Biology subjects

Huang, W.

Publications and source records attributed to Huang, W..

At least 361 records · Page 20Linked to original sources

bioRxiv2019

Functional significance and therapeutic potential of miR-15a mimic in pancreatic ductal adenocarcinoma

Treatment of pancreatic ductal adenocarcinoma (PDAC) remains a clinical challenge. There is an urgent need to develop novel strategies to enhance survival and improve patient prognosis. microRNAs (miRNAs) play critical roles as oncogenes or tumor suppressors in the regulation of cancer development and progression. In this study, we demonstrate that low expression of miR-15a is associated with poor prognosis of PDAC patients. miR-15a expression is reduced in PDAC while closely related miR-16 expression remains relatively unchanged. miR-15a suppresses several important targets such as Wee1, Chk1, Yap-1, and BMI-1 causing cell cycle arrest and inhibiting cell proliferation. Ectopic expression of miR-15a sensitizes PDAC cells to gemcitabine reducing the IC50 over 6.5-fold. To investigate the therapeutic potential of miR-15a, we used a modified miR-15a (5-FU-miR-15a) with uracil (U) residues in the guide strand replaced with 5-fluorouracil (5-FU). We demonstrated enhanced inhibition of PDAC cell proliferation by 5-FU-miR-15a compared to native miR-15a. In vivo we showed the therapeutic power of 5-FU-miR-15a alone or in combination with gemcitabine with near complete elimination of PDAC lung metastatic tumor growth. These results support the future development of 5-FU-miR-15a as a novel therapeutic agent as well as a prognostic biomarker in the clinical management of PDAC.

cancer biology↗

bioRxiv2019

Influence of genetic interactions on polygenic prediction

Prediction of phenotypes from genotypes is an important objective to fulfill the promises of genomics, precision medicine and agriculture. Although its now possible to account for the majority of genetic variation through model fitting, prediction of phenotypes remains a challenge, especially across populations that have diverged in the past. In this study, we designed simulation experiments to specifically investigate the role of genetic interactions in failure of polygenic prediction. We found that non-additive genetic interactions can significantly reduce the accuracy of polygenic prediction. Our study demonstrated the importance of considering genetic interactions in genetic prediction.

genetics↗

bioRxiv2019

Fate mapping via Ms4a3 expression history traces monocyte-derived cells

Most tissue-resident macrophage (RTM) populations are seeded by waves of embryonic hematopoiesis and are self-maintained independently of a bone-marrow contribution during adulthood. A proportion of RTMs, however, is constantly replaced by blood monocytes and their functions compared to embryonic RTM remains unclear. The kinetics and extent of the contribution of circulating monocytes to RTM replacement during homeostasis, inflammation and disease is highly debated. Here, we identified Ms4a3 as a specific marker expressed by granulocyte-monocyte progenitors (GMPs) and subsequently generated Ms4a3TdT reporter and Ms4a3Cre-RosaTdT fate mapper models to follow monocytes and their progenies. Our Ms4a3Cre-RosaTdT model traced efficiently blood monocytes (97%) and granulocytes (100%), but no lymphocytes or tissue dendritic cells. Using this model, we precisely quantified the contribution of monocytes to the RTM pool during homeostasis and inflammation. The unambiguous identification of monocyte-derived cells will permit future studies of their function under any condition.

immunology↗

bioRxiv2019

Phenological matching drives wheat pest range shift under climate change

Shifting geographical ranges of crop pests and pathogens in response to climate change pose a threat to food security (1, 2). The orange wheat blossom midge (Sitodiplosis mosellana Gehin) is responsible for significant yield losses in China (3), the worlds largest wheat producer. Here we report that rising temperatures in the North China Plain have resulted in a mean northward range shift of 3.3{degrees} (58.8 km per decade) from the 1950s to 2010s, which accelerated to 91.3 km per decade after 1985 when the highly toxic pesticide hexachlorocyclohexane (HCH) was banned (4). Phenological matching between wheat midge adult emergence and wheat heading in this new expanded range has resulted in greater damage to wheat production. Around $286.5 million worth of insecticides were applied to around 19 million hectares in an attempt to minimize wheat midge damage to crops between 1985 and 2016. Despite use of these pesticides, wheat midge caused losses of greater than 0.95 million metric tons of grain during this period. Our results demonstrate the potential for indirect negative impacts of climate change on crop production and food security, and constitute the first large scale example of plant pest range shift due to global warming.

ecology↗

bioRxiv2019

Inhibition of Osteoblast Proliferation and Migration by Exogenous and Endogenous Formaldehyde

