Biology subjects

Yang, Y.

Publications and source records attributed to Yang, Y..

At least 1,333 records · Page 74Linked to original sources

bioRxiv2019

Sequencing and comparative analysis of three Chlorella genomes provide insights into strain-specific adaptation to wastewater

Microalgal Chlorella has been demonstrated to process wastewater efficiently from piggery industry, yet optimization through genetic engineering of such a bio-treatment is currently challenging, largely due to the limited data and knowledge in genomics. In this study, we first investigated the differential growth rates among three wastewater-processing Chlorella strains: Chlorella sorokiniana BD09, Chlorella sorokiniana BD08 and Chlorella sp. Dachan, and the previously published Chlorella sorokiniana UTEX 1602, showing us that BD09 maintains the best tolerance in synthetic wastewater. We then performed genome sequencing and analysis, resulting in a high-quality assembly for each genome with scaffold N50 >2 Mb and genomic completeness [≥] 91%, as well as genome annotation with 9,668, 10,240, 9,821 high-confidence gene models predicted for BD09, BD08, and Dachan, respectively. Comparative genomics study unravels that metabolic pathways, which are involved in nitrogen and phosphorus assimilation, were enriched in the faster-growing strains. We found that gene structural variation and genomic rearrangement might contribute to differential capabilities in wastewater tolerance among the strains, as indicated by gene copy number variation, domain reshuffling of orthologs involved, as well as a [~]1 Mb-length chromosomal inversion we observed in BD08 and Dachan. In addition, we speculated that an associated bacterium, Microbacterium chocolatum, which was identified within Dachan, play a possible role in synergizing nutrient removal. Our three newly sequenced Chlorella genomes provide a fundamental foundation to understand the molecular basis of abiotic stress tolerance in wastewater treatment, which is essential for future genetic engineering and strain improvement.

genomics↗

bioRxiv2019

Expression of genes in the 16p11.2 locus during human fetal cortical neurogenesis.

The 593 kbp 16p11.2 copy number variation (CNV) affects the gene dosage of 29 protein coding genes, with heterozygous 16p11.2 microduplication or microdeletion implicated in about 1% of autism spectrum disorder (ASD) cases. The 16p11.2 CNV is frequently associated with macrocephaly or microcephaly indicating early defects of neurogenesis may contribute to subsequent ASD symptoms, but it is unknown which 16p11.2 transcripts are expressed in progenitors and whose levels are likely, therefore, to influence neurogenesis. Analysis of human fetal gene expression data revealed that of all the 16p11.2 transcripts only two, ALDOA and KIF22, are significantly enriched in progenitors. To investigate the role of ALDOA and KIF22 in human cerebral cortex development we used immunohistochemical staining to describe their expression in late first and early second trimester human cerebral cortex. KIF22 protein is restricted to proliferating cells with its levels increasing during the cell cycle and peaking at mitosis. ALDOA protein is expressed in all cell types and does not vary with cell-cycle phase. Our expression analysis suggests the hypothesis that the simultaneous changes in KIF22 and ALDOA dosage in cortical progenitors causes defects in neurogenesis that may contribute to ASD in 16p11.2 CNV patients.

neuroscience↗

bioRxiv2019

Predicting functional long non-coding RNAs validated by low throughput experiments

High-throughput techniques have uncovered hundreds and thousands of long non-coding RNAs (lncRNAs). Among them, only a small fraction has experimentally validated functions (EVlncRNAs) by low-throughput methods. What fraction of lncRNAs from high-throughput experiments (HTlncRNAs) is truly functional is an active subject of debate. Here, we developed the first method to distinguish EVlncRNAs from HTlncRNAs and mRNAs by using Support Vector Machines and found that EVlncRNAs can be well separated from HTlncRNAs and mRNAs with 0.6 for Matthews correlation coefficient, 64% for sensitivity, and 81% for precision for the independent human test set. The most discriminative features are related to sequence conservations at RNA (for separating from HTlncRNAs) and protein (for separating from mRNA) levels. The method is found to be robust as the human-RNA-trained model is applicable to independent mouse RNAs with similar accuracy and to a lesser extent to plant RNAs. The method can recover newly discovered EVlncRNAs with high sensitivity. Its application to randomly selected 2000 human HTlncRNAs indicates that a large number of functional lncRNAs are waiting to be validated. The method is expected to speed up and reduce the cost of the discovery by prioritizing potentially functional lncRNAs prior to experimental validation. EVlncRNA-pred is available as a web server at http://biophy.dzu.edu.cn/lncrnapred/index.html. All datasets used in this study can be obtained from the same website.

