Role of PU.1 and C/EBPα in Remodelling the Interleukin (IL)-1β Enhancer-Promoter Interaction

Background: IL-1b is a potent inflammatory cytokine promptly expressed in activated myeloid immune cells. Among various transcription factors, PU.1 and CCAAT/enhancer-binding protein alpha (C/EBPa) play a key role in the lineage commitment of myeloid cells. To date, however, the exact mechanisms by which these lineage-determining transcription factors employ to regulate the expression of myeloid-specific genes remains elusive; thus, this study explores the role of PU.1 and C/EBPa in remodelling the chromatin conformation that allows ample production of IL-1b. Methods: To examine the mechanism of these lineage-determining transcription factors, production of IL-1b and enhancer-promoter interactions were analyzed in non-myeloid B16-BL6 cells that were ectopically expressed with PU.1 and C/EBPa. Results: Overexpression of PU.1 and C/EBPa rendered B16-BL6 cells response to the bacterial component lipopolysaccharide (LPS) and expressed IL-1b. These cells also expressed a putative enhancer RNA, located ~10 kbs upstream of the IL-1b transcription start site, in response to LPS. Knocking out the enhancer region reduced IL-1b mRNA expression, suggesting that the genomic region is an enhancer. Based on the chromatin conformation capture-qPCR analysis, IL-1b enhancer-promoter interactions were established upon overexpression of PU.1 and C/EBPa, which was further enhanced by LPS. Discussion & Conclusion: These results suggest that PU.1 and C/EBPa are pioneering transcription factors that establish chromatin looping between IL-1b regulatory elements and induce the generation of enhancer RNA, resulting in the production of IL-1b in non-myeloid cells. Interdisciplinary Reflection: Our system that investigates how transcription factors can remodel the chromatin landscape will further expand our understanding of gene regulation

The transcription factor PU.1 mediates enhancer-promoter looping that is required for IL-1 eRNA and mRNA transcription in mouse melanoma and macrophage cell lines

The DNA-binding protein PU.1 is a myeloid lineage– determining and pioneering transcription factor due to its ability to bind “closed” genomic sites and maintain “open” chromatin state for myeloid lineage–specific genes. The precise mechanism of PU.1 in cell type–specific programming is yet to be elucidated. The melanoma cell line B16BL6, although it is nonmyeloid lineage, expressed Toll-like receptors and activated the transcription factor NF-B upon stimulation by the bacterial cell wall component lipopolysaccharide. However, it did not produce cytokines, such as IL-1 mRNA. Ectopic PU.1 expression induced remodeling of a novel distal enhancer (located 10 kbp upstream of the IL-1 transcription start site), marked by nucleosome depletion, enhancer-promoter looping, and histone H3 lysine 27 acetylation (H3K27ac). PU.1 induced enhancer-promoter looping and H3K27ac through two distinct PU.1 regions. These PU.1-dependent events were independently required for subsequent signal-dependent and co-dependent events: NF-B recruitment and further H3K27ac, both of which were required for enhancer RNA (eRNA) transcription. In murine macrophage RAW264.7 cells, these PU.1-dependent events were constitutively established and readily expressed eRNA and subsequently IL-1 mRNA by lipopolysaccharide stimulation. In summary, this study showed a sequence of epigenetic events in programming IL-1 transcription by the distal enhancer priming and eRNA production mediated by PU.1 and the signal-dependent transcription factor NF-B

Pixel-wise classification in graphene-detection with tree-based machine learning algorithms

Mechanical exfoliation of graphene and its identification by optical inspection is one of the milestones in condensed matter physics that sparked the field of 2D materials. Finding regions of interest from the entire sample space and identification of layer number is a routine task potentially amenable to automatization. We propose supervised pixel-wise classification methods showing a high performance even with a small number of training image datasets that require short computational time without GPU. We introduce four different tree-based machine learning algorithms -- decision tree, random forest, extreme gradient boost, and light gradient boosting machine. We train them with five optical microscopy images of graphene, and evaluate their performances with multiple metrics and indices. We also discuss combinatorial machine learning models between the three single classifiers and assess their performances in identification and reliability. The code developed in this paper is open to the public and will be released at github.com/gjung-group/Graphene_segmentation.Comment: 12 pages, 6 figure

Plant Location Selection for Food Production by Considering the Regional and Seasonal Supply Vulnerability of Raw Materials

A production capacity analysis considering market demand and raw materials is very important to design a new plant. However, in the food processing industry, the supply uncertainty of raw materials is very high, depending on the production site and the harvest season, and further, it is not straightforward to analyze too complex food production systems by using an analytical optimization model. For these reasons, this study presents a simulation-based decision support model to select the right location for a new food processing plant. We first define three supply vulnerability factors from the standpoint of regional as well as seasonal instability and present an assessment method for supply vulnerability based on fuzzy quantification. The evaluated vulnerability scores are then converted into raw material supply variations for food production simulation to predict the quarterly production volume of a new food processing plant. The proposed selection procedure is illustrated using a case study of semiprocessed kimchi production. The best plant location is proposed where we can reduce and mitigate risks when supplying raw material, thereby producing a target production volume steadily

Control of Crystal Morphology for Mold Flux During High-Aluminum AHSS Continuous Casting Process

In the present manuscript, the efforts to control the crystal morphology are carried out aiming at improving the lubrication of lime-alumina-based mold flux for casting advanced high-strength steel with high aluminum. Jackson alpha factors for crystals of melt crystallization in multi-component mold fluxes are established and reasonably evaluated by applying thermodynamic databases to understand the crystal morphology control both in lime-alumina-based and lime-silica-based mold fluxes. The results show that Jackson alpha factor and supercooling are the most critical factors to determine the crystal morphology in a mold flux. Crystals precipitating in mold fluxes appear with different morphologies due to their different Jackson alpha factors and are likely to be more faceted with higher Jackson alpha factor. In addition, there is a critical supercooling degree for crystal morphology dendritic transition. When the supercooling over the critical value, the crystals transform from faceted shape to dendritic ones in morphology as the kinetic roughening occurs. Typically, the critical supercooling degrees for cuspidine dendritic transition in the lime-silica-based mold fluxes are evaluated to be between 0.05 and 0.06. Finally, addition of a small amount of Li2O in the mold flux can increase the Jackson alpha factor and decrease the supercooling for cuspidine precipitation; thus, it is favorable to enhance a faceted cuspidine crystal.1132Ysciescopu