Exogenous and endogenous formaldehyde (FA) plays an important role in cell growth and migration; however, its potential role in osteoblasts remains largely unclear. Cell counting kit-8 (CCK-8) and wound healing assays revealed that FA exposure at naturally occurring concentrations inhibited the proliferation and migration of mouse preosteoblast MC3T3-E1 cells. Moreover, RNA sequencing (RNA-seq) analysis revealed that FoxO1 signaling pathway components displayed distinct expression patterns upon FA exposure, reflected through significant enrichment of cell migration. In particular, FoxO1 Sirt1 and FA-induced related protein expression which were closely with cell proliferation and migration were confirmed by western blotting. The present results indicate that the FoxO1 pathway is involved in FA-induced inhibition of cell growth and migration.

cell biology↗

bioRxiv2019

Measuring single cell divisions in human cancers from multi-region sequencing data

Cancer is driven by complex evolutionary dynamics involving billions of cells. Increasing effort has been dedicated to sequence single tumour cells, but obtaining robust measurements remains challenging. Here we show that multi-region sequencing of bulk tumour samples contains quantitative information on single-cell divisions that is accessible if combined with evolutionary theory. Using high-throughput data from 16 human cancers, we measured the in vivo per-cell point mutation rate (mean: 1.69x10-8 bp per cell division) and per-cell survival rate (mean: 0.57) in individual patient tumours from colon, lung and renal cancers. Per-cell mutation rates varied 50-fold between individuals, and per-cell survival rates were between nearly-homeostatic and almost perfect cell doublings, equating to tumour ages between 1 and 19 years. Furthermore, reanalysing a recent dataset of 89 whole-genome sequenced healthy haematopoietic stem cells, we find 1.14 mutations per genome per cell division and near perfect cell doublings (per-cell survival rate: 0.96) during early haematopoietic development. Our analysis measures in vivo the most fundamental properties of human cancer and healthy somatic evolution at single-cell resolution within single individuals.

cancer biology↗

bioRxiv2019

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data. SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

bioinformatics↗

bioRxiv2019

The genomic sequence and comparative genomic analysis of cultivated passion fruit(Passiflora edulis L.)

Cultivated passion fruit is a fruit tree widely cultivated in southern China, but little is known about its genomics, which seriously restricts the molecular genetics research of passion fruit. In this study, we analyzed the 165.7Mb representative genome sequences. The results showed that the passion fruit genome contained a large number of simple sequence repeats (SSR). Compared to the cassava and peach genomes, the passion fruit genome has 23,053 predicted genes. These genes can be aligned to 282 plant genomes. GO annotation indicated that these genes are involved in metabolic pathways of carbohydrates, organic acids, lipids and other molecules. KEGG pathway enrichment assigned these genes into five major categories and 19 secondary functions. Cluster analysis of gene families showed that 12,767 genes could be clustered into 9,868 gene families and 291 unique gene families. On the evolutionary relationship, the passion fruit is closely related to Populus trichocarpa and Ricinus communis, but the rate of evolution is slower. In summary, this genomic analysis result is informative, and will facilitate the future studies on gene functions of passion fruit.

genomics↗

bioRxiv2019

Cyclin A2/Cdk1 is Essential for the in vivo S Phase Entry by Phosphorylating Top2a.

Cyclin-dependent kinase 1 (CDK1) plays essential roles in cell cycle regulation. However, due to the early embryonic lethality of mouse Cdk1 mutants, the in vivo role of CDK1 in regulating cell cycle and embryonic development remains unclear. Here, by generating zebrafish cdk1 mutants using CRISPR/Cas9 system, we show that cdk1-/- embryos exhibit severe microphthalmia accompanied with multiple defects in polarized cell division, S phase entry and M phase progression, cell apoptosis and cell differentiation, but not in interkinetic nuclear migration (IKNM). By informatics analysis, we identified Top2a as a potential downstream target, and Cyclin A2 and Cyclin B1 as partners of Cdk1 in cell cycle. Depletion of either Cyclin A2 or Top2a leads to decreased S phase entry and increased DNA damage response in zebrafish retinal cells, and depletion of Cyclin B1 leads to M phase arrest. Immunoprecipitation shows that Cdk1 and Cyclin A2 physically interact in vivo. Moreover, phosphorylation of Top2a on Serine 1213 (S1213) site is almost absent in either cdk1 or ccna2 mutants, but in not ccnb1 mutants. Furthermore, overexpression of TOP2AS1213, the phosphomimetic form of human TOP2A, rescues S phase entry and microphthalmia defects in cdk1-/- and ccna2-/- embryos. Taken together, our data suggests that Cdk1 interacts with Cyclin A2 to regulate S phase entry through phosphorylating Top2a, and with Cyclin B1 to regulate M phase progression in vivo.

