Lysosomal endonuclease RNase T2 and PLD exonucleases cooperatively generate RNA ligands for TLR7 activation.

Toll-like receptor 7 (TLR7) is essential for recognition of RNA viruses and initiation of antiviral immunity. TLR7 contains two ligand-binding pockets that recognize different RNA degradation products: pocket 1 recognizes guanosine, while pocket 2 coordinates pyrimidine-rich RNA fragments. We found that the endonuclease RNase T2, along with 5' exonucleases PLD3 and PLD4, collaboratively generate the ligands for TLR7. Specifically, RNase T2 generated guanosine 2',3'-cyclic monophosphate-terminated RNA fragments. PLD exonuclease activity further released the terminal 2',3'-cyclic guanosine monophosphate (2',3'-cGMP) to engage pocket 1 and was also needed to generate RNA fragments for pocket 2. Loss-of-function studies in cell lines and primary cells confirmed the critical requirement for PLD activity. Biochemical and structural studies showed that PLD enzymes form homodimers with two ligand-binding sites important for activity. Previously identified disease-associated PLD mutants failed to form stable dimers. Together, our data provide a mechanistic basis for the detection of RNA fragments by TLR7.

Glucose-driven TOR-FIE-PRC2 signalling controls plant development.

Nutrients and energy have emerged as central modulators of developmental programmes in plants and animals1-3. The evolutionarily conserved target of rapamycin (TOR) kinase is a master integrator of nutrient and energy signalling that controls growth. Despite its key regulatory roles in translation, proliferation, metabolism and autophagy2-5, little is known about how TOR shapes developmental transitions and differentiation. Here we show that glucose-activated TOR kinase controls genome-wide histone H3 trimethylation at K27 (H3K27me3) in Arabidopsis thaliana, which regulates cell fate and development6-10. We identify FERTILIZATION-INDEPENDENT ENDOSPERM (FIE), an indispensable component of Polycomb repressive complex 2 (PRC2), which catalyses H3K27me3 (refs. 6-8,10-12), as a TOR target. Direct phosphorylation by TOR promotes the dynamic translocation of FIE from the cytoplasm to the nucleus. Mutation of the phosphorylation site on FIE abrogates the global H3K27me3 landscape, reprogrammes the transcriptome and disrupts organogenesis in plants. Moreover, glucose-TOR-FIE-PRC2 signalling modulates vernalization-induced floral transition. We propose that this signalling axis serves as a nutritional checkpoint leading to epigenetic silencing of key transcription factor genes that specify stem cell destiny in shoot and root meristems and control leaf, flower and silique patterning, branching and vegetative-to-reproduction transition. Our findings reveal a fundamental mechanism of nutrient signalling in direct epigenome reprogramming, with broad relevance for the developmental control of multicellular organisms.

Evolutionary rewiring of the wheat transcriptional regulatory network by lineage-specific transposable elements.

More than 80% of the wheat genome consists of transposable elements (TEs), which act as major drivers of wheat genome evolution. However, their contributions to the regulatory evolution of wheat adaptations remain largely unclear. Here, we created genome-binding maps for 53 transcription factors (TFs) underlying environmental responses by leveraging DAP-seq in Triticum urartu, together with epigenomic profiles. Most TF binding sites (TFBSs) located distally from genes are embedded in TEs, whose functional relevance is supported by purifying selection and active epigenomic features. About 24% of the non-TE TFBSs share significantly high sequence similarity with TE-embedded TFBSs. These non-TE TFBSs have almost no homologous sequences in non-Triticeae species and are potentially derived from Triticeae-specific TEs. The expansion of TE-derived TFBS linked to wheat-specific gene responses, suggesting TEs are an important driving force for regulatory innovations. Altogether, TEs have been significantly and continuously shaping regulatory networks related to wheat genome evolution and adaptation.

An atlas of wheat epigenetic regulatory elements reveals subgenome divergence in the regulation of development and stress responses.

