Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 469 records · Page 26Linked to original sources

bioRxiv2023

Effects of Probiotics, Glucose Oxidase and its Combination on Immune Function, Antioxidant Status, Serum Biochemical Incides and Toxin Residue in Sheep when Challenged with Aflatoxin B1

The objective of this study was to investigate the ability of a mixed mycotoxins detoxification agent (probiotics, Glucose oxidase (GOD) and its combination) to alleviate the toxicity of aflatoxin B1 (AFB1) by assessing growth performance, serum toxin residue, immune function, antioxidant status and serum biochemical indices of sheep. Twenty 8-month-old Mongolian sheep were randomly assigned to 1-5 diet treatment groups: 1) the control (CON) group: basal diet; 2) the AFB1 (A) group: CON + 0.2 mg/kg AFB1; 3) the AFB1 and probiotics (AP) group: A + 0.5% probiotics; 4) the AFB1 and GOD (AG) group: A + 0.5% GOD; 5) the AFB1, probiotics, and GOD (APG) group: A + 0.5% probiotics + 0.5% GOD. Results showed that treatment A adversely affected the health and performance of sheep. However, the treatments AP, AG, or APG exerted a positive effect on health, performance and other indications. In conclusion, probiotics, GOD, and its combination induced injury of Mongolian sheep by alleviating the negative effects of AFB1 on the production performance, immune indexes, antioxidant indexes, and serum biochemical indexes and effectively reducing serum toxin residue. Key ContributionThe study investigated the effects of probiotics, Glucose oxidase and its combination on serum detoxification of AFB1 by assessing growth performance, serum toxin residue, immune function, antioxidant status and blood biochemical indices of sheep. AFB1 - contaminated diets supplemented with probiotics and GOD were effective in improving growth performance, immunity, antioxidant function, liver function, and decreasing serum AFB1 residue of sheep.

zoology↗

bioRxiv2023

GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach

The identification of protein-peptide binding sites significantly advances our understanding of their interaction. Recent advancements in deep learning have profoundly transformed the prediction of protein-peptide binding sites. In this work, we describe the Geometric Attention-based networks for Peptide binding Sites identification (GAPS). The GAPS constructs atom representations using geometric feature engineering and employs various attention mechanisms to update pertinent biological features. In addition, the transfer learning strategy is implemented for leveraging the pre-trained protein-protein binding sites information to enhance training of the protein-peptide binding sites recognition, taking into account the similarity of proteins and peptides. Consequently, GAPS demonstrates state-of-the-art (SOTA) performance in this task. Our model also exhibits exceptional performance across several expanded experiments including predicting the apo protein-peptide, the protein-cyclic peptide, and the predicted protein-peptide binding sites. Overall, the GAPS is a powerful, versatile, stable method suitable for diverse binding site predictions.

bioinformatics↗

bioRxiv2023

Predicting the Trend of SARS-CoV-2 Mutation Frequencies Using Historical Data

As the SARS-CoV-2 virus rapidly evolves, predicting the trajectory of viral variations has become a critical yet complex task. A deep understanding of future mutation patterns, in particular the mutations that will prevail in the near future, is vital in steering diagnostics, therapeutics, and vaccine strategies in the coming months. In this study, we developed a model to forecast future SARS-CoV-2 mutation surges in real-time, using historical mutation frequency data from the USA. To improve upon the accuracy of traditional time-series models, we transformed the prediction problem into a supervised learning framework using a sliding window approach. This involved breaking the time series of mutation frequencies into very short segments. Considering the time-dependent nature of the data, we focused on modeling the first-order derivative of the mutation frequency. We predicted the final derivative in each segment based on the preceding derivatives, employing various machine learning methods, including random forest, XGBoost, support vector machine, and neural network models, in this supervised learning setting. Empowered by the novel transformation strategy and the high capacity of machine learning models, we witnessed low prediction error that is confined within 0.1% and 1% when making predictions for future 30 and 80 days respectively. In addition, the method also led to a notable increase in prediction accuracy compared to traditional time-series models, as evidenced by lower MAE, and MSE for predictions made within different time horizons. To further assess the methods effectiveness and robustness in predicting mutation patterns for unforeseen mutations, we categorized all mutations into three major patterns. The model demonstrated its robustness by accurately predicting unseen mutation patterns when training on data from two pattern categories while testing on the third pattern category, showcasing its potential in forecasting a variety of mutation trajectories. To enhance accessibility and utility, we built our methodology into an R-shiny app (https://swdatpredicts.shinyapps.io/rshiny_predict/), a tool with potential applicability in studying other infectious diseases, thus extending its relevance beyond the current pandemic.

