Biology subjects

Lyu, G.

Publications and source records attributed to Lyu, G..

4 recordsLinked to original sources

bioRxiv2023

Histone lactylation antagonizes senescence and skeletal muscle aging via facilitating gene expression reprogramming

One of the prominent drivers of cellular senescence and/or aging is epigenetic alteration, through which orchestrated regulation of gene expression is achieved during the processes. Accumulating endeavors have been devoted to identifying histone modifications-related mechanisms underlying senescence and aging. Here, we show that histone lactylation, a recently identified histone modification bridging metabolism, epigenetic regulation of gene expression and cellular activities in response to internal and external cues, plays a crucial role in counteracting senescence as well as mitigating dysfunctions of skeletal muscle in aged mice. Mechanistically, the abundance of histone lactylation is markedly decreased during senescence and aging but restored following manipulation of the metabolic environment. Genome-wide distribution profiling and gene expression network analysis uncover that the maintenance of histone lactylation level is critical for suppressing senescence and aging programs via targeting of proliferation- and homeostasis-related pathways. We also confirmed that the level of histone lactylation is not only controlled by glycolysis but also regulated by NAD+ content in vivo. More intriguingly, running exercise enhances the level of histone lactylation and reconstructs the cell landscape and communications of mouse skeletal muscle, leading to rejuvenation and functional improvement. Our study highlights the role of histone lactylation in regulating senescence as well as aging-related tissue function, implying that this modification could be used as a novel marker of senescence, and provides a potential target for aging intervention via metabolic manipulation.

cell biology↗

bioRxiv2023

Spliceosomal mutations decouple 3' splice site fidelity from cellular fitness

The fidelity of splice site selection is thought to be critical for proper gene expression and cellular fitness. In particular, proper recognition of 3'-splice site (3'SS) sequences by the spliceosome is a daunting task considering the low complexity of the 3'SS consensus sequence YAG. Here we show that inactivating the near-essential splicing factor Prp18p results in a global activation of alternative 3'SS, many of which harbor sequences that highly diverge from the YAG consensus, including some highly unusual non-AG 3'SS. We show that the role of Prp18p in 3'SS fidelity is promoted by physical interactions with the essential splicing factors Slu7p and Prp8p and synergized by the proofreading activity of the Prp22p helicase. Strikingly, structure-guided point mutations that disrupt Prp18p-Slu7p and Prp18p-Prp8p interactions mimic the loss of 3'SS fidelity without any impact on cellular growth, suggesting that accumulation of incorrectly spliced transcripts does not have a major deleterious effect on cellular viability. These results show that spliceosomes exhibit remarkably relaxed fidelity in the absence of Prp18p, and that new 3'SS sampling can be achieved genome-wide without a major negative impact on cellular fitness, a feature that could be used during evolution to explore new productive alternative splice sites.

molecular biology↗

bioRxiv2022

Dynamic Interaction Learning and MultimodalRepresentation for Drug Response Prediction

Mining multimodal pharmaceutical data is crucial for in-silico drug candidate screening and discovery. A daunting challenge of integrating multimodal data is to enable dynamic feature modeling generalizable for real-world applications. Unlike conventional approaches using a simple concatenation with fixed parameters, in this paper, we develop a dynamic interaction learning network to adaptively integrate drug and different reactants on multimodal tasks towards robust drug response prediction. The primary objective of dynamic learning falls into two key aspects: at micro-level, we aim to dynamically search specific relational patterns on the whole reactant range for each drug-reactant pair; at macro-level, drug features can be used to adaptively correlate with different reactants. Extensive experiments demonstrate the validity of our approach in both drug protein interaction (DPI) and cancer drug response (CDR) tasks. Our approach achieves superior performance on both DPI (AUC = 0.967) and CDR (AUC = 0.932) tasks, outperforming competitive baselines from four real-world, drug-outcome datasets. In addition, the performance on the challenging blind subsets is remarkably improved, where AUC value increases from 0.843 to 0.937 on blind protein set of DPI task, and Pearsons correlation value increases from 0.516 to 0.566 on blind drug set of CDR task. A series of case studies highlight the potential generalization and interpretability of dynamic learning in the in-silico drug response assessment.

bioinformatics↗

bioRxiv2022

Single cell transcriptomics reveals correct developmental dynamics and high-quality midbrain cell types by improved hESC differentiation.

Stem cell technologies provide new opportunities for modeling cells in the healthy and diseased states and for regenerative medicine. In both cases developmental knowledge as well as the quality and molecular properties of the cells are essential for their future application. In this study we identify developmental factors important for the differentiation of human embryonic stem cells (hESCs) into midbrain dopaminergic (mDA) neurons. We found that Laminin-511, and dual canonical and non-canonical WNT activation followed by GSK3{beta} inhibition plus FGF8b, improved midbrain patterning. In addition, mDA neurogenesis and differentiation was enhanced by activation of liver X receptors and inhibition of fibroblast growth factor signaling. Moreover, single-cell RNA-sequencing analysis revealed a developmental dynamics similar to that of the endogenous human ventral midbrain and the emergence of high quality molecularly-defined midbrain cell types, including mDA neurons that become functional. Thus, our study identifies novel factors important for human midbrain development and opens the door for a future application of molecularly-defined hESC-derived midbrain cell types in Parkinsons disease.

neuroscience↗