Biology subjects

Yang, P.

Publications and source records attributed to Yang, P..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Daily glycome and transcriptome profiling reveals polysaccharide structures and glycosyltransferases critical for cotton fiber growth

Cotton fiber length and strength are key determinants of its quality. Dynamic changes in the pectin, xyloglucan, xylan, and cellulose polysaccharide epitopes content during fiber growth contribute to complex remodeling of fiber cell wall (CW) and quality. Detailed knowledge about polysaccharide compositional and structural alteration in the fiber during fiber elongation and strengthening is vastly limited. Here, large-scale glycome profiling coupled with fiber phenotype and transcriptome profiling was conducted on fiber collected daily covering the most critical fiber developmental window. High temporal resolution profiling allowed us to identify specific polysaccharide epitopes associated with distinct fiber phenotypes that might contribute to fiber quality. This study revealed the critical role of highly branched RG-I pectin epitopes such as, {beta}-1,4-linked-galactans, {beta}-1,6-linked-galactans, and arabinogalactans, in addition to earlier reported homogalacturonans and xyloglucans in the formation of cotton-fiber-middle-lamella and contributing to fiber plasticity and elongation. We also propose the essential role of heteroxylans (Xyl-MeGlcA and Xyl-3Ar), as a guiding factor for secondary CW cellulose-microfibril arrangement, thus contributing to fiber strength. Correlation analysis of glycome and transcriptome data identified several key putative glycosyltransferases involved in synthesizing the critical polysaccharide epitopes. Novel details discovered here provide a foundation to identify molecular factors that dictate important fiber traits.

plant biology↗

bioRxiv2024

Neurodevelopmentally rooted epicenters in schizophrenia: sensorimotor-association spatial axis of cortical thickness alterations

Pathologic perturbations in schizophrenia have been suggested to propagate via the functional and structural connectome across the lifespan. Yet how the connectome guides early cortical reorganization of developing schizophrenia remains unknown. Here, we used early-onset schizophrenia (EOS) as a neurodevelopmental disease model to investigate putative early pathologic origins that propagate through the functional and structural connectome. We compared 95 patients with antipsychotic-naive first-episode EOS and 99 typically developing controls (7-17 years of age, 120 females). Whereas patients showed widespread cortical thickness reductions, thickness increases were observed in primary cortical areas. Using normative connectomics models, we found that epicenters of thickness reductions were situated in association regions linked to language, affective, and cognitive functions, while epicenters of increased thickness in EOS were located in sensorimotor regions subserving visual, somatosensory, and motor functions. Using post-mortem transcriptomic data of six donors, we observed that the epicenter map differentiated oligodendrocyte-related transcriptional changes at its sensory apex and the association end was related to expression of excitatory/inhibitory neurons. More generally, we observed that the epicenter map was associated with neurodevelopmental disease gene dysregulation and human accelerated region genes, suggesting potential shared genetic determinants across various neurodevelopmental disorders. Taken together, our results underscore the developmentally rooted pathologic origins of schizophrenia and their transcriptomic overlap with other neurodevelopmental diseases.

neuroscience↗

bioRxiv2024

PIGOME: An Integrated and Comprehensive Multi-omics Database for Pig Functional Genomics Studies

In addition to being a major source of animal protein, pigs are important model for the study of development and diseases in humans. During the past two decades, thousands of high-throughput sequencing studies in pigs have been performed using a variety of tissues from different breeds and developmental stages. However, the multi-omics database specifically used for pig functional genomic research is still limited. Here, we present a user-friendly database of pig multi-omics named PIGOME. PIGOME contains seven types of pig omics datasets, including whole-genome sequencing, RNA-seq, miRNA-seq, ChIP-seq, ATAC-seq, bisulfite-seq, and MeRIP-seq, from 6,901 samples and 392 projects with manually curated metadata, integrated gene annotation, and quantitative trait locus information. Furthermore, various explore and browse functions have been established for user-friendly access to omics information. PIGOME implemented several tools to visualize genomic variants, gene expression, and epigenetic signals of a given gene in the pig genome, enabling efficient exploration of spatial-temporal expression/epigenetic pattern, function, regulatory mechanism, and associated economic traits. Collectively, PIGOME provides valuable resources for pig breeding and is helpful for human biomedical research. PIGOME is available at https://pigome.com.