bioinformatics↗

bioRxiv2023

FURNA: a database for function annotations of RNA structures

Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by non-coding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions of RNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand binding sites, RNA families, protein binding motifs, and cross-references to related databases. FURNA is available at https://seq2fun.dcmb.med.umich.edu/furna/ to enable quick discovery of RNA functions from their structures and sequences.

bioinformatics↗

bioRxiv2023

Parallel Molecular Data Storage by Printing Epigenetic Bits on DNA

DNA storage has shown potential to transcend current silicon-based data storage technologies in storage density, lifetime and energy consumption. However, writing large-scale data directly into DNA sequences by de novo synthesis remains uneconomical in time and cost. Inspired by the natural genomic modifications, in this work, we report an alternative, parallel strategy that enables the writing of arbitrary data on DNA using premade nucleic acids. With epigenetic modifications as information bits, our system employed DNA self-assembly guided enzymatic methylation to perform movable-type printing on universal DNA templates. By programming with a finite set of 700 DNA movable types and 5 templates, we achieved the synthesis-free writing of [~]270000 bits on an automated platform with 350 bits written per reaction. The data encoded in complex epigenetic patterns were retrieved high-throughput by nanopore sequencing, and algorithms were developed to finely resolve 240 modification patterns per sequencing reaction. Our framework presents a new modality of DNA-based data storage that is parallel, programmable, stable and scalable. Such a modality opens up avenues towards practical data storage and dual-mode data functions in biomolecular systems.

biochemistry↗

bioRxiv2023

Quorum Sensing Positively Regulates CPS-dependent Autographiviridae Phages Infection in Vibrio alginolyticus

Quorum sensing (QS) orchestrates many bacterial behaviors, such as virulence and biofilm formation, across bacterial populations. Nevertheless, the underlying mechanism of QS regulating CPS-dependent phage-bacterium interactions remains unclear. In the present study, we report that QS upregulated the expression of CPS-dependent phage receptors, thus increasing phage adsorption and infection rates in V. alginolyticus. We found that QS upregulated the expression of the ugd gene, leading to increased synthesis of Autographiviridae phage receptor capsular polysaccharide (CPS) synthesis in V. alginolyticus. The signal molecule autoinducer-2 (AI-2) released by V. alginolyticus from different sources can potentially enhance CPS-dependent phage infection. Therefore, our data suggest that inhibiting quorum sensing may reduce rather than improve the therapeutic efficacy of CPS-specific phages. IMPORTANCEPhage resistance is a direct threat to phage therapy, and understanding phage-host interactions, especially bacteria block phage infection, is essential for developing successful phage therapy. In the present study, we demonstrate for the first time that V. alginolyticus uses QS to promote CPS-specific phage infection by upregulating the expression of ugd, which is necessary for the synthesis of Autographiviridae phage receptor capsular polysaccharide (CPS). Although increased CPS-specific phage susceptibility is a novel trade-off mediated by QS, it results in the upregulation of virulence factors, promoting biofilm development and enhanced capsular polysaccharide production in V. alginolyticus. This suggests that inhibiting QS may improve the effectiveness of antibiotic treatment, but it may also reduce the efficacy of phage therapy.

microbiology↗

bioRxiv2023

High-resolution ultrastructure and morphology of marine ammonia-oxidizing archaea Nitrosopumilus maritimus SCM1 revealed by Cryo-ET and STEM-EDS

