Biology subjects

Xiang, R.

Publications and source records attributed to Xiang, R..

31 records · Page 2Linked to original sources

bioRxiv2022

Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle

Many quantitative trait loci (QTL) are located in non-coding genomic regions. Therefore, QTL are assumed to affect gene regulation. Gene expression and RNA splicing are primary steps of transcription so QTL changing gene expression (eQTL) or RNA splicing (sQTL) are expected to significantly contribute to phenotypic variations. Here, we quantify the contribution of eQTL and sQTL detected from 16 tissues (N~5,000) to 37 complex traits of ~120k cattle. Using Bayesian methods, we show that including more regulatory variants in the model explains larger proportions of heritability. Across traits, cis and trans eQTL and sQTL detected from 16 tissues jointly explain ~70% (SE=0.5%) of heritability, 44% more than expected from the same number of random variants, where trans e/sQTL contribute 24% (14% more than expected). Multi-tissue cis and trans e/sQTL also explain 71% (SE=0.3%) of heritability for the metabolome, demonstrating the essential role of proximal and distal regulatory variants in shaping mammalian phenotypes.

genomics↗

bioRxiv2021

The first sheep graph pan-genome reveals the spectrum of structural variations and their effects on different tail phenotypes

Structural variations (SVs) are a major contributor to genetic diversity and phenotypic variations, but their prevalence and functions in domestic animals are largely unexplored. Here, we assembled 26 haplotype-resolved genome assemblies from 13 genetically diverse sheep using PacBio HiFi sequencing. We constructed a graph-based ovine pan-genome and discovered 142,422 biallelic insertions and deletions, 7,028 divergent alleles and 13,419 multiallelic variations. We then used a graph-based approach to genotype the biallelic SVs in 684 individuals from 45 domestic breeds and two wild species. Integration with RNA-seq data allows to identify candidate expression-associated SVs. We demonstrate a direct link of SVs and phenotypes by localizing the putative causative insertion in HOXB13 gene responsible for the long-tail trait and identifying multiple large SVs associated with the fat-tail. Beyond generating a benchmark resource for ovine structural variants, our study highlights that animal genetic research will greatly benefit from using a pan-genome graph rather than a single reference genome.

genomics↗

bioRxiv2021

High-resolution spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae

Drosophila has long been a successful model organism in multiple fields such as genetics and developmental biology. Drosophila genome is relatively smaller and less redundant, yet largely conserved with mammals, making it a productive model in studies of embryogenesis, cell signaling, disease mechanisms, etc. Spatial gene expression pattern is critical for understanding of complex signaling pathways and cell-cell interactions, whereas temporal gene expression changes need to be tracked during highly dynamic activities such as tissue development and disease progression. Systematic studies in Drosophila as a whole are still impeded by lack of these spatiotemporal transcriptomic information. Drosophila embryos and tissues are of relatively small size, limiting the application of current technologies to comprehensively resolve their spatiotemporal gene expression patterns. Here, utilizing SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. Our data recapitulated the spatial transcriptomes of embryonic and larval development in Drosophila. With these data, we identified known and previously undetected subregions in several tissues during development, and revealed known and potential gene regulatory networks of transcription factors within their topographic background. We further demonstrated that Stereo-seq data can be used for 3D reconstruction of Drosophila embryo spatial transcriptomes. Our data provides Drosophila research community with useful resources of spatiotemporally resolved transcriptomic information across developmental stages.

developmental biology↗

bioRxiv2021

B.1.1.7 and B.1.351 variants are highly virulent in K18-ACE2 transgenic mice and show different pathogenic patterns from early SARS-CoV-2 strains

SARS-CoV-2 continues to circulate globally resulting in emergence of several variants of concern (VOC), including B.1.1.7 and B.1.351 that show increased transmissibility and enhanced resistance to antibody neutralization. In a K18-hACE2 transgenic mouse model, we demonstrate that Both B.1.1.7 and B.1.351 are 100 times more lethal than the original SARS-CoV-2 bearing 614D. Mice infected with B.1.1.7 and B.1.351 exhibited more severe lesions in internal organs than those infected with early SARS-CoV-2 strains bearing 614D or 614G. Infection of B.1.1.7 and B.1.351 also results in distinct tissue-specific cytokine signatures, significant D-dimer depositions in vital organs and less pulmonary hypoxia signaling before death as compared to the mice infected with early SARS-CoV-2 strains. However, K18-hACE2 mice with the pre-existing immunity from prior infection or immunization were resistant to the lethal reinfection of B.1.1.7 or B.1.351, despite having reduced neutralization titers against these VOC. Our study reveals distinguishing pathogenic patterns of B.1.1.7 and B.1.351 variants from those early SARS-CoV-2 strains in K18-hACE2 mice, which will help to inform potential medical interventions for combatting COVID-19.

