ARID1A regulates DNA repair through chromatin organization and its deficiency triggers DNA damage-mediated anti-tumor immune response.

AT-rich interaction domain protein 1A (ARID1A), a SWI/SNF chromatin remodeling complex subunit, is frequently mutated across various cancer entities. Loss of ARID1A leads to DNA repair defects. Here, we show that ARID1A plays epigenetic roles to promote both DNA double-strand breaks (DSBs) repair pathways, non-homologous end-joining (NHEJ) and homologous recombination (HR). ARID1A is accumulated at DSBs after DNA damage and regulates chromatin loops formation by recruiting RAD21 and CTCF to DSBs. Simultaneously, ARID1A facilitates transcription silencing at DSBs in transcriptionally active chromatin by recruiting HDAC1 and RSF1 to control the distribution of activating histone marks, chromatin accessibility, and eviction of RNAPII. ARID1A depletion resulted in enhanced accumulation of micronuclei, activation of cGAS-STING pathway, and an increased expression of immunomodulatory cytokines upon ionizing radiation. Furthermore, low ARID1A expression in cancer patients receiving radiotherapy was associated with higher infiltration of several immune cells. The high mutation rate of ARID1A in various cancer types highlights its clinical relevance as a promising biomarker that correlates with the level of immune regulatory cytokines and estimates the levels of tumor-infiltrating immune cells, which can predict the response to the combination of radio- and immunotherapy.

Recent Advances in the Chemobiological Upcycling of Polyethylene Terephthalate (PET) into Value-Added Chemicals.

Polyethylene terephthalate (PET) is a plastic material commonly applied to beverage packaging used in everyday life. Owing to PET's versatility and ease of use, its consumption has continuously increased, resulting in considerable waste generation. Several physical and chemical recycling processes have been developed to address this problem. Recently, biological upcycling is being actively studied and has come to be regarded as a powerful technology for overcoming the economic issues associated with conventional recycling methods. For upcycling, PET should be degraded into small molecules, such as terephthalic acid and ethylene glycol, which are utilized as substrates for bioconversion, through various degradation processes, including gasification, pyrolysis, and chemical/biological depolymerization. Furthermore, biological upcycling methods have been applied to biosynthesize value-added chemicals, such as adipic acid, muconic acid, catechol, vanillin, and glycolic acid. In this review, we introduce and discuss various degradation methods that yield substrates for bioconversion and biological upcycling processes to produce value-added biochemicals. These technologies encourage a circular economy, which reduces the amount of waste released into the environment.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes.

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods-both reference-based and reference-free-generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

Anti-tumor effects of jaceosidin on apoptosis, autophagy, and necroptosis in human glioblastoma multiforme.

Glioblastoma multiforme (GBM) is the most aggressive and common malignant neoplasm. Nevertheless, a 5-year survival rate of patients with GBM has remained below 5%. Artemisia princeps PAMPANINI, used as a food and traditional medicine, have shown beneficial properties including anti-inflammatory, anti-oxidative, and anti-cancer activities. Thus, this study aimed to investigate biological mechanism of a bioactive compound, jaceosidin (JAC), isolated from A. princeps in human GBM T98G cells. Herein, as a result of analysis in terms of cancer survival and death, we found that JAC significantly reduced cell survival against T98G cells. In addition, JAC increased apoptotic cell death via changes on morphological and molecular phenotypes in T98G cells as evidenced by cellular shapes and DNA fragmentation. The apoptotic cell death was confirmed by the cleavage of caspase-3 and PARP, the downregulation of survivin and Bcl-2. Moreover, JAC decreased the expression of cyclinD1 and Cdks and increased the phosphorylation of EKR, JNK, and p38 MAPKs. Specifically, JAC suppressed the PI3K/AKT signaling and its downstream molecules including p70S6, GSK3ß, and ß-catenin. In addition, as a result of analysis in terms of metastasis using wound healing and Boyden chamber assays, JAC showed anti-migrative and anti-invasive activities. Finally, we analyzed in terms of autophagy and necroptosis that are modes of programmed cell survival and death different from apoptosis in T98G cells. We found that JAC inhibited autophgic regulatory proteins including Beclin-1, Atgs, and LC3A/B, thereby reducing autophagic-mediated cell survival, whereas JAC did not affect phosphorylation of key proteins in necroptosis, especially MLKL. Given these findings, our results provided novel evidences on the biological mechanisms of JAC in T98G cells, suggesting that JAC may be a therapeutic agent for patients with GBM.

Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map.

White matter hyperintensities (WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image (MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for all types of dementia and brain aging processes. The automatic WMH segmentation is challenging because of their variable intensity range, size and shape. U-Net tackles this problem through the dense prediction and has shown competitive performances not only on WMH segmentation/detection but also on varied image segmentation tasks. However, its network architecture is high complex. In this study, we propose the use of Saliency U-Net and Irregularity map (IAM) to decrease the U-Net architectural complexity without performance loss. We trained Saliency U-Net using both: a T2-FLAIR MRI sequence and its correspondent IAM. Since IAM guides locating image intensity irregularities, in which WMH are possibly included, in the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The best performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net by recognizing the shape of large WMH more accurately through multi-context learning. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which was the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest training time and the least number of parameters. In conclusion, based on our experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation.