Biology subjects

Yang, W.-Y.

Publications and source records attributed to Yang, W.-Y..

4 recordsLinked to original sources

bioRxiv2024

Stabilization of AMPK/PFKL/RPIA in the Glycolytic Bodies Transduces IL6/STAT3 Signal in Hepatocarcinogenesis

Metabolic reprogramming is a pivotal characteristic of cancer, yet the intricate interplay between glycolysis and the pentose phosphate pathway (PPP) remains elusive. This study unveils the pivotal role of 6-phosphofructokinase liver type (PFKL) in glycolysis and ribose 5-phosphate isomerase A (RPIA) in PPP, orchestrating liver tumorigenesis. PFKL, the rate-limiting enzyme in glycolysis, stabilizes RPIA by impeding ubiquitination/proteasome activity. The pro-inflammatory and tumor cytokine interleukin 6 activates pSTAT3 which binds to the promoter region and activates AMPK and PFKL transcription. Furthermore, pAMPK stabilizes PFKL protein by preventing proteasome degradation in hepatoma cells. Inhibiting PFKL, AMPK, and STAT3 genetically or pharmacologically can reduce glycolysis, ATP production, resulting in reduction of hepatoma cell proliferation and migration. Intriguingly, the PFKL, AMPK, RPIA, and PKM2 are co-localized in the Glycolytic body (G-body) which starts forming at chronic hepatitis, dramatically increases during active hepatitis, and the size of G-bodies becomes bigger from cirrhosis to hepatocellular carcinoma. Furthermore, using Bimolecular fluorescence complementation (BiFC) assay, we demonstrated that PFKL and RPIA direct interacts. Targeting AMPK or STAT3 significantly reduced tumor formation and lipid accumulation in zebrafish models, suggesting the STAT3/AMPK/PFKL axis as a potential therapeutic avenue for liver cancer treatment.

cancer biology↗

bioRxiv2020

Parkin coordinates mitochondrial lipid remodeling to execute mitophagy

Mitochondrial failure caused by Parkin mutations contributes to Parkinsons disease. Parkin binds, ubiquitinates, and targets impaired mitochondria for autophagic destruction. Robust mitophagy involves peri-nuclear concentration of Parkin-tagged mitochondria, followed by dissemination of juxtanuclear mitochondrial aggregates, and efficient sequestration of individualized mitochondria by autophagosomes. Here, we report that the execution of complex mitophagic events requires active mitochondrial lipid remodeling. Parkin recruits phospholipase D2 to the depolarized mitochondria and generate phosphatidic acid (PA). Mitochondrial PA is subsequently converted to diacylglycerol (DAG) by Lipin-1 phosphatase-a process that further requires mitochondrial ubiquitination and ubiquitin-binding autophagic receptors, NDP52 and Optineurin. We show that Optineurin transports, via Golgi-derived vesicles, a PA-binding factor EndoB1 to ubiquitinated mitochondria, thereby facilitating DAG production. Mitochondrial DAG activates both F-actin assembly to drive mitochondrial individualization, and autophagosome biogenesis to efficiently restrict impaired mitochondria. Thus Parkin, autophagic receptors and the Golgi complex orchestrate mitochondrial lipid remodeling to execute robust mitophagy.

cell biology↗

bioRxiv2018

Analysis of contributory gut microbiota and lauric acid against necrotic enteritis in Clostridium perfringens and Eimeria side-by-side challenge model

Gut microbiota has been demonstrated to be involved in intestinal nutrition, defense, and immunity, as well as participating in disease progression. This study was to investigate gut microbiota changes in chickens challenged with netB-positive Clostridium perfringens strain 1 (CP1) and/or the predisposing Eimeria species (Eimeria). In addition, the effects of lauric acid, a medium-chain fatty acid (MCFA), on NE reduction and modulation of microbiota were evaluated. The results demonstrated that microbial communities in the jejunum were distinct from those in the cecum, and the microbial community change was more significant in jejunum. Challenge of CP1 in conjunction with Eimeria significantly reduced species diversity in jejunal microbiota, but cecal microbiota remained stable. In the jejunum, CP1 challenge increased the abundance of the genera of Clostridium sensu stricto 1, Escherichia Shigella, and Weissella, but significantly decreased the population of Lactobacillus. Eimeria infection on its own was unable to promote NE, demonstrating decrements of Clostridium sensu stricto 1 and Lactobacillus. Co-infection with CP1 and Eimeria reproduced the majority of NE lesions with significant increment of Clostridium sensu stricto 1 and reduction in Lactobacillus. The changes of these two taxa increased the severity of NE lesions. Further analyses of metagenomeSeq, STAMP, and LEfSe showed significant overgrowth of Clostridium sensu stricto 1 was associated with NE and Eimeria infection than C. perfringens challenge alone. The supplementation of lauric acid did not reduce NE incidence and severity but decreased the relative abundance of Escherichia Shigella. In conclusion, significant overgrowth of Clostridium sensu stricto 1 in the jejunm is the major microbiota contributory to NE. Controlling proliferation of this taxon in the jejunum should be the niche for developing effective strategies against NE.

microbiology↗

bioRxiv2017

No major flaws in "Identification of individuals by trait prediction using whole-genome sequencing data"

In a recently published PNAS article, we studied the identifiability of genomic samples using machine learning methods [Lippert et al., 2017]. In a response, Erlich [2017] argued that our work contained major flaws. The main technical critique of Erlich [2017] builds on a simulation experiment that shows that our proposed algorithm, which uses only a genomic sample for identification, performed no better than a strategy that uses demographic variables. Below, we show why this comparison is misleading and provide a detailed discussion of the key critical points in our analyses that have been brought up in Erlich [2017] and in the media. Further, not only faces may be derived from DNA, but a wide range of phenotypes and demographic variables. In this light, the main contribution of Lippert et al. [2017] is an algorithm that identifies genomes of individuals by combining multiple DNA-based predictive models for a myriad of traits.

genomics↗