Biology subjects

Miao, J.

Publications and source records attributed to Miao, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Controlling for polygenic genetic confounding in epidemiologic association studies

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically-unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

genetics↗

bioRxiv2023

Xanthomonas euvesicatoria effector XeAvrRxo1 triggers a Rxo1-mediated defense response in Nicotiana benthamiana and its chaperone Xe4429 functions as an antitoxin, an expression repressor, and an enhancer of XeAvrRxo1 secretion

Xanthomonas euvesicatoria (X. euvesicatoria) is the causal agent of bacterial spot disease that threatens pepper and tomato production around the globe. X. euvesicatoria gene Xe4428 encodes a type III effector (T3E) that shares 89.67% amino acid identity with Xanthomonas oryzae pv. oryzicola (Xoc) T3E AvrRxo1. Deletion of Xe4428 in the genome of X. euvesicatoria (strain Xcv85-10) compromised its virulence to infect pepper and Nicotiana benthamiana plants. Transient co-expression of Xe4428 and Rxo1 on pepper and N. benthamiana plant leaves results in a robust hypersensitive reaction. Thus, Xe4428, renamed as XeAvrRxo1, is a bona fide orthologue of XocAvrRxo1 that possesses both virulence and avirulence functions. Expression of XeAvrRxo1 in E. coli and X. euvesicatoria is toxic to both bacterial cells. Another X. euvesicatoria gene Xe4429, encodes a putative chaperone of XeAvrRxo1, which can interact with XeAvrRxo1 to suppress its toxicity in X. euvesicatoria and E. coli bacterial cells. Xe4429 also binds to the promoter region of XeavrRxo1 and represses its transcription/translation in X. euvesicatoria bacterial cells. In addition, expression of Xe4429 can enhance the secretion and translocation of XeAvrRxo1 into plant cells. Therefore, Xe4429 functions as an antitoxin, a transcription repressor, and a type III chaperone that is capable of enhancing the secretion and translocation of XeAvrRxo1 during pathogenesis.

pathology↗

bioRxiv2023

Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease

Almost every recent Alzheimers disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and non-random participation of parental illness survey, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that current AD GWAX provide highly misleading genetic correlations between AD risk and higher education which subsequently affects a variety of genetic epidemiologic applications involving AD and cognition. Our study sheds important light on the design and analysis of mid-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

genetics↗

bioRxiv2023

Vacuolar H+-ATPase subunit a was identified as the target protein of the oomycete inhibitor fluopicolide

Approximately 240 fungicides are currently in use. However, only a few direct targets have been identified, which limits the development of fungicides and rapid resistance monitoring. Fluopicolide, which is an excellent oomycete inhibitor, is classified as delocalization of spectrin-like proteins inhibitors by FRAC. In the current study, a Pc-actinin knockout had no effect on the sensitivity of Phytophthora capsici to fluopicolide. The vacuolar H+-ATPase subunit a (PcVHA-a) was identified using a BSA-seq and DARTS assay. Four kinds of point mutations in PcVHA-a that cause fluopicolide resistance in P. capsici were confirmed using site-directed mutagenesis. The results of MST, molecular docking, and a DARTS assay indicated that PcVHA-a could bind fluopicolide. Sequence analysis and a molecular docking assay proved the specificity of fluopicolide to oomycetes or fish. Our results suggest that PcVHA-a is the target of fluopicolide, and H+-ATPase could be used as a novel target for the development of new fungicides.

pharmacology and toxicology↗

bioRxiv2023

Plasmodium falciparum GCN5 plays a key role in regulating artemisinin resistance-related stress responses

