Biology subjects

Gao, X.

Publications and source records attributed to Gao, X..

At least 343 records · Page 19Linked to original sources

bioRxiv2019

Estradiol modulates gut microbiota in female ob/ob mice fed a high fat diet

Estrogens protect against diet-induced obesity in women and female rodents. In support of these anorectic effects, lack of estrogens in postmenopausal women is associated with weight gain, increasing their risk for cardiovascular diseases and cancer. Estrogens act with leptin, a satiety hormone encoded by the ob gene, to regulate energy homeostasis in females. Leptin-deficient mice (ob/ob) exhibit morbid obesity and insulin resistance. In addition to estrogens and leptin, the gut microbiome (gut microbes and their metabolites), is critical in regulating energy metabolism. The present study investigates whether estrogens and leptin modulate gut microbiota in ovariectomized ob/ob (obese) or heterozygote (lean) control mice fed a high-fat diet (HFD) that received either 17{beta}-Estradiol (E2) or vehicle implants. E2 attenuated weight gain in both genotypes compared to vehicle counterparts. Moreover, both obesity (ob/ob mice) and E2 reduced gut microbial diversity. ob/ob mice exhibited lower species richness than control mice, while E2-treated mice had reduced evenness compared to vehicle mice. Regarding taxa, E2 treatment was associated with higher abundances of the family S24-7. Leptin was associated with higher abundances of Coriobacteriaceae, Clostridium and Lactobacillus. E2 and leptin had overlapping effects on relative abundances of some taxa, suggesting that interaction of these hormones is important in gut microbial homeostasis. Taken together, these findings suggest that E2 and leptin profoundly alter the gut microbiota of HFD-fed female mice. Understanding the function of E2 and leptin in regulating gut microbiota will allow the development of therapies targeting the gut microbiome for hormone-dependent metabolic disorders in women.

microbiology↗

bioRxiv2019

Deep learning in bioinformatics: introduction, application, and perspective in big data era

Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at https://github.com/lykaust15/Deep_learning_examples.

bioinformatics↗

bioRxiv2019

Cardiac Metabolic Limitations Contribute to Diminished Performance of the Heart in Aging

Changes in the myocardial energetics associated with aging--reductions in creatine phosphate (CrP)/ATP ratio, total creatine, and ATP--mirror changes observed in failing hearts compared to healthy controls. Similarly, both aging and heart failure are associated with significant reductions in cardiac performance and maximal left ventricular cardiac power output (CPO) compared to young healthy individuals. Based on these observations, we hypothesize that reductions in the concentrations cytoplasmic adenine nucleotide, creatine, and phosphate pools that occur with aging impair the myocardial capacity to synthesize ATP at physiological free energy levels, and that the resulting changes to myocardial energetic status impair the mechanical pumping ability of the heart. The purpose of this study is to test theses hypotheses using an age-structured population model for myocardial metabolism in the adult female population and to determine the potential impact of reductions in key myocardial metabolite pools in causing metabolic/energetic and cardiac mechanical dysfunction associated with aging. To test these hypotheses, we developed a population model for myocardial energetics to predict myocardial ATP, ADP, CrP, creatine, and inorganic phosphate concentrations as functions of cardiac work and age in the adult female population. Model predictions support our hypotheses and are consistent with previous experimental observations. The major findings provide a novel theoretical and computational framework for further probing complex relationships between the energetics and performance of the heart with aging. SignificanceNormal mechanical function of the heart requires that ATP be continuously synthesized at a hydrolysis potential of roughly -60 kJ mol-1. Yet in both the aging and diseased heart the relationships between cardiac work rate and concentrations of ATP, ADP, and inorganic phosphate are altered. Important outstanding questions are: To what extent do changes in metabolite concentrations that occur in aging and heart disease affect metabolic/molecular processes in the myocardium? How are systolic and diastolic functions affected by changes in metabolite concentrations? This study addresses these questions by analyzing relationships between cardiac energy demand and supply using an age-structured population model for human myocardial energetics in women.