bioinformatics↗

bioRxiv2019

Variation benchmark datasets: update, criteria, quality and applications

Development of new computational methods and testing their performance has to be done on experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets. They have been used for training and benchmarking predictors for various types of variations and their effects. There are 419 new datasets from 109 papers containing altogether 329003373 variants; however there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property predictions for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performance to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and showed that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data.\n\nAUTHOR SUMMARYA prediction method performance can only be assessed in comparison to existing knowledge. For that purpose benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. We collected variation datasets from literature, website and databases. There are 419 separate new datasets, which however contain plenty of redundancy. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property predictions for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. The updated VariBench facilitates development and testing of new methods and comparison of obtained performance to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies and showed that such comparisons are possible and useful when the details of studies and the datasets are shared.

bioinformatics↗

bioRxiv2019

MRGPRX4 is a novel bile acid receptor in cholestatic itch

Patients with liver diseases often suffer from chronic itch or pruritus, yet the itch-causing pruritogen(s) and their cognate receptor(s) remain largely elusive. Using transcriptomics and GPCR activation assays, we found that an orphan, primate specific MRGPRX4 is expressed in human dorsal root ganglia (hDRG) and selectively activated by bile acids. In situ hybridization and immunohistochemistry revealed that MRGPRX4 is expressed in [~]7% of hDRG neurons and co-localizes with HRH1, a known itch-inducing GPCR. Bile acids elicited a robust Ca2+ response in a subset of cultured hDRG neurons, and intradermal injection of bile acids and an MRGPRX4 specific agonist induced significant itch in healthy human subjects. Surprisingly, application of agonist for TGR5, a known sequence conserved bile acid receptor previously implicated in cholestatic itch, failed to elicit Ca2+ response in cultured hDRG neurons, nor did it induce pruritus in human subjects. In situ hybridization and immunostaining results revealed that hTGR5 is selectively expressed in satellite glial cells, unlike mTGR5 (in mouse DRG neurons), likely accounting for the inter-species difference functionally. Finally, we found that patients with cholestatic itch have significantly higher plasma bile acid levels compared to non-itchy patients and the bile acid levels significantly decreased after itch relief. This elevated bile acid level in itchy patients is sufficient to activate MRGPRX4. Taken together, our data strongly suggest that MRGPRX4 is a novel bile acid receptor that likely underlies cholestatic itch, providing a promising new drug target for anti-itch therapies.

neuroscience↗

bioRxiv2019

To Improve Protein Sequence Profile Prediction through Image Captioning on Pairwise Residue Distance Map

Protein sequence profile prediction aims to generate multiple sequences from structural information to advance the protein design. Protein sequence profile can be computationally predicted by energy-based method or fragment-based methods. By integrating these methods with neural networks, our previous method, SPIN2 has achieved a sequence recovery rate of 34%. However, SPIN2 employed only one dimensional (1D) structural properties that are not sufficient to represent 3D structures. In this study, we represented 3D structures by 2D maps of pairwise residue distances. and developed a new method (SPROF) to predict protein sequence profile based on an image captioning learning frame. To our best knowledge, this is the first method to employ 2D distance map for predicting protein properties. SPROF achieved 39.8% in sequence recovery of residues on the independent test set, representing a 5.2% improvement over SPIN2. We also found the sequence recovery increased with the number of their neighbored residues in 3D structural space, indicating that our method can effectively learn long range information from the 2D distance map. Thus, such network architecture using 2D distance map is expected to be useful for other 3D structure-based applications, such as binding site prediction, protein function prediction, and protein interaction prediction.

bioinformatics↗

bioRxiv2019

FIREcaller: an R package for detecting frequently interacting regions from Hi-C data

Hi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of cis-regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller. Highlights- Frequently Interacting Regions (FIREs) can be used to identify tissue and cell-type-specific cis-regulatory regions. - An R software, FIREcaller, has been developed to identify FIREs and clustered FIREs into super-FIREs.

