjects

Guo, W.

Publications and source records attributed to Guo, W..

231 records · Page 13Linked to original sources

bioRxiv2019

Chromosome level draft genomes of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), an alien invasive pest in China

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) is a severely destructive pest native to the Americas, but has now become an alien invasive pest in China, and causes significant economic loss. Therefore, in order to make effective management strategies, it is highly essential to understand genomic architecture and its genetic background. In this study, we assembled two chromosome scale genomes of the fall armyworm, representing one male and one female individual procured from Yunnan province of China. The genome sizes were identified as 542.42 Mb with N50 of 14.16 Mb, and 530.77 Mb with N50 of 14.89 Mb for the male and female FAW, respectively. We predicted about 22,201 genes in the male genome. We found the expansion of cytochrome P450 and glutathione s-transferase gene families, which are functionally related to the intensified detoxification and pesticides tolerance. Further population analyses of corn strain (C strain) and rice strain (R strain) revealed that the Chinese fall armyworm was most likely invaded from Africa. These strain information, genome features and possible invasion source described in this study will be extremely important for making effective strategies to manage the fall armyworms.

genomics↗

bioRxiv2019

The cholinergic basal forebrain links sensory stimuli with delayed reinforcement to support learning

Linking stimuli with delayed reinforcement requires neural circuits that can bridge extended temporal gaps. Auditory cortex (ACx) circuits reorganize to support auditory fear learning, but only when afferent sensory inputs temporally overlap with cholinergic reinforcement signals. Here we show that mouse ACx neurons rapidly reorganize to support learning, even when sensory and reinforcement cues are separated by a long gap. We found that cholinergic basal forebrain neurons bypass the temporal delay through multiplexed, short-latency encoding of sensory and reinforcement cues. At the initiation of learning, cholinergic neurons in Nucleus Basalis increase responses to conditioned sound frequencies and increase functional connectivity with ACx. By rapidly scaling up responses to sounds that predict reinforcement, cholinergic inputs jump the gap to align with bottom-up sensory traces and support associative cortical plasticity.

neuroscience↗

bioRxiv2019

3D RNA-seq - a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists

RNA-seq analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of reliance on specialized bioinformatics skills. Thus, we have developed "3D RNA-seq", an R shiny App and web based service which provides an easy-to-use, flexible and powerful tool for three-component analysis of RNA-seq data: Differential Expression, Differential Alternative Splicing and Differential Transcript Usage. 3D RNA-seq integrates state-of-the-art, highly rated differential expression analysis tools and adopts best practice for RNA-seq analysis. It operates through a user-friendly graphical interface, can handle complex experimental designs, allows setting of statistical parameters, tracks results through graphics and tables, and generates figures and a comprehensive report that will guarantee reproducibility. 3D RNA-seq can be applied to any species and is designed to be run by biologists with no programming skills (or by bioinformaticians) allowing lab scientists to perform rapid and accurate analysis of RNA-seq data.

bioinformatics↗

bioRxiv2019

A web-server framework to explore and visualize large genomic variation data in lab and its applications to wheat and its progenitors

BackgroundThe cost of high-throughput sequencing is rapidly decreasing, allowing researchers to investigate genomic variations across hundreds or even thousands of samples in the post-genomic era. The management and exploration of these large-scale genomic variation data require programming skills. The public genotype querying databases of many species are usually centralized and implemented independently, making them difficult to update with new data over time. Currently, there is a lack of a widely used framework for setting up user-friendly web servers for exploring new genomic variation data in diverse species. ResultsHere, we present SnpHub, a Shiny/R-based server framework for retrieving, analysing and visualizing the large-scale genomic variation data that be easily set up on any Linux server. After a pre-building process based on the provided VCF files and genome annotation files, the local server allows users to interactively access SNPs/INDELs and annotation information by locus or gene and for user-defined sample sets through a webpage. Users can freely analyse and visualize genomic variations in heatmaps, phylogenetic trees, haplotype networks, or geographical maps. Sample-specific sequences can be accessed as replaced by SNPs/INDELs. ConclusionsSnpHub can be applied to any species, and we build up a SnpHub portal website for wheat and its progenitors based on published data in present studies. SnpHub and its tutorial are available as http://guoweilong.github.io/SnpHub/.