physiology↗

bioRxiv2019

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

MotivationThere are now over 500 ontologies in the life sciences. Over the past years, significant resources have been invested into formalizing these biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns, and encode domain background knowledge. At the same time, ontologies have extended their amount of human-readable information such as labels and definitions as well as other meta-data. As a consequence, biomedical ontologies now form large formalized domain knowledge bases and have a potential to improve ontology-based data analysis by providing background knowledge and relations between biological entities that are not otherwise connected. ResultsWe evaluate the contribution of formal axioms and ontology meta-data to the ontology-based prediction of protein-protein interactions and gene-disease associations. We find that the formal axioms that have been created for the Gene Ontology and several other ontologies significantly improve ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute in varying degrees to improving data analysis. Our results have major implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings clearly motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies. Availabilityhttps://github.com/bio-ontology-research-group/tsoe Contactrobert.hoehndorf@kaust.edu.sa, xin.gao@kaust.edu.sa

bioinformatics↗

bioRxiv2019

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

MotivationProper prioritization of candidate genes is essential to the genome-based diagnostics of a range of genetic diseases. However, it is a highly challenging task involving limited and noisy knowledge of genes, diseases and their associations. While a number of computational methods have been developed for the disease gene prioritization task, their performance is largely limited by manually crafted features, network topology, or pre-defined rules of data fusion. ResultsHere, we propose a novel graph convolutional network-based disease gene prioritization method, PGCN, through the systematic embedding of the heterogeneous network made by genes and diseases, as well as their individual features. The embedding learning model and the association prediction model are trained together in an end-to-end manner. We compared PGCN with five state-of-the-art methods on the Online Mendelian Inheritance in Man (OMIM) dataset for tasks to recover missing associations and discover associations between novel genes and diseases. Results show significant improvements of PGCN over the existing methods. We further demonstrate that our embedding has biological meaning and can capture functional groups of genes. AvailabilityThe main program and the data are available at https://github.com/lykaust15/Disease_gene_prioritization_GCN.

bioinformatics↗

bioRxiv2018

Genome-wide association study of diabetic kidney disease highlights biology involved in renal basement membrane collagen

Diabetic kidney disease (DKD) is a heritable but poorly understood complication of diabetes. To identify genetic variants predisposing to DKD, we performed genome-wide association analyses in 19,406 individuals with type 1 diabetes (T1D) using a spectrum of DKD definitions basedon albuminuria and renal function. We identified 16 genome-wide significant loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM) implicated in heritable nephropathies. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of DKD, including albuminuria and end-stage renal disease. Three other loci are in or near genes with known or suggestive involvement in DKD (BMP7) or renal biology (COLEC11 and DDR1). The 16 DKD-associated loci provide novel insights into the pathogenesis of DKD, identifying potential biological targets for prevention and treatment.

genetics↗

bioRxiv2018

Searching and mapping genomic subsequences in nanopore raw signals through novel dynamic time warping algorithms

Genome diagnostics have gradually become a prevailing routine for human healthcare. With the advances in understanding the causal genes for many human diseases, targeted sequencing provides a rapid, cost-efficient and focused option for clinical applications, such as SNP detection and haplotype classification, in a specific genomic region. Although nanopore sequencing offers a perfect tool for targeted sequencing because of its mobility, PCR-freeness, and long read properties, it poses a challenging computational problem of how to efficiently and accurately search and map genomic subsequences of interest in a pool of nanopore reads (or raw signals). Due to its relatively low sequencing accuracy, there is no reliable solution to this problem, especially at low sequencing coverage.\n\nHere, we propose a brand new signal-based subsequence inquiry pipeline as well as two novel algorithms to tackle this problem. The proposed algorithms follow the principle of subsequence dynamic time warping and directly operate on the electrical current signals, without loss of information in base-calling. Therefore, the proposed algorithms can serve as a tool for sequence inquiry in targeted sequencing. Two novel criteria are offered for the consequent signal quality analysis and data classification. Comprehensive experiments on real-world nanopore datasets show the efficiency and effectiveness of the proposed algorithms. We further demonstrate the potential applications of the proposed algorithms in two typical tasks in nanopore-based targeted sequencing: SNP detection under low sequencing coverage, and haplotype classification under low sequencing accuracy.

bioinformatics↗

bioRxiv2018

Proteomic Profile of TGF-β1 treated Lung Fibroblasts identifies Novel Markers of Activated Fibroblasts in the Silica Exposed Rat Lung