bioinformatics↗

bioRxiv2019

Integrated analysis of the miRNA-mRNA network associated with LMP1 gene in nasopharyngeal carcinoma

Epstein-Barr virus oncogenic latent membrane protein 1 (LMP1) has been known to play important roles in nasopharyngeal carcinoma (NPC). LMP1 gene also induced a variety of microRNAs (miRNAs) which bear pivotal roles in regulation of mRNAs expression. However, little was known about the global change of mRNAs and miRNAs induced by LMP1 gene in NPC. In our study, one NPC tissue microarray profile and two LMP1-associated microarray expression profiles data were downloaded from the Gene Expression Omnibus database. A protein-protein interaction network was constructed by using bioinformatics platform Gene-Cloud of Biotechnology Information (GCBI). 78 differentially expressed miRNAs and 3322 differentially expressed genes were identified in order to generate a macroscopic network between miRNAs and mRNAs associated with LMP1 gene. In addition, two significant models were generated to illustrate the expression tendency. Our study provided a way to reveal the interaction between miRNAs and mRNAs in LMP1 axis, bringing insights into the pathogenesis of NPC.

microbiology↗

bioRxiv2019

A 3D molecular atlas of the chick embryonic heart

We present a detailed analysis of gene expression in the 2-day (HH12) embryonic chick heart. RNA-seq of 13 micro-dissected regions reveals regionalised expression of 15,570 genes. Of these, 132 were studied by in situ hybridisation and a subset (38 genes) was mapped by Optical Projection Tomography or serial sectioning to build a detailed 3-dimensional atlas of expression. We display this with a novel interactive 3-D viewer and as stacks of sections, revealing the boundaries of expression domains and regions of overlap. Analysis of the expression domains also defines some sub-regions distinct from those normally recognised by anatomical criteria at this stage of development, such as a previously undescribed subdivision of the atria into two orthogonal sets of domains (dorsoventral and left-right). We also include a detailed comparison of expression in the chick with the mouse and other species.

developmental biology↗

bioRxiv2019

Accurate Prediction of Genome-wide RNA Secondary Structure Profile Based On Extreme Gradient Boosting

MotivationMany studies have shown that RNA secondary structure plays a vital role in fundamental cellular processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. Identification of RNA secondary structure is a key step to understand the common mechanisms underlying the translation process. Recently, a few experimental methods were developed to measure genome-wide RNA secondary structure profile through high-throughput sequencing techniques, and have been successfully applied to genomes including yeast and human. However, these high-throughput methods usually have low precision and are hard to cover all nucleotides on the RNA due to limited sequencing coverage.\n\nResultsIn this study, we developed a new method for the prediction of genome-wide RNA secondary structure profile (TH-GRASP) from RNA sequence based on eXtreme Gradient Boosting (XGBoost). The method achieves an prediction with areas under the receiver operating characteristic curve (AUC) values greater than 0.9 on three different datasets, and AUC of 0.892 by an independent test on the recently released Zika virus RNA dataset. These AUCs represent a consistent increase of >6% than the recently developed method CROSS trained by a shallow neural network. A further analysis on the 1000-Genome Project data showed that our predicted unpaired probability at mutations sites are highly correlated with the minor allele frequencies (MAF) of synonymous, non-synonymous mutations, and mutations in 3 and 5UTR with Pearson Correlation Coefficients all above 0.8. These PCCs are consistently higher than those generated by RNAplfold method. Moreover, an investigation over all human mRNA indicated a periodic distribution of the predicted unpaired probability on codons, and a decrease of paired probability in the boundary with 5 and 3 untranslated regions. These results highlighted TH-GRASP is effective to remove experimental noises and to have ability to make predictions on nucleotides with low or no coverage by fitting high-throughput genomic data for RNA secondary structure profiles, and also suggested that building model on high throughput experimental data might be a future direction to substitute analytical methods.\n\nAvailabilityThe TH-GRASP is available for academic use at https://github.com/sysu-yanglab/TH-GRASP.\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2019

