Biology subjects

Wang, G.

Publications and source records attributed to Wang, G..

At least 271 records · Page 15Linked to original sources

bioRxiv2023

sLASER and PRESS Perform Similarly at Revealing Metabolite-Age Correlations

PurposeTo compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate. MethodsMRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale (CSO) and posterior cingulate cortex (PCC) regions. Acquisition parameters included TR/TE 2000/30 ms; 96 transients; 2048 datapoints sampled at 2 kHz. Spectra were analyzed using Osprey. Signal-to-noise ratio (SNR), full-width-half-maximum linewidth of tCr, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t-tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite-age relationship. Coefficients of variation were calculated to assess inter-subject variability in each metabolite measurement. ResultsSNR and linewidth were significantly higher (p<0.05) for sLASER than PRESS. Paired t-tests showed significant differences between PRESS and sLASER in most metabolite measurements. Metabolite measures were significantly correlated (p<0.05) for most metabolites between the two methods except GABA, Gln and Lac in CSO and GSH, Lac and NAAG in PCC. Metabolite-age relationships were consistently identified using both PRESS and sLASER. Similar CVs were observed for most metabolites. ConclusionThe study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in CSO and PCC data acquired at 3T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in good brain regions at clinical field strength.

neuroscience↗

bioRxiv2023

Metabolite T1 relaxation times differ across the adult lifespan

PurposeTo investigate the age-dependence of metabolite T1 relaxation times at 3T in both gray- and white-matter-rich voxels. MethodsThis manuscript analyzes publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3T using PRESS localization. Voxels were placed in posterior cingulate cortex and centrum semiovale in 102 healthy volunteers across 5 decades of life (20s to 60s). All spectra were analyzed in Osprey v2.4.0. To estimate T1 relaxation times for tNAA2.0 and tCr3.0, the ratio of modeled metabolite residual amplitudes in the metabolite-nulled spectrum to the full metabolite signal was calculated using the single inversion recovery signal equation. Correlations between T1 and subject age were evaluated. ResultsSpearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.43; p < 0.001) and tCr3.0 (rs = -0.23; p = 0.021) decreased significantly with age in white-matter-rich CSO, and less steeply (and not significantly) for tNAA2.0 (rs = -0.15; p = 0.136) and tCr3.0 (rs = -0.10; p = 0.319) in gray-matter-rich PCC. ConclusionThe analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1 relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1 relaxation times for accurate quantification of metabolite levels in studies of aging.

neuroscience↗

bioRxiv2023

DPHL v2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins.

A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v1, a previous pan-human library built from 1096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying 10,943 proteins. However, a major limitation of DPHL v1 is the lack of semi-tryptic peptides and protein isoforms, which are abundant in clinical specimens. Here, we generated DPHL v2 from 1608 DDA-MS data acquired using Orbitrap mass spectrometers. The data included 586 DDA-MS newly acquired from 17 tissue types, while 1022 files were derived from DPHL v1. DPHL v2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v2 to include semi-tryptic peptides and protein isoforms. DPHL v2 was then applied to a publicly available colorectal cancer dataset with 286 DIA-MS files. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v1. Our findings show that the increased human proteome coverage of DPHL v2 provides larger pools of potential protein biomarkers.

bioinformatics↗

bioRxiv2023

Integration of single-cell multiomic measurements across disease states with genetics identifies mechanisms of beta cell dysfunction in type 2 diabetes

Altered function and gene regulation of pancreatic islet beta cells is a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of mechanisms driving T2D is still missing. Here we integrate information from measurements of chromatin activity, gene expression and function in single beta cells with genetic association data to identify disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 non-diabetic, pre-T2D and T2D donors, we robustly identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift in T2D. Subtype-defining active chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is likely induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for identifying mechanisms of complex diseases.

systems biology↗

bioRxiv2022

Cataloguing and profiling of the gut virome in Chinese populations uncover extensive viral signatures across common diseases

