Biology subjects

Li, M. J.

Publications and source records attributed to Li, M. J..

22 records · Page 2Linked to original sources

bioRxiv2022

Nuclear architecture protein Distal antenna balances genome-binding and phase-separation properties to regulate neuroblast competence

Neural progenitors transit through multiple competence states that restrict production of each neural cell type. In Drosophila neuroblasts, a timed genome reorganization relocates the cell fate gene, hunchback, to the nuclear periphery, terminating competence to produce early-born neurons. Distal antenna (Dan), a pipsqueak (Psq) superfamily protein, is transiently downregulated at mid-embryogenesis, which is required for this relocation. Here we find that Dan is a highly intrinsically disordered protein, and when its Psq DNA-binding domain is increasingly disrupted, Dan coalesces into steadily larger, interconnected hubs of rapid protein exchange. Consistent with these phase-separation properties, Dan has a predicted LARKS domain, a structural motif that forms reversible interactions associated with phase-separation. In the embryo, loss of either the Psq motif or the LARKS domain abrogates Dans ability to maintain neuroblast early competence upon misexpression, suggesting that Dan requires both DNA-binding and phase-separation to regulate neuroblast competence. Finally, we found that Dan strongly interacts with proteins of the nuclear pore complex (NPC), and Elys, a core NPC scaffold protein known to regulate genome architecture, binds the hb intron and is required for competence termination. Together, the results support a model for how Dans phase-separation properties can mediate dynamic restructuring by balancing genome-binding, self-association, and interaction among nuclear architecture regulators.

developmental biology↗

bioRxiv2022

Inferring CTCF insulators and anchored loops across human tissues and cell types

CCCTC-binding factor (CTCF) is a transcription regulator which is involved in many cellular processes. How CTCF recognizes DNA sequence to exert chromosome barrier or enhancer blocking effects remains to be fully interrogated. Despite many computational tools were developed to predict CTCF-mediated loops qualitatively or quantitatively, few could specially evaluate the regulatory potential of DNA sequence at CTCF binding sites (CBSs) and how it affects chromatin loop formation. Here, we developed a deep learning model, DeepAnchor, to precisely characterize the binding patterns for different types of CBSs. By incorporating base-wise genomic/epigenomic features, we revealed distinct chromatin and sequence features for CTCF-mediated insulation and looping at a high resolution, such as two sequence motifs flanking the core CTCF motif at loop-associated CBSs. Besides, we leveraged the predicted anchor score to optimize the loop extrusion model and achieved the best performance in predicting CTCF-anchored loops. We established a compendium of context-specific CTCF-anchored loops across 52 human tissue/cell types and found that genomic disruption of CTCF-anchored loops may represent a general causal mechanism of disease pathogenesis. These computational models, together with the established resource, could facilitate the mechanistic research on how the CTCF-mediated cis-regulatory elements (CREs) shapes context-specific gene regulation in cell development and disease progression.

bioinformatics↗

bioRxiv2021

MCGA: a multi-strategy conditional gene-based association framework integrating with isoform-level expression profiles reveals new susceptible and druggable candidate genes of schizophrenia

Linkage disequilibrium and disease-associated variants in non-coding regions make it difficult to distinguish truly associated genes from redundantly associated genes for complex diseases. In this study, we proposed a new conditional gene-based framework called MCGA that leveraged an improved effective chi-squared statistic to control the type I error rates and remove the redundant associations. MCGA initially integrated two conventional strategies to map genetic variants to genes, i.e., mapping a variant to its physically nearby gene and mapping a variant to a gene if the variant is a gene-level expression quantitative trait locus (eQTL) of the gene. We further performed a simulation study and demonstrated that the isoform-level eQTL was more powerful than the gene-level eQTL in the association analysis. Then the third strategy, i.e., mapping a variant to a gene if the variant is an isoform-level eQTL of the gene, was also integrated with MCGA. We applied MCGA to predict the potential susceptibility genes of schizophrenia and found that the potential susceptibility genes identified by MCGA were enriched with many neuronal or synaptic signaling-related terms in the Gene Ontology knowledgebase and antipsychotics-gene interaction terms in the drug-gene interaction database (DGIdb). More importantly, nine susceptibility genes were the target genes of multiple approved antipsychotics in DrugBank. Comparing the susceptibility genes identified by the above three strategies implied that strategy based on isoform-level eQTL could be an important supplement for the other two strategies and help predict more candidate susceptibility isoforms and genes for complex diseases in a multi-tissue context.

bioinformatics↗

bioRxiv2019

Whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

genomics↗