Biology subjects

Xia, J.

Publications and source records attributed to Xia, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Imputation for Lipidomics and Metabolomics (ImpLiMet): Online optimization and method selection for missing data imputation

MotivationMissing values are often unavoidable in modern high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. Three missingness patterns have been conceptualized: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Each describes unique dependencies between the missing and observed data. As a result, the optimal imputation method for each dataset depends on the type of data, the cause of the missing data, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset. ResultsImpLiMet: is a user-friendly UI-platform that enables users to impute missing data using eight different methods. For the users dataset, ImpLiMet can suggest the optimal imputation solution through a grid search-based investigation of the error rate for imputation across three missingness data simulations. The effect of imputation can be visually assessed by histogram, kurtosis and skewness analyses, as well as principal component analysis (PCA) comparing the impact of the chosen imputation method on the distribution and overall behaviour of the data. Availability and implementationImpLiMet is freely available at https://complimet.ca/shiny/implimet/ with software accessible at https://github.com/complimet/ImpLiMet Contactsteffanyann.bennett@uottawa.ca and miroslava.cuperlovic-culf@nrc-cnrc.gca. Supplementary informationSupplementary data are available at Bioinformatics Advances online.

bioinformatics↗

bioRxiv2024

A key residue of the extracellular gate provides quality control contributing to ABCG substrate specificity

For G-type ATP-binding cassette (ABC) transporters, a hydrophobic "di-leucine motif" as part of a hydrophobic extracellular gate has been described to separate a large substrate-binding cavity from a smaller upper cavity and proposed to act as a valve controlling drug extrusion. Here, we show that an L704F mutation in the hydrophobic extracellular gate of Arabidopsis ABCG36/PDR8/PEN3 uncouples the export of the auxin precursor indole-3-butyric acid (IBA) from that of the defense compound camalexin (CLX). Molecular dynamics simulations reveal an increase in free energy and pulling forces for CLX at both the entrance and exit sites of ABCG36L704F, respectively, providing a mechanistic rationale for the transport discrimination of CLX. Mutagenesis of L704 to tyrosine allows export of structurally related non-ABCG36 substrates, indole-3-acetic acid (IAA) and indole, suggesting an allosteric communication between the extracellular gate and the central substrate binding pocket. In summary, our work supports the conclusion that L704 is a key residue of the extracellular gate that provides a final quality control contributing to ABCG substrate specificity.

biochemistry↗

bioRxiv2024

A zebrafish system for identifying genetic dependencies in melanocytes and melanoma

The advent of large-scale sequencing in both development and disease has identified large numbers of candidate genes that may be linked to important phenotypes. We have developed a rapid, scalable system for assessing the role of candidate genes using zebrafish. We generated transgenic zebrafish in which Cas9 was knocked-in to the endogenous mitfa locus, a master transcription factor of the melanocyte lineage. The main advantage of this system compared to existing techniques is maintenance of endogenous regulatory elements. We used this system to identify both cell-autonomous and non-cell autonomous regulators of normal melanocyte development. We then applied this to the melanoma setting to demonstrate that loss of genes required for melanocyte survival can paradoxically promote more aggressive phenotypes, highlighting that in vitro screens can mask in vivo phenotypes. Our genetic approach offers a versatile tool for exploring developmental processes and disease mechanisms that can readily be applied to other cell lineages.

genetics↗

bioRxiv2024

Earlier finish of motor planning in the premotor cortex predicts faster motor command in the primary motor cortex: human intracranial EEG evidence

The human motor system has a hierarchical control during finger movements. The non-primary motor cortex (premotor cortex, PM, and supplementary motor area, SMA) organizes motor planning, while the primary motor cortex (M1) is responsible for motor execution. We utilized the human intracranial EEGs high temporal and spatial resolution to investigate how the temporal dynamics of the high-gamma neural oscillations in the hierarchically organized motor sub-regions, during both pre-movement planning and motor execution, correlated with reaction times (RTs) in a cued finger movement task. Our results showed that the high-gamma power of PM, SMA, and M1 activated sequentially. More importantly, the sustained high-gamma power activation in the non-primary motor cortex and the peak latency of high-gamma power in M1 had a significant predictive relationship with the RTs. In particular, the faster the activation of the non-primary motor cortex returned to baseline, the faster the motor command in M1, and accordingly the shorter the RTs. Further, pairwise phase coherence between the motor areas showed that the more sustained the connection between the motor areas, the longer the RTs would be. The current findings illustrate the relationship between the temporal profiles of high-gamma power in human motor areas and response performance.

neuroscience↗

bioRxiv2024

Constitutive upregulation of transcription factors underlies permissive bradyzoite differentiation in a natural isolate of Toxoplasma gondii.

