Different types of disease‐causing non‐coding variants revealed by genomic and gene expression analyses in families with X‐linked intellectual disability

2021 ◽  
Author(s):  
Michael J. Field ◽  
Raman Kumar ◽  
Anna Hackett ◽  
Sayaka Kayumi ◽  
Cheryl A. Shoubridge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Intellectual Disability ◽  
Gene Expression Analyses ◽  
Different Types ◽  
Coding Variants

scholarly journals Endolysosomal Cation Channels and MITF in Melanocytes and Melanoma

Biomolecules ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1021
Author(s):  
Carla Abrahamian ◽  
Christian Grimm
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Convincing Evidence ◽  
Signaling Cascades ◽  
Cation Channels ◽  
Pore Channel ◽  
Different Types ◽  
Canonical Wnt ◽  
The Relationship

Microphthalmia-associated transcription factor (MITF) is the principal transcription factor regulating pivotal processes in melanoma cell development, growth, survival, proliferation, differentiation and invasion. In recent years, convincing evidence has been provided attesting key roles of endolysosomal cation channels, specifically TPCs and TRPMLs, in cancer, including breast cancer, glioblastoma, bladder cancer, hepatocellular carcinoma and melanoma. In this review, we provide a gene expression profile of these channels in different types of cancers and decipher their roles, in particular the roles of two-pore channel 2 (TPC2) and TRPML1 in melanocytes and melanoma. We specifically discuss the signaling cascades regulating MITF and the relationship between endolysosomal cation channels, MAPK, canonical Wnt/GSK3 pathways and MITF.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Unearthing LTR retrotransposon gag genes co-opted in the deep evolution of eukaryotes

2021 ◽  
Author(s):  
Jianhua Wang ◽  
Guan-Zhu Han
Keyword(s):  
Gene Expression ◽  
Selective Pressure ◽  
Ltr Retrotransposon ◽  
Ltr Retrotransposons ◽  
Cellular Functions ◽  
The Family ◽  
Family Level ◽  
Gene Expression Analyses ◽  
Comprehensive Picture ◽  
Gypsy Retrotransposons

Abstract LTR retrotransposons comprise a major component of the genomes of eukaryotes. On occasion, retrotransposon genes can be recruited by their hosts for diverse functions, a process formally referred to as co-option. However, a comprehensive picture of LTR retrotransposon gag gene co-option in eukaryotes is still lacking, with several documented cases exclusively involving Ty3/Gypsy retrotransposons in animals. Here we use a phylogenomic approach to systemically unearth co-option of retrotransposon gag genes above the family level of taxonomy in 2,011 eukaryotes, namely co-option occurring during the deep evolution of eukaryotes. We identify a total of 14 independent gag gene co-option events across more than 740 eukaryote families, eight of which have not been reported previously. Among these retrotransposon gag gene co-option events, nine, four, and one involve gag genes of Ty3/Gypsy, Ty1/Copia, and Bel-Pao retrotransposons, respectively. Seven, four, and three co-option events occurred in animals, plants, and fungi, respectively. Interestingly, two co-option events took place in the early evolution of angiosperms. Both selective pressure and gene expression analyses further support that these co-opted gag genes might perform diverse cellular functions in their hosts, and several co-opted gag genes might be subject to positive selection. Taken together, our results provide a comprehensive picture of LTR retrotransposon gag gene co-option events that occurred during the deep evolution of eukaryotes, and suggest paucity of LTR retrotransposon gag gene co-option during the deep evolution of eukaryotes.

scholarly journals Gene expression analyses reveal metabolic specifications in acute O2-sensing chemoreceptor cells

2017 ◽  
Vol 595 (18) ◽  
pp. 6091-6120 ◽  
Author(s):  
Lin Gao ◽  
Victoria Bonilla-Henao ◽  
Paula García-Flores ◽  
Ignacio Arias-Mayenco ◽  
Patricia Ortega-Sáenz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Analyses ◽  
Chemoreceptor Cells ◽  
O2 Sensing

RETRACTED ARTICLE: Gene expression analyses to explore the biomarkers and therapeutic targets for gliomas

2014 ◽  
Vol 36 (3) ◽  
pp. 403-409 ◽  
Author(s):  
Lina Wang ◽  
Bo Wei ◽  
Guozhang Hu ◽  
Le Wang ◽  
Ying Jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Therapeutic Targets ◽  
Gene Expression Analyses ◽  
Retracted Article
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