bioinformatics↗

bioRxiv2024

Discovery of the First-in-class G9a/GLP PROTAC Degrader

Aberrantly expressed lysine methyltransferases G9a and GLP, which catalyze mono- and di-methylation of histone H3 lysine 9 (H3K9), have been implicated in numerous cancers. Recent studies have uncovered both catalytic and non-catalytic oncogenic functions of G9a/GLP. As such, G9a/GLP catalytic inhibitors have displayed limited anticancer activity. Here, we report the discovery of the first-in-class G9a/GLP proteolysis targeting chimera (PROTAC) degrader, 10 (MS8709), as a potential anticancer therapeutic. 10 induces G9a/GLP degradation in a concentration-, time, and ubiquitin-proteasome system (UPS)-dependent manner, does not alter the mRNA expression of G9a/GLP and is selective for G9a/GLP over other methyltransferases. Moreover, 10 displays superior cell growth inhibition to the parent G9a/GLP inhibitor UNC0642 in prostate, leukemia, and lung cancer cells and has suitable mouse pharmacokinetic properties for in vivo efficacy studies. Overall, 10 is a valuable chemical biology tool to further investigate the functions of G9a/GLP and a potential therapeutic for treating G9a/GLP-dependent cancers. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/582210v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1b3c923org.highwire.dtl.DTLVardef@5a22f0org.highwire.dtl.DTLVardef@1abbabeorg.highwire.dtl.DTLVardef@1711f29_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

From Sight to Insight: A Multi-task Approach with the Visual Language Decoding Model

Visual neural decoding aims to unlock the mysteries of how the human brain interprets the visual world. While early studies made some progress in decoding visual activity for singular type of information, they failed to concurrently reveal the multi-level interweaving linguistic information in the brain. Here, we developed a novel Visual Language Decoding Model (VLDM) capable of decoding categories, semantic labels, and textual descriptions from visual perceptual activities simultaneously. We selected the large-scale NSD dataset to ensure the efficiency of the decoding model in joint training and evaluation across multiple tasks. For category decoding, we achieved the effective classification of 12 categories with an accuracy of nearly 70%, significantly surpassing the chance level. For label decoding, we attained the precise prediction of 80 specific semantic labels with a 16-fold improvement over the chance level. For text decoding, the scores of the decoded text surpassed the corresponding baseline levels by remarkable margins on six evaluation metrics. This study contributes significantly to extensive applications in multi-layered brain-computer interfaces, potentially leading to more natural and efficient human-computer interaction experiences.

neuroscience↗

bioRxiv2024

Definition of regulatory elements and transcription factors controlling immune cell gene expression at single cell resolution using single nucleus ATAC-seq

The transcriptome of porcine peripheral blood mononuclear cells (PBMC) at single cell (sc) resolution is well described, but little is understood about the cis-regulatory mechanism behind scPBMC gene expression. Here, we profiled the open chromatin landscape of porcine PBMC using single nucleus ATAC sequencing (snATAC-seq). Approximately 22% of the identified peaks overlapped with annotated transcription start sites (TSS). Using clustering based on open chromatin pattern similarity, we demonstrate that cell type annotations using snATAC-seq are highly concordant to that reported for sc RNA sequencing (scRNA-seq). The differentially accessible peaks (DAPs) for each cell type were characterized and the pattern of accessibility of the DAPs near cell type markers across cell types was similar to that of the average gene expression level of corresponding marker genes. Additionally, we found that peaks identified in snATAC-seq have the potential power to predict the cell type specific transcription starting site (TSS). We identified both transcription factors (TFs) whose binding motif were enriched in cell type DAPs of multiple cell types and cell type specific TFs by conducting transcription factor binding motif (TFBM) analysis. Furthermore, we identified the putative enhancer or promoter regions bound by TFs for each differentially expressed gene (DEG) having a DAP that overlapped with its TSS by generating cis-co-accessibility networks (CCAN). To predict the regulators of such DEGs, TFBM analysis was performed for each CCAN. The regulator TF-target DEG pair predicted in this way was largely consistent with the results reported in the ENCODE Transcription Factor Targets Dataset (TFTD). This snATAC-seq approach provides insights into the chromatin accessibility landscape of porcine PBMCs and enables discovery of TFs predicted to control DEG through binding regulatory elements whose chromatin accessibility correlates with the DEG promoter region.