bioinformatics↗

bioRxiv2019

BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq

BackgroundTime consuming computational assembly and quantification of gene expression and splicing analysis from RNA-seq data vary considerably. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants.\n\nResultsA high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts - BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al., 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al., 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5 and 3 UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2,791 differentially alternatively spliced genes and 2,768 transcripts with differential transcript usage.\n\nConclusionA high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

genomics↗

bioRxiv2019

Identifying drug combinations of individual cancer patients by personalized drug controller method

Treating cancer in precision medicine, it is important to identify the personalized combinational drugs under consideration of the individual heterogeneity. Many bioinformatics tools for the personalized driver genes identification have presented promising clues in determining candidate personalized drug targets for the personalized drugs discovery. However, it has not been studied how to fill the gap between personalized driver genes identification and personalized combinational drugs discovery. In this work, we developed a novel algorithm of structure network Controllability-based Personalized driver Genes and combinational Drug identification (CPGD), aiming to mine the personalized driver genes and identify the combinational drugs of an individual cancer patient. On two benchmark cancer datasets, the performance of CPGD for predicting the clinical efficacious combinational drugs is superior to that of other state-of-the-art driver gene-focus algorithms in terms of precision accuracy. In particular, by quantifying and referring the relationships between target genes of pairwise combinatorial drugs and disease module genes on breast cancer data set, CPGD can significantly divide patients into the discriminative high-risk and low-risk groups for risk asessment in combination therapy. In addition, CPGD can further enhance cancer subtyping by providing computationally personalized side effect signatures for individual patients. Collectively, CPGD provided a new and effecient bioinformatics tool from structure network controllability perspective for discovering personalized combinational drugs with personalized side effect consideration, so as to effectively support personalized risk assessement and disease subtyping. SignificanceIt is quite challenging to predict personalized combinational drugs rather than patient-cohort s drugs based on cancer omics data. In this work, a novel structure network Controllability-based algorithm (CPGD) from feedback vertex sets control perspective was developed, for discovering efficacious combinational drugs of an individual cancer patient by targeting the personalized driver genes. The CPGD contains three methodological advances by exploring more precise mathematical models on high-throughput personalized multi-omics data. The first is that a proper network structure is constructed to characterize the gene regulatory mechanism of an individual patient. The second is that considering the weight information of network edges/relations improves the performance for predicting clinical efficacious combinational drugs compared with other drivers-focus methods. And the third is that proper evaluation metrics for personalized combinational drugs prioritization, personalized risk assessment and disease subtyping are designed when evaluating the performance of CPGD.

systems biology↗

bioRxiv2019

SCINA: Semi-Supervised Analysis of Single Cells in silico

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. We reversed this paradigm and developed SCINA, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation-maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. Notably, SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out mouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

bioinformatics↗

bioRxiv2018

Loss of the Heparan Sulfate Proteoglycan Glypican5 facilitates long range Shh signaling.

As a morphogen, Sonic Hedgehog (Shh) mediates signaling at a distance from its sites of synthesis. After secretion, Shh must traverse a distance through the extracellular matrix (ECM) to reach the target cells and activate the Hh response. Extracellular matrix proteins, in particular the Heparan Sulfate Proteoglycans (HSPGs) of the Glypican family have both negative and positive effects on non-cell autonomous Shh signaling, all attributed to their ability to bind Shh. Using mouse embryonic stem cell-derived mosaic tissues with compartments that lack the glycosyltransferases Exostosin1 (Ext1) and Exostosin2 (Ext2), or the HSPG core protein Glypican5 we show that cells surrounded by a mutated extracellular matrix are highly proficient distributing Shh. In contrast, cells that lack Ext1 function poorly secrete Shh. Our results confirm earlier observations that HSPGs can have both positive (Shh export) and negative influences (Shh distribution), and are supporting a model in which Shh presented on the cell surface in the context of HSPGs preferentially distributes into ECM that lacks HSPGs, possibly due to the absence of Shh sequestering molecules.

