scholarly journals A multi-tissue multi-omics analysis reveals distinct kineztics in entrainment of diurnal transcriptomes by inverted feeding

iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102335
Author(s):  
Haoran Xin ◽  
Fang Deng ◽  
Meiyu Zhou ◽  
Rongfeng Huang ◽  
Xiaogen Ma ◽  
...  
Keyword(s):  
2020 ◽  
Author(s):  
Jaswinder Singh Maras ◽  
Shvetank Sharma ◽  
Adil Bhat ◽  
sheetalnath Rooge ◽  
Reshu Agarwal ◽  
...  

2021 ◽  
Vol 10 (10) ◽  
pp. 2100
Author(s):  
Hiroshi Sakagami ◽  
Sachie Nakatani ◽  
Ayame Enomoto ◽  
Sana Ota ◽  
Miku Kaneko ◽  
...  

Efficient utilization of alkaline extracts of several plants for the treatment of oral diseases has been reported. To investigate the mechanism of anti-inflammatory activity of alkaline extract of the leaves of Sasa sp. (SE), multi-omics analysis using metabolomics and DNA array was performed. Human gingival fibroblasts (HGFs) were treated for IL-1β to induce inflammation (detected by PGE2 production in culture medium) in the presence or absence of SE. Both IL-1β and SE showed slight hormetic growth stimulation against HGF. SE inhibited PGE2 production dose- and time-dependently. Its inhibitory action was more pronounced by first treating the cells with SE, rather than with IL-1β. At 3 h after IL-1β treatment, 18 amino acids (except cysteine and glutamic acid), total glutathione (GSH, GSSG, Cys-GSH disulfide), Met-sulfoxide, 5-oxoproline, and SAM declined, whereas DNA expressions of AKT, CASP3, and CXCL3 were elevated. These changes were reversed by simultaneous treatment with SE. The present study suggests that the anti-inflammatory action of SE is mediated via various metabolic pathways for cell survival, apoptosis, and leukocyte recruitment.


2021 ◽  
Vol 164 ◽  
pp. 390-398
Author(s):  
Weibing Tang ◽  
Minjian Chen ◽  
Xuejiang Guo ◽  
Kun Zhou ◽  
Zechao Wen ◽  
...  

2021 ◽  
pp. 338551
Author(s):  
Hanne Røberg-Larsen ◽  
Elsa Lundanes ◽  
Tuula A. Nyman ◽  
Frode S. Berven ◽  
Steven Ray Wilson

2017 ◽  
Vol 34 (2) ◽  
pp. 319-320 ◽  
Author(s):  
Kun-Hsing Yu ◽  
Michael R Fitzpatrick ◽  
Luke Pappas ◽  
Warren Chan ◽  
Jessica Kung ◽  
...  

2021 ◽  
pp. 2101374
Author(s):  
Shengguan Cai ◽  
Qiufang Shen ◽  
Yuqing Huang ◽  
Zhigang Han ◽  
Dezhi Wu ◽  
...  
Keyword(s):  

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 565
Author(s):  
Satoshi Takahashi ◽  
Masamichi Takahashi ◽  
Shota Tanaka ◽  
Shunsaku Takayanagi ◽  
Hirokazu Takami ◽  
...  

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.


2021 ◽  
Author(s):  
Katharina Schönberger ◽  
Nadine Obier ◽  
Mari Carmen Romero-Mulero ◽  
Pierre Cauchy ◽  
Julian Mess ◽  
...  

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