Biology subjects

Chu, Y.

Publications and source records attributed to Chu, Y..

67 records · Page 4Linked to original sources

bioRxiv2020

A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children

Growing evidence suggests that autism spectrum disorder (ASD) is highly associated with dysbiosis in the gut microbiome. However, results of metagenome-based microbiome studies are not always consistent due to great individual diversity that overwhelms disease-associated alterations. Here, we proposed a novel analysis strategy--quasi-paired cohort and applied it to a metagenomic study of ASD microbiomes. By comparing the paired samples of ASD and neurotypical subjects, we identified significant deficiencies in ASD children in detoxifying enzymes and pathways, which showed strong correlations to mitochondrial damage. Diagnostic models with these detoxifying enzymes accurately discriminated ASD individuals from controls, and the dysfunction score inferred from the model increased with the clinical rating scores of ASD. Conclusively, our findings suggest a previously undiscovered mechanism in which impaired microbial detoxification leads to toxicant accumulation and mitochondrion damage contributes to the pathogenesis of ASD. This novel mechanism points to future therapeutic strategies of rebuilding microbial detoxification for ASD.

microbiology↗

bioRxiv2020

Argonaute Binding within 3'-Untranslated Regions Does Not Predict Gene Repression

Despite two decades of study, the full scope of RNAi in mammalian cells has remained obscure. Here we combine: 1) Knockout of argonaute (AGO) variants; 2) RNA sequencing analysis of gene expression changes; and 3) Crosslinking Immunoprecipitation Sequencing (CLIP-seq) using anti-AGO2 antibody to identify potential microRNA (miRNA) binding sites. We find that knocking out AGO1, AGO2, and AGO3 are necessary to achieve full impact on gene expression. CLIP-seq reveals several hundred significant AGO2 associations within the 3-untranslated regions of cytoplasmic transcripts. The standard mechanism of miRNA action would suggest that these associations repress gene expression. Contrary to this expectation, clusters are poorly correlated with gene repression in wild-type versus knockout cells. Many clusters are associated with increased gene expression in wild-type versus knock out cells, including the strongest cluster within the MYC 3-UTR. Our results suggest that assumptions about miRNA action should be re-examined.

molecular biology↗

bioRxiv2020

Analyzing Presymptomatic Tissue to Gain Insights into the Molecular and Mechanistic Origins of Late-Onset Degenerative Trinucleotide Repeat Disease

How genetic defects trigger the molecular changes that cause late-onset disease is important for understanding disease progression and therapeutic development. Fuchs endothelial corneal dystrophy (FECD) is an RNA-mediated disease caused by a trinucleotide CUG expansion in an intron within the TCF4 gene. The mutant intronic CUG RNA is present at 1-2 copies per cell, posing a challenge to understand how a rare RNA can cause disease. Late-onset FECD is a uniquely advantageous model for studying how RNA triggers disease because; 1) Affected tissue is routinely removed during surgery; 2) The expanded CUG mutation is one of the most prevalent disease-causing mutations, making it possible to obtain pre-symptomatic tissue from eye bank donors to probe how gene expression changes precede disease; and 3) The affected tissue is a homogeneous single cell monolayer, facilitating accurate transcriptome analysis. Here we use RNA sequencing (RNAseq) to compare tissue from individuals who are pre-symptomatic (Pre_S) to tissue from patients with late stage FECD (FECD_REP). The abundance of mutant repeat intronic RNA in Pre_S and FECD_REP tissue is elevated due to increased half-life in a corneal cell-specific manner. In Pre_S tissue, changes in splicing and extracellular matrix gene expression foreshadow the changes observed in advanced disease and predict the activation of the fibrosis pathway and immune system seen in late-stage patients. The absolute magnitude of splicing changes is similar in presymptomatic and late stage tissue. Our data identify gene candidates for early drivers of disease and biomarkers that may represent diagnostic and therapeutic targets for FECD. We conclude that changes in alternative splicing and gene expression are observable decades prior to the diagnosis of late-onset trinucleotide repeat disease.

molecular biology↗

bioRxiv2019

An upstream open reading frame represses translation of chicken PPARγ transcript variant 1

