Biology subjects

Gao, Y.

Publications and source records attributed to Gao, Y..

At least 397 records · Page 22Linked to original sources

bioRxiv2022

Urine proteomic analysis of the rat e-cigarette model

Urinary proteomics was used to investigate the potential effects of e-cigarettes on the human body. In this study, a rat e-cigarette model was constructed by smoking for two weeks and urine samples before, during, and after e-cigarette smoking were collected. Urine proteomes before-after smoking of each rat were compared individually, while the control group was set up to rule out differences caused by rat growth and development. After smoking, the differential proteins produced by rats shows strong individual variation. Fetuin-B, a biomarker of COPD, and annexin A2, which is recognized as a multiple tumor marker, were identified as the differential proteins in five out of six smoking rats on day 3. To our surprise, odorant-binding proteins expressed in the olfactory epithelium were also found and were significantly upregulated, which may help explain olfactory adaptation. Pathways enriched by the differential proteins shows the evidence that smoking e-cigarettes affects the immune system, cardiovascular system, respiratory system, etc., which provides clues for further exploration of the mechanism of e-cigarettes on the human body.

biochemistry↗

bioRxiv2022

A stable reference human transcriptome and proteome as a standard for reproducible omics experiments

In recent years, the development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits its application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. However, the transcriptome and proteome of most cell lines shift during culturing, which limits their applicability to serve as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (R2=0.966-0.995) and proteome (R2=0.934-0.976 for DDA, R2=0.942-0.986 for DIA) after 9 subculturing generations, which allows this stable standard sample to be stably produced on an industrial scale for several decades. Moreover, this stability was maintained across labs and platforms. In sum, our results justified a omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

cell biology↗

bioRxiv2022

A compendium of genetic regulatory effects across pig tissues

The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.

genomics↗

bioRxiv2022

Aryl hydrocarbon receptor utilises cellular zinc signals to maintain the gut epithelial barrier

Both zinc and plant-derived ligands of the aryl hydrocarbon receptor (AHR) are dietary components which regulate intestinal epithelial barrier function and protect against Inflammatory Bowel Disease (IBD)1,2. Here, we explore whether zinc and AHR pathway are linked using a mouse IBD model with follow-on studies on human and mouse ileum organoids. Our data demonstrate that AHR regulates cellular zinc uptake, and that zinc is an integral part of AHR signalling processes. We show that dietary supplementation in mice with the plant-derived AHR ligand precursor, indole-3-carbinol (I3C), offers a high level of protection against dextran sulfate sodium induced IBD while protection fails in mice with AHR deleted in the intestinal epithelium. AHR agonist treatment is also ineffective in mice with a nutritional zinc deficiency. Experiments in the human Caco-2 cell line and ileum organoids showed that AHR activation increases total cellular zinc and cytosolic free Zn2+ concentrations through transcriptional upregulation of several SLC39 zinc importers. As a consequence, genes for tight junction (TJ) proteins were upregulated in a zinc-dependent manner involving zinc inhibition of signalling to NF-{kappa}B and attenuated degradation of TJ proteins through zinc inhibition of calpain activity. Thus, our data indicate that AHR activation by plant-derived dietary ligands improves gut barrier function via zinc-dependent cellular pathways, suggesting that combined dietary supplementation with AHR ligands and zinc might be effective in preventing and treating inflammatory gut disorders.

cell biology↗

bioRxiv2022

Machine learning-guided directed evolution for the development of small-molecule antibiotics originating from antimicrobial peptides

Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics that possess a variety of potent biological activities by exerting immunomodulatory effects to clear difficult-to-treat infections. Understanding the structure-activity relationships (SARs) of AMPs can direct the synthesis of desirable therapeutics. In this study, we use machine learning-guided directed evolution to develop the lipopolysaccharide-binding domain (LBD), which acts as a functional domain of anti-lipopolysaccharide factor (ALF), a family of AMPs, identified from Marsupenaeus japonicus. We report the identification of LBDA-D as an output of this algorithm with the input of the original LBDMj sequence and show the NMR solution structure of LBDB, which possesses a circular extended structure with a disulfide crosslink in each terminus and two 310-helices and exhibits a broad antimicrobial spectrum. Scanning electron microscopy and transmission electron microscopy showed LBDB induced the formation of a cluster of bacteria wrapped in a flexible coating that ruptured and consequently killed the bacteria. The co-injection of LBDB and Vibrio alginolyticus, Staphylococcus aureus and another major pathogen in shrimp aquaculture white spot syndrome virus in vivo improved the survival of M. japonicus, indicating a promising therapeutic role of LBDB for infectious disease. The findings of this study pave the way for the rational drug design of activity-enhanced peptide antibiotics.

synthetic biology↗

bioRxiv2022

Chromosome-level genome assembly of Torreya grandis provides insights into the origin and evolution of gymnosperm-specific sciadonic acid biosynthesis

Species in genus Torreya are nut trees that produce dry fruits with a wide assortment of functions. Here, we report the 19-Gb chromosome-level genome assembly of T. grandis. The genome is shaped by an ancient whole genome duplication and recurrent LTR retrotransposon bursts. Comparative genomic analyses reveal key genes involved in reproductive organ development, cell wall biosynthesis and seed storage. Two genes encoding a C18 {Delta}9-elongase and a C20 {Delta}5-desaturase are identified in T. grandis to be responsible for sciadonic acid biosynthesis and both are present in diverse plant lineages except angiosperms. We demonstrate that the histidine-rich boxes of the {Delta}5-desaturase are crucial for its catalytic activity. Methylome analysis reveals that methylation valleys of the T. grandis seed genome harbor genes associated with important seed activities, including cell wall and lipid biosynthesis. Moreover, seed development is accompanied by DNA methylation changes that possibly fuel energy production. This study provides important genomic resource for gymnosperms and unravels key enzymes for biosynthesis of sciadonic acid as a hallmark metabolite of gymnosperms.

plant biology↗

bioRxiv2022

The endogenous modulation of visual plasticity in human adults

The adult human visual system can exhibit a degree of neuroplastic change under the right conditions which has implications for future treatments to recover vision loss that have occurred in infancy. The exogenous factors that promote neuroplastic change have been the focus of human psychophysical, electrophysiological and neuroimaging research over the last decade. What has not been considered is the importance of endogenous factors. In this study we modulate the neural oscillations that determines the internal neural state to demonstrate that endogenous factors play a critical role in not only baseline contrast sensitivity but also the extent to which the adult visual system can undergo neuroplastic change in binocular balance.

neuroscience↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗

bioRxiv2022

Multi-omics of Circular RNAs and Their Responses to Hormones in Moso Bamboo (Phyllostachys edulis)

Circular RNAs are endogenous non-coding RNAs with covalently closed structures, which have important functions in plants. However, their biogenesis, degradation, and function upon treatment with gibberellins (GA) and auxins (NAA) remain unknown. Here, we systematically identified and characterized expression patterns, evolutionary conservation, genomic features, and internal structures of circRNAs using RNase R-treated libraries from moso bamboo (Phyllostachys edulis) seedlings. Moreover, we investigated the biogenesis of circRNAs dependent on both cis- and trans-regulation. We determined details regarding the function of circRNAs, including their roles in regulating microRNA-related genes and modulating the alternative splicing of their linear counterparts. Importantly, we developed a customized degradome sequencing approach to detect microRNA-mediated cleavage of circRNAs. Finally, we present a comprehensive view of the participation of circRNAs in the regulation of hormone metabolism upon treatment of bamboo seedlings with gibberellins (GA) and auxins (NAA). Collectively, our study uncovers important features of circRNAs including overall characteristics, biogenesis, function, and microRNA-mediated degradation of circRNAs in moso bamboo.