Toxoplasma gondii bradyzoites play a critical role in pathology due to their long-term persistence in intermediate hosts and their potential to reactivate, resulting in severe diseases in immunocompromised individuals. Currently there is no effective treatment for eliminating bradyzoites. Hence, better in vitro models of T. gondii cyst development would facilitate identification of therapeutic targets for bradyzoites. Herein we characterized a natural isolate of T. gondii, called Tg68, which showed slower in vitro replication of tachyzoites, and permissive bradyzoite development under stress conditions in vitro. Transcriptional analysis revealed constitutive expression in Tg68 tachyzoites of the key regulators of bradyzoite development including BFD1, BFD2, and several AP2 factors. Consistent with this finding, Tg68 tachyzoites expressed high levels of bradyzoite-specific genes including BAG1, ENO1, and LDH2. Moreover, after stress induced differentiation, Tg68 bradyzoites exhibited gene expression profiles of mature bradyzoites, even at early time points. These data suggest that Tg68 tachyzoites exist in a pre-bradyzoite stage primed to readily develop into mature bradyzoites under stress conditions in vitro. Tg68 presents a novel model for differentiation in vitro that will serve as a useful tool for investigation of bradyzoite biology and development of therapeutics. SignificanceToxoplasma gondii is a widespread protozoan that chronically infects [~]30% of the worlds population. T. gondii can differentiate between the fast-growing life stage that causes acute infection and the slow-growing stage that persists in the host for extended periods of time. The slow-growing stage cannot be eliminated by the host immune response or currently known antiparasitic drugs. Studies on the slow-growing stage have been limited due to the limitations of in vivo experiments and the challenges of in vitro manipulation. Here, we characterize a natural isolate of T. gondii, which constitutively expresses factors that drive development and that is permissive to convert to the slow-growing stage under stress conditions in vitro. The strain presents a novel in vitro model for studying the chronic phase of toxoplasmosis and identifying new therapeutic treatments for chronic infections.

microbiology↗

bioRxiv2024

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics. Author SummaryAll life processes involve the consumption, creation, and interconversion of metabolites. Metabolomics is the comprehensive study of these small molecules, often using mass spectrometry, to provide critical information of health and disease. Automated processing of such metabolomics data is desired, especially for the bioinformatics community with familiar tools and infrastructures. Despite of Pythons popularity in bioinformatics and machine learning, the Python ecosystem in computational metabolomics still misses a complete data pipeline. We have developed an end-to-end computational metabolomics data processing pipeline, based on the raw data preprocessor Asari [1]. Our pipeline takes experimental data in .mzML or .raw format and outputs annotated feature tables for subsequent biological interpretation. We demonstrate the application of this pipeline to multiple metabolomics and lipidomics datasets. Accompanying the pipeline, we have designed a set of reusable data structures, released as the MetDataModel package, which shall promote more consistent terminology and software interoperability in this area.

bioinformatics↗

bioRxiv2023

Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability

Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a factor proportional to mean stimulus response and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast-dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation-dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models, and provide a parsimonious explanation for the stimulus-dependence of correlated variability within and between visual areas.

neuroscience↗

bioRxiv2023

Discovery of a Novel Chemotype as DYRK1A Inhibitors against Alzheimer's disease: Computational Modeling and Biological Evaluation