Human Chrysomya bezziana myiasis: A systematic review

BackgroundMyiasis due to Old World screw-worm fly, Chrysomya bezziana, is an important obligate zoonotic disease in the OIE-list of diseases and is found throughout much of Africa, the Indian subcontinent, southeast and east Asia. C. bezziana myiases cause not only morbidity and death to animals and humans, but also economic losses in the livestock industries. Because of the aggressive and destructive nature of this disease in hosts, we initiated this study to provide a comprehensive understanding of human myiasis caused by C. bezziana.\n\nMethodsWe conducted a systematic search of the databases in English (PubMed, Embase and African Index Medicus) and Chinese (CNKI, Wanfang, and Duxiu), and international government online reports to 6th February, 2019, to identify studies concerning Chrysomya bezziana. Another ten human cases in China and Papua New Guinea that our team had recorded were also included.\n\nResultsWe retrieved 1,048 reports from which 202 studies were ultimately eligible for inclusion in the present descriptive analyses. Since the first human case due to C. bezziana was reported in 1909, we have summarized 291 cases and found that these cases often occurred in poor hygiene, low socio-economic conditions, old age, and underlying diseases including infections, age-related diseases, and noninfectious chronic diseases. But C. bezziana myiasis appears largely neglected as a serious medical or veterinary condition, with human and animal cases only reported in 16 and 24 countries respectively, despite this fly species recorded as present in 44 countries worldwide.\n\nConclusionOur findings indicate that cryptic myiasis cases due to the obligate parasite, C. bezziana, are under-recognized. Through this in-depth study to clarify the knowledge of human C. bezziana myiasis including its etiology, clinical features, diagnosis, treatment, epidemiology, prevention and control, we call for more vigilance and awareness of the disease from governments, health authorities, clinicians, veterinary workers, nursing homes, and also the general public.\n\nAuthor summaryChrysomya bezziana larvae are characterized by feeding aggressively on the living tissues and body fluids of the host. The nightmare-like feelings of patients suffering from this myiasis, severe tissues and bones destruction, even death, and enormous economic loss in the livestock industries have been described in the previous reports. But our findings indicate that C. bezziana myiases still appear to be under-recognized as a serious medical or veterinary condition throughout the world. Both in China and the world at large, it is probable that C. bezziana distribution could be greater than currently reported. For the first time, we have therefore systematically investigated human myiasis caused by C. bezziana. Our study provides an opportunity for clinicians and health authorities to gain a better understanding of this disease from its etiology, pathology, clinical features, diagnosis, treatment, epidemiology, prevention and control. In addition, our findings will hopefully engage governments, health staff, veterinary workers, the elderly homes, and also the general public in efforts to recognize, prevent and control such infestations.

epidemiology↗

bioRxiv2019

Pan-genome analysis reveals the molecular basis of niche adaptation of Staphylococcus epidermidis strains

Staphylococcus epidermidis is the most commonly isolated species from human skin and the second leading cause of bloodstream infections. Here, we performed a large-scale comparative study without any pre-assigned reference to identify genomic determinants associated with their diversity and adaptation as a \"double-side spy\", a skin dominant colonization, and a successful pathogen. The pan-genome of S. epidermidis is open with 435 core proteins and a pan-genome size of 8034 proteins. Genome-wide phylogenetic tree shows that whole genome sequence is a powerful tool to analyze the complex evolutionary process of S. epidermidis and investigate the source of infection. Comparative genome analyses demonstrate the high diversity of antimicrobial resistances, especially mobile genetic elements. The complicated relationships of host-bacterium and bacterium-bacterium help S. epidermidis to play a vital role in balancing the epithelial microflora. The highly variable and dynamic nature of the S. epidermidis genome may be the result of its success in adapting to broad habitats, which is necessary to deal with complex environments. This study gives the general landscape of S. epidermidis pan-genome and provides valuable insights into mechanisms for genome evolution and lifestyle adaptation of this ecologically flexible species.

microbiology↗

bioRxiv2019

Inhibiting TG2 sensitize lung cancer to radiotherapy through interfering TOPOIIα-mediated DNA repair