developmental biology↗

bioRxiv2018

The heparin-binding proteome in normal pancreas and murine experimental acute pancreatitis

Acute pancreatitis (AP) is acute inflammation of the pancreas, mainly caused by gallstones and alcohol, driven by changes in communication between cells. Heparin-binding proteins (HBPs) play a central role in health and diseases. Therefore, we used heparin affinity proteomics to identify extracellular HBPs in pancreas and plasma of normal mice and in a caerulein mouse model of AP. Many new extracellular HBPs (360) were discovered in the pancreas, taking the total number of HBPs known to 786. Extracellular pancreas HBPs form highly interconnected protein-protein interaction networks in both normal pancreas (NP) and AP. Thus, HBPs represent an important set of extracellular proteins with significant regulatory potential in the pancreas. HBPs in NP are associated with biological functions such as molecular transport and cellular movement that underlie pancreatic homeostasis. However, in AP HBPs are associated with additional inflammatory processes such as acute phase response signalling, complement activation and mitochondrial dysfunction, which has a central role in the development of AP. Plasma HBPs in AP included known AP biomarkers such as serum amyloid A, as well as emerging targets such as histone H2A. Other HBPs such as alpha 2-HS glycoprotein (AHSG) and histidine-rich glycoprotein (HRG) need further investigation for potential applications in the management of AP. Pancreas HBPs are extracellular and so easily accessible and are potential drug targets in AP, whereas plasma HBPs represent potential biomarkers for AP. Thus, their identification paves the way to determine which HBPs may have potential applications in the management of AP.

biochemistry↗

bioRxiv2018

Identifying the Biosynthetic Gene Cluster for Triacsins with an N-hydroxytriazene Moiety

Triacsins are a family of natural products containing an N-hydroxytriazene moiety not found in any other known secondary metabolites. Though many studies have examined the biological activity of triacsins in lipid metabolism, the biosynthesis of triacsins has remained unknown. Here, we report the identification of the triacsin biosynthetic gene cluster in Streptomyces aureofaciens ATCC 31442. Bioinformatic analysis of the gene cluster led to the discovery of the tacrolimus producer Streptomyces tsukubaensis NRRL 18488 as a new triacsin producer. In addition to targeted gene disruption to identify necessary genes for triacsin production, stable isotope feeding was performed in vivo to advance the understanding of N-hydroxytriazene biosynthesis.

biochemistry↗

bioRxiv2018

Proteomic analysis of the Pseudomonas aeruginosa iron starvation response reveals PrrF sRNA-dependent regulation of amino acid metabolism, iron-sulfur cluster biogenesis, motility, and zinc homeostasis

Iron is a critical nutrient for most microbial pathogens, and the innate immune system exploits this requirement by sequestering iron and other metals through a process termed nutritional immunity. The opportunistic pathogen Pseudomonas aeruginosa provides a model system for understanding the microbial response to host iron depletion, as this organism exhibits a high requirement for iron as well as an exquisite ability to overcome iron deprivation during infection. Hallmarks of P. aeruginosas iron starvation response include the induction of multiple high affinity iron acquisition systems and an \"iron sparing response\" that is post-transcriptionally mediated by the PrrF small regulatory RNAs (sRNAs). Here, we used liquid chromatography-tandem mass spectrometry to conduct label-free proteomics of the P. aeruginosa iron starvation response, revealing several iron-regulated processes that have not been previously described. Iron starvation induced multiple proteins involved in branched chain and aromatic amino acid catabolism, providing the capacity for iron-independent entry of carbons into the TCA cycle. Proteins involved in sulfur assimilation and cysteine biosynthesis were reduced upon iron starvation, while proteins involved in iron-sulfur cluster biogenesis were paradoxically increased, highlighting the central role of iron in P. aeruginosa metabolism. Iron starvation also resulted in changes in the expression of several zinc-responsive proteins, as well as the first experimental evidence for increased levels of twitching motility proteins upon iron starvation. Subsequent proteomics analyses demonstrated that the PrrF sRNAs were required for iron regulation of many of these newly-identified proteins, and we identified PrrF complementarity with mRNAs encoding key iron-regulated proteins involved in amino acid metabolism, iron-sulfur biogenesis, and zinc homeostasis. Combined, these results provide the most comprehensive view of the P. aeruginosa iron starvation response to date and outline novel roles for the PrrF sRNAs in the P. aeruginosa iron sparing response and pathogenesis.\n\nAUTHOR SUMMARYIron is central for the metabolism of almost all microbial pathogens, and as such this element is sequestered by the host innate immune system to restrict microbial growth. Defining the response of microbial pathogens to iron starvation is therefore critical for understanding how pathogens colonize and propagate within the host. The opportunistic pathogen Pseudomonas aeruginosa, which causes significant morbidity and mortality in compromised individuals, provides an excellent model for studying this response due to its high requirement for iron yet well-documented ability to overcome iron starvation. Here we used label-free proteomics to investigate the P. aeruginosa iron starvation response, revealing a broad landscape of metabolic and metal homeostasis changes that have not previously been described. We further provide evidence that many of these processes are regulated through the iron responsive PrrF small regulatory RNAs, which are integral to P. aeruginosa iron homeostasis and virulence. These results demonstrate the power of proteomics for defining stress responses of microbial pathogens, and they provide the most comprehensive analysis to date of the P. aeruginosa iron starvation response.