We performed liquid chromatography-tandem mass spectrometry (LC-MS/MS) on control and TGF-{beta}1-exposed rat lung fibroblasts to identify proteins differentially expressed between cell populations. A total of 1648 proteins were found to be differentially expressed in response to TGF-{beta}1 treatment and 196 proteins were expressed at [≥] 1.2 fold relative to control. Guided by these results, we next determined whether similar changes in protein expression were detectable in the rat lung after chronic exposure to silica dust. Of the five proteins selected for further analysis, we found that levels of all proteins were markedly increased in the silica-exposed rat lung, including the proteins for the very low density lipoprotein receptor (VLDLR) and the transmembrane (type I) heparin sulfate proteoglycan called syndecan 2 (SDC2). Because VLDLR and SDC2 have not, to our knowledge, been previously linked to the pathobiology of silicosis, we next examined whether knockdown of either gene altered responses to TGF-{beta}1 in MRC-5 lung fibroblasts. Interestingly, we found knockdown of either VLDLR or SDC2 dramatically reduced collagen production to TGF-{beta}1, suggesting that both proteins might play a novel role in myofibroblast biology and pathogenesis of silica-induced pulmonary fibrosis. In summary, our findings suggest that performing LC-MS/MS on TGF-{beta}1 stimulated lung fibroblasts can uncover novel molecular targets of activated myofibroblasts in silica-exposed lung.\n\nHighlightsWe identified 196 proteins differentially expressed between control and TGF-{beta}1 treated fibroblastsby LC-MS/MS.\n\nSeveral proteins identified by LC-MS/MS were also found to be differentially expressed in whole lung tissues and isolated fibroblasts after chronic exposure to silica dust, including the very low density lipoprotein receptor (VLDLR) and the transmembrane type I heparan sulfate proteoglycan called syndecan 2\n\nKnockdown of SDC2 or VLDLR markedly inhibited collagen production in MRC-5 fibroblasts, suggesting a novel pathogenic role for these proteins in myofibroblast biology.

pharmacology and toxicology↗

bioRxiv2018

Carbon starvation-induced lipoprotein Slp directs the synthesis of catalase and expression of OxyR regulator to protect against hydrogen peroxide stress in Escherichia coli

Escherichia coli can induce a group of stress-response proteins, including carbon starvation-induced lipoprotein (Slp), which is an outer membrane lipoprotein expressed in response to stressful environments. In this paper, slp null mutant\n\nE. coli were constructed by insertion of the group II intron, and then the growth sensitivity of the slp mutant strain was measured under 0.6% (vol/vol) hydrogen peroxide. The changes in resistance to hydrogen peroxide stress were investigated by detecting antioxidant activity and gene expression in the slp mutant strain. The results showed that deletion of the slp gene increased the sensitivity of E. coli under 0.6% (vol/vol) hydrogen peroxide oxidative stress. Analysis of the unique mapping rates from the transcriptome libraries revealed that four of thirteen remarkably up/down-regulated genes in E. coli were involved in antioxidant enzymes after mutation of the slp gene. Mutation of the slp gene caused a significant increase in catalase activity, which contributed to an increase in glutathione peroxidase activity. The katG gene was activated by the OxyR regulator, which was activated directly by 0.6% (vol/vol) hydrogen peroxide, and HPI encoded by katG was induced against oxidative stress. Therefore, the carbon starvation-induced lipoprotein Slp regulates the expression of antioxidant enzymes and the transcriptional activator OxyR in response to the hydrogen peroxide environment, ensuring that cells are protected from hydrogen peroxide oxidative stress at the level of enzyme activity and gene expression.