Wheat (Triticum aestivum) has a large allohexaploid genome. Subgenome-divergent regulation contributed to genome plasticity and the domestication of polyploid wheat. However, the specificity encoded in the wheat genome determining subgenome-divergent spatio-temporal regulation has been largely unexplored. The considerable size and complexity of the genome are major obstacles to dissecting the regulatory specificity. Here, we compared the epigenomes and transcriptomes from a large set of samples under diverse developmental and environmental conditions. Thousands of distal epigenetic regulatory elements (distal-epiREs) were specifically linked to their target promoters with coordinated epigenomic changes. We revealed that subgenome-divergent activity of homologous regulatory elements is affected by specific epigenetic signatures. Subgenome-divergent epiRE regulation of tissue specificity is associated with dynamic modulation of H3K27me3 mediated by Polycomb complex and demethylases. Furthermore, quantitative epigenomic approaches detected key stress responsive cis- and trans-acting factors validated by DNA Affinity Purification and sequencing, and demonstrated the coordinated interplay between epiRE sequence contexts, epigenetic factors, and transcription factors in regulating subgenome divergent transcriptional responses to external changes. Together, this study provides a wealth of resources for elucidating the epiRE regulomics and subgenome-divergent regulation in hexaploid wheat, and gives new clues for interpreting genetic and epigenetic interplay in regulating the benefits of polyploid wheat.

CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer.

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

Analysis of structural variation among inbred mouse strains.

BACKGROUND: 'Long read' sequencing methods have been used to identify previously uncharacterized structural variants that cause human genetic diseases. Therefore, we investigated whether long read sequencing could facilitate genetic analysis of murine models for human diseases. RESULTS: The genomes of six inbred strains (BTBR T + Itpr3tf/J, 129Sv1/J, C57BL/6/J, Balb/c/J, A/J, SJL/J) were analyzed using long read sequencing. Our results revealed that (i) Structural variants are very abundant within the genome of inbred strains (4.8 per gene) and (ii) that we cannot accurately infer whether structural variants are present using conventional short read genomic sequence data, even when nearby SNP alleles are known. The advantage of having a more complete map was demonstrated by analyzing the genomic sequence of BTBR mice. Based upon this analysis, knockin mice were generated and used to characterize a BTBR-unique 8-bp deletion within Draxin that contributes to the BTBR neuroanatomic abnormalities, which resemble human autism spectrum disorder. CONCLUSION: A more complete map of the pattern of genetic variation among inbred strains, which is produced by long read genomic sequencing of the genomes of additional inbred strains, could facilitate genetic discovery when murine models of human diseases are analyzed.

Extended application of genomic selection to screen multiomics data for prognostic signatures of prostate cancer.

Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e. miRNAs) significantly increased outcome predictability when compared with panels consisting of a small number of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multiomics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multiomics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

Mesenchymal stem cells-derived extracellular vesicles protect against oxidative stress-induced xenogeneic biological root injury via adaptive regulation of the PI3K/Akt/NRF2 pathway.

Xenogeneic extracellular matrices (xECM) for cell support have emerged as a potential strategy for addressing the scarcity of donor matrices for allotransplantation. However, the poor survival rate or failure of xECM-based organ transplantation is due to the negative impacts of high-level oxidative stress and inflammation on seed cell viability and stemness. Herein, we constructed xenogeneic bioengineered tooth roots (bio-roots) and used extracellular vesicles from human adipose-derived mesenchymal stem cells (hASC-EVs) to shield bio-roots from oxidative damage. Pretreatment with hASC-EVs reduced cell apoptosis, reactive oxygen species generation, mitochondrial changes, and DNA damage. Furthermore, hASC-EV treatment improved cell proliferation, antioxidant capacity, and odontogenic and osteogenic differentiation, while significantly suppressing oxidative damage by activating the phosphatidylinositol 3-kinase (PI3K)/Akt pathway and nuclear factor erythroid 2 (NFE2)-related factor 2 (NRF2) nuclear translocation via p62-associated Kelch-like ECH-associated protein 1 (KEAP1) degradation. Inhibition of PI3K/Akt and Nrf2 knockdown reduced antioxidant capacity, indicating that the PI3K/Akt/NRF2 pathway partly mediates these effects. In subcutaneous grafting experiments using Sprague-Dawley rats, hASC-EV administration significantly enhanced the antioxidant effect of the bio-root, improved the regeneration efficiency of periodontal ligament-like tissue, and maximized xenograft function. Conclusively, therefore, hASC-EVs have the potential to be used as an immune modulator and antioxidant for treating oxidative stress-induced bio-root resorption and degradation, which may be utilized for the generation and restoration of other intricate tissues and organs.

