Small Tweaks, Major Changes: Post-Translational Modifications That Occur within M2 Macrophages in the Tumor Microenvironment

TWEAK is an activator of Hippo-YAP signaling protecting against hepatic Ischemia/ reperfusion injury

Hepatic ischemia-reperfusion injury (IRI) represents a formidable complication commonly linked with hemorrhagic shock, liver resection, and transplantation. This study aims to elucidate the role of Tumor Necrosis Factor-like Weak Inducer of Apoptosis (TWEAK) in the pathogenesis of hepatic I/R injury and to delineate the underlying mechanisms involved. Utilizing a hypoxia-reoxygenation model in human liver organoids (HLOs) alongside a murine model of warm ischemia-reperfusion injury, we systematically investigated the interplay between TWEAK, its receptor Fn14, and the HIPPO signaling pathway. Our findings indicate that TWEAK pretreatment significantly mitigates IRI in murine livers as well as hypoxia/reoxygenation injury in HLOs. Notably, administration of adeno-associated virus (AAV) to knock down Fn14 abrogated the protective effects of TWEAK in the murine model. Transcriptome sequencing analysis revealed that the interaction between TWEAK and Fn14 enhances cellular resistance to IRI by activating the HIPPO signaling pathway. Overall, TWEAK emerges as a promising therapeutic target for mitigating hepatic I/R injury, potentially improving outcomes in liver transplantation.

Transcriptional regulation and post-translational modifications in the glycolytic pathway for targeted cancer therapy

Cancer cells largely rely on aerobic glycolysis or the Warburg effect to generate essential biomolecules and energy for their rapid growth. The key modulators in glycolysis including glucose transporters and enzymes, e.g. hexokinase 2, enolase 1, pyruvate kinase M2, lactate dehydrogenase A, play indispensable roles in glucose uptake, glucose consumption, ATP generation, lactate production, etc. Transcriptional regulation and post-translational modifications (PTMs) of these critical modulators are important for signal transduction and metabolic reprogramming in the glycolytic pathway, which can provide energy advantages to cancer cell growth. In this review we recapitulate the recent advances in research on glycolytic modulators of cancer cells and analyze the strategies targeting these vital modulators including small-molecule inhibitors and microRNAs (miRNAs) for targeted cancer therapy. We focus on the regulation of the glycolytic pathway at the transcription level (e.g., hypoxia-inducible factor 1, c-MYC, p53, sine oculis homeobox homolog 1, N6-methyladenosine modification) and PTMs (including phosphorylation, methylation, acetylation, ubiquitination, etc.) of the key regulators in these processes. This review will provide a comprehensive understanding of the regulation of the key modulators in the glycolytic pathway and might shed light on the targeted cancer therapy at different molecular levels.

Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme

Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PTMs alter the oncogenic signaling events and offer a novel avenue in GBM therapeutics research through PTM enzymes as potential biomarkers for drug targeting. In addition, PTMs are critical regulators of chromatin architecture, gene expression, and tumor microenvironment (TME), that play a crucial function in tumorigenesis. Moreover, the implementation of artificial intelligence and machine learning algorithms enhances GBM therapeutics research through the identification of novel PTM enzymes and residues. Herein, we briefly explain the mechanism of protein modifications in GBM etiology, and in altering the biologics of GBM cells through chromatin remodeling, modulation of the TME, and signaling pathways. In addition, we highlighted the importance of PTM enzymes as therapeutic biomarkers and the role of artificial intelligence and machine learning in protein PTM prediction.