cell biology↗

bioRxiv2018

Structure of the receptor-activated human TRPC6 ion channel

TRPC6 is a receptor-activated nonselective cation channel that belongs to the family of canonical transient receptor potential (TRPC) channels. It is activated by diacylglycerol, a lipid second messenger. TRPC6 is involved in many physiological processes and implicated in human genetic diseases. Here we present the structure of human TRPC6 homotetramer in complex with a newly identified high affinity inhibitor BTDM solved by single-particle cryo-electron microscopy to 3.8 [A] resolution. The structure shows a two-layer architecture, in which the bell-shaped cytosolic layer holds the transmembrane layer. Extensive inter-subunit interactions of cytosolic domain, including N terminal ankyrin repeats and C terminal coiled-coil, contribute to the tetramer assembly. The high affinity inhibitor BTDM wedges between S5-S6 pore domain and voltage sensor-like domain to inhibit channel opening. Our structure uncovers the molecular architecture of TRPC channels and provides a structural basis for understanding the mechanism of these channels.

biophysics↗

bioRxiv2018

The cortical dynamics orchestrating skilled prehension

Skillful control of movement is central to our ability to sense and manipulate the world. A large body of work in nonhuman primates has demonstrated that motor cortex provides flexible, time-varying activity patterns that control the arm during reaching and grasping. Previous studies have suggested that these patterns are generated by strong local recurrent dynamics operating autonomously from inputs during movement execution. An alternative possibility is that motor cortex requires coordination with upstream brain regions throughout the entire movement in order to yield these patterns. Here, we developed an experimental preparation in the mouse to directly test these possibilities using optogenetics and electrophysiology during a skilled reach-to-grab-to-eat task. To validate this preparation, we first established that a specific, time-varying pattern of motor cortical activity was required to produce coordinated movement. Next, in order to disentangle the contribution of local recurrent motor cortical dynamics from external input, we optogenetically held the recurrent contribution constant, then observed how motor cortical activity recovered following the end of this perturbation. Both the neural responses and hand trajectory varied from trial to trial, and this variability reflected variability in external inputs. To directly probe the role of these inputs, we used optogenetics to perturb activity in the thalamus. Thalamic perturbation at the start of the trial prevented movement initiation, and perturbation at any stage of the movement prevented progression of the hand to the target; this demonstrates that input is required throughout the movement. By comparing motor cortical activity with and without thalamic perturbation, we were able to estimate the effects of external inputs on motor cortical population activity. Thus, unlike pattern-generating circuits that are local and autonomous, such as those in the spinal cord that generate left-right alternation during locomotion, the pattern generator for reaching and grasping is distributed across multiple, strongly-interacting brain regions.

neuroscience↗

bioRxiv2018

Rapid and dynamic alternative splicing impacts the Arabidopsis cold response transcriptome

BackgroundPlants have adapted to tolerate and survive constantly changing environmental conditions by re-programming gene expression. The scale of the contribution of alternative splicing (AS) to stress responses has been underestimated due to limitations in RNA-seq analysis programs and poor representation of AS transcripts in plant databases. Significantly, the dynamics of the AS response have not been investigated but this is now possible with accurate transcript quantification programs and AtRTD2, a new, comprehensive transcriptome for Arabidopsis.\n\nResultsUsing ultra-deep RNA-sequencing of a time-course of Arabidopsis thaliana plants exposed to cold treatment, we identified 8,949 genes with altered expression of which 2,442 showed significant differential alternative splicing (DAS) and 1,647 genes were regulated only at the level of AS (DAS-only). The high temporal resolution demonstrated the rapid induction of both transcription and AS resulting in coincident waves of differential expression (transcription) and differential alternative splicing in the first 6-9 hours of cold. The differentially expressed and DAS gene sets were largely non-overlapping, each comprising thousands of genes. The dynamic analysis of AS identified genes with rapid and sensitive AS within 3 h of transfer to the cold (early AS genes), which were enriched for splicing and transcription factors. A detailed investigation of the novel cold-response DAS-only gene, U2B\"-LIKE, suggested that it regulates AS and is required for tolerance to freezing.\n\nConclusionsOur data indicate that transcription and AS are the major regulators of transcriptome reprogramming that together govern the physiological and survival responses of plants to low temperature.