Plasmodium falciparum causes the most severe malaria and is exposed to various environmental and physiological stresses in the human host. Given that GCN5 plays a critical role in regulating stress responses in model organisms, we aimed to elucidate PfGCN5s function in stress responses in P. falciparum. The protein level of PfGCN5 was substantially induced under three stress conditions (heat shock, low glucose starvation, and dihydroartemisinin, the active metabolite of artemisinin (ART)). With a TetR-DOZI conditional knockdown (KD) system, we successfully down-regulated PfGCN5 to [~]50% and found that KD parasites became more sensitive to all three stress conditions. Transcriptomic analysis via RNA-seq identified [~]1,000 up-and down-regulated genes in the wildtype (WT) and KD parasites under these stress conditions. Importantly, DHA induced transcriptional alteration of many genes involved in many aspects of stress responses, which were heavily shared among the altered genes under heat shock and low glucose conditions, including ART-resistance-related genes such as K13 and coronin. Based on the expression pattern between WT and KD parasites under three stress conditions, [~]300-400 genes were identified to be involved in PfGCN5-dependent, general and stress-condition-specific responses with high levels of overlaps among three stress conditions. Notably, using ring-stage survival assay (RSA), we found that KD or inhibition of PfGCN5 could sensitize the ART-resistant parasites to the DHA treatment. All these indicate that PfGCN5 is pivotal in regulating general and ART-resistance-related stress responses in malaria parasites, implicating PfGCN5 as a potential target for malaria intervention. IMPORTANCEMalaria leads to about half a million deaths annually and these casualties were majorly caused by the infection of Plasmodium falciparum. This parasite strives to survive by defending against a variety of stress conditions, such as malaria cyclical fever (heat shock), starvation due to low blood sugar (glucose) levels (hypoglycemia), and drug treatment. Previous studies have revealed that P. falciparum has developed unique stress responses to different stresses including ART treatment, and ART-resistant parasites harbor elevated stress responses. In this study, we provide critical evidence on the role of PfGCN5, a histone modifier, and a chromatin coactivator, in regulating general and stress-specific responses in malaria parasites, indicating that PfGCN5 can be used as a potential target for anti-malaria intervention.

microbiology↗

bioRxiv2022

Reimagining Gene-Environment Interaction Analysis for Human Complex Traits

In this study, we introduce PIGEON--a novel statistical framework for quantifying and estimating polygenic gene-environment interaction (GxE) using a variance component analytical approach. Based on PIGEON, we outline the main objectives in GxE studies, demonstrate the flaws in existing GxE approaches, and introduce an innovative estimation procedure which only requires summary statistics as input. We demonstrate the statistical superiority of PIGEON through extensive theoretical and empirical analyses and showcase its performance in multiple analytic settings, including a quasi-experimental GxE study of health outcomes, gene-by-sex interaction for 530 traits, and gene-by-treatment interaction in a randomized clinical trial. Our results show that PIGEON provides an innovative solution to many long-standing challenges in GxE inference and may fundamentally reshape analytical strategies in future GxE studies.

genetics↗

bioRxiv2022

Single-cell Transcriptomic Analysis of Systemic Autoinflammatory Diseases with Anti-TNFα Therapy

Systemic autoinflammatory diseases (SAIDs) are a group of rare diseases characterized by recurrent or continuous inflammation, typically accompanied by genetic variants. Good responses to anti-TNF therapy were observed in SAIDs patients. However, the mechanisms underlying the disease flare and the response to TNF blocking therapy have not been fully elucidated. Here, we use single-cell RNA sequencing technology to describe the transcriptomic profile of PBMCs and PMNs in SAIDs patients both before and after anti-TNF treatment. We found that interferon responses are involved in the active phase of the disease. After anti-TNF therapy, we observed remission from clinical symptoms while unexpected elevation of TNF and IL-1, indicating that these inflammatory cytokines are not positively correlated with disease activity. Trajectory analysis showed that inhibition of macrophage differentiation, rather than reduction of the inflammatory cytokines, as the potential mechanism of anti-TNF treatment response in SAIDs.

immunology↗

bioRxiv2022

Deep Learning for Predicting 16S rRNA Copy Number

BackgroundCulture-independent 16S rRNA gene metabarcoding is a commonly used method in microbiome profiling. However, this approach can only reflect the proportion of sequencing reads, rather than the actual cell fraction. To achieve more quantitative cell fraction estimates, we need to resolve the 16S gene copy numbers (GCN) for different community members. Currently, there are several bioinformatic tools available to estimate 16S GCN, either based on taxonomy assignment or phylogeny. MethodHere we develop a novel algorithm, Stacked Ensemble Model (SEM), that estimates 16S GCN directly from the 16S rRNA gene sequence strings, without resolving taxonomy or phylogeny. For accessibility, we developed a public, end-to-end, web-based tool based on the SEM model, named Artificial Neural Network Approximator for 16S rRNA Gene Copy Number (ANNA16). ResultsBased on 27,579 16S rRNA gene sequence data (rrnDB database), we show that ANNA16 outperforms the most commonly used 16S GCN prediction algorithms. The prediction error range in the 5-fold cross validation of SEM is completely lower than all other algorithms for the 16S full-length sequence and partially lower at 16S subregions. The final test and a mock community test indicate ANNA16 is more accurate than all currently available tools (i.e., rrnDB, CopyRighter, PICRUSt2, & PAPRICA). SHAP value analysis indicates ANNA16 mainly learns information from rare insertions. ConclusionANNA16 represents a deep learning based 16S GCN prediction tool. Compared to the traditional GCN prediction tools, ANNA16 has a simple structure, faster inference speed without precomputing, and higher accuracy. With increased 16S GCN data in the database, future studies could improve the prediction errors for rare, high-GCN taxa due to current under sampling.