microbiology↗

bioRxiv2018

ACME: Pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks

Prediction of peptide binding to MHC molecules plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Although numerous computational methods have been developed to this end, several challenges still remain in predicting peptide-MHC interactions. Many previous methods are allele-specific, training separate models for individual alleles and are thus unable to yield accurate predictions for those alleles with limited training data. Despite that there exist several pan-specific algorithms that train a common model for different alleles, they only adopt simple model structures that generally have limited performance in capturing the complex underlying patterns of peptide-MHC interactions. Here we present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson Correlation Coefficient by up to 23 percent. In addition, its ability to identify strong-binding peptides has been experimentally validated. Moreover, by integrating the convolutional neural network with attention mechanism, ACME is able to extract interpretable patterns that can provide useful and detailed insights into the binding preferences between peptides and their MHC partners. All these results have demonstrated that ACME can provide a powerful and practically useful tool for the studies of peptide-MHC class I interactions.

bioinformatics↗

bioRxiv2018

Root volatiles in plant-plant interactions I: Characterization of root sesquiterpene emissions from Centaurea stoebe and their effects on other plants

Volatile organic compounds (VOCs) emitted by plant leaves can influence the physiology of neighboring plants. In contrast to interactions above ground, little is known about the role of VOCs in belowground plant-plant interactions. Here, we characterize constitutive root volatile emissions of the spotted knapweed (Centaurea stoebe) and explore the impact of these volatiles on the germination and growth of different sympatric plant species. We show that C. stoebe roots emit high amounts of sesquiterpenes, with estimated release rates of (E)-{beta}-caryophyllene above 3 g g-1 dw h-1. Sesquiterpene emissions show little variation between different C. stoebe populations, but vary substantially between different Centaurea species. Through root transcriptome sequencing, we identify six root-expressed sesquiterpene synthases (TPSs). Two root-specific TPSs, CsTPS4 and CsTPS5, are sufficient to produce the full blend of emitted root sesquiterpenes. Volatile exposure experiments demonstrate that C. stoebe root volatiles have neutral to positive effects on the germination and growth of different sympatric neighbors. Thus, constitutive root sesquiterpenes produced by two C. stoebe TPSs are associated with facilitation of sympatric neighboring plants. The release of root VOCs may thus influence C. stoebe abundance and plant community structure in nature.

plant biology↗

bioRxiv2018

Root volatiles in plant-plant interactions II: Root terpenes from Centaurea stoebe modify Taraxacum officinale root chemistry and root herbivore growth

Volatile organic compounds (VOCs) emitted by plant roots can influence the germination and growth of neighboring plants. However, little is known about the effects of root VOCs on plant-herbivore interactions. The spotted knapeed (Centaurea stoebe) constitutively releases high amounts of sesquiterpenes into the rhizosphere. Here, we examine the impact of C. stoebe root VOCs on primary and secondary metabolites of sympatric Taraxacum officinale plants and the resulting plant-mediated effects on a generalist root herbivore, the white grub Melolontha melolontha. We show that exposure of T. officinale to C. stoebe root VOCs does not affect the accumulation of defensive secondary metabolites, but modulates carbohydrate and total protein levels in T. officinale roots. Furthermore, VOC exposure increases M. melolontha growth on T. officinale plants. Exposure of T. officinale to a major C. stoebe root VOC, the sesquiterpene (E)-{beta}-caryophyllene, partially mimics the effect of the full root VOC blend on M. melolontha growth. Thus, releasing root VOCs can modify plant-herbivore interactions of neighboring plants. The release of VOCs to increase the susceptibility of other plants may be a form of plant offense.

