Biology subjects

Yang, J.

Publications and source records attributed to Yang, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2018

UDP-glucose:anthocyanidin 3-O-glucoside-2”-O-glucosyltransferase catalyzes further glycosylation of anthocyanins in purple Ipomoea batatas

Glycosylation contributes to the diversity and stability of anthocyanins in plants. The process is catalyzed by various glucosyltransferases using different anthocyanidin aglycones and glycosyl donors. An anthocyanidin 3-O-glucoside-2\"-O-glucosyltransferase (3GGT) from purple sweetpotato (cv. Ayamurasaki) served for the catalytic conversion of anthocyanidin 3-O-glucoside into anthocyanidin 3-O-sophoroside, which is functionally different from the 3GGT ortholog of Arabidopsis. The phylogenetic analysis indicates regioselectivity of 3GGT using UDP-xylose or UDP-glucose as the glycosyl is divergent between Convolvulaceae and Arabidopsis. Homology-based protein modeling and site-directed mutagenesis of Ib3GGT and At3GGT suggested that the Thr-138 of Ib3GGT is a key amino acid residue for UDP-glucose recognition and plays a major role in sugar donor selectivity. The wild type and ugt79b1 mutants of Arabidopsis plants overexpressing Ib3GGT produced the new component cyanidin 3-O-sophoroside. Moreover, Ib3GGT expression was associated with anthocyanin accumulation in different tissues during Ayamurasaki plant development and was regulated by the transcription factor IbMYB1. The localization assay of Ib3GGT showed that further glycosylation occurs in the cytosol and not endoplasmic reticulum. The present study revealed the function of Ib3GGT in further glycosylation of anthocyanins and its Thr-138 is the key amino acid residue for UDP-glucose recognition.

plant biology↗

bioRxiv2018

Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing

DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvaths across-tissue age predictor.

bioinformatics↗

bioRxiv2018

A novel approach of human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseases

A key goal of geroscience research is to discover effective interventions to extend human healthspan, the years of healthy life. Currently, majority of the geroprotectors are found by testing compounds in model organisms; whether these compounds will be effective in humans is largely unknown. Here we present a novel strategy called ANDRU (aging network based drug discovery) to help the discovery of human geroprotectors. Instead of relying on model organisms, this approach is driven by human genomic and pharmacogenomic data. It first identifies human aging subnetworks that putatively function at the interface between aging and age-related diseases; it then screens for pharmacological or genetic interventions that may \"reverse\" the age-associated transcriptional changes seen in these subnetworks. We applied ANDRU to human adipose and artery tissues. In adipose tissue, PTPN1, a target for diabetes treatment and APOE, a known genetic factor for human longevity and diseases like Alzheimers disease, were ranked at the top. For small molecules, conjugated linoleic acid and metformin, a drug commonly used to treat type 2 diabetes, were ranked among the top compounds. In artery tissue, N-methyl-D-aspartate antagonists and curcumin were ranked at the top. In summary, ANDRU represents a promising human data-driven strategy that may speed up the discovery of interventions to extend human healthspan.

systems biology↗

bioRxiv2018

Granzyme B is an essential mediator in CD8+ T cell killing of Theileria parva-infected cells

There is established evidence that cytotoxic CD8+ T cells are important mediators of immunity against the bovine intracellular protozoan parasite T. parva. However, the mechanism by which the specific CD8+ T cells kill parasitized cells is not understood. Although the predominant pathway used by human and murine CD8+ T cells to kill pathogen-infected cells is granule exocytosis, involving release of perforin and granzyme B, there is to date a lack of published information on the biological activities of bovine granzyme B. The present study set out to define the functional activities of bovine granzyme B and determine its role in mediating killing of T. parva-parasitized cells. DNA constructs encoding functional and non-functional forms of bovine granzyme B were produced and the proteins expressed in Cos-7 cells were used to establish an enzymatic assay to detect and quantify expression of functional granzyme B protein. Using this assay, the levels of killing of different T. parva-specific CD8+ T cell clones were found to be significantly correlated with levels of granzyme B protein, but not mRNA transcript, expression. Experiments using inhibitors specific for perforin and granzyme B confirmed that CD8+ T cell killing of parasitized cells is dependent on granule exocytosis and specifically granzyme B. Further studies showed that granzyme B-mediated death of parasitized cells is independent of caspases, but involves activation of the pro-apoptotic molecule Bid.

