Biology subjects

Wu, C.

Publications and source records attributed to Wu, C..

At least 325 records · Page 18Linked to original sources

bioRxiv2019

High-depth resequencing reveals hybrid population and insecticide resistance characteristics of fall armyworm (Spodoptera frugiperda) invading China

The rapid wide-scale spread of fall armyworm (Spodoptera frugiperda) has caused serious crop losses globally. However, differences in the genetic background of subpopulations and the mechanisms of rapid adaptation behind the invasion are still not well understood. Here we report a 393.25-M chromosome-level genome assembly of fall armyworm with scaffold N50 of 13.3 M consisting of 23281 annotated protein-coding genes. Genome-wide resequencing of 105 samples from 16 provinces in China revealed that the fall armyworm population comprises a complex inter-strain hybrid, mainly with the corn-strain genetic background and less of the rice-strain genetic background, which highlights the inaccuracy of strain identification using mitochondrial or Tpi genes. An analysis of genes related to pesticide- and Bt-resistance showed that the risk of fall armyworm developing resistance to conventional pesticides is very high, while remaining currently susceptible to Bt toxins. Laboratory bioassay results showed that insects invading China carry resistance to organophosphate and pyrethroid pesticides, but are sensitive to genetically modified maize expressing Cry1Ab in field experiments. Additionally, we found that two mitochondrial fragments are inserted into the nuclear genome, and the insertion event occurred after the differentiation of the two strains. This study represents a valuable advancement toward the analysis of genetic differences among subpopulations and improving management strategies for fall armyworm.

genomics↗

bioRxiv2019

Nonsense suppression position effect implicates poly(A)-binding protein in the regulation of translation termination

Readthrough of translation termination codons, also known as nonsense suppression, is a relatively inefficient process mediated by ribosomal A site recognition and insertion of near-cognate tRNAs. Multiple factors influence readthrough efficiency, including nonsense codon specificity and context. To determine whether nonsense codon position in a gene influences the extent of readthrough, we generated a series of LUC nonsense alleles and quantitated both readthrough and termination efficiencies at each nonsense codon in yeast cells lacking nonsense-mediated mRNA decay (NMD) activity. Readthrough efficiency for premature termination codons (PTCs) manifested a marked dependence on PTC proximity to the mRNA 3-end, decreasing progressively across the LUC ORF but increasing with 3-UTR lengthening. These effects were eliminated, and translation termination efficiency decreased considerably, in cells harboring pab1 mutations. Our results support a critical role for poly(A)-binding protein in the regulation of translation termination and suggest that inefficient termination is the trigger for NMD.

molecular biology↗

bioRxiv2019

Metabolic remodeling by TIGAR overexpression is a therapeutic target in esophageal squamous-cell carcinoma

Whole-genome sequencing has identified many amplified genes in esophageal squamous-cell carcinoma (ESCC); however, their roles and the clinical relevance have yet elucidated. Here we show TP53-induced glycolysis and apoptosis regulator (TIGAR) is a major player in ESCC progression and chemoresistance. TIGAR reprograms glucose metabolism from glycolysis to the glutamine pathway through AMP-activated kinase, and its overexpression is correlated with poor disease outcomes. Tigar knockout mice have reduced ESCC growth and tumor burden. Treatment of TIGAR-overexpressed ESCC cell xenografts and patient-derived tumor xenografts in mice with combination of glutaminase inhibitor and chemotherapeutic agents achieves significant more efficacy than chemotherapy alone. These findings shed light on an important role of TIGAR in ESCC and provide evidence for targeted treatment of TIGAR-overexpressed ESCC.\n\nSignificanceEffective and target therapies are required for ESCC, one of the most common types of digestive OR cancer. Little has been known about the biology of ESCC progression or potential molecular targets OR for treatment. Whole-genome sequencing and RNA sequencing studies in ESCC have identified OR many recurrent copy number gain genes; however, the roles and druggable relevance of these OR genes remains poorly understood. Herein we demonstrate that TIGAR overexpression leads to OR metabolic remodeling, promoting ESCC progression and resistance to chemotherapeutic agents. OR Inhibiting the glutamine pathway significantly represses TIGAR-overexpressing ESCC growth and OR enhances tumor cell sensitization to cytotoxic agents. These findings might provide the rationale OR for clinical trials testing glutamine pathway inhibitors in combination with chemotherapy in OR TIGAR-expressing ESCC.