bioinformatics↗

bioRxiv2023

The current landscape and emerging challenges of benchmarking single-cell methods

With the rapid development of computational methods for single-cell sequencing data, benchmarking serves as a valuable resource. As the number of benchmarking studies surges, it is timely to assess the current state of the field. We conducted a systematic literature search and assessed 282 papers, including all 130 benchmark-only papers from the search and an additional 152 method development papers containing benchmarking. This collective effort provides the most comprehensive quantitative summary of the current landscape of single-cell benchmarking studies. We examine performances across nine broad categories, including often ignored aspects such as role of datasets, robustness of methods and downstream evaluation. Our analysis highlights challenges such as how to effectively combine knowledge across multiple benchmarking studies and in what ways can the community recognise the risk and prevent benchmarking fatigue. This paper highlights the importance of adopting a community-led research paradigm to tackle these challenges and establish best practice standards.

bioinformatics↗

bioRxiv2023

Structural basis for VLDLR recognition by eastern equine encephalitis virus

Alphaviruses are arthropod-borne enveloped RNA viruses that include several important human pathogens with outbreak potential. Among them, eastern equine encephalitis virus (EEEV) is the most virulent, and many survivors develop neurological sequelae, including paralysis and intellectual disability. The spike proteins of alphaviruses comprise trimers of heterodimers of their envelope glycoproteins E2 and E1 that mediate binding to cellular receptors and fusion of virus and host cell membranes during entry. We recently identified very-low density lipoprotein receptor (VLDLR) and apolipoprotein E receptor 2 (ApoER2), two closely related proteins that are expressed in the brain, as cellular receptors for EEEV and a distantly related alphavirus, Semliki forest virus (SFV)1. The EEEV and SFV spike glycoproteins have low sequence homology, and how they have evolved to bind the same cellular receptors is unknown. Here, we used single-particle cryo-electron microscopy (cryo-EM) to determine structures of the EEEV and SFV spike glycoproteins bound to the VLDLR ligand-binding domain. The structures reveal that EEEV and SFV use distinct surfaces to bind VLDLR; EEEV uses a cluster of basic residues on the E2 subunit of its spike glycoprotein, while SFV uses two basic residues at a remote site on its E1 glycoprotein. Our studies reveal that different alphaviruses interact with the same cellular receptor through divergent binding modes. They further suggest that the ability of LDLR-related proteins to interact with viral spike proteins through very small footprints with flexible binding modes results in a low evolutionary barrier to the acquisition of LDLR-related proteins as cellular receptors for diverse sets of viruses.

microbiology↗

bioRxiv2023

A machine learning-based approach to identify reliable gold standards for protein complex composition prediction

Co-Fractionation Mass Spectrometry (CFMS) enables the discovery of protein complexes and the systems-level analyses of multimer dynamics that facilitate responses to environmental and developmental conditions. A major challenge in the CFMS analyses, and other omics approaches in general, is to conduct validation experiments at scale and develop precise methods to evaluate the performance of the analyses. For protein complex composition predictions, CORUM is commonly used as a source of known complexes; however, the subunit pools in cell extracts are very rarely in the assumed fully assembled states. Therefore, a fundamental conflict exists between the assumed multimerization of the CORUM "gold standards" and the CFMS experimental datasets to be evaluated. In this paper, we develop a machine learning-based "small world" data analysis method. This method uses size exclusion chromatography profiles of predicted CORUM complex subunits to identify relatively rare instances of fully assembled complexes, as well as bona fide stable CORUM subcomplexes. Our method involves a two-stage machine learning approach that is designed to leverage evolutionarily conserved sequences among CORUM subunits and integrate it with size exclusion chromatography profile data from CFMS experiments. The generated gold standards are evaluated by both statistical significance and size comparison between calculated and predicted complexes. We expect these gold standards to serve as improved benchmarks to assess the overall reliability of CFMS-based protein complex composition predictions.

