scholarly journals pH Changes Have a Profound Effect on Gene Expression, Hydrolytic Enzyme Production, and Dimorphism in Saccharomycopsis fibuligera

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohamed El-Agamy Farh ◽  
Najib Abdellaoui ◽  
Jeong-Ah Seo
Keyword(s):  
Gene Expression ◽  
Yeast Cell ◽  
Enzyme Production ◽  
Hydrolytic Enzyme ◽  
Cell Formation ◽  
Hyphal Growth ◽  
Acidic Ph ◽  
Hybrid Strain ◽  
Ph Change ◽  
Saccharomycopsis Fibuligera

Saccharomycopsis fibuligera is an amylolytic yeast that plays an important role within nuruk (a traditional Korean fermentation starter) used for the production of makgeolli (Korean rice wine), which is characterized by high acidity. However, the effect of pH change (neutral to acidic) on the yeast cell to hyphal transition and carbohydrate-hydrolyzing enzyme activities for S. fibuligera has not been investigated yet. In this study, S. fibuligera strains were cultured under the different pH conditions, and the effect on the enzyme production and gene expression were investigated. An acidic pH induced a hyphal transition from yeast cell of S. fibuligera KPH12 and the hybrid strain KJJ81. In addition, both strains showed a gradual decrease in the ability to degrade starch and cellulose as the pH went down. Furthermore, a transcriptome analysis demonstrated that the pH decline caused global expression changes in genes, which were classified into five clusters. Among the differentially expressed genes (DEGs) under acidic pH, the downregulated genes were involved in protein synthesis, carbon metabolism, and RIM101 and cAMP-PKA signaling transduction pathways for the yeast-hyphal transition. A decrease in pH induced a dimorphic lifestyle switch from yeast cell formation to hyphal growth in S. fibuligera and caused a decrease in carbohydrate hydrolyzing enzyme production, as well as marked changes in the expression of genes related to enzyme production and pH adaptation. This study will help to elucidate the mechanism of adaptation of S. fibuligera to acidification that occur during the fermentation process of makgeolli using nuruk.

scholarly journals Glucose-Dependent Insulinotropic Polypeptide Suppresses Foam Cell Formation of Macrophages through Inhibition of the Cyclin-Dependent Kinase 5-CD36 Pathway

Biomedicines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 832
Author(s):  
Michishige Terasaki ◽  
Hironori Yashima ◽  
Yusaku Mori ◽  
Tomomi Saito ◽  
Yoshie Shiraga ◽  
...  
Keyword(s):  
Gene Expression ◽  
Foam Cell ◽  
Cell Formation ◽  
Foam Cell Formation ◽  
U937 Cells ◽  
Cyclin Dependent Kinase ◽  
Macrophage Foam Cell ◽  
Cyclin Dependent Kinase 5 ◽  
Gip Receptor ◽  
Cd36 Gene

Glucose-dependent insulinotropic polypeptide (GIP) has been reported to have an atheroprotective property in animal models. However, the effect of GIP on macrophage foam cell formation, a crucial step of atherosclerosis, remains largely unknown. We investigated the effects of GIP on foam cell formation of, and CD36 expression in, macrophages extracted from GIP receptor-deficient (Gipr−/−) and Gipr+/+ mice and cultured human U937 macrophages by using an agonist for GIP receptor, [D-Ala2]GIP(1–42). Foam cell formation evaluated by esterification of free cholesterol to cholesteryl ester and CD36 gene expression in macrophages isolated from Gipr+/+ mice infused subcutaneously with [D-Ala2]GIP(1–42) were significantly suppressed compared with vehicle-treated mice, while these beneficial effects were not observed in macrophages isolated from Gipr−/− mice infused with [D-Ala2]GIP(1–42). When macrophages were isolated from Gipr+/+ and Gipr−/− mice, and then exposed to [D-Ala2]GIP(1–42), similar results were obtained. [D-Ala2]GIP(1–42) attenuated ox-LDL uptake of, and CD36 gene expression in, human U937 macrophages as well. Gene expression level of cyclin-dependent kinase 5 (Cdk5) was also suppressed by [D-Ala2]GIP(1–42) in U937 cells, which was corelated with that of CD36. A selective inhibitor of Cdk5, (R)-DRF053 mimicked the effects of [D-Ala2]GIP(1–42) in U937 cells. The present study suggests that GIP could inhibit foam cell formation of macrophages by suppressing the Cdk5-CD36 pathway via GIP receptor.

scholarly journals Impact of the acidic environment on gene expression and functional parameters of tumors in vitro and in vivo

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Mandy Rauschner ◽  
Luisa Lange ◽  
Thea Hüsing ◽  
Sarah Reime ◽  
Alexander Nolze ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Death ◽  
Western Blot ◽  
Tumor Cell ◽  
Low Ph ◽  
Strong Increase ◽  
Acidic Ph ◽  
The Impact