cancer biology↗

bioRxiv2019

Both Determinants of Allosteric and Active Sites Responsible for Catalytic Activity of Delta 12 Fatty Acid Desaturase by Domain Swapping

Cheese lacks essential fatty acids (EFAs). Delta 12 fatty acid desaturase (FADS12) is a critical enzyme required for EFA biosynthesis in fermentation of the predominant strains of cheese. Previously, we identified the FADS12 gene and characterized its function for the first time in Geotrichum candidum, a dominant strain used to manufacture soft cheese with white rind. In this study, we analyzed the molecular mechanism of FADS12 function by swapping domains from Mortierella alpina and G. candidum that had, respectively, high and low oleic acid conversion rates. The results revealed three regions that are essential to this process, including regions from the end of the second transmembrane domain to the beginning of the third transmembrane domain, from the end of the third transmembrane domain to the beginning of the fourth transmembrane domain, and from the 30-amino acid from the end of the sixth transmembrane domain to the C-terminal end region. Based on our domain swapping analyses, nine pairs of amino acids including H112, S118, H156, Q161, K301, R306, E307, A309 and S323 in MaFADS12 (K123, A129, N167, M172, T302, D307, I308, E310 and D324 in GcFADS12) were identified as having a significantly effect on FADS12 catalytic efficiency, and linoleic acid and its analogues (12,13-cyclopropenoid fatty acid) were found to inhibit the catalytic activity of FADS12 and related recombinant enzymes. Furthermore, the molecular mechanism of FADS12 inhibition was analyzed. The results revealed two allosteric domains, including one domain from the N-terminal region to the beginning of the first transmembrane domain and another from the 31st amino acid from the end of the sixth transmembrane domain to the C terminus. Y4 and F398 amino acid residues from MaFADS12 and eight pairs of amino acids including G56, L60, L344, G10, Q13, S24, K326 and L344 in MaFADS12 (while Y66, F70, F345, F20, Y23, Y34, F327 and F345 in GcFADS12) played a pivotal role in FADS12 inhibition. Finally, we found that both allosteric and active sites were responsible for the catalytic activity of FADS12 at various temperatures, pH, and times. This study offers a solid theoretical basis to develop preconditioning methods to increase the rate at which GcFADS12 converts oleic and linoleic acids to produce higher levels of EFAs in cheese.

molecular biology↗

bioRxiv2019

A Missense Point Mutation in Nerve Growth Factor (NGFR100W) Results in Selective Peripheral Sensory Neuropathy

A missense point mutation in nerve growth factor (NGFR100W) is associated with hereditary sensory autonomic neuropathy V (HSAN V), originally discovered in a Swedish family. These patients develop severe loss of perception to deep pain but with apparently normal cognitive functions. To better understand the disease mechanism, we have generated the first NGFR100W knockin mouse model of HSAN V. Mice homozygous for the NGFR100W mutation (NGFfln/fln) showed significant structural deficits in intra-epidermal nerve fibers (IENFs) at birth. These mice had a total loss of pain perception at [~]2 months of age and they often failed to survive to full adulthood. Heterozygous mice (NGF+/fln) developed a progressive degeneration of small sensory fibers both behaviorally and functionally: they showed a progressive loss of IENFs starting at the age of 9 months accompanied with progressive loss of perception to painful stimuli such as noxious temperature. Quantitative analysis of lumbar 4/5 dorsal root ganglia (DRG) revealed a significant reduction in small size neurons positive for calcitonin gene-related peptide, while analysis of sciatic nerve fibers revealed the mutant NGF+/fln mice had no reduction in myelinated nerve fibers. Significantly, the amount of NGF secreted from fibroblasts were reduced in heterozygous and homozygous mice compared to their wild-type littermates. Interestingly, NGF+/fln showed no apparent structural alteration in the brain: neither the anterior cingulate cortex nor the medial septum including NGF-dependent basal forebrain cholinergic neurons. Accordingly, these animals did not develop appreciable deficits in tests for central nervous system function. Our study provides novel insights into the selective impact of NGFR100W mutation on the development and function of the peripheral sensory system.