immunology↗

bioRxiv2021

Bayesian genome-wide analysis of cattle traits using variants with functional and evolutionary significance

ContextFunctional genomics studies have revealed genomic regions with regulatory and evolutionary significance. Such information independent of association analysis may benefit fine-mapping and genomic selection of economically important traits. However, systematic evaluation of the use of functional information in mapping, and genomic selection of cattle traits is lacking. Also, Single Nucleotide Polymorphisms (SNPs) from the high-density (HD) panel are known to tag informative variants, but the performance of genomic prediction using HD SNPs together with variants supported by different functional genomics is unknown. AimsWe selected six sets of functionally important variants and modelled each set together with HD SNPs in Bayesian models to map and predict protein, fat, and milk yield as well as mastitis, somatic cell count and temperament of dairy cattle. MethodsTwo models were used: 1) BayesR which includes priors of four distribution of variant-effects, and 2) BayesRC which includes additional priors of different functional classes of variants. Bayesian models were trained in 3 breeds of 28,000 cows of Holstein, Jersey and Australian Red and predicted into 2,600 independent bulls. Key resultsAdding functionally important variants significantly increased the enrichment of genetic variance explained for mapped variants, suggesting improved genome-wide mapping precision. Such improvement was significantly higher when the same set of variants were modelled by BayesRC than by BayesR. Combining functional variant sets with HD SNPs improves genomic prediction accuracy in the majority of the cases and such improvement was more common and stronger for non-Holstein breeds and traits like mastitis, somatic cell count and temperament. In contrast, adding a large number of random sequence variants to HD SNPs reduces mapping precision and has a worse or similar prediction accuracy, compared to using HD SNPs alone to map or predict. While BayesRC tended to have better genomic prediction accuracy than BayesR, the overall difference in prediction accuracy between the two models was insignificant. ConclusionsOur findings demonstrate the usefulness of functional data in genomic mapping and prediction. ImplicationsWe highlight the need for effective tools exploiting complex functional datasets to improve genomic prediction.

genetics↗

bioRxiv2021

Mutant alleles differentially shape cattle complex traits and fitness

Classical mutant alleles (MAs), with large effects on phenotype, tend to be deleterious to traits and fitness. Is this the case for mutations with small effects? We infer MAs for 8 million sequence variants in 113k cattle and quantify the effects of MA on 37 complex traits. Heterozygosity for variants at genomic sites conserved across 100 vertebrates increase fertility, stature, and milk production, positively associating these traits with fitness. MAs decrease stature and fat and protein concentration in milk, but increase gestation length and somatic cell count in milk (the latter indicative of mastitis). However, the allele frequency of MAs that decrease fat and protein concentration and stature and increase gestation length and somatic cell count is lower than the allele frequency of MAs with the opposite effect. These results suggest bias in the direction of effect of mutation (e.g. towards reduced protein in milk), but selection operating to reduce the frequency of these MAs. Taken together, our results imply two classes of genomic sites subject to long-term selection: sites conserved across vertebrates show hybrid vigour while sites subject to less long-term selection show a bias in mutation towards alleles that are selected against.

