A novel homozygous splice-site mutation of JK gene leads to Jk(a-b-) phenotype.

OBJECTIVES: This study aimed to investigate the molecular mechanism of the Jk(a-b-) phenotype in a Chinese transfusion patient. BACKGROUND: Many different mutation types relating to Jk(a-b-) phenotype have been reported. However, the splice-site mutation is relatively rare and the related functional verification is lacking. MATERIALS AND METHODS: In this study, the blood sample was collected from a transfusion patient with the Jk(a-b-) phenotype. Serotyping was performed using routine serological methods. The exons sequences and coding regions of the JK gene were amplified using polymerase chain reaction and directly sequenced. To perform a minigene splicing assay, the intronic mutation sequences were cloned into a pSPL3 splice reporting vector. The splicing reporter minigene assay was performed in HEK 293T cells. RESULTS: The Jk(a-b-) phenotype of the blood sample was identified through serological testing. Sequencing results revealed that the sample had a novel homozygous splice-site mutation JK*02N (NM_015865.7: c.663+3A>C). Further analysis, including cDNA sequencing and minigene splicing assay, confirmed that the novel splice-site mutation resulted in exon skipping. Interestingly, different numbers of exons being skipped were obtained by the two methods. CONCLUSION: This study revealed a novel homozygous splicing-site mutation associated with the Jk(a-b-) phenotype in Chinese population. Our results emphasise the importance of the in vitro functional method minigene splicing assay, while also acknowledging its potential limitations when compared to cDNA sequencing.

CRISPR/Cas9-Editing K562 Cell Line as a Potential Tool in Transfusion Applications: Knockout of Vel Antigen Gene.

Introduction: The Vel- phenotype is a rare blood group, and it is challenging for identifying this phenotype due to limited available reagents. Moreover, there are relatively few studies on genomic editing of erythroid antigens and generation of knockout (KO) cell lines at present. Methods: To identify the high-efficiency small-guiding RNA (sgRNA) sequence, candidate sgRNAs were transfected into HEK 293T cells and analyzed using Sanger sequencing. Following this, the high-efficiency sgRNA was transfected into K562 cells using lentivirus transduction to generate KO Vel blood group gene cells. The expression of the Vel protein was detected using Western blot on single-cell clones. Additionally, flow cytometry was used to detect the erythroid markers CD235a and CD71. Hemoglobin quantification and Giemsa staining were also performed to evaluate the erythroid differentiation of KO clones induced by hemin. Results: The high-efficiency sgRNA was successfully obtained and used for CRISPR-Cas9 editing in K562 cells. After limiting dilution and screening, two KO clones had either deleted 2 or 4 bases and showed no expression of the Vel protein. In the hemin-induced KO clone, there was a significant difference in erythroid marker and hemoglobin quantification compared to untreated cells. The morphological changes were also observed for the hemin-induced KO clone. Conclusion: In this study, a highly efficient sgRNA was screened out and used to generate Vel erythroid antigen KO single-cell clones in K562 cells. The edited cells could then be induced to undergo erythroid differentiation with the use of hemin.

Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing.

BACKGROUND: Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data. RESULTS: In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts. CONCLUSIONS: Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.