neuroscience↗

bioRxiv2019

Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank

Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits and may yield inflated Type I error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type I error rates were well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

genetics↗

bioRxiv2019

3D mapping of host-parasite-microbiome interactions reveals metabolic determinants of tissue tropism and disease tolerance in Chagas disease

Chagas disease (CD) is a parasitic infection caused by Trypanosoma cruzi protozoa. Over 8 million people worldwide are T. cruzi-positive, 20-30% of which will develop cardiomyopathy, megaoesophagus and/or megacolon. The mechanisms leading to gastrointestinal (GI) symptom development are however poorly understood. To address this issue, we systematically characterized the spatial impact of experimental T. cruzi infection on the microbiome and metabolome across the GI tract. The largest microbiota perturbations were observed in the proximal large intestine in both acute and chronic disease, with chronic-stage effects also observed in the cecum. Strikingly, metabolomic impact of acute-to-chronic stage transition differed depending on the organ, with persistent large-scale effects of infection primarily in the oesophagus and large intestine, providing a potential mechanism for GI pathology tropism in CD. Infection particularly affected acylcarnitine and lipid metabolism. Building on these observations, treatment of infected mice with carnitine-supplemented drinking water prevented acute-stage mortality with no changes in parasite burden. Overall, these results identified a new mechanism of disease tolerance in CD, with potential for the development of new therapeutic regimens. More broadly, these results highlight the potential of spatially-resolved metabolomic approaches to provide insight into disease pathogenesis, with translational applications for infectious disease drug development.

microbiology↗

bioRxiv2019

Innovations in Primate Interneuron Repertoire

Primates and rodents, which descended from a common ancestor more than 90 million years ago, exhibit profound differences in behavior and cognitive capacity. Modifications, specializations, and innovations to brain cell types may have occurred along each lineage. We used Drop-seq to profile RNA expression in more than 184,000 individual telencephalic interneurons from humans, macaques, marmosets, and mice. Conserved interneuron types varied significantly in abundance and RNA expression between mice and primates, but varied much more modestly among primates. In adult primates, the expression patterns of dozens of genes exhibited spatial expression gradients among neocortical interneurons, suggesting that adult neocortical interneurons are imprinted by their local cortical context. In addition, we found that an interneuron type previously associated with the mouse hippocampus--the \"ivy cell\", which has neurogliaform characteristics--has become abundant across the neocortex of humans, macaques, and marmosets. The most striking innovation was subcortical: we identified an abundant striatal interneuron type in primates that had no molecularly homologous cell population in mouse striatum, cortex, thalamus, or hippocampus. These interneurons, which expressed a unique combination of transcription factors, receptors, and neuropeptides, including the neuropeptide TAC3, constituted almost 30% of striatal interneurons in marmosets and humans. Understanding how gene and cell-type attributes changed or persisted over the evolutionary divergence of primates and rodents will guide the choice of models for human brain disorders and mutations and help to identify the cellular substrates of expanded cognition in humans and other primates.

neuroscience↗

bioRxiv2019

Quantitative Personality Predictions from a Brief EEG Recording

The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. However, most existing ways to quantify personality traits rely on self-reported scales, which are susceptible to biases such as self-presentational concerns. In this study, we propose and evaluate a novel implicit measure of personality that uses machine learning (ML) algorithms to predict an individuals levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Results from a large test sample of 196 volunteers indicated that the personality scores derived from the proposed measure converged significantly with a commonly used questionnaire, predicted behavioral indices and psychological adjustment in a manner similar to self-reported scores, and were relatively stable across time. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice.

neuroscience↗

bioRxiv2019

STING recruits NLRP3 to the ER and deubiquitinates NLRP3 to activate the inflammasome