Nitrososphaerota, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their significance in Earths ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant question unanswered of how the machinery of these organisms underpins metabolic functions. In this study, we combined chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaerota. Our tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules in which one type is possibly composed of polyphosphate, while the other type is polyhydroxyalkanoate. Collectively, these findings provide visual evidence for the resilience of AOA in the vast oligotrophic marine environment.

microbiology↗

bioRxiv2023

Structural insights into specific recognition of PTENα/β-NTE by WDR5 in cancer progression

PTEN/{beta}, two variants of PTEN, play a key role in promoting tumor growth by interacting with WDR5 through their N-terminal extensions (NTEs). This interaction facilitates the recruitment of the SET1/MLL methyltransferase complex, resulting in histone H3K4 trimethylation and upregulation of oncogenes such as NOTCH3, which in turn promotes tumor growth. However, the molecular mechanism underlying this interaction has remained elusive. In this study, we determined the crystal structure of PTEN-NTE in complex with WDR5, which reveal that PTEN specifically binds to the WIN site of WDR5 through a novel binding motif specifically found in the NTE domain of PTEN/{beta}. Disruption of this interaction significantly impedes cell proliferation and tumor growth, highlighting the potential of the WIN site inhibitors of WDR5 as therapeutic agents for PTEN/{beta} associated cancers. These findings not only shed light on the important role of the PTEN/{beta}-WDR5 interaction in carcinogenesis, but also present a promising avenue for developing cancer treatments that target this pathway.

cancer biology↗

bioRxiv2023

Lithocholic acid targets TULP3 to activate sirtuins and AMPK to retard ageing

Lithocholic acid (LCA), accumulated in the body during calorie restriction (CR), can confer administered metazoans with the ability to activate AMP-activated protein kinase (AMPK) and retard ageing. However, how LCA is signalled to activate AMPK and elicit the biological effects is unclear. Here, we show that LCA can enhance sirtuins (SIRTs) to deacetylate and subsequently inhibit vacuolar H+-ATPase (v-ATPase), thereby triggering AMPK activation via the lysosomal glucose-sensing pathway. Through proteomic analysis of SIRT1-coimmunoprecipitated proteins, we identify and validate that TUB like protein 3 (TULP3) is a constitutive component of SIRTs. Surprisingly, we found that TULP3 is an LCA receptor, and that the LCA-bound TULP3 activates SIRTs. The activated SIRTs in turn deacetylate the V1E1 subunit of v-ATPase on K52, K99 and K191 residues. Muscle-specific expression of the 3KR mutant of V1E1, mimicking the deacetylated state, dominantly activates AMPK and rejuvenates muscles in aged mice. Moreover, LCA once administered also activates AMPK and extends lifespan and healthspan in nematodes and flies, depending on the TULP3 homologues tub-1 and ktub, respectively. Our study thus elucidates that LCA triggers the TULP3-sirtuin-v-ATPase- AMPK route to manifest benefits of calorie restriction.

physiology↗

bioRxiv2023

Lithocholic acid phenocopies rejuvenating and life-extending effects of calorie restriction

Calorie restriction (CR) is a dietary intervention to promote health and longevity. CR causes various metabolic changes in both the production and circulation of metabolites; however, it remains unclear which of the changed metabolite(s) can account for the physiological benefits of CR. Through metabolomic analysis of metabolites undergoing abundance changes during CR and subsequent functional validation, we found that lithocholic acid (LCA) is the only metabolite that alone can recapitulate the effects of CR, including activation of AMPK and the rejuvenating effects of muscle regeneration, grip strength and running capacity in mice. Interestingly, LCA also activates AMPK and exerts life- and health-extending effects in Caenorhabditis elegans and Drosophila melanogaster, indicating that these animal models are able to transmit the signalling of LCA once administered. Knockout of AMPK abrogates LCA-induced phenotypes, in nematodes and flies, as well as in mice. Together, we have identified that administration of the CR-upregulated metabolite LCA alone can confer anti-ageing benefits to metazoans, in an AMPK-dependent manner.