genomics↗

bioRxiv2021

New loci and neuronal pathways for resilience to heat stress in animals

Climate change and resilience to warming climates have implications for humans, livestock, and wildlife. The genetic mechanisms that confer thermotolerance to mammals are still not well characterized. We used dairy cows as a model to study heat tolerance because they are lactating, and therefore often prone to thermal stress. The data comprised almost 0.5 million milk records (milk, fat, and proteins) of 29,107 Australian Holsteins, each having around 15 million imputed sequence variants. Dairy animals often reduce their milk production when temperature and humidity rise; thus, the phenotypes used to measure an individuals heat tolerance were defined as the rate of milk production decline (slope traits) with a rising temperature-humidity index. With these slope traits, we performed a genome-wide association study (GWAS) using different approaches, including conditional analyses, to correct for the relationship between heat tolerance and level of milk production. The results revealed multiple novel loci for heat tolerance, including 61 potential functional variants at sites highly conserved across vertebrate species. Moreover, it was interesting that specific candidate variants and genes are related to the neuronal system (ITPR1, ITPR2, and GRIA4) and neuroactive ligand-receptor interaction functions for heat tolerance (NPFFR2, CALCR, and GHR), providing a novel insight that can help to develop genetic and management approaches to combat heat stress. Author summaryWhile understanding the genetic basis of heat tolerance is crucial in the context of global warmings effect on humans, livestock, and wildlife, the specific genetic variants and biological features that confer thermotolerance in animals are still not well characterized. The ability to tolerate heat varies across individuals, with substantial genetic control of this complex trait. Dairy cattle are excellent model in which to find genes associated with individual variations in heat tolerance since they significantly suffer from heat stress due to the metabolic heat of lactation. By genome-wide association studies of more than 29,000 cows with 15 million sequence variants and controlled phenotype measurements, we identify many new loci associated with heat tolerance. The biological functions of these loci are linked to the neuronal system and neuroactive ligand-receptor interaction functions. Also, several putative causal mutations for heat tolerance are at genomic sites that are otherwise evolutionarily conserved across 100 vertebrate species. Overall, our findings provide new insight into the molecular and biological basis of heat tolerance that can help to develop genetic and management approaches to combat heat stress.

genomics↗

bioRxiv2021

A systematic CRISPR screen reveals an IL-20/IL20RA-mediated peritoneal immune crosstalk to prevent the ovarian cancer metastasis

Transcoelomic spread of cancer cells across the peritoneal cavity occurs in most initially diagnosed ovarian cancer (OC) patients and accounts for most cancer-related death. However, how OC cells interact with peritoneal stromal cells to evade the immune surveillance remains largely unexplored. Here, through an in vivo genome-wide CRISPR/Cas9 screen, we identified IL20RA, which decreased dramatically in OC patients during peritoneal metastasis, as a key factor preventing the transcoelomic metastasis of OC. Reconstitution of IL20RA in highly metastatic OC cells greatly suppresses the transcoelomic metastasis. OC cells, when disseminate into the peritoneal cavity, greatly induce peritoneum mesothelial cells to express IL-20 and IL-24, which in turn activate the IL20RA downstream signaling in OC cells to produce mature IL-18, eventually resulting in the polarization of macrophages into the M1-like subtype to clear the cancer cells. Thus, we show an IL-20/IL20RA-mediated crosstalk between OC and mesothelial cells that supports a metastasis-repressing immune microenvironment.

cancer biology↗

bioRxiv2020

Single-cell screening of SARS-CoV-2 target cells in pets, livestock, poultry and wildlife

A few animals have been suspected to be intermediate hosts of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, a large-scale single-cell screening of SARS-CoV-2 target cells on a wide variety of animals is missing. Here, we constructed the single-cell atlas for 11 representative species in pets, livestock, poultry, and wildlife. Notably, the proportion of SARS-CoV-2 target cells in cat was found considerably higher than other species we investigated and SARS-CoV-2 target cells were detected in multiple cell types of domestic pig, implying the necessity to carefully evaluate the risk of cats during the current COVID-19 pandemic and keep pigs under surveillance for the possibility of becoming intermediate hosts in future coronavirus outbreak. Furthermore, we screened the expression patterns of receptors for 144 viruses, resulting in a comprehensive atlas of virus target cells. Taken together, our work provides a novel and fundamental strategy to screen virus target cells and susceptible species, based on single-cell transcriptomes we generated for domesticated animals and wildlife, which could function as a valuable resource for controlling current pandemics and serve as an early warning system for coping with future infectious disease threats.

cell biology↗

bioRxiv2020

Proteogenomics analysis of non-coding region encoded peptides in normal tissues and five cancer types