The gut viral community has been linked to human physiology and health, but our knowledge of its genetic and functional contents and disease dependence is far from complete. Here, we collected 11,327 bulk or viral metagenomes from fecal samples from large-scale Chinese populations to establish a Chinese gut virus catalogue (cnGVC) comprising 67,096 nonredundant viral genomes. This catalogue included [~]70% of novel viruses that are not represented in existing gut viral databases, and allowed us to characterize the functional diversity and specificity of the gut virome. Using cnGVC, we 1) profiled the gut virome in large-scale populations and evaluated their sex- and age-related variations, 2) investigated the diversity and compositional patterns of the gut virome across common diseases by analyzing 6,314 bulk metagenomes spanning 28 disease or unhealthy statuses, and 3) identified a large number of universal viral signatures of diseases and validated their predictive ability for health status. Overall, our resources and results would contribute to the grand effort of expanding the knowledge of the human gut virome and addressing a full picture of the associations between viruses and common diseases.

microbiology↗

bioRxiv2022

FCGRT, a cancer-derived immunoglobulin G binding protein, mediates the malignant phenotype of glioma

CIgG has received increasing attention, and was first discovered by our group to indicate poor prognosis in glioma. Furthermore, by protein mass spectrometry, we found that FCGRT can combine with CIgG. However, the study of FCGRT in glioma has not been reported. We used the CGGA325, TCGA dataset and immunohistochemistry to verify the importance of FCGRT on the prognosis of glioma patients. Single cell sequencing data analysis evaluated that the role of FCGRT in the microenvironment of glioma. Estimate, ssGSEA, EPIC and xCell were used for immune infiltration analysis. FCGRT was knocked down in U251 cells to detect the effect of FCGRT on the malignant development of glioma. These results showed that patients with higher FCGRT expression had a shorter overall survival. FCGRT was closely related to the tumor microenvironment, especially to macrophages in the tumor microenvironment (r=0.743, p<0.001). Interestingly we also found that FCGRT was positively correlated with IGHG1. Finally, we found that knock-down of FCGRT resulted in a decrease in proliferation, migration and invasion of U251 cells. Taken together, we believe that FCGRT is an independent prognostic factor for glioma patients, and its possible mechanism is to promote proliferation and invasion of tumor cells by interacting with CIgG.

bioinformatics↗

bioRxiv2022

Reduced progranulin increases tau and alpha-synuclein inclusions and alters phenotypes of tauopathy mice via glucocerebrosidase

Comorbid proteinopathies are observed in many neurodegenerative disorders including Alzheimers disease (AD), increase with age, and influence clinical outcomes, yet the mechanisms remain ill-defined. Here, we show that reduction of progranulin (PGRN), a lysosomal protein associated with TDP-43 proteinopathy, also increases tau inclusions, causes concomitant accumulation of -synuclein and worsens mortality and disinhibited behaviors in tauopathy mice. The increased inclusions paradoxically protect against spatial memory deficit and hippocampal neurodegeneration. PGRN reduction with tau pathology attenuates activity of {beta}-glucocerebrosidase (GCase), a protein previously associated with synucleinopathy, while increasing glucosylceramide (GlcCer)-positive tau inclusions. In neuronal culture, GCase inhibition enhances tau aggregation induced by AD-tau. Furthermore, purified GlcCer directly promotes tau aggregation in vitro. Neurofibrillary tangles in human tauopathies are also GlcCer-immunoreactive. Thus, in addition to TDP-43, PGRN regulates tau- and synucleinopathies via GCase and GlcCer. A lysosomal PGRN-GCase pathway may be a common therapeutic target for age-related comorbid proteinopathies.

neuroscience↗

bioRxiv2022

Translating Membrane Geometry into Protein Function: Multifaceted Membrane Interactions of Human Atg3 Promote LC3-Phosphatidylethanolamine Conjugation during Autophagy