Radiotherapy is an indispensable strategy for lung cancer, however, treatment failure or reoccurrence is often found in patients due to the developing radioresistance. Novel approaches are required for radiosensitizing to improve the therapeutic efficacy. In present study, we found that transglutaminase 2 (TG2) confers radioresistance in non-small cell lung cancer (NSCLC) cells through regulating TOPOII and promoting DNA repair. Our data showed that TG2 inhibitor or knockdown increased NSCLC radiosensitivity in vivo and in vitro. We found that TG2 translocated into nucleus and located to DSB sites, surprisingly, knockdown TG2 or glucosamine inhibited the phosphorylation of ATM, ATR and DNA-Pkcs. Through IP-MS assay and functional experiments, we identified that TOPOII as an downstream factor of TG2. Moreover, we found that TGase domain account for the interaction with TOPOII. Finally, we found that TG2 expression was correlated with poor survival in lung adenocarcinoma instead of squamous cell carcinoma. In conclusion, we demonstrated that inhibiting TG2 sensitize NSCLC to IR through interfere TOPOII mediated DNA repair, suggesting TG2 as a potential radiosensitizing target in NSCLC.

cancer biology↗

bioRxiv2019

Predicting Drug Protein Interaction using Quasi-Visual Question Answering System

Identifying novel drug-protein interactions is crucial for drug discovery. For this purpose, many machine learning-based methods have been developed based on drug descriptors and one-dimensional (1D) protein sequences. However, protein sequence cant accurately reflect the interactions in 3D space. On the other hand, a direct input of 3D structure is of low efficiency due to the sparse 3D matrix, and is also prevented by limited number of co-crystal structures available for training. In this work, we propose an end-to-end deep learning framework to predict the interactions by representing proteins with 2D distance map from monomer structures (Image), and drugs with molecular linear notation (String), following the Visual Question Answering mode. For an efficient training of the system, we introduced a dynamic attentive convolutional neural network to learn fixed-size representations from the variable-length distance maps and a self-attentional sequential model to automatically extract semantic features from the linear notations. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on the DUD-E, Human and BindingDB benchmark datasets. Further attention visualization provides biological interpretation to depict highlighted regions of both protein and drug molecules.

bioinformatics↗

bioRxiv2019

Three-dimensional genome reorganization during mouse spermatogenesis

Three-dimensional genome organization plays an important role in many biological processes. Yet, how the genome is packaged at the molecular level during mammalian spermatogenesis remains unclear. Here, we performed Hi-C in seven sequential stages during mouse spermatogenesis. We found that topological associating domains (TADs) and chromatin loops underwent highly dynamic reorganization. They displayed clear existence in primitive type A spermatogonia, disappearance at pachytene stage, and reestablishment in spermatozoa. Surprisingly, even in the absence of TADs and chromatin loops at pachytene stage, CTCF remained bound at TAD boundary regions (identified in primitive type A spermatogonia). Additionally, many enhancers and promoters exhibited features of open chromatin and transcription remained active at pachytene stage. A/B compartmentalization and segmentation ratio were conserved in different stages of spermatogenesis in autosomes, although there were A/B compartment switching events correlated with gene activity changes. Intriguingly, A/B compartment structure on the X chromosome disappeared during pacSC, rST and eST stages. Together, our work uncovered a dynamic three-dimensional chromatin organization during mouse spermatogenesis and suggested that transcriptional regulation could be independent of TADs and chromatin loops at specific developmental stages.

genomics↗

bioRxiv2019

Plasma Circular RNA Panel to Diagnose Hepatitis B Virus-Related Hepatocellular Carcinoma