immunology↗

bioRxiv2018

Reduced but not Enhanced Default Mode Network Functional Connectivity in Major Depressive Disorder: Evidence from 25 Cohorts in the REST-meta-MDD Project

Major Depressive Disorder (MDD) is common and disabling, but its neural pathophysiology remains unclear. Functional brain network studies in MDD have largely had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. The 25 research groups in China composing the REST-meta-MDD Project contributed R-fMRI data of 1,300 patients with MDD and 1,128 normal controls (NCs). The data were preprocessed locally with a standardized protocol prior to aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to show increased FC in MDD. We found decreased instead of increased DMN FC when comparing 848 MDDs with 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naive MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDDs. Exploratory analyses also revealed alterations of local intrinsic activity in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates treatment response. This manuscript announces the publicly available resting-state fMRI indices of the REST-meta-MDD consortium shared via the R-fMRI Maps Project.\n\nSIGNIFICANCE STATEMENTFunctional connectivity within the default mode network in major depressive disorder patients has been frequently reported abnormal but with contradicting directions in previous small sample size studies. By creating the REST-meta-MDD consortium containing neuroimaging data of 1,300 depressed patients and 1,128 normal controls from 25 research groups in China, we found decreased default mode network functional connectivity in depressed patients, driven by patients with recurrent depression, and associated with current medication treatment but not with disease duration. These findings suggest that default mode network functional connectivity remains a prime target for understanding the pathophysiology of depression, with particular relevance to revealing mechanisms of effective treatments.

neuroscience↗

bioRxiv2018

Genetic Dissection of Haploid Male Fertility in Maize (Zea mays L.)

Haploid genome doubling is a key limiting step of haploid breeding in maize. Spontaneous restoration of haploid male fertility (HMF) provides a method by which costs can be saved and which does not require the use of toxic chemicals, in contrast to the artificial doubling process. To reveal the genetic basis of HMF, haploids were obtained from the offspring of 285 F2:3 families, derived from the cross Zheng58x K22. The F2:3 families were used as female donor and YHI-1 as the male inducer line. The rates of HMF from each family line were evaluated at two field sites over two planting seasons. Quantitative trait loci (QTL) for HMF were identified using a genetic linkage map containing 157 simple sequence repeat (SSR) markers. QTL for HMF displayed incomplete dominance. Transgressive segregation of haploids from F2:3 families was observed relative to haploids derived from the two parents of the mapping population. A total of nine QTL were detected, which were distributed on chromosomes 1, 3, 4, 7, and 8. Three QTL, qHMF3b, qHMF7a, and qHMF7b were detected in both locations, respectively. In our mapping population, HMF was controlled by three major QTL. These QTL could be useful to predict the ability of spontaneous haploid genome doubling in related breeding materials, and to accelerate the haploid breeding process by introgression or aggregation of those QTL.

genetics↗

bioRxiv2018

Evaluation of the GenoType MTBDRplus and MTBDRsl for the detection of drug-resistant Mycobacterium tuberculosis on isolates from Beijing,China