Abstract Background The low extracellular pH (pHe) of tumors resulting from glycolytic metabolism is a stress factor for the cells independent from concomitant hypoxia. The aim of the study was to analyze the impact of acidic pHe on gene expression on mRNA and protein level in two experimental tumor lines in vitro and in vivo and were compared to hypoxic conditions as well as combined acidosis+hypoxia. Methods Gene expression was analyzed in AT1 prostate and Walker-256 mammary carcinoma of the rat by Next Generation Sequencing (NGS), qPCR and Western blot. In addition, the impact of acidosis on tumor cell migration, adhesion, proliferation, cell death and mitochondrial activity was analyzed. Results NGS analyses revealed that 147 genes were uniformly regulated in both cell lines (in vitro) and 79 genes in both experimental tumors after 24 h at low pH. A subset of 25 genes was re-evaluated by qPCR and Western blot. Low pH consistently upregulated Aox1, Gls2, Gstp1, Ikbke, Per3, Pink1, Tlr5, Txnip, Ypel3 or downregulated Acat2, Brip1, Clspn, Dnajc25, Ercc6l, Mmd, Rif1, Zmpste24 whereas hypoxia alone led to a downregulation of most of the genes. Direct incubation at low pH reduced tumor cell adhesion whereas acidic pre-incubation increased the adhesive potential. In both tumor lines acidosis induced a G1-arrest (in vivo) of the cell cycle and a strong increase in necrotic cell death (but not in apoptosis). The mitochondrial O2 consumption increased gradually with decreasing pH. Conclusions These data show that acidic pHe in tumors plays an important role for gene expression independently from hypoxia. In parallel, acidosis modulates functional properties of tumors relevant for their malignant potential and which might be the result of pH-dependent gene expression.

Abstract 356: IKK-Beta Deletion Alleviates the Atherogenetic Effect of Intermittent Hypoxia Induced Macrophage Foam Cell Formation

2015 ◽  
Vol 35 (suppl_1) ◽  
Author(s):  
Toshihiro Imamura ◽  
Iain S Hartley ◽  
Abdull J Massri ◽  
Orit Poulsen ◽  
Dan Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Intermittent Hypoxia ◽  
Foam Cell ◽  
Cell Formation ◽  
Foam Cell Formation ◽  
Wild Type ◽  
Nf Kappa B ◽  
Macrophage Foam Cell ◽  
Obstructive Sleep ◽  
Intracellular Cholesterol

Background: Obstructive sleep apnea syndrome (OSAS) is a common sleeping disorder characterized by intermittent hypoxia (IH). Clinical studies have previously shown an independent association between obstructive sleep apnea and atherosclerosis. Furthermore, it has been previously shown that such a predisposition to atherosclerosis in OSAS patient can be caused by various inflammatory mediators, particularly the NF-kappa B (NF-kB) pathway. Foam cells or lipid-laden macrophages in the atherosclerotic lesion have been well documented as a hallmark of atherosclerosis; however, the contribution of IH, such as in OSAS, to foam cell formation is not yet fully understood. Previous observations have led us to hypothesized that IH induces macrophage foam cell formation due to the activation of NF-kappa B pathway. Methods: Myeloid restricted IKK-beta deleted mice were generated by a Cre/lox recombination system to inactivate the NF-kB pathway in macrophages. Thioglycollate-elicited peritoneal macrophages were incubated with 200 μg/ml of low-density lipoprotein and simultaneously exposed to either IH (Normoxia: 8min, 0.5% O2: 10min) or normoxia for 24 hours. After exposure, the extent of foam cell formation was assessed by quantification of intracellular cholesterol. Finally, we compared the differences in gene expression using RNA-seq between wild type and IKK-beta deleted macrophages exposed to either IH or normoxia for 24 hours. Results: IH significantly increased total cholesterol in wild type macrophages (63.4±3.3 μg/mg of cellular protein, n=9) in comparison to normoxia (51.2±1.6). Interestingly, such increase in intracellular cholesterol in response to IH-exposure was abolished by IKK-beta deletion (IH 52.4±1.1; normoxia 50.0±1.6 n=8), suggesting that NF-kB pathway regulated gene expression is critical for IH-induced foam cell formation. Indeed, we have found that NF-kB knockout abolished IH-induced expressional alterations in 364 genes, which are potential candidates for regulating intracellular cholesterol. Conclusion: NF-kB activation plays a critical role in IH-induced macrophage foam cell formation.

Cultivable Microbial Diversity in Domestic Bentonites and Their Hydrolytic Enzyme Production

2019 ◽  
Vol 47 (1) ◽  
pp. 125-131
Author(s):  
Dong-Ho Seo ◽  
Eui-Sang Cho ◽  
Chi Young Hwang ◽  
Deok Jun Yoon ◽  
Jeonghye Chun ◽  
...  
Keyword(s):  
Microbial Diversity ◽  
Enzyme Production ◽  
Hydrolytic Enzyme

scholarly journals A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

2020 ◽  
Vol 117 (31) ◽  
pp. 18869-18879 ◽  
Author(s):  
Christopher Culley ◽  
Supreeta Vijayakumar ◽  
Guido Zampieri ◽  
Claudio Angione
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Machine Learning ◽  
Cell Growth ◽  
Yeast Cell ◽  
Metabolic Flux ◽  
Data Augmentation ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Metabolic Reconstruction

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143Saccharomyces cerevisiaemutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

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