Peroxisome proliferator-activated receptor {gamma} (PPAR{gamma}) is a master regulator of adipogenesis. The PPAR{gamma} gene produces various transcripts with different 5'-untranslated regions (5' UTRs) because of alternative promoter usage and splicing. The 5' UTR plays important roles in posttranscriptional gene regulation. However, to date, the regulatory role and underlying mechanism of 5' UTRs in the posttranscriptional regulation of PPAR{gamma} expression remain largely unclear. In this study, we investigated the effects of 5' UTRs on posttranscriptional regulation using reporter assays. Our results showed that the five PPAR{gamma} 5' UTRs exerted different effects on reporter gene activity. Bioinformatics analysis showed that chicken PPAR{gamma} transcript 1 (PPAR{gamma}1) possessed an upstream open reading frame (uORF) in its 5' UTR. Mutation analysis showed that a mutation in the uORF led to increased Renilla luciferase activity and PPAR{gamma} protein expression, but decreased Renilla luciferase and PPAR{gamma}1 mRNA expression. mRNA stability analysis using real-time RT-PCR showed that the uORF mutation did not interfere with mRNA stability, but promoter activity analysis of the cloned 5' UTR showed that the uORF mutation reduced promoter activity. Furthermore, in vitro transcription/translation assays demonstrated that the uORF mutation markedly increased the translation of PPAR{gamma}1 mRNA. Collectively, our results indicate that the uORF represses the translation of chicken PPAR{gamma}1 mRNA.

molecular biology↗

bioRxiv2019

Genetic analysis of seed and pod traits in a set of Recombinant Inbred Lines (RILs) in peanut (Arachis hypogaea L.)

Although seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explorethe distribution of recombination and potential chromosomal rearrangements. These traits were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.

plant biology↗

bioRxiv2019

It takes two vanilloid ligand bindings per channel to transduce painful capsaicin stimuli

The capsaicin receptor TRPV1 in mammals evolved the capability of integrating pain arising from combinations of noxious temperature or chemical irritants. Four-fold repetition of TRPV1 subunits makes an ion channel endowed with excellent sensitivity for pain detection, assisting this ionotropic receptor to differentiate graded injuries. We manipulated the stoichiometry and relative steric coordination of capsaicin binding at the molecular level, explicating rules with which a receptor codes pain within a broad range of intensity. The first ligand binding delivers small but clear initiation of channel activation. Maximal agonist action has already been reached in a receptor-in-tandem containing two or three wild-type receptor units, displaying activity comparable to the full liganded all-wild-type tandem tetramers. When the binding sites outnumbered ligands, independent action dominates in each channel. The non-vanilloid agonist 2-APB differs from capsaicin by adopting a distinct open mechanism since it does not demand a vanilloid group to activate. The sharing of the same pore greatly simplifies synergism to transduce relevant inputs by summation for pain signaling. And questions the need to explore deeper into other aspects of nociception.

molecular biology↗

bioRxiv2019

A single TRPV1 amino acid controls species sensitivity to capsaicin

Chili peppers produce capsaicin, the principle chemical that accounts for the culinary sensations of heat. Capsaicin activates the transient receptor potential cation channel subfamily V member 1 (TRPV1) on sensory neurons to alter the membrane potential to induce pain. While structural studies have identified residues important for the binding of capsaicin to rat TRPV1, there is still no clear understanding of differential capsaicin sensitivity of TRPV1 between birds and mammals. To determine the residues dictating relative capsaicin sensitivity among species, we have used intracellular Ca2+ imaging to characterize chimeras composed of capsaicin-sensitive rat TRPV1 (rTRPV1) and capsaicin-insensitive chicken TRPV1 (cTRPV1) with a series of capsaicinoids. We find that chimeras containing rat E570-V686 swapped into chicken receptors displays capsaicin sensitivity, and that a single amino acid substitution in the S4-S5 helix, changing the alanine at position 578 in the chick receptor to a glutamate, is sufficient to endow micromolar capsaicin sensitivity. Moreover, introduction of lysine, glutamine or proline at A578 also install capsaicin sensitivity in cTRPV1. Comparing the derivatives Cap-EA and Cap-EMA with capsaicin, these two compounds with the hydrophilic vanilloid-like moiety affect the protein-ligand interaction. The ability of 10 M Cap-EA to activate cTRPV1-A578E and the differential response of mutants to the analogs suggests that chick A578 may participate in vanilloid binding, as does the corresponding rTRPV1 site. The hydrophilic vanilloid agonist zingerone 500 M failed to activate any A578 mutants that retained capsaicin sensitivity, suggesting that the vanilloid group alone is not sufficient for receptor activation. Replacing the rTRPV1-E570 residue with K, Q shows a similar tendency to maintain the receptor capsaicin sensitivity. Our study demonstrates a subtle modification on different species TRPV1 globally alter their capsaicin response.