bioinformatics↗

bioRxiv2022

Optimizing and benchmarking polygenic risk scores with GWAS summary statistics

BackgroundPolygenic risk score (PRS) is a major research topic in human genetics. However, a significant gap exists between PRS methodology and applications in practice due to often unavailable individual-level data for various PRS tasks including model fine-tuning, benchmarking, and ensemble learning. ResultsWe introduce an innovative statistical framework to optimize and benchmark PRS models using summary statistics of genome-wide association studies. This framework builds upon our previous work and can fine-tune virtually all existing PRS models while accounting for linkage disequilibrium. In addition, we provide an ensemble learning strategy named PUMAS-ensemble to combine multiple PRS models into an ensemble score without requiring external data for model fitting. Through extensive simulations and analysis of many complex traits in the UK Biobank, we demonstrate that this approach closely approximates gold-standard analytical strategies based on external validation, and substantially outperforms state-of-the-art PRS methods. ConclusionsOur method is a powerful and general modeling technique that can continue to combine the best-performing PRS methods out there through ensemble learning and could become an integral component for all future PRS applications.

genetics↗

bioRxiv2022

MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites

Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSED, for predicting chromatin profiles in malaria parasites. The MalariaSED performance was validated by published ChIP-qPCR and TF motifs results. Applying MalariaSED to [~]1.3 million variants shows that geographically differentiated noncoding variants are associated with parasite invasion and drug resistance. Further analysis reveals chromatin accessibility changes at Plasmodium falciparum rings are partly associated with artemisinin resistance. MalariaSED illuminates the potential functional roles of noncoding variants in malaria parasites.

bioinformatics↗

bioRxiv2022

Genetically-Encoded Discovery of Perfluoroaryl-Macrocycles that Bind to Albumin and Exhibit Extended Circulation in-vivo

In this paper, we report selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine SNAr chemistry. Modification of phage-displayed libraries SXCXnC-phage, n=3-5, where X is any amino acid except for cysteine by decafluoro-diphenylsulfone (DFS), yields genetically-encoded library of octafluoro-diphen-ylsulfone-crosslinked macrocycles (OFS-SXCXnC-phage). Selection from these libraries using albumin as a bait identified a family of significantly enriched perfluoroaryl-macrocycles. Synthesis of perfluoroaryl-macrocycles predicted by phage display and testing their binding properties by 19F NMR and fluorescent polarization identified OFS-macrocycle with SICRFFC sequence as the most potent albumin binder. We observed that OFS-macrocycles slowly react with biological nucleophiles such as glutathione. Replacing decafluoro-diphenylsulfone by nearly isosteric pentafluorophenyl sulfide yielded perfluorophenylsulfide (PFS)-crosslinked macrocycles devoid of undesired reactivity. The augmented lead PFS-macrocycle with SICRFFC sequence exhibited KD = 4-6 M towards human serum albumin and similar affinities towards rat and mouse albumins. When injected in mouse, the PFS-SICRFFCGGG compound was significantly retained in circulation in vivo when compared to control PFS-macrocyclic peptide. The perfluoroaryl-macrocycles with SICRFFC motif are the smallest known peptide macrocycle with significant affinity for human albumin and they are a productive starting point for future development of compact macrocycles with predictable circulation half-life in vivo. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/504611v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@cd54d2org.highwire.dtl.DTLVardef@7bd9aborg.highwire.dtl.DTLVardef@10f7515org.highwire.dtl.DTLVardef@1cfc49a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2022

Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics

Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting its clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a novel statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs a novel annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 18.7%-122.1% gain in predictive R2 compared to state-of-the-art methods based on GWAS summary statistics. Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic prediction.

genetics↗

bioRxiv2022

A Saturated Map of Common Genetic Variants Associated with Human Height from 5.4 Million Individuals of Diverse Ancestries