physiology↗

bioRxiv2023

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

neuroscience↗

bioRxiv2023

Yeast9: A Consensus Yeast Metabolic Model Enables Quantitative Analysis of Cellular Metabolism By Incorporating Big Data

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This have increased the quality and scope of this model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or normal conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through the central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference in nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine-learning models. Based on those findings we anticipate that Yeast9 will empower systems biology studies of yeast metabolism.

systems biology↗

bioRxiv2023

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Tracing cellular dynamic changes across conditions, time, and space is crucial for understanding the molecular mechanisms underlying complex biological systems. However, integrating multi-sample data in a unified and flexible way to explore cellular heterogeneity remains a major challenge. Here, we present Stereopy, a flexible and versatile framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this flexible framework, we have developed three key components: a multi-sample tailored data container, a scope controller, and an analysis transformer. Furthermore, Stereopy showcases three transformative applications supported by pivotal algorithms. Firstly, the multi-sample cell community detection (CCD) algorithm introduces an innovative capability to detect specific cell communities and identify genes responsible for pathological changes in comparable datasets. Secondly, the spatially resolved temporal gene pattern inference (TGPI) algorithm represents a notable advancement in detecting important spatiotemporal gene patterns while concurrently considering spatial and temporal features, which enhances the identification of important genes, domains and regulatory factors closely associated with temporal datasets. Finally, the 3D niche-based regulation inference tool, named NicheReg3D, reconstructs the 3D cell niches to enable the inference of cell-gene interaction network within the spatial texture, thus bridging intercellular communications and intracellular regulations to unravel the intricate regulatory mechanisms that govern cellular behavior. Overall, Stereopy serves as both a bioinformatics toolbox and an extensible framework that provides researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data.

bioinformatics↗

bioRxiv2023

Armillaria korhonenii, the sixteenth biological species of Armillaria from China

Armillaria korhonenii, designated as Chinese biological species Q (CBSQ) here, is described as a new species based on phylogenetic and morphological evidence as well as the results of mating tests. This species is the sixteenth biological species of Armillaria reported from China following the discovery of 15 previously known biological species. Phylogenetically, A. korhonenii is the earliest divergent lineage of the genus Armillaria as inferred from the nucleotide sequences of the Elongation Factor 1-Alpha (EF1a). Morphologically, it is characterized by its ellipsoid to elongate basidiospores, yellowish orange to honey yellow pileus with fibrillose squamules. Mating tests indicated that A. korhonenii is a tetrapolar heterogeneous species and is inter-sterile with all the tested species except partial compatibility with CBSH.

microbiology↗

bioRxiv2023

Epigenetic reader ZMYND11 noncanonical function restricts HNRNPA1-mediated stress granule formation and oncogenic activity

ZMYND11 encodes an epigenetic reader of histone methylation, functioning as a transcriptional corepressor. However, whether and how ZMYND11 contributes to cancer progression and therapy remains unclear. Here we report that ZMYND11 downregulation is prevalent in cancers and profoundly correlates with adverse prostate cancer patient outcomes. Depletion of ZMYND11 promotes tumor cell growth, migration and invasion in vitro as well as tumor formation and metastasis in vivo. Mechanistically, we find that ZMYND11 exhibits tumor suppressive roles through recognizing arginine-194-methylated HNRNPA1 dependent on its MYND domain, thereby squeezing HNRNPA1 in nucleus and inhibiting the formation of stress granules in cytoplasm. Furthermore, ZMYND11 antagonizes HNRNPA1-driven high PKM2/PKM1 ratio and counteracts PKM2-induced aggressive tumor phenotype. Remarkably, ZMYND11 recognition of HNRNPA1 could be disrupted by pharmaceutical inhibition of arginine methyltransferase PRMT5 while ZMYND11 low-expressing tumors are sensitive to the treatment of PRMT5 inhibitors. Collectively, our study unravels a novel and noncanonical function of ZMYND11 as the nonhistone methyl reader and discovers a mechanism for the requirement of arginine-methylation-mediated ZMYND11-HNRNPA1 association to restrict tumor progression and offers cancer therapeutic targets and potential biomarkers.