molecular biology↗

bioRxiv2019

The consequences of lipid remodelling of adipocyte cell membranes being functionally distinct from global lipid storage during obesity

Obesity is a complex disorder where the genome interacts with diet and environmental factors to ultimately influence body mass, composition and shape. Numerous studies have investigated how bulk lipid metabolism of adipose tissue changes with obesity, and in particular how the composition of triglycerides (TGs) changes with increased adipocyte expansion. However, reflecting the analytical challenge posed by examining non-TG lipids in extracts dominated by TGs, the glycerophospholipid (PL) composition of cell membranes has been seldom investigated. PLs contribute to a variety of cellular processes including maintaining organelle functionality, providing an optimised environment for membrane-associated proteins and as pools for metabolites (e.g. choline for one-carbon metabolism and for methylation of DNA). We have conducted a comprehensive lipidomic study of white adipose tissue in mice who become obese either through genetic modification (ob/ob), diet (high fat diet) or a combination of the two using both solid phase extraction and ion mobility to increase coverage of the lipidome. Composition changes in seven classes of lipid (free fatty acids, diglycerides, TGs, phosphatidylcholines, lyso-phosphatidylcholines, phosphatidylethanolamines, and phosphatidylserines) correlated with perturbations in one-carbon metabolism and transcriptional changes in adipose tissue. We demonstrate that changes in TGs that dominate the overall lipid composition of white adipose tissue are distinct from diet-induced alterations of PLs, the predominant components of the cell membranes. PLs correlate better with transcriptional and one-carbon metabolism changes within the cell, suggesting the compositional changes that occur in cell membranes during adipocyte expansion have far-reaching functional consequences.

systems biology↗

bioRxiv2019

DTI-CDF: a CDF model towards the prediction of DTIs based on hybrid features

Drug-target interactions play a crucial role in target-based drug discovery and exploitation. Computational prediction of DTIs has become a popular alternative strategy to the experimental methods for identification of DTIs of which are both time and resource consuming. However, the performances of the current DTIs prediction approaches suffer from a problem of low precision and high false positive rate. In this study, we aimed to develop a novel DTIs prediction method, named DTI-CDF, for improving the prediction precision based on a cascade deep forest model which integrates hybrid features, including multiple similarity-based features extracted from the heterogeneous graph, fingerprints of drugs, and evolution information of target protein sequences. In the experiments, we built five replicates of 10 fold cross-validations under three different experimental settings of data sets, namely, corresponding DTIs values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training set are missed, but existed in the test set. The experimental results show that our proposed approach DTI-CDF achieved significantly higher performance than the state-of-the-art methods.

bioinformatics↗

bioRxiv2019

Linking genotypes with multiple phenotypes in single-cell CRISPR screens

CRISPR/Cas9 based functional screening coupled with single-cell RNA-seq (\"single-cell CRISPR screening\") unravels gene regulatory networks and enhancer-gene regulations in a large scale. We propose scMAGeCK, a computational framework to systematically identify genes and non-coding elements associated with multiple expression-based phenotypes in single-cell CRISPR screening. scMAGeCK identified genes and enhancers that modulate the expression of a known proliferation marker, MKI67 (Ki-67), a result that resembles the outcome of proliferation-linked CRISPR screening. We further performed single-cell CRISPR screening on mouse embryonic stem cells (mESC), and identified key genes associated with different pluripotency states. scMAGeCK enabled an unbiased construction of genotype-phenotype network, where multiple phenotypes can be regulated by different gene perturbations. Finally, we studied key factors that improve the statistical power of single-cell CRISPR screens, including target gene expression and the number of guide RNAs (gRNAs) per cell. Collectively, scMAGeCK is a novel and effective computational tool to study genotype-phenotype relationships at a single-cell level.

bioinformatics↗

bioRxiv2019

Major QTLs for resistance to early and late leaf spot diseases are identified on chromosomes 3 and 5 in peanut (Arachis hypogaea)