Autophagosome formation is the hallmark of macroautophagy (herein referred to as autophagy) and requires the covalent conjugation of LC3 proteins (or Atg8 in yeast) to the amino headgroup of PE (phosphatidylethanolamine) lipids. Atg3 is an enzyme that catalyzes the final step of this reaction by transferring LC3 from an LC3-Atg3 intermediate to PEs in targeted membranes. Here, we determine the solution structure of human Atg3 (hAtg3) and demonstrate that the catalytically important regions of hAtg3 are conformationally dynamic. Furthermore, we reveal that these regions and hAtg3s N-terminal membrane curvature-sensing amphipathic helix concurrently interact with the membrane. These structural studies indicate that hAtg3 exploits a multifaceted membrane-association mechanism to position its catalytic center at the membrane surface and to bring the reaction substrates of LC3 and PE lipids to proximity for effective LC3-PE conjugation. In addition, our studies demonstrate that the interaction of the His266 residue with the membrane is primarily responsible for hAtg3s pH-dependent activity. Our investigations advance an emerging concept that the interactions of Atg3 with the highly curved membrane rims of the phagophore spatially regulate autophagosome biogenesis.

biochemistry↗

bioRxiv2022

Structural basis for BIRC6 to balance apoptosis and autophagy

Caspase-9 is the initiator caspase for the intrinsic apoptotic cell death pathway, and is critical to the activation of effector caspases during apoptosis, but how its levels and activities are maintained remains unclear. The gigantic Inhibitor of Apoptosis Protein (IAP) BIRC6/BRUCE/Apollon not only inhibits apoptosis, but also promotes ubiquitination of the key autophagic protein LC3 and inhibits autophagy. Here we show that BIRC6 forms an anti-parallel U-shaped dimer in a 3.6-[A] cryo-EM structure with multiple previously unannotated domains, including a ubiquitin-like domain, and discover that the mitochondria-derived pro-apoptotic factor Smac/DIABLO binds BIRC6 by interacting with one BIR domain, two carbohydrate-binding modules and two helices in the central cavity. Notably, Smac outcompetes the effector caspase 3 and the pro-apoptotic protease HtrA2, but not caspase 9, for binding BIRC6. BIRC6 strongly inhibits cellular activity of caspase 9, but weakly suppresses that of caspase 3. Meanwhile, BIRC6 binds LC3 through an LC3-interacting region, probably following dimer disruption of this BIRC6 region. Deficiency in LC3 ubiquitination promotes autophagy and autophagic degradation of BIRC6, and inhibits apoptosis. Moreover, induction of autophagy promotes autophagic degradation of both procaspase-9 and active caspase-9, but not of effector caspases. These results are important to understand how the balance between apoptosis and autophagy is regulated under pathophysiological conditions.

cell biology↗

bioRxiv2022

Discovering Haematoma-Stimulated Circuits for Secondary Brain Injury after Intraventricular Haemorrhage by Spatial Transcriptome Analysis

Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important yet challenging to study due to disease leisions locations and other complexities. Utilizing the powerful spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular haemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications. Our data identified three main 3D global pseudospace-time trajectory bundles which represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex induced by experimental intraventricular haematoma stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH. These results are informative in understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injuries and strategizing more effective clinical management regimens. The bioinformatics strategies will also be useful for the study of other CNS diseases.

bioinformatics↗

bioRxiv2022

Fractions strategy differences in those born extremely preterm

IntroductionTo investigate the effects of different strategies and cognitive load we explored brain hemodynamic responses associated with the use of different strategies to solve subtraction of fractions. We focused on those born extremely preterm (EPT; <28 weeks gestation) as they are known to have cognitive challenges and struggle with mathematics. We also included a group of full-term (FT) peers for comparison. MethodsFunctional MRI was acquired while the participants mentally solved fraction equations using either a strategy based on improper or mixed fractions. Different fraction item types were given, which affected respective required cognitive loads per strategy. Diffusion and T1-weighted structural images were also acquired. ResultsThe EPT and FT groups differed in terms of task-related hemodynamic responses. Functional group differences were greatest when strategies were applied to item types that result in high cognitive load. Other findings showed reduced white and grey matter volume and reduced white matter connectivity in widespread areas in the EPT group compared to the FT group. ConclusionThe understanding of function and structure presented here may help inform pedagogical practices by allowing for tailoring of mathematical education through identifying suitable strategy adoption that depends on item type, to circumvent weaknesses in cognitive skills.

