Biology subjects

Xia, F.

Publications and source records attributed to Xia, F..

35 records · Page 2Linked to original sources

bioRxiv2024

Related PP2C phosphatases Pic3 and Pic12 negatively regulate immunity in tomato to Pseudomonas syringae

Type 2C protein phosphatases (PP2Cs) constitute a large family in most plant species but relatively few of them have been implicated in immunity. To identify and characterize PP2C phosphatases that affect tomato immunity, we used CRISPR/Cas9 to generate loss-of-function mutations in 11 PP2C-encoding genes whose expression is altered in response to immune elicitors or pathogens. We report that two closely related PP2C phosphatases, Pic3 and Pic12, are involved in regulating resistance to the bacterial pathogen Pseudomonas syringae pv. tomato (Pst). Loss-of-function mutations in Pic3 lead to enhanced resistance to Pst in older but not younger leaves, whereas such mutations in Pic12 resulted in enhanced resistance in both older and younger leaves. Overexpression of Pic3 and Pic12 proteins in leaves of Nicotiana benthamiana inhibited resistance to Pst and this effect was dependent on Pic3/12 phosphatase activity and an N-terminal palmitoylation motif associated with localization to the cell periphery. Pic3 but not Pic12 had a slight negative effect on flagellin-associated reactive oxygen species generation, although their involvement in the response to Pst appeared independent of flagellin. RNA-sequencing analysis of Rio Grande (RG)-PtoR wild-type plants and two independent RG-pic3 mutants revealed that the enhanced disease resistance in RG-pic3 older leaves is associated with increased transcript abundance of multiple defense related genes. RG-pic3/RG-pic12 double mutant plants exhibited stronger disease resistance than RG-pic3 or RG-pic12 single mutants. Together, our results reveal that Pic3 and Pic12 negatively regulate tomato immunity in an additive manner through flagellin-independent pathways.

plant biology↗

bioRxiv2024

Dual-targeted nanoparticulate drug delivery systems for enhancing triple-negative breast cancer treatment

The efficacy of DNA-damaging agents, such as the topoisomerase I inhibitor SN38, is often compromised by the robust DNA repair mechanisms in tumor cells, notably homologous recombination (HR) repair. Addressing this challenge, we introduce a novel nano-strategy utilizing binary tumor-killing mechanisms to enhance the therapeutic impact of DNA damage and mitochondrial dysfunction in cancer treatment. Our approach employs a synergistic drug pair comprising SN38 and the BET inhibitor JQ-1. We synthesized two prodrugs by conjugating linoleic acid (LA) to SN38 and JQ-1 via a cinnamaldehyde thioacetal (CT) bond, facilitating co-delivery. These prodrugs co-assemble into a nanostructure, referred to as SJNP, in an optimal synergistic ratio. SJNP was validated for its efficacy at both the cellular and tissue levels, where it primarily disrupts the transcription factor protein BRD4. This disruption leads to downregulation of BRCA1 and RAD51, impairing the HR process and exacerbating DNA damage. Additionally, SJNP releases cinnamaldehyde (CA) upon CT linkage cleavage, elevating intracellular ROS levels in a self-amplifying manner and inducing ROS-mediated mitochondrial dysfunction. Our results indicate that SJNP effectively targets murine triple-negative breast cancer (TNBC) with minimal adverse toxicity, showcasing its potential as a formidable opponent in the fight against cancer.