Early and late leaf spots are the major foliar diseases of peanut responsible for severely decreased yield in the absence of intensive fungicide spray programs. Pyramiding host resistance to leaf spots in elite cultivars is a sustainable solution to mitigate the diseases. In order to determine the genetic control of leaf spot disease resistance in peanut, a recombinant inbred line population (Florida-07 x GP-NC WS16) segregating for resistance to both diseases was used to construct a SNP-based linkage map consisting of 855 loci. QTL mapping revealed three resistance QTLs for late leaf spot qLLSA05 (phenotypic variation explained, PVE=7-10%), qLLSB03 (PVE=5-7%), and qLLSB05 (PVE=15-41%) that were consistently expressed over multi-year analysis. Two QTL, qLLSA05 and qLLSB05, confirmed our previously published QTL-seq results. For early leaf spot, three resistance QTLs were identified in multiple years, two on chromosome A03 (PVE=8-12%) and one on chromosome B03 (PVE=13-20%), with the locus qELSA03_1.1 coinciding with the previously published genomic region for LLS resistance in GPBD4. Comparative analysis of the genomic regions spanning the QTLs suggests that resistance to early and late leaf spots are largely genetically independent. In addition, QTL analysis on yield showed that the presence of resistance allele in qLLSB03 and qLLSB05 loci might result in protection from yield loss caused by LLS disease damage. Finally, post hoc analysis of the RIL subpopulation that was not utilized in the QTL mapping revealed that the flanking markers for these QTLs can successfully select for resistant and susceptible lines, confirming the effectiveness of pyramiding these resistance loci to improve host-plant resistance in peanut breeding programs using marker-assisted selection.

genetics↗

bioRxiv2018

Machine learning as an effective method for identifying true SNPs in polyploid plants

Single Nucleotide Polymorphisms (SNPs) have many advantages as molecular markers since they are ubiquitous and co-dominant. However, the discovery of true SNPs especially in polyploid species is difficult. Peanut is an allopolyploid, which has a very low rate of true SNP calling. A large set of true and false SNPs identified from the Arachis 58k Affymetrix array was leveraged to train machine learning models to select true SNPs straight from sequence data. These models achieved accuracy rates of above 80% using real peanut RNA-seq and whole genome shotgun (WGS) re-sequencing data, which is higher than previously reported for polyploids. A 48K SNP array, Axiom Arachis2, was designed using the approach which revealed 75% accuracy of calling SNPs from different tetraploid peanut genotypes. Using the method to simulate SNP variation in peanut, cotton, wheat, and strawberry, we show that models built with our parameter sets achieve above 98% accuracy in selecting true SNPs. Additionally, models built with simulated genotypes were able to select true SNPs at above 80% accuracy using real peanut data, demonstrating that our model can be used even if real data are not available to train the models. This work demonstrates an effective approach for calling highly reliable SNPs from polyploids using machine learning. A novel tool was developed for predicting true SNPs from sequence data, designated as SNP-ML (SNP-Machine Learning, pronounced \"snip mill\"), using the described models. SNP-ML additionally provides functionality to train new models not included in this study for customized use, designated SNP-MLer (SNP-Machine Learner, pronounced \"snip miller\"). SNP-ML is freely available for public use.

bioinformatics↗

bioRxiv2017

Insight Into The Mechanism Of Protein Thermostability Based On The Residue Interaction Degrees

Understanding the basis of protein thermostability raises a general question: which residue with specific interaction degrees is more important to the protein thermostability? A strictly selected dataset of 131 pairs of thermophilic (TPs) and mesophilic proteins (MPs) was constructed. There were 6.4% and 8.4% of the total residues in sequences did not interact with others in TPs and MPs. The amino acid contents in sequences are closest to those with the interaction degrees of 3 according to the Chi-squared distances. Only Glu, Gln and the amide residues showed significant differences in sequences, which was the same as identified at low residue interaction degrees. However, we observed significant Phe, Lys, Leu, Gln and the charged, aliphatic, aromatic, positive charged and small residues at high interaction degree. Among them, Phe was rarely reported previously although aromatic residues were well-known contributor to protein thermostability. Finally, we took aspartate transcarbamylases as an example to explain how a residue with various interaction degrees contributed differently to their thermostability. Our results clearly demonstrated the differences of amino acids in sequence between TPs and MPs could only represent those involved in low interaction degrees. Much more residues with significant differences existed at high interaction degrees even if they had few significant amino acids in sequences. The interaction degree-based method should be an alternative tool in extracting valuable eigenvalues for predicting proteins attributes in bioinformatics. It could also provide a new perspective for studying the thermostability of proteins and engineering novel thermostable proteins.\n\nList of abbreviations

bioinformatics↗