neuroscience↗

bioRxiv2022

Genome-wide Identification and Expression Profile Analysis of Laccase Family Genes in the Hypsizygus marmoreus

Laccase exists widely in plants and fungi. It is a copper-containing polyphenol oxidase that can degrade lignin, oxidate, and phenolic substances, inhibit heterophytes, promote fruiting body formation, and improve the quality of mushrooms. In this study, 18 laccase genes were identified from the whole genome of a white strain (HM62) of Hypsizygus marmoreus, and the mapping, structure, and evolution of laccase genes were analyzed at the whole genome level, while the spatiotemporal expression was evaluated at different developmental stages. The laccase genes mainly distributed on chromosomes 1, 2, 3, 4, 6, 9, and 10, and 9 genes were clustered linearly on chromosome 6, indicating gene doubling. Phylogenetic tree analysis showed that the laccase gene family was divided into three subfamilies. The spatiotemporal expression analysis of the laccase gene family showed that HmLac09 and HmLac10 were highly expressed in different periods and might be involved in lignin degradation and fruit body formation, respectively. The expression levels of HmLac02, HmLac05, HmLac08, and HmLac17 genes in gray or gray and white heterozygous strains were higher than those in white strains, which might be related to the difference in lignin decomposition in gray strains, and one of the factors leading to different growth rates. The present study investigated the characterization of the H. marmoreus laccase gene family, extending our understanding of laccase mediated fruiting body development and growth rate mechanisms in this fungi.

microbiology↗

bioRxiv2022

Extracellular disintegration of viral proteins as an innovative strategy for developing broad-spectrum antivirals against coronavirus

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed millions of lives worldwide, not to mention innumerable losses in the global economy and disruptions in social relationships. Unfortunately, state-of-the-art treatments still lag behind the fast emergence of new variants of concern. The key to resolve this issue is to develop broad-spectrum antivirals with innovative antiviral mechanisms in which coronaviruses are deactivated regardless of their variant development. Herein, we report a new antiviral strategy involving extracellular disintegration of viral proteins that are indispensable for viral infection with hyperanion-grafted enediyne molecules. The sulfate groups ensure low cellular permeability and rather low cytotoxicity of the molecules, while the core enediyne generates reactive radical species and causes significant damage to the spike (S) protein of coronavirus. The enediyne compounds exhibit antiviral activity at micromolar to nanomolar concentrations, and the selectivity index of up to 20,000 against four kinds of human coronaviruses, including the SARS-CoV-2 omicron variant, suggesting the high potential of this new strategy in combating the COVID-19 pandemic.

biochemistry↗

bioRxiv2022

Deep Domain Adversarial Neural Network for the Deconvolution of Cell Type Mixtures in Tissue Proteome Profiling

Cell type deconvolution is a computational method for the determination/resolution of cell type proportions from bulk sequencing data, frequently used for the analysis of divergent cell types in tumor tissue samples. However, deconvolution technology is still in its infancy for the analysis of cell types using proteomic data due to challenges with repeatability/reproducibility, variable reference standards and the lack of single-cell proteomic reference data. Here, we developed a novel deep learning-based deconvolution method (scpDeconv) tailored to proteomic data. scpDeconv uses an autoencoder to leverage the information from bulk proteomic data to improve the quality of single-cell proteomic data, and employs a domain adversarial architecture to bridge the single-cell and bulk data distributions and transfer labels from single-cell data to bulk data. Extensive experiments validated the performance of scpDeconv in the deconvolution of proteomic data produced from various species/sources and different proteomic technologies. This method should find broad applicability to areas including tumor microenvironment interpretation and clinical diagnosis/classification.