molecular biology↗

bioRxiv2018

RBM-5 modulates U2AF large subunit-dependent alternative splicing in C. elegans

A key step in pre-mRNA splicing is the recognition of 3 splicing sites by the U2AF large and small subunits, a process regulated by numerous trans-acting splicing factors. How these trans-acting factors interact with U2AF in vivo is unclear. From a screen for suppressors of the temperature-sensitive (ts) lethality of the C. elegans U2AF large subunit gene uaf-1(n4588) mutants, we identified mutations in the RNA binding motif gene rbm-5, a homolog of the tumor suppressor RBM5. rbm-5 mutations can suppress uaf-1(n4588) ts-lethality by loss of function and neuronal expression of rbm-5 was sufficient to rescue the suppression. Transcriptome analyses indicate that uaf-1(n4588) affected the expression of numerous genes and rbm-5 mutations can partially reverse the abnormal gene expression to levels similar to that of wild type. Though rbm-5 mutations did not obviously affect alternative splicing per se, they can suppress or enhance, in a gene-specific manner, the altered splicing of genes in uaf-1(n4588) mutants. Specifically, the recognition of a weak 3 splice site was more susceptible to the effect of rbm-5. Our findings provide novel in vivo evidence that RBM-5 can modulate UAF-1-dependent RNA splicing and suggest that RBM5 might interact with U2AF large subunit to affect tumor formation.\n\nAuthor summaryRNA splicing is a critical regulatory step for eukaryotic gene expression and has been involved in the pathogenesis of multiple diseases. How RNA splicing factors interact in vivo to affect the splicing and expression of genes is unclear. In studying the temperature-sensitive lethal phenotypes of a mutation affecting the splicing factor U2AF large subunit gene uaf-1 in the nematode Caenorhabditis elegans, we isolated suppressive mutations in the rbm-5 gene, a homolog of the human tumor suppressor gene RBM5. rbm-5 is broadly expressed in neurons to enhance the lethality of the uaf-1 mutants. We found that the uaf-1 mutation causes aberrant expression of genes in numerous biological pathways, a large portion of which can be corrected by rbm-5 mutations. The abnormal splicing of multiple genes caused by the uaf-1 mutation is either corrected or enhanced by rbm-5 mutations in a gene-specific manner. We propose that RBM-5 interacts with UAF-1 to affect RNA splicing and the tumor suppressor function of RBM5 might involve U2AF-dependent RNA splicing.

genetics↗

bioRxiv2018

Genome-wide characterization, evolutionary analysis of WRKY genes in Cucurbitaceae species and assessment of its roles in resisting to powdery mildew disease

The WRKY proteins constitute a large family of transcription factors that have been known to play a wide range of regulatory roles in multiple biological processes. Over the past few years, many reports have focused on analysis of evolution and biological function of WRKY genes at the whole genome level in different plant species. However, little information is known about WRKY genes in melon (Cucumis melo L.). In the present study, a total of 56 putative WRKY genes were identified in melon, which were randomly distributed on their respective chromosomes. A multiple sequence alignment and phylogenetic analysis using melon, cucumber and watermelon predicted WRKY domains indicated that melon WRKY proteins could be classified into three main groups (I-III). Our analysis indicated that no recent duplication events of WRKY genes were detected in melon, and strong purifying selection was observed among the 85 orthologous pairs of Cucurbitaceae species. Expression profiles of CmWRKY derived from RNA-seq data and quantitative RT-PCR (qRT-PCR) analyses showed distinct expression patterns in various tissues, and the expression of 16 CmWRKY were altered following powdery mildew infection in melon. Besides, we also found that a total of 24 WRKY genes were co-expressed with 11 VQ family genes in melon. Our comparative genomic analysis provides a foundation for future functional dissection and understanding the evolution of WRKY genes in cucurbitaceae species, and will promote powdery mildew resistance study in melon.

plant biology↗

bioRxiv2018

Validation of Prostate Cancer Risk Variants by CRISPR/Cas9 Mediated Genome Editing

GWAS have identified numerous SNPs associated with prostate cancer risk. One such SNP is rs10993994. It is located in the MSMB promoter, associates with MSMB encoded {beta}-microseminoprotein prostate secretion levels, and is associated with mRNA expression changes in MSMB and the adjacent gene NCOA4. In addition, our previous work showed a second SNP, rs7098889, is in LD with rs10993994 and associated with MSMB expression independent of rs10993994. Here, we generate a series of clones with single alleles removed by double guide RNA (gRNA) mediated CRISPR/Cas9 deletions, through which we demonstrate that each of these SNPs independently and greatly alters MSMB expression in an allele-specific manner. We further show that these SNPs have no substantial effect on the expression of NCOA4. These data demonstrate that a single SNP can have a large effect on gene expression and illustrate the importance of functional validation to deconvolute observed correlations. The method we have developed is generally applicable to test any SNP for which a relevant heterozygous cell line is available.\n\nAuthor summaryIn pursuing the underlying biological mechanism of prostate cancer pathogenesis, scientists utilized the existence of common single nucleotide polymorphisms (SNPs) in human genome as genetic markers to perform large scale genome wide association studies (GWAS) and have so far identified more than a hundred prostate cancer risk variants. Such variants provide an unbiased and systematic new venue to study the disease mechanism, and the next big challenge is to translate these genetic associations to the causal role of altered gene function in oncogenesis. The majority of these variants are waiting to be studied and lots of them may act in oncogenesis through gene expression regulation. To prove the concept, we took rs10993994 and its linked rs7098889 as an example and engineered single cell clones by allelic-specific CRISPR/Cas9 deletion to separate the effect of each allele. We observed that a single nucleotide difference would lead to surprisingly high level of MSMB gene expression change in a gene specific and tissue specific manner. Our study strongly supports the notion that differential level of gene expression caused by risk variants and their associated genetic locus play a major role in oncogenesis and also highlights the importance of studying the function of MSMB encoded {beta}-MSP in prostate cancer pathogenesis.