Dual specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) plays an essential role in tau and A{beta} pathology closely related to Alzheimers disease (AD). Accumulative evidence has demonstrated DYRK1A inhibition is able to reduce the pathological features of AD. Nevertheless, there is no approved DYRK1A inhibitors for clinical use as anti-AD drugs. This is somewhat the lack of effective and safe chemotypes of DYRK1A inhibitors. To address this issue, we carried out in silico screening, in vitro assays and in vivo efficacy evaluation with the aim to discover a new class of DYRK1A inhibitors for potential treatment of AD. By in silico screening, we selected and purchased 16 potential DYRK1A inhibitors from the Specs chemical library. Among them, compound Q17 (Specs ID: AO-476/40829177) potently inhibited DYRK1A. The hydrogen bonds between compound Q17 and each of three amino acid residues named GLU239, LEU241 and LYS188, were uncovered by molecular docking and molecular dynamics simulation. The cell-based assays showed that compound Q17 could protect SH-SY5Y cells from okadaic acid (OA)-induced injury by targeting DYRK1A. More importantly, compound Q17 significantly improved cognitive dysfunction in 3xTg-AD mice, ameliorated pathological changes, and reduced the expression of DYRK1A, GSK-3{beta} and GSK-3{beta} (pSer9), attenuated tau hyperphosphorylation and A{beta} deposition as well. In summary, our computational modeling strategy is effective to identify novel chemotypes of DYRK1A inhibitors with great potential to treat AD, and the identified compound Q17 in this study is worthy of further study. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/565431v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@75abecorg.highwire.dtl.DTLVardef@16a3611org.highwire.dtl.DTLVardef@39aeborg.highwire.dtl.DTLVardef@afc8ab_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2023

Deep Reinforcement Learning Enables Better Bias Control in Benchmark for Virtual Screening

Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the- box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt- BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.

bioinformatics↗

bioRxiv2023

Computational divergence analysis reveals the existence of regulatory degeneration and supports HDAC1 as a potential drug target for Alzheimer's disease

Epigenetic dysregulation has been widely reported in patients of Alzheimers disease (AD) and epigenetic drugs are gaining particular interest as a potential candidate therapy target. However, it is less clear how epigenetic dysregulation contributes to AD development. In this work, we performed regulatory divergence analysis using large-scale AD brain RNA-seq data and reported a widespread existence of regulatory degeneration among AD patients. It seems that transcription factor (TF)-mediated regulations get weakened or lost during AD development, resulting in disruption of normal neuronal function, especially including protein degradation, neuroinflammation, mitochondria and synaptic dysfunction. The regulatory degeneration burden (RDB) is well correlated with the detrimental clinical manifestations of AD patients. Studies of epigenetic marks, including histone modification, open chromatin accessibility and three TF binding sites supported the existence of regulatory degeneration. It suggested that epigenetic dysregulation contributed to regulatory degeneration, which also explained the consequence of epigenetic dysregulation. Among the epigenetic regulators, HDAC1 was proposed as a potential participator in such a process. Overall, our computational analysis suggested a novel causal mechanism of AD development and proposed HDAC1 as a drug target to treat AD.

systems biology↗

bioRxiv2023

AutoMolDesigner for Antibiotic Discovery: An AI-based Open-source Software for Automated Design of Small-molecule Antibiotics

Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in the holistic and efficient tool specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative deep learning-enabled molecular generation and automated machine learning based-antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole cell-based antibiotic screening and design. It is open-source thus allows for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/8366085).

bioinformatics↗

bioRxiv2023

Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, the genetic mechanisms and target genes underlying these loci are largely unknown, as most risk-associated-variants might regulate gene expression in a context-specific manner. Here, we generated a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Accessible chromatin peak detection identified cell-type-specific candidate cis-regulatory elements (cCREs) from each lung cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs prioritized the variants for 68% of the GWAS loci, a subset of which was also supported by transcription factor abundance and footprinting. cCRE colocalization and single-cell based trait relevance score nominated epithelial and immune cells as the main cell groups contributing to lung cancer susceptibility. Notably, cCREs of rare proliferating epithelial cell types, such as AT2-proliferating (0.13%) and basal cells (1.8%), overlapped with CCVs, including those in TERT. A multi-level cCRE-gene linking system identified candidate susceptibility genes from 57% of lung cancer loci, including those not detected in tissue- or cell-line-based approaches. cCRE-gene linkage uncovered that adjacent genes expressed in different cell types are correlated with distinct subsets of coinherited CCVs, including JAML and MPZL3 at the 11q23.3 locus. Our data revealed the cell types and contexts where the lung cancer susceptibility genes are functional.