cancer biology↗

bioRxiv2023

Short tandem repeat mutations regulate gene expression in colorectal cancer

Short tandem repeat (STR) mutations are prevalent in colorectal cancer (CRC), especially in tumours with the microsatellite instability (MSI) phenotype. While STR length variations are known to regulate gene expression under physiological conditions, the functional impact of STR mutations in CRC remains unclear. Here, we integrate STR mutation data with clinical information and gene expression levels to study the gene regulatory effects of STR mutations in CRC. We confirm that STR mutability in CRC highly depends on the MSI status, repeat unit size, and repeat length. Furthermore, we present a set of 1244 putative expression STRs (eSTRs) for which the STR length is associated with gene expression levels in CRC tumours. The length of 73 eSTRs is associated with expression levels of cancer-related genes, nine of which are CRC-specific genes. We show that linear models describing eSTR-gene expression relationships allow for predictions of gene expression changes in response to eSTR mutations. Moreover, we found an increased mutability of eSTRs in MSI tumours. Our evidence of gene regulatory roles for eSTRs in CRC highlights a mostly overlooked way through which tumours may modulate their phenotypes. The increased mutability of eSTRs in MSI tumours may be an early indication that eSTR mutations can confer a selective advantage to tumours. Future extensions of our findings into larger cohorts could uncover new STR-based targets in the treatment of cancer.

cancer biology↗

bioRxiv2023

Neural signatures of stress susceptibility and resilience in the amygdala-hippocampal network

The neural dynamics that underlie divergent anhedonic responses to stress remain unclear. Here, we identified neuronal dynamics in an amygdala-hippocampal circuit that distinguish stress resilience and susceptibility. In a reward-choice task, basolateral amygdala (BLA) activity in resilient mice showed enhanced discrimination of upcoming reward choices. In contrast, a rumination-like signature emerged in the BLA of susceptible mice; a linear decoder could classify the intention to switch or stay on a previously chosen reward. Spontaneous activity in the BLA of susceptible mice was higher dimensional than controls, reflecting the exploration of a larger number of distinct neural states. Manipulation of vCA1-BLA inputs rescued dysfunctional neural dynamics and anhedonia in susceptible mice, suggesting that targeting this pathway can enhance BLA circuit function and ameliorate of depression-related behaviors. One-Sentence SummaryIdentification and rescue of dysfunctional vCA1-BLA population dynamics and behavior in stress-susceptible mice.

neuroscience↗

bioRxiv2023

Polysaccharides from Balanophora harlandii Hook: Isolation, Characterization, and Anti-Inflammation Activities

Balanophora harlandii Hook (B. harlandii), a folk medicine, has been traditionally employed to treat traumatic bleeding, gastroenteritis, icteric hepatitis, hemorrhoids, and other conditions. In this work, polysaccharides with anti-inflammatory effects were extracted and purified from B. harlandii. The extraction conditions were optimized, and the properties of one purified neutral fraction, denoted as BHPs-W-S3, were analyzed. Gel permeation chromatography (GPC) was carried out to measure the molecular weight. The structure of BHPs-W-S3 was assessed based on monosaccharide composition analysis, Fourier transform infrared (FT-IR) spectroscopy, methylation analysis, and nuclear magnetic resonance (NMR) spectroscopy. BHPs-W-S3 has a molecular weight of 14.1 kDa, and its three main monosaccharides are glucose, galactose, and mannose with a molar ratio of 6.4:1.7:1.1. Its main chain consists of [->]6)--D-Glcp-(1[->], [->]4,6)--D-Glcp-(1[->], [->]6)-{beta}-D-Galp-(1[->], [->]3,6)-{beta}-D-Galp-(1[->], and it has branch chains at the O-4 and/or O-3 positions. In addition, in vitro experiments show that the polysaccharides from B. harlandi can decrease the phosphorylation level of p65 and IKB- in LPS-induced RAW264.7 cells to reduce the expression of the pro-inflammatory genes such as TNF-, IL-6, and IL-1{beta}. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/559774v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@17ccfeforg.highwire.dtl.DTLVardef@137726borg.highwire.dtl.DTLVardef@139100dorg.highwire.dtl.DTLVardef@14cce50_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2023

Simultaneous multimodal optical coherence and three-photon microscopy of the mouse brain in the 1700 nm optical window in vivo