One of the fundamental reactions of the innate immune responses to pathogen infection is the release of pro-inflammatory cytokines, including IL-1{beta}, processed by the NLRP3 inflammasome. STING is essential for innate immune responses and inflammasome activation. Here we reveal a distinct mechanism by which STING regulates the NLRP3 inflammasome activation, IL-1{beta} secretion, and inflammatory responses in human cell lines, mice primary cells, and mice. Interestingly, upon HSV-1 infection and cytosolic DNA stimulation, STING binds to NLRP3 and promotes the inflammasome activation through two approaches. First, STING recruits NLRP3 and promotes NLRP3 translocation to the endoplasmic reticulum, thereby facilitating the inflammasome formation. Second, STING interacts with NLRP3 and removes K48- and K63-linked polyubiquitination of NLRP3, thereby promoting the inflammasome activation. Collectively, we demonstrate that the cGAS-STING-NLRP3 signaling is essential for host defense against DNA virus infection.

immunology↗

bioRxiv2019

Using Machine Learning to Facilitate Classification of Somatic Variants from Next-Generation Sequencing

BackgroundMolecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. Here we present a machine learning-based method to distinguish artifacts from bona fide Single Nucleotide Variants (SNVs) detected by NGS from tumor specimens.\n\nMethodsA cohort of 11,278 SNVs identified through clinical sequencing of tumor specimens were collected and divided into training, validation, and test sets. Each SNV was manually inspected and labeled as either real or artifact as part of clinical laboratory workflow. A three-class (real, artifact and uncertain) model was developed on the training set, fine-tuned using the validation set, and then evaluated on the test set. Prediction intervals reflecting the certainty of the classifications were derived during the process to label \"uncertain\" variants.\n\nResultsThe optimized classifier demonstrated 100% specificity and 97% sensitivity over 5,587 SNVs of the test set. 1,252 out of 1,341 true positive variants were identified as real, 4,143 out of 4,246 false positive calls were deemed artifacts, while only 192(3.4%) SNVs were labeled as \"uncertain\" with zero misclassification between the true positives and artifacts in the test set.\n\nConclusionsWe presented a computational classifier to identify variant artifacts detected from tumor sequencing. Overall, 96.6% of the SNVs received a definitive label and thus were exempt from manual review. This framework could improve quality and efficiency of variant review process in clinical labs.

bioinformatics↗

bioRxiv2019

A rectal cancer model establishes a platform to study individual responses to chemoradiation

Rectal cancer (RC) is a challenging disease to treat that requires chemotherapy, radiation, and surgery to optimize outcomes for individual patients. No accurate model of RC exists to answer fundamental research questions relevant to individual patients. We established a biorepository of 32 patient-derived RC organoid cultures (tumoroids) from patients with primary, metastatic, or recurrent disease. RC tumoroids retained molecular features of the tumors from which they were derived, and their ex vivo responses to clinically relevant chemotherapy and radiation treatment correlate well with responses noted in individual patients tumors. Upon engraftment into murine rectal mucosa, human RC tumoroids gave rise to invasive rectal cancer followed by metastasis to lung and liver. Importantly, engrafted tumors closely reflected the heterogenous sensitivity to chemotherapy observed clinically. Thus, the biology and drug sensitivity of RC clinical isolates can be efficiently interrogated using an organoid-based, in vitro platform coupled with endoluminal propagation in animals.

cancer biology↗

bioRxiv2019

Efficient RNA-mediated reprogramming of human somatic cells to naive pluripotency facilitated by tankyrase inhibition

In contrast to conventional human pluripotent stem cells (hPSC) that are related to post-implantation embryo stages, naive hPSC exhibit features of pre-implantation epiblast. Naive hPSC are established by resetting conventional hPSC, or are derived from dissociated embryo inner cell masses. Here we investigate conditions for transgene-free reprogramming of human somatic cells to naive pluripotency. We find that tankyrase inhibition promotes RNA-mediated induction of naive pluripotency. We demonstrate application to independent human fibroblast cultures and endothelial progenitor cells. We show that induced naive hPSC can be clonally expanded with a diploid karyotype and undergo somatic lineage differentiation following formative transition. Induced naive hPSC lines exhibit distinctive surface marker, transcriptome, and methylome properties of naive epiblast identity. This system for efficient, facile, and reliable induction of transgene free naive hPSC offers a robust platform, both for delineation of human reprogramming trajectories and for evaluating the attributes of isogenic naive versus conventional hPSC.