genomics↗

bioRxiv2023

A vast repertoire of secondary metabolites influences community dynamics and biogeochemical processes in cold seeps

In deep sea cold seeps, diverse microbial communities thrive on the geological seepage of hydrocarbons and inorganic compounds. These chemosynthetically-driven communities are unique in composition, ecology, and biogeochemical activities compared to photosynthetically-driven ecosystems. However, their biosynthetic capabilities remain largely unexplored. Here, we analyzed 81 metagenomes, 33 metatranscriptomes, and seven metabolomes derived from nine globally distributed areas of cold seeps to investigate the secondary metabolites produced by cold seep microbiomes. Cold seep microbiomes encode diverse, abundant, and novel biosynthetic gene clusters (BGCs). Most BGCs are affiliated with understudied bacteria and archaea, including key mediators of methane and sulfur cycling, and multiple candidate phyla. The BGCs encode diverse antimicrobial compounds (e.g. NRPS, PKSs, RiPPs) that potentially shape community dynamics, as well as compounds predicted to influence biogeochemical cycling, such as phosphonates, iron-acquiring siderophores, nitrogenase-protecting glycolipids, and methyl-CoM reductase-modifying proteins. BGCs from key players in cold seeps are widely distributed and highly expressed, with their abundance and expression levels varying with different sediment depths. Numerous unique natural products were detected through untargeted sediment metabolomics, demonstrating a vast, unexplored chemical space and validating in situ expression of the BGCs in cold seep sediments. Overall, these results demonstrate cold seep sediments potentially serve as a reservoir of hidden natural products and provide insights into microbial adaptation in chemosynthetically-driven ecosystems.

microbiology↗

bioRxiv2023

Ultrasound Imaging Enables Longitudinal Tracking of Vascular Changes that Correlate with Immune Cell Infiltration After Radiotherapy

BackgroundWhile immunotherapy shows great promise in patients with triple negative breast cancer, many will not respond to treatment. Radiotherapy has the potential to prime the tumor-immune microenvironment for immunotherapy. However, predicting response is difficult due to tumor heterogeneity across patients, which necessitates personalized medicine strategies that incorporate tumor tracking into the therapeutic approach. Here, we investigated the use of ultrasound (US) imaging of the tumor vasculature to monitor the tumor response to treatment. MethodsWe utilized ultrafast power doppler US to track the vascular response to radiotherapy over time. We used 4T1 (metastatic) and 67NR (non-metastatic) breast cancer models to determine if US measurements corroborate conventional immunostaining analysis of the tumor vasculature. To evaluate the effects of radiation, tumor volume and vascular index were calculated using US, and the correlation between vascular changes and immune cell infiltration was determined. ResultsUS tumor measurements and the quantified vascular response to radiation were confirmed with caliper measurements and immunostaining, respectively, demonstrating a proof-of-principle method for non-invasive vascular monitoring. Additionally, we found significant infiltration of CD8+ T cells into irradiated tumors 10 days after radiation, which followed a sustained decline in vascular index and an increase in splenic CD8+ T cells that was first observed 1 day post-radiation. ConclusionsOur findings reveal that ultrafast power doppler US can evaluate changes in tumor vasculature that are indicative of shifts in the tumor-immune microenvironment. This work may lead to improved patient outcomes through observing and predicting response to therapy.

cancer biology↗

bioRxiv2023

Computational prediction of protein interactions on single cells by proximity sequencing

Proximity sequencing (Prox-seq) measures gene expression, protein expression, and protein complexes at the single cell level, using information from dual-antibody binding events and a single cell sequencing readout. Prox-seq provides multi-dimensional phenotyping of single cells and was recently used to track the formation of receptor complexes during inflammatory signaling in macrophages and to discover a new interaction between CD9/CD8 proteins on naive T cells. The distribution of protein abundance affects identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model for protein dimer formation on single cells and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq single-cell data, which resulted in more accurate and robust quantification of protein complexes. Finally, our model offers a simple way to investigate the behavior of Prox-seq under various biological conditions and guide users toward selecting the best analysis method for their data.