plant biology↗

bioRxiv2022

Citrus genomic resources unravel putative genetic determinants of Huanglongbing, a pathogen-triggered immune disease

Citrus is one of the most important tree crops worldwide and citrus production is threatened by Huanglongbing (HLB), a devasting citrus disease caused by Candidatus Liberibacter. HLB is a pathogen-triggered immune disease, in which the pathogen initiates systemic and chronic immune responses including the excessive production of reactive oxidative species, which subsequently lead to cell death of phloem tissues and HLB disease symptoms. Here, we identified putative genetic determinants of HLB pathogenicity by integrating citrus genomic resources to characterize the pan-genome of accessions that differ in their response to HLB. Genome-wide association mapping and analysis of allele-specific expression between susceptible, tolerant, and resistant accessions further refined candidates underlying the response to HLB. To enable these analyses we first developed a phased diploid assembly of Citrus sinensis Newhall genome and produced resequencing data for 91 citrus accessions that differ in their response to HLB. These data were combined with previous resequencing data from 356 sequenced accessions for genome-wide association mapping of the HLB response. Genes with HLB pathogenicity were associated with the host immune response, ROS production, and antioxidants. Overall, this study has provided a significant recourse of citrus genomic data and we have identified candidate genes to be further explored to understand the genetic determinants of HLB pathogenicity and to generate HLB resistant/tolerant citrus varieties.

genomics↗

bioRxiv2022

Pseudogenes limit the identification of novel common transcripts generated by their parent genes

The human genome contains numerous duplicated regions, such as parent-pseudogene pairs, causing sequencing reads to align equally well to either gene. The extent to which this ambiguity complicates transcriptomic analyses is currently unknown. This is concerning as many parent genes have been linked to disease, including GBA1, causally linked to both Parkinsons and Gaucher disease. We find that most of the short sequencing reads that map to GBA1, also map to its pseudogene, GBAP1. Using long-read RNA-sequencing in human brain, where all reads mapped uniquely, we demonstrate significant differences in expression compared to short-read data. We identify novel transcripts from both GBA1 and GBAP1, including protein-coding transcripts that are translated in vitro and detected in proteomic data, but that lack GCase activity. By combining long-read with single-nuclear RNA-sequencing to analyse brain-relevant cell types we demonstrate that transcript expression varies by brain region with cell-type-selectivity. Taken together, these results suggest a non-lysosomal function for both GBA1 and GBAP1 in brain. Finally, we demonstrate that inaccuracies in annotation are widespread among parent genes, with implications for many human diseases.

genetics↗

bioRxiv2022

Knowledge Mapping of Alternative Splicing of Cancer From 2012 to 2021: A Bibliometric Analysis

BackgroundAs a processing method of RNA precursors, alternative splicing plays a critical role in normal cell physiological activities. Aberrant alternative splicing events are associated with cancer development and are promising targets for cancer treatment. However, no detailed and unbiased study describes the current state of alternative splicing of cancer research. This study aims to conduct a bibliometric analysis of alternative splicing of cancer research in the last decade to measure and recognize the current state and trends. MethodsWeb of Science Core Collection was used to acquire the articles. Utilizing three bibliometric tools (CiteSpace, VOSviewer, R-bibliometrix), we were able to measure and recognize the influence and collaboration data of individual articles, journals, and co-citations. Analysis of co-occurrence and bursts information helped us identify the trending research areas related to alternative splicing of cancer. ResultsFrom 2012 to 2021, the total number of papers on alternative splicing of cancer published in 766 academic journals was 3,507, authored by 20,406 researchers in 405 institutions from 80 countries/regions. Research involving alternative splicing of cancer genes was primarily conducted in the United States and China, with the United States retaining the prime status; simultaneously, the Chinese Academy of Sciences, Fudan University and National Cancer Institute were the institutions with strong research capabilities. Scorilas Andreas is the scholar with the most publications, while the most co-citations were generated by Wang, Eric T. Plos One published the most papers on alternative splicing of cancer, while J biol chem was the most co-cited academic journal in this field. Furthermore, these publications covered a wide range of areas, but were still primarily focused on the fields of molecular, biology, and immunology. The results of keyword co-occurrence analysis can be divided into three types: molecular (P53, CD44, Androgen receptor, SRSF3, ESRP1), pathological process (apoptosis, EMT, metastasis, angiogenesis, proliferation), and disease (breast cancer, colorectal cancer, prostate cancer, hepatocellular carcinoma, gastric cancer). ConclusionResearch on alternative splicing of cancer has been a hot topic over the past decade, increasing in intensity each year. Current alternative splicing of cancer studies focused on hallmarks of alternative splicing in cancer, and alternative splicing signatures including diagnostic and therapeutic targets. Among them, the current trends are splicing factors regulating epithelial-mesenchymal transition and other hallmarks, aberrant splicing events in tumors, and further mechanisms. These might give researchers interested in this field a forward-looking perspective and inform further research.