bioinformatics↗

bioRxiv2023

A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing

Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multiregion gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.

bioinformatics↗

bioRxiv2023

scPipe: An extended preprocessing pipeline for comprehensive single-cell ATAC-Seq data integration in R/Bioconductor

scPipe is a flexible R/Bioconductor package originally developed to analyse platform-independent single-cell RNA-Seq data. To expand its preprocessing capability to accommodate new single-cell technologies, we further developed scPipe to handle single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data. After executing multiple data cleaning steps to remove duplicated reads, low abundance features and cells of poor quality, a SingleCellExperiment object is created that contains a sparse count matrix with features of interest in the rows and cells in the columns. Quality control information (e.g. counts per cell, features per cell, total number of fragments, fraction of fragments per peak) and any relevant feature annotations are stored as metadata. We demonstrate that scPipe can efficiently identify "true" cells and provides flexibility for the user to fine-tune the quality control thresholds using various feature and cell-based metrics collected during data preprocessing. Researchers can then take advantage of various downstream single-cell tools available in Bioconductor for further analysis of scATAC-Seq data such as dimensionality reduction, clustering, motif enrichment, differential accessibility and cis-regulatory network analysis. The scPipe package enables a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.

bioinformatics↗

bioRxiv2023

Epigenetic regulation of p63 blocks squamous-to-neuroendocrine transdifferentiation in esophageal development and malignancy

While cell fate determination and maintenance are important in establishing and preserving tissue identity and function during development, aberrant cell fate transition leads to cancer cell heterogeneity and resistance to treatment. Here, we report an unexpected role for the transcription factor p63 (Trp63/TP63) in the fate choice of squamous versus neuroendocrine lineage in esophageal development and malignancy. Deletion of p63 results in extensive neuroendocrine differentiation in the developing mouse esophagus and esophageal progenitors derived from human embryonic stem cells. In human esophageal neuroendocrine carcinoma (eNEC) cells, p63 is transcriptionally silenced by EZH2-mediated H3K27 trimethylation (H3K27me3). Upregulation of the major p63 isoform {Delta}Np63, through either ectopic expression or EZH2 inhibition, promotes squamous transdifferentiation of eNEC cells. Together these findings uncover p63 as a rheostat in coordinating the transition between squamous and neuroendocrine cell fates during esophageal development and tumor progression.

cell biology↗

bioRxiv2023

Multi-omics data and analysis reveal the formation of key pathways of different colors in Torenia fournieri flowers

AbstractTorenia fournieri Lind. is an ornamental plant, popular for its numerous flowers and variety of colors. However, its genomic evolution, as well as the genetic and metabolic basis of flower color formation, remain poorly understood. Here we report a chromosome-level reference genome of T. fournieri comprising 164.4 Mb. Phylogenetic analysis revealed the phylogenetic placement of the species, and comparative genomics analysis indicated that T. fournieri shared a whole genome duplication (WGD) event with Antirrhinum majus. Through joint transcriptomics and metabolomics analyses, we characterized the differential genes and metabolites in the anthocyanin synthesis pathway in five T. fournieri varieties. We identified many metabolites related to pelargonidin, peonidin, and naringenin in Rose (R) color samples. On the other hand, the blue (B) and blue-violet (D) color samples contained many metabolites related to petunidin, cyanidin, quercetin, and malvidin. The formation of different flower colors in T. fournieri involves multiple genes and metabolites. We analyzed the results and obtained significantly different genes and metabolites related to the biosynthesis of flavonoids and anthocyanins, which are key metabolites in the formation of different flower colors. Our T. fournieri genome data provide a basis for studying the differentiation of this species and provide a valuable model genome enabling genetic studies and genomics-assisted breeding of T. fournieri. HighlightThe genome of Torenia fournieri was reported for the first time, and the formation mechanism of different colors in Torenia fournieri flowers was analyzed by genomics, transcriptomics and metabolomics.