genomics↗

bioRxiv2017

Reconciling the clk-1 and aging paradox and categorizing lifespan curves by taking individual specificity into account

The clk-1 gene encodes the demethoxyubiquinone (DMQ) hydroxylase that is required for biosynthesis of ubiquinone (coenzyme Q). Deletion of clk-1 was lethal in mice, and its mutation in C. elegans mildly extended lifespan, slowed physiological rate and led to sickness. We found that if growth retardation was taken into account the average lifespan of clk-1 mutants would not be prolonged or would be shortened. In addition, recent study showed that knocking down of clk-1 shortened lifespan. Although the extension of lifespan in clk-1 mutants was mild and was not observed sometimes, some progenies indeed had prolonged maximum lifespan even if retardation of growth was taking into account. These paradoxes implicate the existence of individual specificity in the aging process even in the same cohort, just like a drug is beneficial for some people while for others it is detrimental. We further categorized lifespan curves into five kinds of patterns according to the lifespan alternations observed in organisms: N (normal); L (long-lived); S (short-lived); F (flattened); ST (steepened), and found that the curve of clk-1 mutants fit into the F pattern. The reasons behind the individual specificity and its implications in aging process deserves further investigations.

cell biology↗

bioRxiv2017

DeepMetabolism: A Deep Learning System To Predict Phenotype From Genome Sequencing

Life science is entering a new era of petabyte-level sequencing data. Converting such \"big data\" to biological insights represents a huge challenge for computational analysis. To this end, we developed DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data. By integrating unsupervised pre-training with supervised training, DeepMetabolism is able to predict phenotypes with high accuracy (PCC>0.92), high speed (<30 min for >100 GB data using a single GPU), and high robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine.

systems biology↗

bioRxiv2017

Data-Driven Prediction of CRISPR-Based Transcription Regulation for Programmable Control of Metabolic Flux

Multiplex and multi-directional control of metabolic pathways is crucial for metabolic engineering to improve product yield of fuels, chemicals, and pharmaceuticals. To achieve this goal, artificial transcriptional regulators such as CRISPR-based transcription regulators have been developed to specifically activate or repress genes of interest. Here, we found that by deploying guide RNAs to target on DNA sites at different locations of genetic cassettes, we could use just one synthetic CRISPR-based transcriptional regulator to simultaneously activate and repress gene expressions. By using the pairwise datasets of guide RNAs and gene expressions, we developed a data-driven predictive model to rationally design this system for fine-tuning expression of target genes. We demonstrated that this system could achieve programmable control of metabolic fluxes when using yeast to produce versatile chemicals. We anticipate that this master CRISPR-based transcription regulator will be a valuable addition to the synthetic biology toolkit for metabolic engineering, speeding up the \"design-build-test\" cycle in industrial biomanufacturing as well as generating new biological insights on the fates of eukaryotic cells.

synthetic biology↗

bioRxiv2016

Inference of Multiple-wave Admixtures by Length Distribution of Ancestral Tracks

The ancestral tracks in admixed genomes are of valuable information for population history inference. A few methods have been developed to infer admixture history based on ancestral tracks. Nonetheless, these methods suffered the same flaw that only population admixture history under some specific models can be inferred. In addition, the inference of history might be biased or even unreliable if the specific model is deviated from the real situation. To address this problem, we firstly proposed a general discrete admixture model to describe the admixture history with multiple ancestral populations and multiple-wave admixtures. We next deduced the length distribution of ancestral tracks under the general discrete admixture model. We further developed a new method, MultiWaver, to explore the multiple-wave admixture histories. Our method could automatically determine an optimal admixture model based on the length distribution of ancestral tracks, and estimate the corresponding parameters under this optimal model. Specifically, we used a likelihood ratio test (LRT) to determine the number of admixture waves, and implemented an expectation??maximization (EM) algorithm to estimate parameters. We used simulation studies to validate the reliability and effectiveness of our method. Finally, good performance was observed when our method was applied to real datasets of African Americans, Mexicans, Uyghurs, and Hazaras.

genetics↗