cancer biology↗

bioRxiv2023

Patient-Specific Vascularized Tumor Model: Blocking TAM Recruitment with Multispecific Antibodies Targeting CCR2 and CSF-1R

Tumor-associated inflammation drives cancer progression and therapy resistance, with the infiltration of monocyte-derived tumor-associated macrophages (TAMs) associated with poor prognosis in diverse cancers. Targeting TAMs holds potential against solid tumors, but effective immunotherapies require testing on immunocompetent human models prior to clinical trials. Here, we develop an in vitro model of microvascular networks that incorporates tumor spheroids or patient tissues. By perfusing the vasculature with human monocytes, we investigate monocyte trafficking into the tumor and evaluate immunotherapies targeting the human tumor microenvironment. Our findings demonstrate that macrophages in vascularized breast and lung tumor models can enhance monocyte recruitment via TAM-produced CCL7 and CCL2, mediated by CSF-1R. Additionally, we assess a novel multispecific antibody targeting CCR2, CSF-1R, and neutralizing TGF-{beta}, referred to as CSF1R/CCR2/TGF-{beta} Ab, on monocytes and macrophages using our 3D models. This antibody repolarizes TAMs towards an anti-tumoral M1-like phenotype, reduces monocyte chemoattractant protein secretion, and effectively blocks monocyte migration. Finally, we show that the CSF1R/CCR2/TGF-{beta} Ab inhibits monocyte recruitment in patient-specific vascularized tumor models. Overall, this vascularized tumor model offers valuable insights into monocyte recruitment and enables functional testing of innovative therapeutic antibodies targeting TAMs in the tumor microenvironment (TME).

bioengineering↗

bioRxiv2023

A genome-wide association study of neonatal metabolites

The hereditary component significantly influences the concentration of metabolites in adults. Nevertheless, the precise influence of genetic factors on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified a total 17 previous reported associations and 13 novel discovered associations between single nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was calculated to be 76.2%, with a range of 69-78.8%. The aforementioned findings offer valuable insights pertaining to the genetic architecture of neonatal metabolism. In BriefLarge-scale genomes of maternal non-invasive prenatal testing provide insights into the genetic contribution to neonatal metabolism. HighlightsGWAS of 27,785 low-pass genomes revealed 13 novel associations of neonatal metabolic components. Estimated an average of 76.2% heritability of neonatal metabolic components and showed the individual concentration can be accurately predicted from polygenic risk scores. A total of 17 established relationships have been observed, providing evidence that maternal genomes can be utilized in neonatal metabolite GWAS.

genetics↗

bioRxiv2023

Integrating leaf gas exchange and chlorophyll fluorescence to reveal the long-term regulation of photosynthesis in situ

Understanding the diurnal and seasonal regulation of photosynthesis is an essential step in quantifying and modeling the impact of the environment on plant function. Although the dynamics of photosynthesis have been widely investigated in terms of CO2 exchange measurements, a more comprehensive view can be obtained when combining gas exchange and chlorophyll fluorescence (ChlF) measurements. However, such integrated measurements have been so far restricted to short term analysis using portable systems that combine IRGA and PAM-ChlF techniques. Here we introduce and demonstrate a new method for integrated, long-term and in situ measurements of leaf gas exchange and ChlF, based on an autonomous gas exchange system and a new miniature PAM- fluorometer. The method is used to simultaneously track the dynamics of the light and carbon reactions of photosynthesis at a 20-minute resolution in leaves of silver birch during summer time. The potential of the method is initially demonstrated using the ratio between electron transport and net assimilation (ETR/ANET). We successfully captured the diurnal patterns in the ETR/ANET during summer time, including a drastic increase in ETR/ANET upon a high-temperature period. We suggest that these measurements can provide valuable data to model and quantify the regulation of leaf photosynthesis in situ. HighlightWe introduce new integrated measurements to help resolve the seasonal and diurnal dynamics of photosynthesis regulation by combining long-term simultaneous measurements of gas exchange and chlorophyll fluorescence in field conditions.

plant biology↗