genomics↗

bioRxiv2023

Target-identification and mechanism-of-action studies of indole terpenoid mimics reveal that proper spindle microtubule assembly is essential for Cdh1-mediated proteolysis of CENP-A

Centromere protein A (CENP-A), a histone H3 variant specific to centromeres, is crucial for kinetochore positioning and chromosome segregation. However, its regulatory mechanism in human cells remains incompletely understood. We conducted a structure-activity relationship (SAR) study of the cell cycle-arresting indole terpenoid mimic JP18 and found two more potent analogues, (+)-6-Br-JP18 and (+)-6-Cl-JP18. Tubulin was identified as a potential cellular target of these halogenated analogues by using the drug affinity responsive target stability (DARTS) based method. X-ray crystallography analysis revealed that both molecules bind to the colchicine-binding site of {beta}-tubulin. Furthermore, we discovered that treatment of human cells with microtubule-targeting agents (MTAs), including these two compounds, led to CENP-A accumulation by destabilizing Cdh1, a co-activator of the APC/C E3 ubiquitin ligase. Our study establishes a link between microtubule dynamics and CENP-A accumulation using small-molecule tools and highlights the role of Cdh1 in CENP-A proteolysis.

cell biology↗

bioRxiv2023

Key common genes and pathways in ulcerative colitis and ankylosing spondylitis based on bioinformatics analysis

Associations between ulcerative colitis (UC) and ankylosing spondylitis (AS) have been observed in multiple studies, but the common etiology of UC and AS remain unknown. Thus, the current research was conducted to investigate the shared genes and relevant mechanisms in UC and AS. GSE87466 and GSE25101 datasets were used to identify DEGs involved in UC and AS, respectively. The clusterProfiler R package was utilized to detect the biological processes of DEGs in UC and AS. The performance of common DEGs in distinguishing UC or AS samples from control ones were evaluated by ROC curves. The miRWalk, Cistrome and TransmiR database were utilized to construct the network of TF-miRNA-diagnostic biomarker. GSEA method and CTD database were used to investigate the common KEGG pathways shared by UC and AS. In addition, CTD database was also used to detect the interaction score between diagnostic biomarkers and diseases associated with UC or AS. Moreover, prospective diagnostic biomarker-targeting drugs were identified using the DGIdb database. A total of 20 common DEGs were obtained by analyzing data in GSE97466 and GSE25101 datasets. ROC curves revealed that GMFG, GNG11, CLEC4D, CMTM2, VAMP5, S100A8, S100A12 and DGKQ may serve as diagnostic biomarkers for the individuals with AS and UC. A network of TF-miRNA-diagnostic biomarker, composed of 212 nodes and 721 edges, was constructed and visualized by Cytoscape software. Toll-like receptor signaling pathway, antigen processing and presentation, allograft rejection, viral myocarditis, pathways in cancer, graft versus host disease and natural killer cell mediated cytotoxicity were identified as common pathways in UC and AS. For the first time, our study identified 8 common key genes and 7 common pathways in UC and AS. These findings may help to clarify the relationship between UC and AS, and provide guidance in the diagnosis and treatment of UC and AS patients. Author SummaryAnkylosing spondylitis (AS) and Ulcerative colitis (UC) are two types of autoimmune diseases that often co-occur. The simultaneous onset of both diseases often results in severe clinical manifestations and limited therapeutic efficacy. So there is an urgent need to gain a deeper understanding of the causes of UC and AS in order to develop more effective treatment strategies. In this study, we explored the genes and pathways commonly involved in two diseases, constructed a transcriptional network, and further investigated potential drugs. The discoveries could potentially offer insights into the connection between UC and AS, and assist in identifying and managing patients with UC and AS.

bioinformatics↗

bioRxiv2023

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data

MotivationRecent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterisation of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. ResultsWe propose SnapCCESS for clustering cells by integrating data modalities in multimodal singlecell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterisation of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. Availability and implementationSnapCCESS is implemented as a Python package and is freely available from https://github.com/yulijia/SnapCCESS.

bioinformatics↗

bioRxiv2023

Avirulent ME49Δgra5 Protecting Hosts Against Toxoplasma gondii Infection and Breast Tumors