The incidence of tuberculosis (TB) and especially multidrug-resistant TB (MDR) and extreme drug resistance (XDR-TB) continue to increase alarmingly worldwide and reliable and fast diagnosis of MDR-TB and XDR-TB is essential for the adequate treatment of patients. So molecular line probe assays (LPAs) for detection of MDR-TB and XDR-TB have been endorsed by the World Health Organization (WHO). We analyzed 96 isolates from Beijing comparing culture-based drug susceptibility testing (DST) to LPAs detecting rifampicin (RFP), isoniazid (INH), and levofloxacin (LFX), amikacin (AM), capreomycin (CMP), ethambutol (EMB) resistance. Compared to phenotypic DST, the GenoType MTBDRplus and MTBDRsl showed a sensitivity of 98.7% and a specificity of 88.9% for detection of RFP resistance, 82.1% and 94.4% for INH, 89.7% and 94.4% for LFX, 60.0% and 98.7% for AM/CPM, 57.5% and 98.2% for EMB, respectively. The sensitivity and specificity of LPAs for MDR-TB and XDR-TB were 80.8%, 100% and 50.0%, 97.6%. Mutations in codon S531L of the rpoB gene and S315T1 of KatG gene were dominated in MDR-TB strains. The most frequently observed mutations were in codon A90V of the gyrA gene, A1401G of the rrs gene and M306V of the embB gene, according to the MTBDRsl results. Our study showed that, in combination to phenotypic DST, application of the LPAs might be an efficient and reliable supplementary DST assay for rapid susceptibility screening of MDR-TB and XDR-TB. Using LPAs in high MDR/XDR burden countries allows for appropriate and timely treatment, which will reduce transmission rates, morbidity and improve treatment outcomes in patients.

microbiology↗

bioRxiv2018

Optimizing the identification of causal variants across varying genetic architectures in crops

BackgroundAssociation studies use statistical links between genetic markers and variation in a phenotypes value across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome-wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures.\n\nResultsHere we employ simulation studies utilizing real-world genotype datasets from association populations in four species with distinct minor allele frequency distributions, population structures, and patterns linkage disequilibrium to evaluate the impact of variation in both heritability and trait complexity on both conventional mixed linear model based GWAS and two new approaches specifically developed for complex traits. Mixed linear model based GWAS rapidly losses power for more complex traits. FarmCPU, a method based on multi-locus mixed linear models, provides the greatest statistical power for moderately complex traits. A Bayesian approach adopted from genomic prediction provides the greatest statistical power to identify causal genetic loci for extremely complex traits.\n\nConclusionsUsing estimates of the complexity of the genetic architecture of target traits can guide the selection of appropriate statistical methods and improve the overall accuracy and power of GWAS.

genomics↗

bioRxiv2018

Expectation of the intercept from bivariate LD score regression in the presence of population stratification

Linkage disequilibrium (LD) score regression is an increasingly popular method used to quantify the level of confounding in genome-wide association studies (GWAS) or to estimate heritability and genetic correlation between traits. When applied to a pair of GWAS, the LD score regression (LDSC) methodology produces a statistic, referred to as the bivariate LDSC intercept, which deviation from 0 is classically interpreted as an indication of sample overlap between the two GWAS. Here we propose an extension of the theory underlying the bivariate LDSC methodology, which accounts for population stratification within and between GWAS. Our extended theory predicts an inflation of the bivariate LDSC intercept when sample sizes and heritability are large, even in the absence of sample overlap. We illustrate our theoretical results with simulations based on actual SNP genotypes and we propose a re-interpretation of previously published results in the light of our extended theory.

genetics↗

bioRxiv2018

Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry

One in four adults worldwide are either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, is most informative for predicting risk of obesity sequellae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio adjusted for BMI (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

genetics↗

bioRxiv2018

Imprint of Assortative Mating on the Human Genome

Non-random mate-choice with respect to complex traits is widely observed in humans, but whether this reflects true phenotypic assortment, environment (social homogamy) or convergence after choosing a partner is not known. Understanding the causes of mate choice is important, because assortative mating (AM) if based upon heritable traits, has genetic and evolutionary consequences. AM is predicted under Fishers classical theory1 to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify AM on a specific trait by estimating the correlation ({theta}) between genetic predictors of the trait from SNPs on odd versus even chromosomes. We show by theory and simulation that the effect of AM can be distinguished from population stratification. We applied this approach to 32 complex traits and diseases using SNP data from [~]400,000 unrelated individuals of European ancestry. We found significant evidence of AM for height ({theta}=3.2%) and educational attainment ({theta}=2.7%), both consistent with theoretical predictions. Overall, our results imply that AM involves multiple traits, affects the genomic architecture of loci that are associated with these traits and that the consequence of mate choice can be detected from a random sample of genomes.

genetics↗

bioRxiv2018

Recombinant expression of Proteorhodopsin and biofilm regulators in Escherichia coli for nanoparticle binding and removal in a wastewater treatment model

