scholarly journals A Computational Pipeline to Identify New Potential Regulatory Motifs in Melanoma Progression

Author(s):  
Gianfranco Politano ◽  
Alfredo Benso ◽  
Stefano Di Carlo ◽  
Francesca Orso ◽  
Alessandro Savino ◽  
...  
2013 ◽  
Vol 11 (666) ◽  
pp. 1-2 ◽  
Author(s):  
W. Zeng ◽  
J. Su ◽  
L. Wu ◽  
D. Yang ◽  
T. Long ◽  
...  
Keyword(s):  

2009 ◽  
Vol 21 (9) ◽  
pp. 2606-2623 ◽  
Author(s):  
Mark Spensley ◽  
Jae-Yean Kim ◽  
Emma Picot ◽  
John Reid ◽  
Sascha Ott ◽  
...  

Genetics ◽  
1996 ◽  
Vol 144 (1) ◽  
pp. 317-328 ◽  
Author(s):  
Sheri P Kernodle ◽  
John G Scandalios

Abstract Two highly similar cytosolic Cu/Zn Sod (Sod4 and Sod4A) genes have been isolated from maize. Sod4A contains eight exons and seven introns. The Sod4 partial sequence contains five introns. The introns in both genes are located in the same position and have highly homologous sequences in several regions. The largest intron (>1200 bp) interrupts the 5′ leader sequence. The presence of different regulatory motifs in the promoter region of each gene may indicate distinct responses to various conditions. Zymogram and RNA blot analyses show that Sod4 and Sod4A are expressed in all tissues of the maize plant. The developmental profiles of Sod4 and Sod4A mRNA accumulation differ in scutella during sporophytic development. RNA blot analysis of the respective Sod mRNAs indicates a differential, tissue-specific response of each gene to certain stressors. RNA isolated from stem tissue of ethephon-treated seedlings shows an increase in the Sod4 but not the Sod4A transcript while there is no change in transcripts of either gene in leaves or roots. There is differential mRNA accumulation between the two genes in leaf and stem tissue of paraquat-treated seedlings. Other agents that can cause oxidative stress were also tested for differential expression of the genes.


Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 726
Author(s):  
Christopher Groth ◽  
Ludovica Arpinati ◽  
Merav E. Shaul ◽  
Nina Winkler ◽  
Klara Diester ◽  
...  

Background: Despite recent improvement in the treatment of malignant melanoma by immune-checkpoint inhibitors, the disease can progress due to an immunosuppressive tumor microenvironment (TME) mainly represented by myeloid-derived suppressor cells (MDSC). However, the relative contribution of the polymorphonuclear (PMN) and monocytic (M) MDSC subsets to melanoma progression is not clear. Here, we compared both subsets regarding their immunosuppressive capacity and recruitment mechanisms. Furthermore, we inhibited PMN-MDSC migration in vivo to determine its effect on tumor progression. Methods: Using the RET transgenic melanoma mouse model, we investigated the immunosuppressive function of MDSC subsets and chemokine receptor expression on these cells. The effect of CXCR2 inhibition on PMN-MDSC migration and tumor progression was studied in RET transgenic mice and in C57BL/6 mice after surgical resection of primary melanomas. Results: Immunosuppressive capacity of intratumoral M- and PMN-MDSC was comparable in melanoma bearing mice. Anti-CXCR2 therapy prolonged survival of these mice and decreased the occurrence of distant metastasis. Furthermore, this therapy reduced the infiltration of melanoma lesions and pre-metastatic sites with PMN-MDSC that was associated with the accumulation of natural killer (NK) cells. Conclusions: We provide evidence for the tumor−promoting properties of PMN-MDSC as well as for the anti-tumor effects upon their targeting in melanoma bearing mice.


2021 ◽  
Vol 154 (15) ◽  
pp. 154105
Author(s):  
Zahra Shadfar ◽  
Oussama Yahiaoui ◽  
Thomas A. Collier ◽  
Thomas Fallon ◽  
Jane R. Allison

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Satu Salmi ◽  
Anton Lin ◽  
Benjamin Hirschovits-Gerz ◽  
Mari Valkonen ◽  
Niina Aaltonen ◽  
...  

Abstract Background FoxP3+ Regulatory T cells (Tregs) and indoleamine-2,3-dioxygenase (IDO) participate in the formation of an immunosuppressive tumor microenvironment (TME) in malignant cutaneous melanoma (CM). Recent studies have reported that IDO expression correlates with poor prognosis and greater Breslow’s depth, but results concerning the role of FoxP3+ Tregs in CM have been controversial. Furthermore, the correlation between IDO and Tregs has not been substantially studied in CM, although IDO is known to be an important regulator of Tregs activity. Methods We investigated the associations of FoxP3+ Tregs, IDO+ tumor cells and IDO+ stromal immune cells with tumor stage, prognostic factors and survival in CM. FoxP3 and IDO were immunohistochemically stained from 29 benign and 29 dysplastic nevi, 18 in situ -melanomas, 48 superficial and 62 deep melanomas and 67 lymph node metastases (LNMs) of CM. The number of FoxP3+ Tregs and IDO+ stromal immune cells, and the coverage and intensity of IDO+ tumor cells were analysed. Results The number of FoxP3+ Tregs and IDO+ stromal immune cells were significantly higher in malignant melanomas compared with benign lesions. The increased expression of IDO in melanoma cells was associated with poor prognostic factors, such as recurrence, nodular growth pattern and increased mitotic count. Furthermore, the expression of IDO in melanoma cells was associated with reduced recurrence˗free survival. We further showed that there was a positive correlation between IDO+ tumor cells and FoxP3+ Tregs. Conclusions These results indicate that IDO is strongly involved in melanoma progression. FoxP3+ Tregs also seems to contribute to the immunosuppressive TME in CM, but their significance in melanoma progression remains unclear. The positive association of FoxP3+ Tregs with IDO+ melanoma cells, but not with IDO+ stromal immune cells, indicates a complex interaction between IDO and Tregs in CM, which demands further studies.


2019 ◽  
Vol 10 (36) ◽  
pp. 8374-8383 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Aditya Sonpal ◽  
Mojtaba Haghighatlari ◽  
Andrew J. Schultz ◽  
Johannes Hachmann

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.


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