bioinformatics↗

bioRxiv2022

A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes

Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by leveraging effects that are shared across phenotypes. However, effects can be shared across phenotypes in a variety of ways, so computationally efficient statistical methods are needed that can accurately and flexibly capture patterns of effect sharing. Here, we describe new Bayesian multivariate, multiple regression methods that, by using flexible priors, are able to model and adapt to different patterns of effect sharing and specificity across phenotypes. Simulation results show that these new methods are fast and improve prediction accuracy compared with existing methods in a wide range of settings where effects are shared. Further, in settings where effects are not shared, our methods still perform competitively with state-of-the-art methods. In real data analyses of expression data in the Genotype Tissue Expression (GTEx) project, our methods improve prediction performance on average for all tissues, with the greatest gains in tissues where effects are strongly shared, and in the tissues with smaller sample sizes. While we use gene expression prediction to illustrate our methods, the methods are generally applicable to any multi-phenotype applications, including prediction of polygenic scores and breeding values. Thus, our methods have the potential to provide improvements across fields and organisms. Author summaryPredicting phenotypes from genotypes is a fundamental problem in quantitative genetics. Thanks to recent advances, it is increasingly feasible to collect data on many phenotypes and genome-wide genotypes in large samples. Here, we tackle the problem of predicting multiple phenotypes from genotypes using a new method based on a multivariate, multiple linear regression model. Although the use of a multivariate, multiple linear regression model is not new, in this paper we introduce a flexible and computationally efficient empirical Bayes approach based on this model. This approach uses a prior that captures how the effects of genotypes on phenotypes are shared across the different phenotypes, and then the prior is adapted to the data in order to capture the most prominent sharing patterns present in the data. We assess the benefits of this flexible Bayesian approach in simulated genetic data sets, and we illustrate its application in predicting gene expression measured in multiple human tissues. We show that our methods can outperform competing methods in terms of prediction accuracy, and the computations involved in fitting the model and making the predictions scale well to large data sets.

genetics↗

bioRxiv2022

An HBV mRNA vaccine with favorable virological suppression and superior immunity

BRIEF ABSTRACTHere we report on the development and comprehensive evaluations of an mRNA vaccine for chronic hepatitis B (CHB) treatment. In two different HBV carrier mouse models generated by viral vector-mediated HBV transfection (pAAV-HBV1.2 and rAAV8-HBV1.3), this vaccine demonstrates sufficient and persistent virological suppression, and robust immunogenicity in terms of induction of strong innate immune activation, high-level virus-specific antibodies, memory B cells and T cells. mRNA platform therefore holds prospects for therapeutic vaccine development to combat CHB.

immunology↗

bioRxiv2022

Targeted degradation of PRC1 components, BMI1 and RING1B, via a novel protein complex degrader strategy

Polycomb repressive complex 1 (PRC1) is an essential epigenetic regulator that mainly controls histone H2A Lys119 mono-ubiquitination (H2AK119ub). BMI1 and RING1B are PRC1 core components and play critical roles in the development of various cancers. However, therapeutic agents targeting PRC1 are very limited, and small-molecule inhibitors of PRC1 displayed limited effectiveness in killing cancer cells. In this study, MS147, the first degrader of PRC1 core components, BMI1 and RING1B, was discovered via a novel protein complex degradation strategy that utilizes the target proteins interacting partner protein (EED) to degrade BMI1 and RING1B. MS147, which comprises an EED small-molecule binder linked to a ligand of the E3 ligase VHL, degrades BMI1 and RING1B in an EED-, VHL-, ubiquitination- and time-dependent manner. MS147 is selective and preferentially degrades BMI1 and RING1B over PRC2 core components: EED, EZH2 and SUZ12. Consequently, MS147 effectively reduces H2AK119ub, but not H3K27me3, which is catalyzed by PRC2. Furthermore, MS147, but not the parent EED binder or known PRC2 degraders, effectively inhibits the proliferation of cancer cell lines that are insensitive to EZH2 knockout or PRC2 degraders. Overall, this study provides a novel degrader targeting BMI1 and RING1B, which is a useful chemical tool to further investigate the roles of PRC1 in cancer, and a novel protein complex degradation strategy, which could potentially expand the degradable human proteome.

biochemistry↗

bioRxiv2022

Rapid and Signal Crowdedness-Robust In-Situ Sequencing through Hybrid Block Coding

Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening new possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108 gene panel from 453,843 cells from four mouse brain coronal slices in less than two days. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimers disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms.

genomics↗