genetics↗

bioRxiv2018

ClusterMap: Compare analysis across multiple Single Cell RNA-Seq profiling

Single cell RNA-Seq facilitates the characterization of cell type heterogeneity and developmental processes. Further study of single cell profiles across different conditions enables the understanding of biological processes and underlying mechanisms at the sub-population level. However, developing proper methodology to compare multiple scRNA-Seq datasets remains challenging. We have developed ClusterMap, a systematic method and workflow to facilitate the comparison of scRNA profiles across distinct biological contexts. Using hierarchical clustering of the marker genes of each sub-group, ClusterMap matches the sub-types of cells across different samples and provides \"similarity\" as a metric to quantify the quality of the match. We introduce a purity tree cut method designed specifically for this matching problem. We use Circos plot and regrouping method to visualize the results concisely. Furthermore, we propose a new metric \"separability\" to summarize sub-population changes among all sample pairs. In three case studies, we demonstrate that ClusterMap has the ability to offer us further insight into the different molecular mechanisms of cellular sub-populations across different conditions. ClusterMap is implemented in R and available at https://github.com/xgaoo/ClusterMap.

bioinformatics↗

bioRxiv2018

SupportNet: a novel incremental learning framework through deep learning and support data

MotivationIn most biological data sets, the amount of data is regularly growing and the number of classes is continuously increasing. To deal with the new data from the new classes, one approach is to train a classification model, e.g., a deep learning model, from scratch based on both old and new data. This approach is highly computationally costly and the extracted features are likely very different from the ones extracted by the model trained on the old data alone, which leads to poor model robustness. Another approach is to fine tune the trained model from the old data on the new data. However, this approach often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as the catastrophic forgetting problem. To our knowledge, this problem has not been studied in the field of bioinformatics despite its existence in many bioinformatic problems.\n\nResultsHere we propose a novel method, SupportNet, to solve the catastrophic forgetting problem efficiently and effectively. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to ensure the robustness of the learned model. Comprehensive experiments on various tasks, including enzyme function prediction, subcellular structure classification and breast tumor classification, show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and reaches similar performance as the deep learning model trained from scratch on both old and new data.\n\nAvailabilityOur program is accessible at: https://github.com/lykaust15/SupportNet.

bioinformatics↗

bioRxiv2018

Transcription Factor Regulation of RNA polymerase’s Torsional Capacity

During transcription, RNA polymerase (RNAP) supercoils DNA as it forsward-translocates. Accumulation of this torsional stress in DNA can become a roadblock for an elongating RNAP and thus should be subject to regulation during transcription. Here, we investigate whether, and how, a transcription factor may regulate the torque generation capacity of RNAP and torque-induced RNAP stalling. Using a real-time assay based on an angular optical trap, we found that under a resisting torque, RNAP was highly prone to extensive backtracking. However, the presence of GreB, a transcription factor that facilitates the cleavage of the 3 end of the extruded RNA transcript, greatly suppressed backtracking and remarkably increased the torque that RNAP was able to generate by 65%, from 11.2 to 18.5 pN{middle dot}nm. Analysis of the real-time trajectories of RNAP position at a stall revealed the kinetic parameters of backtracking and GreB rescue. These results demonstrate that backtracking is the primary mechanism that limits transcription against DNA supercoiling and the transcription factor GreB effectively enhances the torsional capacity of RNAP. These findings broaden the potential impact of transcription factors on RNAP functionality.