The small size of nanoparticles is both an advantage and a problem. Their high surface-area-to-volume ratio enables novel medical, industrial, and commercial applications. However, their small size also allows them to evade conventional filtration during water treatment, posing health risks to humans, plants, and aquatic life. This project aims to remove nanoparticles during wastewater treatment using genetically modified Escherichia coli in two ways: 1) binding citrate-capped nanoparticles with the membrane protein Proteorhodopsin, and 2) trapping nanoparticles using Escherichia coli biofilm produced by overexpressing two regulators: OmpR234 and CsgD. We demonstrate experimentally that Escherichia coli expressing Proteorhodopsin binds to 60 nm citrate-capped silver nanoparticles. We also successfully upregulate biofilm production and show that Escherichia coli biofilms are able to trap 30 nm gold particles. Finally, both Proteorhodopsin and biofilm approaches are able to bind and remove nanoparticles in simulated wastewater treatment tanks. We envision integrating our trapping system in both rural and urban wastewater treatment plants to efficiently capture all nanoparticles before treated water is released into the environment.\n\nFinancial DisclosureThis work was funded by the Taipei American School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\n\nCompeting InterestsThe authors have declared that no competing interests exist.\n\nEthics StatementN/A\n\nData AvailabilityYes - all data are fully available without restriction. Sequences for the plasmids used in this study are available through the Registry of Standard Biological Parts. Links to raw data are included in Supplementary Information.

synthetic biology↗

bioRxiv2018

Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes

We conducted a meta-analysis of genome-wide association studies (GWAS) with [~]16 million genotyped/imputed genetic variants in 62,892 type 2 diabetes (T2D) cases and 596,424 controls of European ancestry. We identified 139 common and 4 rare (minor allele frequency < 0.01) variants associated with T2D, 42 of which (39 common and 3 rare variants) were independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2,765) and other T2D-relevant tissues (n = up to 385) with the GWAS results identified 33 putative functional genes for T2D, three of which were targeted by approved drugs. A further integration of DNA methylation (n = 1,980) and epigenomic annotations data highlighted three putative T2D genes (CAMK1D, TP53INP1 and ATP5G1) with plausible regulatory mechanisms whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. We further found evidence that the T2D-associated loci have been under purifying selection.

genetics↗

bioRxiv2018

A novel role for the HLH protein Inhibitor of Differentiation 4 (ID4) in the DNA damage response in basal-like breast cancer.

Basal-like breast cancer (BLBC) is a poorly characterised, heterogeneous disease. Patients are diagnosed with aggressive, high-grade tumours and often relapse with chemotherapy resistance. Detailed understanding of the molecular underpinnings of this disease is essential to the development of personalised therapeutic strategies. Inhibitor of Differentiation 4 (ID4) is a helix-loop-helix transcriptional regulator required for mammary gland development. ID4 is overexpressed in a subset of BLBC patients, associating with a stem-like poor prognosis phenotype, and is necessary for the growth of cell line models of BLBC, through unknown mechanisms. Here, we have defined a molecular mechanism of action for ID4 in BLBC and the related disease highgrade serous ovarian cancer (HGSOV), by combining RIME proteomic analysis and ChIP-Seq mapping of genomic binding sites. Remarkably, these studies have revealed novel interactions with DNA damage response proteins, in particular, mediator of DNA damage checkpoint protein 1 (MDC1). Through MDC1, ID4 interacts with other DNA repair proteins ({gamma}H2AX and BRCA1) at fragile chromatin sites. ID4 does not affect transcription at these sites, instead binding to chromatin following DNA damage and regulating DNA damage signalling. Clinical analysis demonstrates that ID4 is amplified and overexpressed at a higher frequency in BRCA1-mutant BLBC compared with sporadic BLBC, providing genetic evidence for an interaction between ID4 and DNA damage repair pathways. These data link the interactions of ID4 with MDC1 to DNA damage repair in the aetiology of BLBC and HGSOV.