To explore whether plasma circular RNAs (circRNAs) can diagnose hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), microarray and qPCR were used to identify plasma circRNAs that were increased in HCC patients compared with controls (including healthy controls, chronic hepatitis B, HBV-related liver cirrhosis and HCC patients). A logistic regression model was constructed using a training set (n=313) and then validated using another two independent sets (n=306 and 526, respectively). Area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. We identified a plasma circRNA panel (CircPanel) containing three circRNAs (hsa_circ_0000976, hsa_circ_0007750 and hsa_circ_0139897) that could detect HCC. CircPanel showed a higher accuracy than AFP (alpha-fetoprotein) to distinguish individuals with HCC from controls in all three sets (AUC 0.863 [95% CI 0.819-0.907] vs 0.790 [0.738-0.842], P=0.036 in training set; 0.843 [0.796-0.890] vs 0.747 [0.691-0.804], P=0.011 in validation set 1 and 0.864 [0.830-0.898] vs 0.769 [0.728-0.810], P<0.001 in validation set 2). CircPanel also performed well in detecting Small-HCC (solitary, [≤]3cm), AFP-negative HCC and AFP-negative Small-HCC.\n\nSignificance of this studyWhat is already known about this subject? O_LIThe diagnostic accuracy of alpha-fetoprotein (AFP) in detecting hepatocellular carcinoma (HCC) is unsatisfactory.\nC_LIO_LICircular RNA (circRNA) expression profiles in HCC and adjacent nontumor liver tissues are significantly different.\nC_LIO_LIPlasma circRNAs are enriched, stable and can be biomarkers for various diseases.\nC_LI\n\nWhat are the new findings? O_LIThe expression of circRNAs in the plasma from HCC patients and chronic hepatitis B is significantly different.\nC_LIO_LIPlasma circRNA panel (CircPanel, including hsa_circ_0000976, hsa_circ_0007750 and hsa_circ_0139897) has a higher accuracy than AFP to distinguish individuals with HCC or Small-HCC (solitary, [≤]3cm) from controls (healthy controls, chronic hepatitis B and HBV-related liver cirrhosis).\nC_LIO_LICircPanel also performs well in diagnosing AFP-negative HCC and AFP-negative Small-HCC.\nC_LI\n\nHow might it impact on clinical practice in the foreseeable future? Plasma CircPanel can be a diagnostic biomarker in detecting HCC and improves the diagnostic accuracy.

cancer biology↗

bioRxiv2019

Identification of 67 pleiotropic genes for seven autoimmune diseases using multivariate statistical analysis

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitation in detecting complex genotype-phenotype correlations. It is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune diseases with a multivariate analysis. In this study, the GWAS summary statistics including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions was analyzed to identify shared variants of seven autoimmune diseases using metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological function of the identified genes. After metaCCA analysis, 4,962 SNPs (P<1.21x10-6) and 1,044 pleotropic genes (P<4.34x10-6) were identified. By screening the results of gene-based p-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes which achieved significance threshold in metaCCA analysis and were also associated with at least one autoimmune disease in the VEGAS2 analysis. The metaCCA method could identify novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for some common therapeutic approaches of autoimmune diseases based on the pleiotropic genes and common mechanisms identified. Author summaryAlthough previous researches have clearly indicated varying degrees of overlapping genetic sensitivities in autoimmune diseases, it has proven GWAS only explain small percent of heritability. Here, we take advantage of recent technical and methodological advances to identify pleiotropic genes that act on common biological mechanisms and the overlapping pathophysiological pathways of autoimmune diseases. After selection using multivariate analysis and verification using gene-based analyses, we successfully identified a total of 67 pleiotropic genes and performed the functional term enrichment analysis. In particularly, 27 genes were identified to be pleiotropic in previous different types of studies, which were validated by our present study. Forty significant genes (16 genes were associated with one disease earlier, and 24 were novel) might be the novel pleiotropic candidate genes for seven autoimmune diseases. The improved detection not only yielded the shared genetic components but also provided better understanding for exploring the potential common biological pathogenesis of these major autoimmune diseases.

genetics↗

bioRxiv2019

Comparing 3D genome organization in multiple species using Phylo-HMRF

Recent developments in whole-genome mapping approaches for the chromatin interactome (such as Hi-C) have offered new insights into 3D genome organization. However, our knowledge of the evolutionary patterns of 3D genome structures in mammalian species remains surprisingly limited. In particular, there are no existing phylogenetic-model based methods to analyze chromatin interactions as continuous features across different species. Here we develop a new probabilistic model, named phylogenetic hidden Markov random field (Phylo-HMRF), to identify evolutionary patterns of 3D genome structures based on multi-species Hi-C data by jointly utilizing spatial constraints among genomic loci and continuous-trait evolutionary models. The effectiveness of Phylo-HMRF is demonstrated in both simulation evaluation and application to real Hi-C data. We used Phylo-HMRF to uncover cross-species 3D genome patterns based on Hi-C data from the same cell type in four primate species (human, chimpanzee, bonobo, and gorilla). The identified evolutionary patterns of 3D genome organization correlate with features of genome structure and function, including long-range interactions, topologically-associating domains (TADs), and replication timing patterns. This work provides a new framework that utilizes general types of spatial constraints to identify evolutionary patterns of continuous genomic features and has the potential to reveal the evolutionary principles of 3D genome organization.

bioinformatics↗