biophysics↗

bioRxiv2018

Inference of significant microbial interactions from longitudinal metagenomics sequencing data

Data of next-generation sequencing (NGS) and their analysis have been facilitating advances in our understanding of microbial ecosystems such as human gut microbiota. However, inference of microbial interactions occurring within an ecosystem is still a challenge mainly due to metagenomics sequencing (e.g., 16S rDNA sequences) providing relative abundance of microbes instead of absolute cell count. In order to describe the population dynamics in microbial communities and estimate the involved microbial interactions, we introduce a procedure by integrating generalized Lotka-Volterra equations, forward stepwise regression and bootstrap aggregation. First, we successfully identify experimentally confirmed microbial interactions with relative abundance data of a cheese microbial community. Then, we apply the procedure to time-series of 16S rDNA sequences of gut microbiomes of children who were progressing to Type 1 diabetes (T1D progressors), and compare their gut microbial interactions to a healthy control group. Our results suggest that the number of inferred microbial interactions increased over time during the first three years of life. More microbial interactions are found in the gut flora of healthy children than the T1D progressors. The inhibitory effects from Actinobacteria and Bacilli to Bacteroidia, from Bacteroidia to Clostridia, and the benifit effect from Clostridia to Bacteroidia are shared between healthy children and T1D progressors. An inhibition of Clostridia by Gammaproteobacteria is found in healthy children that maintains through their first three years of life. This suppression appears in T1D progressors during the first year of life, which transforms to a commensalism relationship at the age of three years old. Gammaproteobacteria is found exerting an inhibition on Bacteroidia in the T1D progressors, which is not identified in the healthy controls.

ecology↗

bioRxiv2018

Probing compression versus stretch activated recruitment of cortical actin and apical junction proteins using mechanical stimulations of suspended doublets.

We report an experimental approach to study the mechanosensitivity of cellcell contact upon mechanical stimulation in suspended cell-doublets. The doublet is placed astride an hourglass aperture, and a hydrodynamic force is selectively exerted on only one of the cells. The geometry of the device concentrates the mechanical shear over the junction area. Together with mechanical shear, the system also allows confocal quantitative live imaging of the recruitment of junction proteins (e.g. E-cadherin, ZO-1, Occludin and actin). We observed the time sequence over which proteins were recruited to the stretched region of the contact. The compressed side of the contact showed no response. We demonstrated how this mechanism polarizes the stress-induced recruitment of junctional components within one single junction. Finally, we demonstrated that stabilizing the actin cortex dynamics abolishes the mechanosensitive response of the junction. Our experimental design provides an original approach to study the role of mechanical force at a cell-cell contact with unprecedented control over stress application and quantitative optical analysis.

biophysics↗

bioRxiv2018

Whole-exome sequencing identified rare variants associated with body length and girth in cattle

Body measurements can be used in determining body size to monitor the cattle growth and examine the response to selection. Despite efforts putting into the identification of common genetic variants, the mechanism understanding of the rare variation in complex traits about body size and growth remains limited. Here, we firstly performed GWAS study for body measurement traits in Simmental cattle, however there were no SNPs exceeding significant level associated with body measurements. To further investigate the mechanism of growth traits in beef cattle, we conducted whole exome analysis of 20 cattle with phenotypic differences on body girth and length, representing the first systematic exploration of rare variants on body measurements in cattle. By carrying out a three-phase process of the variant calling and filtering, a sum of 1158, 1151, 1267, and 1303 rare variants were identified in four phenotypic groups of two growth traits, higher/ lower body girth (BG_H and BG_L) and higher/lower body length (BL_H and BL_L) respectively. The subsequent functional enrichment analysis revealed that these rare variants distributed in 886 genes associated with collagen formation and organelle organization, indicating the importance of collagen formation and organelle organization for body size growth in cattle. The integrative network construction distinguished 62 and 66 genes with different co-expression patterns associated with higher and lower phenotypic groups of body measurements respectively, and the two sub-networks were distinct. Gene ontology and pathway annotation further showed that all shared genes in phenotypic differences participate in many biological processes related to the growth and development of the organism. Together, these findings provide a deep insight into rare genetic variants of growth traits in cattle and this will have a promising application in animal breeding.

genetics↗