developmental biology↗

bioRxiv2019

Deep-learning enables proteome-scale identification of phase-separated protein candidates from immunofluorescence images

Intrinsically disordered region (IDR) analysis has been widely used in the screening of phase-separated proteins. However, the precise sequences determining phase separation remain unclear. Furthermore, a large number of phase-separated proteins that exhibit relatively low IDR content remain uncharacterized. Phase-separated proteins appear as spherical droplet structures in immunofluorescence (IF) images, which renders them distinguishable from non-phase-separated proteins. Here, we transformed the problem of phase-separated protein recognition into a binary classification problem of image recognition. In addition, we established a method named IDeepPhase to identify IF images with spherical droplet structures based on convolutional neural networks. Using IDeepPhase on proteome-scale IF images from the Human Protein Atlas database, we generated a comprehensive list of phase-separated candidates which displayed spherical droplet structures in IF images, allowing nomination of proteins, antibodies and cell lines for subsequent phase separation study.

bioinformatics↗

bioRxiv2019

Using distant supervision to augment manually annotated data for relation extraction

Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.

bioinformatics↗

bioRxiv2019

piNET: a versatile web platform for downstream analysis and visualization of proteomics data

Large proteomics data, including those generated by mass spectrometry, are being generated to characterize biological systems at the protein level. Computational methods and tools to identify and quantify peptides, proteins and post-translational modifications (PTMs) that are captured in modern mass spectrometers have matured over the years. On the other hand, tools for downstream analysis, interpretation and visualization of proteomics data sets, in particular those involving PTMs, require further improvement and integration to accelerate scientific discovery and maximize the impact of proteomics studies by connecting them better with biological knowledge across not only proteomics, but also other Omics domains. With the goal of addressing these challenges, the piNET server has been developed as a versatile web platform to facilitate mapping, annotation, analysis and visualization of peptide, PTM, and protein level quantitative data generated by either targeted, shotgun or other proteomics approaches. Building on our experience with large scale analysis of gene and protein expression profiles as part of the Library of Integrated Network Cellular Signatures (LINCS) project, piNET has been designed as a fast, versatile and easy to use web-based tool with three modules that provide mapping from peptides (with PTMs) to proteins, from PTM sites to modifying enzymes that target those sites, and finally from proteins (with PTMs) to pathways, and for further mechanistic insights to LINCS signatures of chemical and genetic perturbations. piNET is freely available at http://www.pinet-server.org.

bioinformatics↗

bioRxiv2019

Retrieval of whole human genome clinical variant information through search motors

The interpretation of variation in the human genome constitutes one of the most pressing challenges in biomedicine. There are many academic and proprietary resources that provide annotation, interpretation, scoring and knowledge of genetic variants under multiple access modalities. For these resources, information is available through portals, wikis, APIs, curated content and databases, and pipelines. Here we explore the use of search motors to provide facilitated access to the main sources of information that are used by clinical geneticists and researchers. We also support the browsing experience with natural language processing and automated summaries of available information. The resulting tool, ai-OMNI.com, is intrinsically flexible, expandable and intuitive, thus providing a different experience for querying the human genome for the consequences of variation.

genomics↗

bioRxiv2019

Magnus Representation of Genome Sequences

We introduce an alignment-free method, the Magnus Representation, to analyze genome sequences. The Magnus Representation captures higher-order information in genome sequences. We combine our approach with the idea of k-mers to define an effectively computable Mean Magnus Vector. We perform phylogenetic analysis on three datasets: mosquito-borne viruses, filoviruses, and bacterial genomes. Our results on ebolaviruses are consistent with previous phylogenetic analyses, and confirm the modern viewpoint that the 2014 West African Ebola outbreak likely originated from Central Africa. Our analysis also confirms the close relationship between Bundibugyo ebolavirus and Tai Forest ebolavirus. For bacterial genomes, our method is able to classify relatively well at the family and genus level, as well as at higher levels such as phylum level. The bacterial genomes are also separated well into Gram-positive and Gram-negative subgroups.

genomics↗