Multimodal microscopy combining various imaging approaches can provide complementary information about tissue in a single imaging session. Here, we demonstrate a multimodal approach combining three-photon microscopy (3PM) and spectral-domain optical coherence microscopy (SD-OCM). We show that an optical parametric chirped-pulse amplification (OPCPA) laser source, which is the standard source for three-photon fluorescence excitation and third harmonic generation (THG), can be used for simultaneous OCM, 3-photon (3P) fluorescence and THG imaging. We validated the system performance in deep mouse brains in vivo with an OPCPA source operating at 1620 nm center wavelength. We visualized small structures such as myelinated axons, neurons, and large fiber tracts in white matter with high spatial resolution non-invasively using linear and nonlinear contrast at >1 mm depth in intact adult mouse brain. Our results showed that simultaneous OCM and 3PM at the long wavelength window can be conveniently combined for deep tissue imaging in vivo.

bioengineering↗

bioRxiv2023

Reconstruction of TrkB complex assemblies and localizing an-tidepressant targets using Artificial Intelligence

Since Major Depressive Disorder (MDD) represents a neurological pathology caused by inter-synaptic messaging errors, membrane receptors, the source of signal cascades, constitute appealing drugs targets. G protein-coupled receptors (GPCRs) and ion channel receptors chelated antidepressants (ADs) high-resolution architectures were reported to realize receptors physical mechanism and design prototype compounds with minimal side effects. Tyrosine kinase receptor 2 (TrkB), a receptor that directly modulates synaptic plasticity, has a finite three-dimensional chart due to its high molecular mass and intrinsically disordered regions (IDRs). Leveraging breakthroughs in deep learning, the meticulous architecture of TrkB was projected employing Alphfold 2 (AF2). Furthermore, the Alphafold Multimer algorithm (AF-M) models the coupling of intra- and extra-membrane topologies to chaperones: mBDNF, SHP2, Etc. Conjugating firmly dimeric transmembrane helix with novel compounds like 2R,6R-hydroxynorketamine (2R,6R-HNK) expands scopes of drug screening to encompass all coding sequences throughout genomes. The operational implementation of TrkB kinase-SHP2, PLC{gamma}1, and SHC1 ensembles has paved the path for machine learning in which it can forecast structural transitions in the self-assembly and self-dissociation of molecules during trillions of cellular mechanisms. In silicon, the cornerstone of the alteration will be artificial intelligence (AI), empowering signal networks to operate at the atomic level and picosecond timescales.

bioinformatics↗

bioRxiv2021

Impact of Grain for Green Project on Water Resources and Ecological Water Stress in Yanhe River Basin

Grain for Green project (GGP) initialed by China government since 1999 has achieved substantial achievements accompanied with surface runoff decrease in the Loess Plateau but impacts of large-scale afforestation on regional water resources are uncertain. Hence, the objective of this study is to explore the impact of land use change on generalized water resources and ecological water stress using blue and green water concept taking Yanhe River Basin as a case study. Soil and Water Assessment Tool (SWAT) is applied to quantify summary of green and blue water which is defined as generalized water resources, ecological water requirement of vegetation (forest and grass), agricultural water footprint and virtual water flow are considered as regional water requirements. Land use types of 1980 (scenario I), 2017 (scenario II) are input in SWAT model while keeps other parameters constant in order to isolate the influence of land use changes. Results show that average annual difference of blue, green and generalized water resources is -72.08 million m3, 24.34 million m3, -47.74 million m3 respectively when simulation results of scenario II subtracts scenario I and it presents that land use change caused by GGP leads to decrease in blue and generalized water resources whereas increase in green water resources. SURQ in scenario I is more than that in scenario IIin all the study period from 1980-2017, green water storage in scenario I is more than that in scenario II in all the study period except in 1998; whereas LATQ in scenario I is less than that in scenario II except in 2000 and 2015, GWQ in 1992, 2000 and 2015, green water flow in 1998. Blue water, green water storage and green water flow in scenario II is less than that in scenario I in the whole basin, 12.89 percent of the basin and 99.21 percent of the basin respectively. Total WF increases from 1995 to 2010 because forest WF increases significantly in this period though agricultural WF and grass WF decreases. Ecological water stress index has no obvious temporal change trend in both land use scenarios but ecological water stress index in scenario II is more than that in scenario I which illustrates that GGP leads to increase of ecological water stress from perspective of generalized water resources