cancer biology↗

bioRxiv2018

The expression tractability of a biological trait

Understanding how gene expression is translated to phenotype is central to modern molecular biology, but the success is contingent on the intrinsic tractability of the specific traits under examination. However, an a priori estimate of trait tractability from the perspective of gene expression is unavailable. Motivated by the concept of entropy in a thermodynamic system, we here propose such an estimate (ST) by gauging the number (N) of different expression states that underlie the same trait abnormality, with large ST corresponding to large N. By analyzing over 200 yeast morphological traits we show that ST is constrained by natural selection, which builds co-regulated gene modules to minimize the total number of possible expression states. We further show that ST is a good measure of the titer of recurrent patterns of an expression-trait relationship, predicting the extent to which the trait could be deterministically understood with gene expression data.

genomics↗

bioRxiv2018

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes associated with brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top associated cis-expression (cis-eQTLs or cis-mQTLs) between brain and blood for genes expressed (or CpG sites methylated) in both tissues, while accounting for errors in their estimated effects (rb). Using publicly available data (n = 72 to l,366), we find that the genetic effects of cis-eQTLs (PeQTL < 5x10-8) or mQTLs (PmQTL < 1x10-10) are highly correlated between independent brain and blood samples ([Formula] with SE = 0.015 for cis-eQTL and [Formula] with SE = 0.006 for cis-mQTLs). Using meta-analyzed brain eQTL/mQTL data (n = 526 to 1,194), we identify 61 genes and 167 DNA methylation (DNAm) sites associated with 4 brain-related traits and disorders. Most of these associations are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = l,980 to 14,115). We further find that cis-eQTLs with tissue-specific effects are approximately uniformly distributed across all the functional annotation categories, and that mean difference in gene expression level between brain and blood is almost independent of the difference in the corresponding cis-eQTL effect. Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL or cis-mQTL data with large sample sizes.

genetics↗

bioRxiv2018

Meta-analysis of genome-wide association studies for height and body mass index in ~700,000 individuals of European ancestry

Genome-wide association studies (GWAS) stand as powerful experimental designs for identifying DNA variants associated with complex traits and diseases. In the past decade, both the number of such studies and their sample sizes have increased dramatically. Recent GWAS of height and body mass index (BMI) in [~]250,000 European participants have led to the discovery of [~]700 and [~]100 nearly independent SNPs associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in [~]450,000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N[~]700,000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3,290 and 716 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of p<1 x 10-8), including 1,185 height-associated SNPs and 554 BMI-associated SNPs located within loci not previously identified by these two GWAS. The genome-wide significant SNPs explain [~]24.6% of the variance of height and [~]5% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were 0.44 and 0.20, respectively. From analyses of integrating GWAS and eQTL data by Summary-data based Mendelian Randomization (SMR), we identified an enrichment of eQTLs amongst lead height and BMI signals, prioritisting 684 and 134 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow up studies.

genetics↗

bioRxiv2018

MicroRNAs as potential therapeutics to enhance chemosensitivity in advancedprostate cancer

Docetaxel and cabazitaxel are taxane chemotherapy treatments for metastatic castration-resistant prostate cancer (CRPC). However, therapeutic resistance remains a major issue. MicroRNAs are short non-coding RNAs that can silence multiple genes, regulating several signalling pathways simultaneously. Therefore, synthetic microRNAs may have therapeutic potential in CRPC by regulating genes involved in taxane response and minimise compensatory mechanisms that cause taxane resistance. To identify microRNAs that can improve the efficacy of taxanes in CRPC, we performed a genome-wide screen of 1280 microRNAs in the CRPC cell lines PC3 and DU145 in combination with docetaxel or cabazitaxel treatment. Mimics of miR-217 and miR-181b-5p enhanced apoptosis significantly in PC3 cells in the presence of these taxanes. These mimics downregulated at least a thousand different transcripts, which were enriched for genes with cell proliferation and focal adhesion functions. Individual knockdown of a selection of 46 genes representing these transcripts resulted in toxic or taxane sensitisation effects, indicating that these genes may be mediating the effects of the microRNA mimics. A range of these genes are expressed in CRPC metastases, suggesting that these microRNA mimics may be functional in CRPC. With further development, these microRNA mimics may have therapeutic potential to improve taxane response in CRPC patients.

cancer biology↗