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Li, R. Y.

Publications and source records attributed to Li, R. Y..

2 recordsLinked to original sources

Developmental conversion of the nucleolus into an RNA Polymerase II transcriptional platform in Drosophila spermatocytes

The nucleolus is widely regarded as a specialized compartment for RNA polymerase I (Pol I)-driven ribosomal RNA transcription and ribosome biogenesis. Yet the presence of "atypical nucleoli", or nucleolus-like bodies (NLBs), which lack rRNA transcription despite containing canonical nucleolar components, has long been recognized, most notably during mammalian oogenesis and spermatogenesis. NLBs have been shown to have an essential function independent of rRNA transcription, but the nature of that function remained unclear. Here, we demonstrate that the nucleolus becomes an NLB during spermatocyte development in Drosophila melanogaster and, surprisingly, that this NLB serves as a platform for RNA polymerase II (Pol II)-mediated transcription. We find that the Y chromosome-linked fertility genes, which are heterochromatic in most cell types but highly expressed in spermatocytes, are transcribed at the spermatocyte NLB. We further show that the recruitment of active Pol II to the NLB requires known spermatocyte-specific transcriptional regulators. In their absence, the Y-linked fertility genes embedded within heterochromatin are not properly transcribed. Our findings reveal an active role for an NLB as a Pol II platform, and we propose that other NLBs may have similar functionality.

developmental biology↗

A novel machine learning-based algorithm for eQTL identification reveals complex pleiotropic effects in the MHC region

Expression quantitative trait loci (eQTLs) are regulatory variants that affect the expression level of their target genes and have significant impact on disease biology. However, eQTL mapping has been done mostly in one tissue at a time, despite the known prevalence of correlations among tissues. Multivariate analyses incorporating multiple phenotypes are available, but they emphasize linear combinations of phenotypes. We present MTClass, a machine learning framework that attempts to classify an individuals genotype based on a vector of multi-phenotype expression levels of a given gene. We conduct simulation studies and multiple case studies using real and imputed data, and we demonstrate that MTClass detects more functionally relevant variants and genes compared to existing single-tissue approaches as well as multi-phenotype association tests. Our results suggest that the importance of expression regulation at the MHC region may have been underestimated, and they provide fresh biological insights into genetic variants that have pleiotropic effects, influencing gene expression in a complex manner. Key pointsO_LIMTClass is a machine learning-based approach that classifies genotypes based on multi-phenotype expression data, providing a novel method for identifying eQTLs. C_LIO_LIMTClass outperforms traditional linear methods like MultiPhen and MANOVA in detecting eQTLs with greater functional impact and in capturing complex genotype-phenotype relationships. C_LIO_LIMTClass identified immune-related variants in the HLA region, suggesting that existing approaches may have underestimated the complexity of these variants effects across tissues. C_LIO_LIMTClass is more flexible and reliable than linear multivariate methods, handling multicollinearity, zero-expressed features, and various input values with greater ease. C_LI

bioinformatics↗