ecology↗

bioRxiv2021

Genome-wide in vivo screen of circulating tumor cells identifies SLIT2 as a regulator of metastasis

Circulating tumor cells (CTCs) break free from primary tumors and travel through the bloodstream and lymphatic system to seed metastatic tumors, which are the major cause of death from cancer. The identification of the major genetic factors that enhance production and persistence of CTCs in the bloodstream at a whole genome level would enable more comprehensive molecular mechanisms of metastasis to be elucidated and the identification of novel therapeutic targets, but this remains a challenging task due to the heterogeneity and extreme rarity of CTCs. Here, we describe the first in vivo genome-wide CRISPR KO screen using CTCs directly isolated from a mouse xenograft. This screen elucidated SLIT2 - a gene encoding a secreted protein acting as a cellular migration cue - as the most significantly represented gene knockout in the CTC population. SLIT2 knockout cells are highly metastatic with hypermigratory and mesenchymal phenotype. Reduced expression of SLIT2 is observed in human tumors, indicating its role as a negative modulator of tumor progression and metastasis.

cancer biology↗

bioRxiv2020

Hydrogel-coating improves the in-vivo stability of electrochemical aptamer-based biosensors

The ability to track the levels of specific molecules, such as drugs, metabolites, and biomarkers, in the living body, in real time and for long durations would improve our understanding of health and our ability to diagnose, treat and monitor disease. To this end, we are developing electrochemical aptamer-based (E-AB) biosensors, a general platform supporting high-frequency, real-time molecular measurements in the living body. Here we report that the addition of an agarose hydrogel protective layer to E-AB sensors significantly improves their baseline stability when deployed in the complex, highly time-varying environments found in vivo. The improved stability is sufficient that these hydrogel-protected sensors achieved good baseline stability when deployed in situ in the veins, muscles, bladder, or tumors of living rats without the use of the drift correction approaches traditionally required in such placements. Finally, this improved stability is achieved without any significant, associated "costs" in terms of detection limits, response times, or biocompatibility.

bioengineering↗

bioRxiv2019

Identifying Operons with Deep Neural Networks

Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the networks decisions to be subsequently cross-checked by human experts.

bioinformatics↗

bioRxiv2019

Quantitative analysis of 1300-nm three-photon calcium imaging in the mouse brain

1300-nm three-photon calcium imaging has emerged as a useful technique to allow calcium imaging in deep brain regions. Application to large-scale neural activity imaging entails a careful balance between recording fidelity and tissue heating. We calculated and experimentally verified the excitation pulse energy to achieve the minimum photon count required for the detection of calcium transients in GCaMP6s-expressing neurons for 920-nm two-photon and 1320-nm three-photon excitation, respectively. Brain tissue heating by continuous three-photon imaging was simulated with Monte Carlo method and experimentally validated with immunohistochemistry. We observed increased immunoreactivity with 150 mW excitation power at 1.0- and 1.2-mm imaging depths. Based on the data, we explained how three-photon excitation achieves better calcium imaging fidelity than two-photon excitation in the deep brain and quantified the imaging depth where three-photon microscopy should be applied. Our analysis presents a translatable model for the optimization of three-photon calcium imaging based on experimentally tractable parameters.