Previous proteogenomics studies have identified peptides encoded by non-coding sequences such as pseudogenes and long non-coding RNAs (lncRNAs) in healthy human tissues as well as in cancers. However, these studies are either limited to analyze only healthy or cancerous tissues, lacking direct comparison between them. In this study, we used an established proteogenomics analysis workflow to analyze proteomics data from 926 cancer samples of five cancer types and 31 different healthy human tissues. We observed the protein level expression of pseudogenes can be classified as ubiquitous or lineage expression. The ubiquitously translated pseudogenes are homologous to house-keeping genes. Our results suggest there is common mechanism underlying the translation of pseudogenes in both normal and tumors. Moreover, we discovered several translated non-coding genes such as DGCR5 and RHOXF1P3 that were up-regulated in tumors compared to normal. These translated pseudogenes imply the biological function of pseudogenes extends to protein level yet to be studied. Further, from the non-coding region encoded peptides specifically detected in tumors we have predicted a large number of potential neoantigens which can be developed as cancer vaccine.

bioinformatics↗

bioRxiv2020

Single cell atlas of trisomy 21 cerebral cortex

Down syndrome (DS) is one of the most common human birth defects caused by trisomy 21 (T21), leading to a variety of cognitive impairments. The cellular composition of human brain has been explored using single cell RNA sequencing in both physiological and pathological conditions. However, the cellular heterogeneity of human brain with chromosome aneuploidy is largely unknown. Here, we profiled the transcriptome of 36046 cells in cerebral cortex of T21 human fetus, covering frontal lobe, parietal lobe, occipital lobe and temporal lobe. Intriguingly, we detected several genes positively associated with neurons maturation was dysregulated in T21 frontal cortex (HIC2, POU2F2, ZGLP1 and FOXK1). To share, explore and utilized the data resources of T21 cerebral cortex, we developed a comprehensive platform named T21atlas, composing of two functional modules (T21cluster and T21talk). Overall, our study provides, as far as we know, the first single cell atlas for T21 cerebral cortex, which could promote our understanding of the molecular mechanism of DS at an unprecedented resolution and could potentially facilitate the development of novel clinical therapeutics against T21.

neuroscience↗

bioRxiv2019

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (eQTLs) and concentration of metabolites (mQTLs), and under histone modification marks in several tissues were discovered from multi-omics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants and for each set we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r>0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone modification marks and selection signatures. From these results, we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In 7,551 Danish cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution and trait heritability to rank variants and the publicly available FAETH data provides a set of biological priors for cattle genomic selection worldwide.

genomics↗

bioRxiv2017

Genome variants altering gene splicing in bovine are extensively shared between tissues

BackgroundMammalian phenotypes are shaped by numerous genome variants, many of which may regulate gene transcription or RNA splicing. To identify variants with regulatory functions in cattle, an important economic and model species, we used sequence variants to map a type of expression quantitative trait loci (expression QTLs) that are associated with variations in the RNA splicing, i.e., sQTLs. To further the understanding of regulatory variants, sQTLs were compare with other two types of expression QTLs, 1) variants associated with variations in gene expression, i.e., geQTLs and 2) variants associated with variations in exon expression, i.e., eeQTLs, in different tissues.\n\nResultsUsing whole genome and RNA sequence data from four tissues of over 200 cattle, sQTLs identified using exon inclusion ratios were verified by matching their effects on adjacent intron excision ratios. sQTLs contained the highest percentage of variants that are within the intronic region of genes and contained the lowest percentage of variants that are within intergenic regions, compared to eeQTLs and geQTLs. Many geQTLs and sQTLs are also detected as eeQTLs. Many expression QTLs, including sQTLs, were significant in all four tissues and had a similar effect in each tissue. To verify such expression QTL sharing between tissues, variants surrounding ({+/-}1Mb) the exon or gene were used to build local genomic relationship matrices (LGRM) and estimated genetic correlations between tissues. For many exons, the splicing and expression level was determined by the same cis additive genetic variance in different tissues. Thus, an effective but simple-to-implement meta-analysis combining information from three tissues is introduced to increase power to detect and validate sQTLs. sQTLs and eeQTLs together were more enriched for variants associated with cattle complex traits, compared to geQTLs. Several putative causal mutations were identified, including an sQTL at Chr6:87392580 within the 5th exon of kappa casein (CSN3) associated with milk production traits.\n\nConclusionsUsing novel analytical approaches, we report the first identification of numerous bovine sQTLs which are extensively shared between multiple tissue types. The significant overlaps between bovine sQTLs and complex traits QTL highlight the contribution of regulatory mutations to phenotypic variations.

genomics↗