BackgroundAltering the biochemical makeup of chromatin by the incorporation of histone variants during development represents a key mechanism in regulating gene expression. The histone variant H2A.B, H2A.B.3 in mice, appeared late in evolution and is most highly expressed in the testis. In the mouse, it is encoded by three different genes. H2A.B expression is spatially and temporally regulated during spermatogenesis being most highly expressed in the haploid round spermatid stage. Active genes gain H2A.B where it directly interacts with polymerase II and RNA processing factors within splicing speckles. However, the importance of H2A.B for gene expression and fertility are unknown.ResultsHere, we report the first mouse knockout of this histone variant and its effects on fertility, nuclear organization, and gene expression. In view of the controversy related to the generation of off-target mutations by gene editing approaches, we test the specificity of TALENs by disrupting the H2A.B multi-copy gene family using only one pair of TALENs. We show that TALENs do display a high level of specificity since no off-target mutations are detected by bioinformatics analyses of exome sequences obtained from three consecutive generations of knockout mice and by Sanger DNA sequencing. Male H2A.B.3 knockout mice are subfertile and display an increase in the proportion of abnormal sperm and clogged seminiferous tubules. Significantly, a loss of proper RNA Pol II targeting to distinct transcription–splicing territories and changes to pre-mRNA splicing are observed.ConclusionWe have produced the first H2A.B knockout mouse using the TALEN approach.Electronic supplementary materialThe online version of this article (10.1186/s13059-019-1633-3) contains supplementary material, which is available to authorized users.
The COVID-19 pandemic has affected all individuals across the globe in some way. Despite large numbers of reported seroprevalence studies, there remains a limited understanding of how the magnitude and epitope utilization of the humoral immune response to SARS-CoV-2 viral anti-gens varies within populations following natural infection. Here, we designed a quantitative, multi-epitope protein microarray comprising various nucleocapsid protein structural motifs, including two structural domains and three intrinsically disordered regions. Quantitative data from the microarray provided complete differentiation between cases and pre-pandemic controls (100% sensitivity and specificity) in a case-control cohort (n = 100). We then assessed the influence of disease severity, age, and ethnicity on the strength and breadth of the humoral response in a multi-ethnic cohort (n = 138). As expected, patients with severe disease showed significantly higher antibody titers and interestingly also had significantly broader epitope coverage. A significant increase in antibody titer and epitope coverage was observed with increasing age, in both mild and severe disease, which is promising for vaccine efficacy in older individuals. Additionally, we observed significant differences in the breadth and strength of the humoral immune response in relation to ethnicity, which may reflect differences in genetic and lifestyle factors. Furthermore, our data enabled localization of the immuno-dominant epitope to the C-terminal structural domain of the viral nucleocapsid protein in two independent cohorts. Overall, we have designed, validated, and tested an advanced serological assay that enables accurate quantitation of the humoral response post natural infection and that has revealed unexpected differences in the magnitude and epitope utilization within a population.
Objective Conventional immunoassays detect autoantibodies related to systemic lupus erythematosus (SLE) via recognition of epitopes on autoantigens expressed in their denatured rather than native conformational state, casting difficulty in evaluating the genuine pathogenicity of the autoantibodies. We aimed to use a novel high-throughput protein microarray platform to identify autoantibodies against native autoantigens in SLE sera. Methods Sera from SLE patients and those of gender-, age-, and ethnicity-matched healthy controls (HC) were screened against more than 1,600 immune-related antigens of native conformation. The relative fluorescent unit readout from post-assay imaging were subjected to bioinformatics pre-processing and composite normalization. A penetrance fold change (pFC) analysis between SLE and HC samples shortlisted 50 autoantigens that were subjected to an unsupervised cluster analysis. Correlations between the pFC of putative autoantigens and clinical parameters including SLE disease activity index (SLEDAI-2K) and recent SLE flares were explored. Results 381 autoantigens were identified when 15 SLE and 15 HC serum samples were compared. The top 20 autoantigens which elicited autoantibody responses in SLE sera filtered based on the highest pFC were further analyzed. Autoantigens which the putative autoantibodies reacted against are those involved in chromatin organization such as DEK, regulation of transcription activity including REOX4 and ELF4, and negative regulation of NFkB activity such as TRIB3. Additionally, the pFC of these autoantibodies significantly and positively correlated with SLEDAI-2K and recent SLE flares. Conclusion A high-throughput protein microarray platform allows detection and quantification of putative lupus-related autoantibodies which are of potential pathophysiological and prognostic significance in SLE patients.
Abnormal immune reactivity in patients with beta-thalassemia (beta-thal) major can be associated with poor prognosis. Immunome protein-array analysis represents a powerful approach to identify novel biomarkers. The Sengenics Immunome Protein Array platform was used for high-throughput quantification of autoantibodies in 12 serum samples collected from nine beta-thal major patients and three non-thalassemia controls, which were run together with two pooled normal sera (Sengenics Internal QC samples). To obtain more accurate and reliable results, the evaluation of the biological relevance of the shortlisted biomarkers was analyzed using an Open Target Platform online database. Elevated autoantibodies directed against 23 autoantigens on the immunome array were identified and analyzed using a penetrance fold change-based bioinformatics method. Understanding the autoantibody profile of beta-thal major patients would help to further understand the pathogenesis of the disease. The identified autoantigens may serve as potential biomarkers for the prognosis of beta-thal major.
Anti-drug antibody (ADAb) development is associated with secondary therapeutic failure in biologic-treated rheumatoid arthritis (RA) patients. With a treat-to-target goal, we aimed to identify biomarkers for predicting ADAb development and therapeutic response in adalimumab-treated patients. Three independent cohorts were enrolled. In Cohort-1, 24 plasma samples (6 ADAb-positive and 6 ADAb-negative patients at baseline and week 24 of adalimumab therapy, respectively) were assayed with immune-related microarray containing 1,636 correctly folded functional proteins. Next, we executed statistically powered autoantibody profiling analysis of 50 samples in Cohort-2 (24 ADAb-positive and 26 ADAb-negative patients). Subsequently, immunofluorescence assay was performed on 48 samples in Cohort-3 to correlate with ADAb titers and drug levels. The biomarkers were identified for predicting ADAb development and therapeutic response using the immune-related microarray and machine learning approach. ADAb-positive patients had lower drug levels at week 24 ( median = 0.024 μ g / ml ) compared with ADAb-negative patients ( median = 6.38 μ g / ml , p < 0.001 ). ROC analysis based on the ADAb status revealed the top 20 autoantibodies with AUC ≥ 0.7 in differentiating both groups in Cohort-1. Analysis of Cohort-2 dataset identified a panel of 8 biomarkers (TROVE2, SSB, NDE1, ZHX2, SH3GL1, CARD9, PTPN20, and KLHL12) with 80.6% specificity, 77.4% sensitivity, and 79.0% accuracy in discriminating poor from EULAR responders. Immunofluorescence assay validated that anti-TROVE2 antibody could highly predict ADAb development and poor EULAR response (AUC 0.79 and 0.89, respectively). Multivariate regression analysis proved anti-TROVE2 antibody to be an independent predictor for developing ADAb. Immune-related protein microarray and replication analysis identified anti-TROVE2 antibody as a useful biomarker for predicting ADAb development and therapeutic response in adalimumab-treated patients.
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