Integrative Analysis
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2021 ◽  
Author(s):  
Tengfei Dou ◽  
Shixiong Yan ◽  
Lixian Liu ◽  
Kun Wang ◽  
Zonghui Jian ◽  
...  

Abstract Background: Melanin is an important antioxidant in food, and has been used in medicine and cosmetology. Chicken meat with high melanin content from black-boned chickens have been considered a high nutritious food with potential medicinal properties. The molecular mechanism of melanogenesis of skeletal muscle in black-boned chickens remain poorly understood. This study investigated the biological gene-metabolite associations regulating the muscle melanogenesis pathways in Wuliangshan black-boned chickens with two normal boned chicken breeds as control.Results: We identified 25 differentially expressed genes and 11 transcription factors in the melanogenesis pathways. High levels of the meat flavor compounds inosine monophosphate, hypoxanthine, lysophospholipid, hydroxyoctadecadienoic acid, and nicotinamide mononucleotide were found in Wuliangshan black-boned chickens.Conclusion: Integrative analysis of transcriptomics and metabolomics revealed the dual physiological functions of the PDZK1 gene, involved in pigmentation and/or melanogenesis and regulating the phospholipid signaling processes in muscle of black boned chickens.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shiqing Yu ◽  
Mathias Drton ◽  
Daniel E. L. Promislow ◽  
Ali Shojaie

Abstract Background Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression. Results We present a new R package, , that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in , and , and is available at https://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html. Conclusions Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.


Author(s):  
Yong Wu ◽  
Xiao-Lin Yu ◽  
Xiao Xiao ◽  
Ming Li ◽  
Yi Li

2021 ◽  
Author(s):  
Takahisa Miyao ◽  
Maki Miyauchi ◽  
S. Thomas Kelly ◽  
Tommy W. Terooatea ◽  
Tatsuya Ishikawa ◽  
...  

SummaryMedullary thymic epithelial cells (mTECs) are critical for self-tolerance induction in T cells via promiscuous expression of tissue-specific antigens (TSAs), which are controlled by transcriptional regulator AIRE. Whereas AIRE-expressing (Aire+) mTECs undergo constant turnover in the adult thymus, mechanisms underlying differentiation of postnatal mTECs remain to be discovered. Integrative analysis of single-cell assays for transposase accessible chromatin (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) suggested the presence of proliferating mTECs with a specific chromatin structure, which express high levels of Aire and co-stimulatory molecules CD80 (Aire+CD80hi). Proliferating Aire+CD80hi mTECs detected by using Fucci technology express a minimal level of Aire-dependent TSAs and are converted into quiescent Aire+CD80hi mTECs expressing high levels of TSAs after a transit amplification. These data provide evidence for the existence of transit amplifying Aire+mTEC precursors during Aire+mTEC differentiation process of the postnatal thymus.


Gene Reports ◽  
2021 ◽  
pp. 101376
Author(s):  
Opeyemi Soremekun ◽  
Chisom Ezenwa ◽  
Oluwatomiwa Paimo ◽  
Chijioke Madu ◽  
Olabode Omotoso ◽  
...  

Author(s):  
Yiming Li ◽  
Jaro Karppinen ◽  
Kathryn S. E. Cheah ◽  
Danny Chan ◽  
Pak C. Sham ◽  
...  

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4745
Author(s):  
Tatiane Katsue Furuya ◽  
Claudio Bovolenta Murta ◽  
Alexis Germán Murillo Carrasco ◽  
Miyuki Uno ◽  
Laura Sichero ◽  
...  

Penile cancer (PeC) carcinogenesis is not fully understood, and no biomarkers are reported in clinical practice. We aimed to investigate molecular signatures based on miRNA and mRNA and perform an integrative analysis to identify molecular drivers and pathways for PeC development. Affymetrix miRNA microarray was used to identify differentially expressed miRNAs (DEmiRs) comparing 11 tumoral tissues (TT) paired with non-neoplastic tissues (NNT) with further validation in an independent cohort (n = 13). We also investigated the mRNA expression of 83 genes in the total sample. Experimentally validated targets of DEmiRs, miRNA-mRNA networks, and enriched pathways were evaluated in silico. Eight out of 69 DEmiRs identified by microarray analysis were validated by qRT-PCR (miR-145-5p, miR-432-5p, miR-487b-3p, miR-30a-5p, miR-200a-5p, miR-224-5p, miR-31-3p and miR-31-5p). Furthermore, 37 differentially expressed genes (DEGs) were identified when comparing TT and NNT. We identified four downregulated DEmiRs (miR-30a-5p, miR-432-5p, miR-487b-3p, and miR-145-5p) and six upregulated DEGs (IL1A, MCM2, MMP1, MMP12, SFN and VEGFA) as potential biomarkers in PeC by their capacity of discriminating TT and NNT with accuracy. The integration analysis showed eight dysregulated miRNA-mRNA pairs in penile carcinogenesis. Taken together, our findings contribute to a better understanding of the regulatory roles of miRNAs and altered transcripts levels in penile carcinogenesis.


Author(s):  
Qian Li ◽  
Ye Meng ◽  
Linhui Hu ◽  
Alice Charwudzi ◽  
Weiwei Zhu ◽  
...  

PLoS Genetics ◽  
2021 ◽  
Vol 17 (9) ◽  
pp. e1009809
Author(s):  
Enrique Audain ◽  
Anna Wilsdon ◽  
Jeroen Breckpot ◽  
Jose M. G. Izarzugaza ◽  
Tomas W. Fitzgerald ◽  
...  

Author(s):  
Jian Zhou ◽  
Zhongmeng Zhao ◽  
Lu Zhang ◽  
Zhipeng Huang ◽  
Han Zhao ◽  
...  

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