neuroscience↗

bioRxiv2019

Identifying Genomic Islands with Deep Neural Networks

BackgroundHorizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, often classified by the content of their cargo genes or their means of integration and mobility. Various computational methods have been devised to detect different types of GIs, but no single method currently is capable of detecting all GIs. ResultsWe propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. ConclusionsWe used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was retrained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

bioinformatics↗

bioRxiv2018

AdaFDR: a Fast, Powerful and Covariate-Adaptive Approach to Multiple Hypothesis Testing

Multiple hypothesis testing is an essential component of modern data science. Its goal is to maximize the number of discoveries while controlling the fraction of false discoveries. In many settings, in addition to the p-value, additional information/covariates for each hypothesis are available. For example, in eQTL studies, each hypothesis tests the correlation between a variant and the expression of a gene. We also have additional covariates such as the location, conservation and chromatin status of the variant, which could inform how likely the association is to be due to noise. However, popular multiple hypothesis testing approaches, such as Benjamini-Hochberg procedure (BH) and independent hypothesis weighting (IHW), either ignore these covariates or assume the covariate to be univariate. We introduce AdaFDR, a fast and flexible method that adaptively learns the optimal p-value threshold from covariates to significantly improve detection power. On eQTL analysis of the GTEx data, AdaFDR discovers 32% and 27% more associations than BH and IHW, respectively, at the same false discovery rate. We prove that AdaFDR controls false discovery proportion, and show that it makes substantially more discoveries while controlling FDR in extensive experiments. AdaFDR is computationally efficient and can process more than 100 million hypotheses within an hour and allows multi-dimensional covariates with both numeric and categorical values. It also provides exploratory plots for the user to interpret how each covariate affects the significance of hypotheses, making it broadly useful across many applications.

bioinformatics↗

bioRxiv2018

Loss-of-function in IRF2BPL is associated with neurological phenotypes

The Interferon Regulatory Factor 2 Binding Protein Like (IRF2BPL) gene encodes a member of the IRF2BP family of transcriptional regulators. Currently the biological function of this gene is obscure, and the gene has not been associated with a Mendelian disease. Here we describe seven individuals affected with neurological symptoms who carry damaging heterozygous variants in IRF2BPL. Five cases carrying nonsense variants in IRF2BPL resulting in a premature stop codon display severe neurodevelopmental regression, hypotonia, progressive ataxia, seizures, and a lack of coordination. Two additional individuals, both with missense variants, display global developmental delay and seizures and a relatively milder phenotype than those with nonsense alleles. The bioinformatics signature for IRF2BPL based on population genomics is consistent with a gene that is intolerant to variation. We show that the IRF2BPL ortholog in the fruit fly, called pits (protein interacting with Ttk69 and Sin3A), is broadly expressed including the nervous system. Complete loss of pits is lethal early in development, whereas partial knock-down with RNA interference in neurons leads to neurodegeneration, revealing requirement for this gene in proper neuronal function and maintenance. The nonsense variants in IRF2BPL identified in patients behave as severe loss-of-function alleles in this model organism, while ectopic expression of the missense variants leads to a range of phenotypes. Taken together, IRF2BPL and pits are required in the nervous system in humans and flies, and their loss leads to a range of neurological phenotypes in both species.

genetics↗

bioRxiv2018

Phenotypic expansion in DDX3X -- a common cause of intellectual disability in females

De novo variants in DDX3X account for 1-3% of unexplained intellectual disability (ID), one of the most common causes of ID, in females. Forty-seven patients (44 females, 3 males) have been described. We identified 29 additional individuals carrying 27 unique DDX3X variants in the setting of complex clinical presentations including developmental delay or ID. In addition to previously reported manifestations, rare or novel phenotypes were identified including respiratory problems, congenital heart disease, skeletal muscle mitochondrial DNA depletion, and late-onset neurologic decline. Our findings expand the spectrum of DNA variants and phenotypes associated with DDX3X disorders.

genetics↗

bioRxiv2017

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to apidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within {+/-}1 two-fold dilution factor, is 92%. Individual accuracies are[≥]90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

bioinformatics↗