Values of OAS gene family in the expression signature, immune cell infiltration and prognosis of human bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is one of the most common genitourinary malignancies in the world, but its pathogenic genes have not been fully identified and the treatment outcomes are still unsatisfactory. Although the members of 2', 5'-oligoadenylate synthetase (OAS) gene family are known involved in some tumorous biological processes, the roles of the OAS gene family in BLCA are still undetermined. METHODS: By combining vast bioinformatic datasets analyses of BLCA and the experimental verification on clinical BLCA specimen, we identified the expressions and biological functions of OAS gene family members in BLCA with comparison to normal bladder tissues. RESULTS: The expression levels of OAS gene family members were higher in BLCA than in normal bladder tissues. The expression levels of most OAS genes had correlations with genomic mutation and methylation, and with the infiltration levels of CD4 + T cells, CD8 + T cells, neutrophils, and dendritic cells in the microenvironment of BLCA. In addition, high expressions of OAS1, OAS2, OAS3, and OASL predicted better overall survival in BLCA patients. CONCLUSIONS: The highly expressed OAS genes in BLCA can reflect immune cells infiltration in the tumor microenvironment and predict the better overall survival of BLCA, and thus may be considered as a signature of BLCA. The study provides new insights into the diagnosis, treatment, and prognosis of BLCA.

Conservation and trans-regulation of histone modification in the A and B subgenomes of polyploid wheat during domestication and ploidy transition.

BACKGROUND: Polyploidy has played a prominent role in the evolution of plants and many other eukaryotic lineages. However, how polyploid genomes adapt to the abrupt presence of two or more sets of chromosomes via genome regulation remains poorly understood. Here, we analyzed genome-wide histone modification and gene expression profiles in relation to domestication and ploidy transition in the A and B subgenomes of polyploid wheat. RESULTS: We found that epigenetic modification patterns by two typical euchromatin histone markers, H3K4me3 and H3K27me3, for the great majority of homoeologous triad genes in A and B subgenomes were highly conserved between wild and domesticated tetraploid wheats and remained stable in the process of ploidy transitions from hexaploid to extracted tetraploid and then back to resynthesized hexaploid. However, a subset of genes was differentially modified during tetraploid and hexaploid wheat domestication and in response to ploidy transitions, and these genes were enriched for particular gene ontology (GO) terms. The extracted tetraploid wheat manifested higher overall histone modification levels than its hexaploid donor, and which were reversible and restored to normal levels in the resynthesized hexaploid. Further, while H3K4me3 marks were distally distributed along each chromosome and significantly correlated with subgenome expression as expected, H3K27me3 marks showed only a weak distal bias and did not show a significant correlation with gene expression. CONCLUSIONS: Our results reveal overall high stability of histone modification patterns in the A and B subgenomes of polyploid wheat during domestication and in the process of ploidy transitions. However, modification levels of a subset of functionally relevant genes in the A and B genomes were trans-regulated by the D genome in hexaploid wheat.

Deshrinking ridge regression for genome-wide association studies.

MOTIVATION: Genome-wide association studies (GWAS) are still the primary steps toward gene discovery. The urgency is more obvious in the big data era when GWAS are conducted simultaneously for thousand traits, e.g. transcriptomic and metabolomic traits. Efficient mixed model association (EMMA) and genome-wide efficient mixed model association (GEMMA) are the widely used methods for GWAS. An algorithm with high computational efficiency is badly needed. It is interesting to note that the test statistics of the ordinary ridge regression (ORR) have the same patterns across the genome as those obtained from the EMMA method. However, ORR has never been used for GWAS due to its severe shrinkage on the estimated effects and the test statistics. RESULTS: We introduce a degree of freedom for each marker effect obtained from ORR and use it to deshrink both the estimated effect and the standard error so that the Wald test of ORR is brought back to the same level as that of EMMA. The new method is called deshrinking ridge regression (DRR). By evaluating the methods under three different model sizes (small, medium and large), we demonstrate that DRR is more generalized for all model sizes than EMMA, which only works for medium and large models. Furthermore, DRR detect all markers in a simultaneous manner instead of scanning one marker at a time. As a result, the computational time complexity of DRR is much simpler than EMMA and about m (number of genetic variants) times simpler than that of GEMMA when the sample size is way smaller than the number of markers. CONTACT: shizhong.xu@ucr.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fundamental Contribution and Host Range Determination of ANP32A and ANP32B in Influenza A Virus Polymerase Activity.