cancer biology↗

bioRxiv2022

Chemical inhibition of PIN auxin transporters by the anti-inflammatory drug Naproxen

The phytohormone auxin plays central roles in many growth and developmental processes in plants. Development of chemical tools targeting the auxin pathway is useful for both plant biology and agriculture. Here we uncover that Naproxen, a synthetic compound with anti-inflammatory activity in humans, acts as an auxin transport inhibitor targeting PIN transporters in plants. Physiological experiments indicate that exogenous Naproxen treatment affects pleiotropic auxin-regulated developmental processes. Further cellular and biochemical evidence supports that Naproxen suppresses auxin transport, specifically PIN-mediated auxin efflux. Moreover, biochemical and structural analysis confirms that Naproxen binds directly to PIN1 protein, via the same binding cavity as the IAA substrate. Thus, by combining cellular, biochemical, and structural approaches, this study well establishes that Naproxen is a PIN inhibitor and elucidates the underlying mechanisms. Further use of the compound may advance our understanding on the molecular mechanisms of PIN-mediated auxin transport, and expand our toolkit in auxin biology and agriculture.

plant biology↗

bioRxiv2022

Chemico-genetic Analysis of Native Autism Proteomes Reveals Shared Biology Predictive of Functional Modifiers

One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving high specificity spatial interactomes compared to prior methods. The resulting native proximity interactomes are enriched for human genes dysregulated in the brain of autistic individuals and reveal unexpected and highly significant interactions with other lower-confidence autism risk gene products, positing new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.

neuroscience↗

bioRxiv2022

Denitrifying bradyrhizobia retain strong N2O reduction during periods of starvation

Rhizobia living as microsymbionts inside nodules have stable access to carbon substrates, but also have to survive as free-living bacteria in soil where they are starved for carbon and energy most of the time. Many rhizobia can denitrify, thus switch to anaerobic respiration under low O2 tension using N-oxides as electron acceptors. The cellular machinery regulating this transition is relatively well-known from studies under optimal laboratory conditions, while little is known about this regulation in starved organisms. It is, for example, not known if the strong preference for N2O-over NO3 --reduction in bradyrhizobia is retained under carbon limitation. Here we show that starved cultures of a Bradyrhizobium strain with respiration rates 1-18% of well-fed cultures, reduced all available N2O before touching provided NO3-. Proteomics showed similar abundance of Nap (periplasmic NO3 - reductase) and NosZ (N2O reductase), suggesting that competition between electron pathways to Nap and NosZ favoured N2O reduction also in starved cells, similar to well-fed cultures. This contrasts the general notion that NosZ activity diminishes under carbon limitation. The results suggest that bradyrhizobia carrying NosZ can act as strong sinks for N2O under natural conditions and that this criterion should be considered in the development of biofertilizers. ImportanceLegume cropped farmlands account for substantial N2O emissions globally. Legumes are commonly inoculated with N2-fixing bacteria, rhizobia, to improve crop yields. Rhizobia belonging to Bradyrhizobium, the micro-symbionts of several economically important legumes, are generally capable of denitrification but many lack genes encoding N2O reductase and will be N2O sources. Bradyrhizobia with complete denitrification will instead act as sinks since N2O-reduction efficiently competes for electrons over nitrate reduction in these organisms. This phenomenon has only been demonstrated under optimal conditions and it is not known how carbon substrate limitation, which is the common situation in most soils, affects the denitrification phenotype. Here we demonstrate that bradyrhizobia retain their strong preference for N2O under carbon starvation. The findings add basic knowledge about mechanisms controlling denitrification and support the potential for developing novel methods for greenhouse gas mitigation based on legume inoculants with the dual capacity to optimize N2-fixation and minimize N2O emission.