Toxoplasma gondii is the causative agent of toxoplasmosis, a zoonotic disease that poses a threat to human health and a considerable loss to livestock farming. At present, clinical therapeutic drugs mainly target T. gondii tachyzoites and fail to eradicate bradyzoites. Developing a safe and effective vaccine against toxoplasmosis is urgent and important. T. gondii possesses dense granule organelles that secrete immunogenic dense granule proteins (GRAs). GRA5 localizes to the parasitophorous vacuole membrane (PVM) in the tachyzoite stage and to the cyst wall in the bradyzoite stage. Here we report that gra5 knockout T. gondii strain is avirulent and fails to form cysts, but stimulates sero-conversion in mice. We next investigated the protective efficacy of ME49{Delta}gra5 vaccination against T. gondii infection. All the immunized mice survived the challenge infection with either wild-type RH, ME49, VEG tachyzoites or ME49 cysts. Remarkably, ME49{Delta}gra5 tachyzoites inoculation in situ attenuated the growth of murine breast tumor (4T1) in mice and prevented 4T1s lung metastasis. ME49{Delta}gra5 inoculation might upregulate the levels of Th1 cytokines and tumor-infiltrating T cells in tumor microenvironment (TME) and trigger anti-tumor responses by increasing the number of NK, B, T cells, macrophages, and dendritic cells (DCs) in spleen. Collectively, the results support the potential of ME49{Delta}gra5 as attenuated live vaccine against T. gondii infection and breast cancer. Author summaryThe zoonotic toxoplasmosis poses a great disease burden to humans and big loss to livestock farming. At present, clinical therapeutic drugs mainly target T. gondii tachyzoites but have no effect on bradyzoites. A safe and effective vaccine against toxoplasmosis is urgent and important. Dense granule proteins (GRAs) are a group of immunoactive proteins secreted by T. gondii dense granule organelles. GRA5 is located on the parasitophorous vacuole membrane (PVM) at the tachyzoite stage and on the cyst wall at the bradyzoite stage. We discovered that the gra5 knockout T. gondii strain became avirulent and failed to form cysts in mice. ME49{Delta}gra5 immunized mice survived the challenge infection of wild-type RH, ME49, and VEG tachyzoites, and ME49 cysts. Meanwhile, ME49{Delta}gra5 tachyzoites inoculation in situ remarkably attenuated the growth of murine breast tumor (4T1) in mice including the injected and distant tumors, as well as prevented 4T1s lung metastasis. This results support the potential of ME49{Delta}gra5 as attenuated live vaccine against T. gondii infection and breast cancer.

microbiology↗

bioRxiv2023

Regional vulnerability in a neurodegenerative disease: Delineating SCA1 CNS and muscle therapeutic targets using a conditional mutant ATXN1 mouse

Spinocerebellar ataxia type 1 (SCA1) is a fatal neurodegenerative disease caused by an expanded polyglutamine tract in the widely expressed ATXN1 protein. To elucidate anatomical regions and cell types that underlie mutant ATXN1-induced disease phenotypes, we developed a floxed conditional knockout mouse model (f-ATXN1146Q/2Q) having mouse Atxn1 coding exons replaced by human exons encoding 146 glutamines. F-ATXN1146Q/2Q mice manifest SCA1-like phenotypes including motor and cognitive deficits, wasting, and decreased survival. CNS contributions to disease were revealed using ATXN1146Q/2Q;Nestin-Cre mice, that showed improved rotarod, open field and Barnes maze performances. Striatal contributions to motor deficits were examined using f-ATXN1146Q/2Q;Rgs9-Cre mice. Mice lacking striatal ATXN1146Q/2Q had improved rotarod performance late in disease. Muscle contributions to disease were revealed in f-ATXN1146Q/2Q;ACTA1-Cre mice which lacked muscle pathology and kyphosis seen in f-ATXN1146Q/2Q mice. Kyphosis was not improved in f-ATXN1146Q/2Q;Nestin-Cre mice. Thus, optimal SCA1 therapeutics will require targeting mutant ATXN1 toxic actions in multiple brain regions and muscle.

neuroscience↗