The polymerase of the influenza virus is part of the key machinery necessary for viral replication. However, the avian influenza virus polymerase is restricted in mammalian cells. The cellular protein ANP32A has been recently found to interact with viral polymerase and to influence both polymerase activity and interspecies restriction. We report here that either human ANP32A or ANP32B is indispensable for human influenza A virus RNA replication. The contribution of huANP32B is equal to that of huANP32A, and together they play a fundamental role in the activity of human influenza A virus polymerase, while neither human ANP32A nor ANP32B supports the activity of avian viral polymerase. Interestingly, we found that avian ANP32B was naturally inactive, leaving avian ANP32A alone to support viral replication. Two amino acid mutations at sites 129 to 130 in chicken ANP32B lead to the loss of support of viral replication and weak interaction with the viral polymerase complex, and these amino acids are also crucial in the maintenance of viral polymerase activity in other ANP32 proteins. Our findings strongly support ANP32A and ANP32B as key factors for both virus replication and adaptation.IMPORTANCE The key host factors involved in the influenza A viral polymerase activity and RNA replication remain largely unknown. We provide evidence here that ANP32A and ANP32B from different species are powerful factors in the maintenance of viral polymerase activity. Human ANP32A and ANP32B contribute equally to support human influenza viral RNA replication. However, unlike avian ANP32A, the avian ANP32B is evolutionarily nonfunctional in supporting viral replication because of a mutation at sites 129 and 130. These sites play an important role in ANP32A/ANP32B and viral polymerase interaction and therefore determine viral replication, suggesting a novel interface as a potential target for the development of anti-influenza strategies.

A coordinate descent approach for sparse Bayesian learning in high dimensional QTL mapping and genome-wide association studies.

MOTIVATION: Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait's variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model. RESULTS: We developed a sparse Bayesian learning (SBL) method for quantitative trait locus mapping and genome-wide association studies. This new method adopts a coordinate descent algorithm to estimate parameters (marker effects) by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100 000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small P-values, indicating that SBL is insensitive to stringent thresholds in significance testing. AVAILABILITY AND IMPLEMENTATION: An R package (sbl) is available on the comprehensive R archive network (CRAN) and https://github.com/MeiyueComputBio/sbl/tree/master/R%20packge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide probing RNA structure with the modified DMS-MaPseq in Arabidopsis.

Transcripts have intrinsic propensity to form stable secondary structure that is fundamental to regulate RNA transcription, splicing, translation, RNA localization and turnover. Numerous methods that integrate chemical reactions with next-generation sequencing (NGS) have been applied to study in vivo RNA structure, providing new insights into RNA biology. Dimethyl sulfate (DMS) probing coupled with mutational profiling through NGS (DMS-MaPseq) is a newly developed method for revealing genome-wide or target-specific RNA structure. Herein, we present our experimental protocol of a modified DMS-MaPseq method for plant materials. The DMS treatment condition was optimized, and library preparation procedures were simplified. We also provided custom scripts for bioinformatic analysis of genome-wide DMS-MaPseq data. Bioinformatic results showed that our method could generate high-quality and reproducible data. Further, we assessed sequencing depth and coverage for genome-wide RNA structure profiling in Arabidopsis, and provided two examples of in vivo structure of mobile RNAs. We hope that our modified DMS-MaPseq method will serve as a powerful tool for analyzing in vivo RNA structurome in plants.

Oscillating shear stress mediates mesenchymal transdifferentiation of EPCs by the Kir2.1 channel.

Although endothelial progenitor cells (EPCs) are considered to be an essential source of vascular endothelial repair, their bidirectional differentiation determines that they play a double-edged role in the restoration of endothelial injury. In this research, we investigated the effect of Kir2.1 ion channel on the transdifferentiation of endothelial progenitor cells (EPCs) under the oscillating shear stress (OSS) and the molecular mechanisms underlying the pathological vascular remodeling. EPCs were treated with OSS (± 3.5 dynes/cm2, 1 Hz) simulated with the parallel flow chamber system. The results have shown that OSS promoted the expression of α-SMA and SM22, markers of mesenchymal cells on EPCs. Moreover, OSS also increased expression of Kir2.1 in EPCs. The down-regulation of Kir2.1 reduced OSS-induced EPC mesenchymal transdifferentiation. The overexpression of Kir2.1 suppressed the angiogenic abilities of EPCs in vitro. In parallel, the overexpression of Kir2.1 on EPCs thickened the carotid tunica intima in rat carotid artery balloon injured model in vivo. Taken together, those data indicated that the OSS could facilitate the transdifferentiation of EPCs by increasing Kir2.1 expression. This study provides a novel insight into the pathogenesis of cardiovascular diseases and gives evidence for Kir2.1 as a potential therapeutic target.

Reducing expression of a nitrate-responsive bZIP transcription factor increases grain yield and N use in wheat.