microbiology↗

bioRxiv2022

Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous Studies

Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifiers reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations presented, and could successfully remove batch effects within the same population. We also showed that the machine learning classifiers prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches. Author summaryOvercoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. From both the simulation studies and realistic applications, we showed that batch normalization is essential for improving phenotype prediction performance by machine learning classifiers when incorporating multiple heterogeneous datasets. Combined with batch normalization, merging strategy and ensemble weighted learning methods both can boost machine learning classifiers performance in phenotype predictions. In addition, we explored that rank aggregation methods should be considered as alternative way to boost prediction performances, given that these methods showed similar robustness as ensemble weighted learning methods.

bioinformatics↗

bioRxiv2022

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

Current methods for evaluating the accuracy of germline variant calls are restricted to easy-to-detect high-confidence regions, thus ignoring a substantial portion of difficult variants beyond the benchmark regions. We established four DNA reference materials from immortalized cell lines derived from a Chinese Quartet including parents and monozygotic twins. We integrated benchmark calls of 4.2 million small variants and 15,000 structural variants from multiple platforms and bioinformatic pipelines for evaluating the reliability of germline variant calls inside the benchmark regions. The genetic built-in-truth of the Quartet family design not only improved sensitivity of benchmark calls by removing additional false positive variants with apparently high quality, but also enabled estimation of the precision of variants calls outside the benchmark regions. Batch effects of variant calling in large-scale DNA sequencing efforts can be effectively identified with the concurrent use of the Quartet DNA reference materials along with study samples, and can be alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Matched RNA and protein reference materials were also established in the Quartet project, thereby enabling benchmark calls constructed from DNA reference materials for evaluation of variants calling performance on RNA and protein data. The Quartet DNA reference materials from this study are a resource for objective and comprehensive assessment of the accuracy of germline variant calls throughout the whole-genome regions.

bioinformatics↗

bioRxiv2022

Deep transfer learning provides a Pareto improvement for multiethnic genomic prediction of diseases

Accurate genomic predisposition assessment is essential for the prevention and early detection of diseases. Polygenic scores and machine learning models have been developed for disease prediction based on genetic variants and other risk factors. However, over 80% of existing genomic data were acquired from individuals of European descent. As a result, clinico-genomic risk prediction is less accurate for non-European populations. Here we employ a transfer learning strategy to improve the clinico-genomic prediction of disease occurrence for the data-disadvantaged populations. Our multi-ancestral machine learning experiments on clinico-genomic datasets of cancers and Alzheimers disease and synthetic datasets with built-in data inequality and subpopulation shift show that transfer learning can significantly improve disease prediction accuracy for data-disadvantaged populations. Under the transfer learning scheme, the prediction accuracy for the data-disadvantaged populations can be improved without compromising the prediction accuracy for other populations. Therefore, transfer learning provides a Pareto improvement toward equitable machine learning for genomic medicine.

bioinformatics↗