Common SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

genetics↗

bioRxiv2021

Agrochemical control of gene expression using evolved split RNA polymerase

Chemical inducible gene expression systems are valuable tools for rational control of gene expression both for basic research and biotechnology. However, most chemical inducers are confined to certain groups of organisms. Therefore, dissecting interaction between different organisms could be challenging using existing chemical inducible systems. We engineered a mandipropamid induced gene expression system (Mandi-T7) based on evolved split T7 RNAP system. As a proof-of-principle, we induced GFP expression in E. coli cells grown inside plant tissue.

synthetic biology↗

bioRxiv2021

A quantile integral linear model to quantify genetic effects on phenotypic variability

Detecting genetic variants associated with the variance of complex traits, i.e. variance quantitative trait loci (vQTL), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (N=375,791), QUAIL identified 11 novel vQTL for body mass index (BMI). Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Further, variance polygenic scores (vPGS) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

genetics↗

bioRxiv2020

SARS-CoV-2 infecting the inner ear results in potential hearing damage at the early stage or prognosis of COVID-19 in rodents

ObjectivesIn order to find out the association between the sensorineural hearing loss and COVID-19, we detected the expression ACE2 and TMPRSS2 in the mouse the hamster. DesignUsing the public data from NCBI and GISAID, we assessed the expression of ACE2 and TMPRSS2 at the transcriptomic, DNA, and protein levels of ACE2 in the brain, inner ear, and muscle from the golden Syrian hamster (Mesocricetus auratus) and mouse (Mus musculus). ResultsWe identified ACE2 and TMPRSS2 expressed at different level in the inner ear and brain at DNA and transcriptomic levels of both mouse and the hamster. The protein expression shows a similar pattern of the brain and inner ear, while the expression of ACE2 from the inner ear was relatively higher than it from the muscle. ConclusionSARS-CoV-2 could infect the hearing system potentially and SSNHL could be a characteristic to detect asymptomatic patients of COVID-19.

bioinformatics↗

bioRxiv2020

An effective, safe and cost-effective cell-based chimeric vaccine against SARS-CoV2

More than one hundred vaccines against SARS-CoV-2 have been developed and some of them have entered clinical trials, but the latest results revealed that these vaccines still face great challenges. Here, we developed a novel cell-based gp96-Ig-secreting chimeric vaccine which is composed of two viral antigens, the RBD of spike protein, and a truncated nucleocapsid protein that could induce epitope-specific cytotoxic T lymphocytes but low antibody response. Syrian hamsters immunized with the cell-based vaccine produced high level of SARS-CoV-2 specific NAbs and specific T cell immunity which could eliminate RBD-truncated N-expressing cells, without the induction of antibody against N protein and other observed toxicity. This study provides a proof of concept for clinical testing of this safe, effective and cost-effective vaccine against SARS-CoV2 infection.

immunology↗

bioRxiv2020

Impact of cell-type and context-dependent regulatory variants on human immune traits

The effects of trait-associated variants are often studied in a single relevant cell-type or context. However, for many complex traits, multiple cell-types are involved. This applies particularly to immune-related traits, for which many immune cell-types and contexts play a role. Here, we studied the impact of immune gene regulatory variants on complex traits to better understand genetic risk mediated through immune cell-types. We identified 26,271 expression quantitative trait loci (QTLs) and 23,121 splicing QTLs in 18 immune cell-types, and analyzed their overlap with trait-associated loci from 72 genome-wide association studies (GWAS). We showed that effects on RNA expression and splicing in immune cells colocalize with an average of 40.4% and 27.7% GWAS loci for immune-related and non-immune traits, respectively. Notably, we found that a large number of loci (mean: 14%) colocalize with splicing QTLs but not expression QTLs. The 60% GWAS loci without colocalization harbor genes that have lower expression levels, are less tolerant to loss-of-function mutations, and more enhancerrich than genes at colocalized loci. To further investigate the 60% GWAS loci not explained by our regulatory QTLs, we collected H3K27ac CUT&Tag data from rheumatoid arthritis (RA) and healthy controls. We found several unexplained GWAS hits lying within regions with higher H3K27ac activity in RA patients. We also observed that enrichment of RA GWAS heritability is greater in H3K27ac regions in immune cell-types from RA patients compared to healthy controls. Our study paves the way for future QTL studies to elucidate the mechanisms of as yet unexplained GWAS loci.

genomics↗