Nitrogen (N) plays critical role in plant growth; manipulating N assimilation could be a target to increase grain yield and N use. Here, we show that ABRE-binding factor (ABF)-like leucine zipper transcription factor TabZIP60 mediates N use and growth in wheat. The expression of TabZIP60 is repressed when the N-deprived wheat plants is exposed to nitrate. Knock down of TabZIP60 through RNA interference (RNAi) increases NADH-dependent glutamate synthase (NADH-GOGAT) activity, lateral root branching, N uptake and spike number, and improves grain yield more than 25% under field conditions, while overexpression of TabZIP60-6D had the opposite effects. Further investigation shows TabZIP60 binds to ABRE-containing fragment in the promoter of TaNADH-GOGAT-3B and negatively regulates its expression. Genetic analysis reveals that TaNADH-GOGAT-3B overexpression overcomes the spike number and yield reduction caused by overexpressing TabZIP60-6D. As such, TabZIP60-mediated wheat growth and N use is associated with its negative regulation on TaNADH-GOGAT expression. These findings indicate that TabZIP60 and TaNADH-GOGAT interaction plays important roles in mediating N use and wheat growth, and provides valuable information for engineering N use efficiency and yield in wheat.

Statistical power in genome-wide association studies and quantitative trait locus mapping.

Power calculation prior to a genetic experiment can help investigators choose the optimal sample size to detect a quantitative trait locus (QTL). Without the guidance of power analysis, an experiment may be underpowered or overpowered. Either way will result in wasted resource. QTL mapping and genome-wide association studies (GWAS) are often conducted using a linear mixed model (LMM) with controls of population structure and polygenic background using markers of the whole genome. Power analysis for such a mixed model is often conducted via Monte Carlo simulations. In this study, we derived a non-centrality parameter for the Wald test statistic for association, which allows analytical power analysis. We show that large samples are not necessary to detect a biologically meaningful QTL, say explaining 5% of the phenotypic variance. Several R functions are provided so that users can perform power analysis to determine the minimum sample size required to detect a given QTL with a certain statistical power or calculate the statistical power with given sample size and known values of other population parameters.

Statistics of Mendelian segregation-A mixture model.

Mendel's law of segregation explains why genetic variation can be maintained over time. In diploid organisms, an offspring receives one allele from each parent, not just half of the blended genetic material of the parents. Which of the two alleles is received is purely random. This stochastic process generates genetic variation among members of the same family, called Mendelian segregation variance or within-family variance. In statistics, the genetic value of a quantitative trait for an offspring follows a mixture distribution consisting of the four alleles of the two parents, guided by a Mendelian variable from each parent. The mixture model allows us to partition the total genetic variance into between-family and within-family variances. In the absence of inbreeding, the genetic variance splits half to the between-family variance and half to the within-family variance. With inbreeding, however, the between-family variance is increased at the cost of the within-family variance, leading to a net increase of the total genetic variance. This study defines multiple Mendelian variables and develops a mixture model of quantitative genetics. The phenomenon that allelic variance is maintained over time is guided by "the law of conservation of allelic variance" in biology, which is comparable to "the law of conservation of mass" in physics.

Identification and characterization of a common B-cell epitope on EIAV capsid proteins.

The equine infectious anemia virus (EIAV) capsid protein (p26) is one of the major immunogenic proteins during EIAV infection and is widely used for the detection of EIAV antibodies in horses. However, few reports have described the use of EIAV-specific monoclonal antibodies (MAbs) in etiological and immunological detection. Previously, we developed an antigen capture enzyme-linked immunosorbent assay (AC-ELISA) for the quantification of the EIAV p26 protein level. However, the epitopes recognized by the MAbs were not identified, and the utilization of the MAbs needs to be evaluated. In this study, we characterized two monoclonal antibodies (9H8 and 1G11 MAbs) against EIAV p26. Two B-cell epitopes are located in amino acid residues, 73NLDKIAEE81 (HE) and 199KNAMRHLRPEDTLEEKMYAC218 (GE) for the 9H8 and 1G11 MAbs, respectively. The 1G11 epitope (GE) varied among viruses isolated worldwide but can be recognized by anti-EIAV sera from different regions, including China, the USA, and Argentina. Meanwhile, 1G11 MAb could react with the mutants of almost all the EIAV strains. Furthermore, we found that the histidine at position 204 (H204), leucine at position 205 (L205), and aspartic acid at position 209 (D209) of EIAV p26 individually played pivotal roles in binding with the 1G11 MAb. Our results revealed that the GE peptide might be a common B-cell binding epitope of EIAV antibodies. This is also the first report to identify a broad-spectrum monoclonal antibody (1G11) against p26 of EIAV. These findings may provide a useful basis for the development of new diagnostic assays for EIAV.