plant biology↗

bioRxiv2018

The Novel Mnk1/2 Degrader VNLG-152 Potently Inhibits TNBC Tumor Growth and Metastasis

Currently, there are no effective therapies for patients with triple-negative breast cancer (TNBC), an aggressive and highly metastatic disease. Activation of eukaryotic initiation factor 4E (eIF4E) by mitogen-activated protein kinase (MAPK)-interacting kinases 1 and 2 (Mnk1/2) play a critical role in the development, progression and metastasis of TNBC. Herein, we undertook a comprehensive study to evaluate the activity of a first-in-class Mnk1/2 protein degraders, in clinically relevant models of TNBC. These studies enabled us to identify racemic VNLG-152R as the most efficacious Mnk1/2 degrader. By targeting Mnk1/2 protein degradation (activity), VNLG-152R potently inhibited both Mnk-eIF4E and mTORC1 signaling pathways and strongly regulated downstream factors involved in cell cycle regulation, apoptosis, pro-inflammatory cytokines/chemokines secretion, epithelial-mesenchymal transition (EMT) and metastasis. Most importantly, orally bioavailable VNLG-152R exhibited remarkable antitumor and antimetastatic activities against cell line and patient-derived TNBC xenograft models, with no apparent host toxicity. Collectively, these studies demonstrate that targeting Mnk-eIF4E/mTORC1 signaling with a potent Mnk1/2 degrader, VNLG-152R, is a novel therapeutic strategy that can be developed as monotherapy for effective treatment of patients with primary/metastatic TNBC.

cancer biology↗

bioRxiv2018

Nuclear export of chimeric mRNAs depends on an lncRNA-triggered autoregulatory loop

Aberrant chromosomal translocations leading to tumorigenesis have been ascribed to the heterogeneously oncogenic functions. However, how fusion transcripts exporting remains to be declared. Here, we showed that the nuclear speckle-specific long non coding RNA MALAT1 controls chimeric mRNA export processes and regulates myeloid progenitor cell differentiation in malignant hematopoiesis. We demonstrated that MALAT1 regulates chimeric mRNAs export in an m6A-dependent manner and thus controls hematopoietic cell differentiation. Specifically, reducing MALAT1 or m6A methyltransferases and the reader YTHDC1 result in the universal retention of distinct oncogenic gene mRNAs in nucleus. Mechanically, MALAT1 hijacks both the chimeric mRNAs and fusion proteins in nuclear speckles during chromosomal translocations and mediates the colocalization of oncogenic fusion proteins with METTL14. MALAT1 and fusion protein complexes serve as a functional loading bridge for the interaction of chimeric mRNA and METTL14. This study demonstrated a universal mechanism of chimeric mRNA transport that involves lncRNA-fusion protein-m6A autoregulatory loop for controlling myeloid cell differentiation. Targeting the lncRNA-triggered autoregulatory loop to disrupt chimeric mRNA transport might represent a new common paradigm for treating blood malignancies.

cancer biology↗

bioRxiv2018

Quantifying Heterogeneity in the Genetic Architecture of Complex Traits Between Ethnically Diverse Groups using Random Effect Interaction Models

In humans, most genome-wide association studies have been conducted using data from Caucasians and many of the reported findings have not replicated in other populations. This lack of replication may be due to statistical issues (small sample size, confounding) or perhaps more fundamentally to differences in the genetic architecture of traits between ethnically diverse subpopulations. What aspects of the genetic architecture of traits vary between subpopulations and how can this be quantified? We consider studying effect heterogeneity using random-effect Bayesian interaction models. The proposed methodology can be applied using shrinkage and variable selection methods and produces useful information about effect heterogeneity in the form of whole-genome summaries (e.g., SNP-heritability and the average correlation of effects) as well as SNP-specific attributes. Using simulations, we show that the proposed methodology yields (nearly) unbiased estimates of genomic heritability and of the average correlation of effects between groups when the sample size is not too small relative to the number of SNPs used. Subsequently, we used the proposed methodology for the analyses of four complex human traits (standing height, high-density lipoprotein, low-density lipoprotein, and serum urate levels) in European-Americans (EAs) and African-Americans (AAs). The estimated correlations of effects between the two subpopulations was well below unity for all the traits, ranging from 0.73 to 0.50. The extent of effect heterogeneity varied between traits and SNP-sets. Height showed less differences in SNP effects between AAs and EAs whereas HDL, a trait highly influenced by life-style, exhibited greater extent of effect heterogeneity. For all the traits we observed substantial variability in effect heterogeneity across SNPs, suggesting it varies between regions of the genome.

genetics↗