systems biology↗

bioRxiv2023

Learning Complete Protein Representation by Deep Coupling of Sequence and Structure

Learning effective representations is crucial for understanding proteins and their biological functions. Recent advancements in language models and graph neural networks have enabled protein models to leverage primary or tertiary structure information to learn representations. However, the lack of practical methods to deeply co-model the relationships between protein sequences and structures has led to suboptimal embeddings. In this work, we propose CoupleNet, a network that couples protein sequence and structure to obtain informative protein representations. CoupleNet incorporates multiple levels of features in proteins, including the residue identities and positions for sequences, as well as geometric representations for tertiary structures. We construct two types of graphs to model the extracted sequential features and structural geometries, achieving completeness on these graphs, respectively, and perform convolution on nodes and edges simultaneously to obtain superior embeddings. Experimental results on a range of tasks, such as protein fold classification and function prediction, demonstrate that our proposed model outperforms the state-of-the-art methods by large margins.

bioinformatics↗

bioRxiv2023

The transcription factor OsbZIP48 governs rice responses to zinc deficiency

Zinc deficiency is the most prevalent micronutrient disorder in rice and leads to delayed development and decreased yield. Nevertheless, despite its primary importance, how rice responds to zinc deficiency remains poorly understood. Herein, we present genetic evidence that OsbZIP48 is essential for regulating rice responses to zinc deficiency. Using the reverse genetics approach, genetic inactivation of OsbZIP48 in rice seedlings caused a hyper sensitivity to zinc deficiency, associated with a significant decrease in the root-to-shoot translocation of zinc. Consistently, OsbZIP48 was constitutively expressed in roots, slightly induced by zinc deficiency in shoots, and localized into nuclei induced by Zn deficiency. Comparative transcriptome analysis of the wild-type plants and osbzip48 mutant grown under zinc deficiency enabled the identification of OsbZIP48 target genes, including key zinc transporter genes (OsZIP4 and OsZIP8). We demonstrated that OsbZIP48 controlled the expressions of these genes by directly binding to their promoters, specifically to the zinc deficiency response element (ZDRE) motif. Collectively, we showed that the OsbZIP48 gene encodes for a transcription factor in rice, and demonstrates its critical role in the response to zinc deficiency in this crop. This knowledge is crucial for the design of rice plants that are resilient to the globally prevalent zinc limitation through zinc bio-fortification programs.

plant biology↗

bioRxiv2023

TEAD1 regulates ITGA1 and ITGA2 to control prostate cancer progression

The extracellular matrix (ECM) undergoes significant changes during prostate cancer (PCa) progression and actively regulates PCa growth and invasion. Here, we performed a meta-analysis of PCa cohorts and found that downregulation or loss of ITGA1 and ITGA2 integrin genes was associated with tumor progression to metastasis and poor prognosis in PCa patients. Genomic deletion of both 1- and 2-integrins activated epithelial-to-mesenchymal transition (EMT) in benign prostate epithelial cells, thereby enhancing their invasive potential in vitro and converting them into tumorigenic cells in vivo. Mechanistically, EMT was induced by enhanced secretion and subsequent activation of autocrine TGF{beta}1 and nuclear targeting of YAP1. Our unbiased genome-wide co-expression analysis of large PCa cohort datasets identified the transcription factor TEAD1 as a key regulator of ITGA1 and ITGA2 expression in PCa cells while TEAD1 loss phenocopied the dual loss of 2- and 2-integrins in vitro and in vivo. Notably, clinical data analysis revealed that TEAD1 downregulation or loss was associated with aggressive PCa and could synergize with ITGA1 and ITGA2 expression to impact PCa prognosis and progression. Altogether, our results demonstrate that loss of 1- and 2-integrins, either via deletion/inactivation of the ITGA1/ITGA2 locus or via loss of TEAD1, contributes to PCa progression by inducing TGF{beta}1-driven EMT.

cancer biology↗