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Genome Editing and Gene Therapies: Complex and Expensive Drugs
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2020
Pertea et al. (2018) published the results of an investigation (entitled “Thousands of large-scale RNA sequencing experiments yield a comprehensive new human gene list and reveal extensive transcriptional noise”) leading to a new human gene database which contains 43,162 genes, of which nearly 50% (21,306) are protein-coding. According to the Online Mendelian Inheritance in Man (www.omim.org/statistics/geneMap) compendium of mutations (updated May 2019), 4,000 genes within the human have so far been linked to disease
Single-Cell Analysis in Cancer
Published in Inna Kuperstein, Emmanuel Barillot, Computational Systems Biology Approaches in Cancer Research, 2019
Inna Kuperstein, Emmanuel Barillot
Lineage inference can be a challenging task, especially when multiple differentiation trajectories from a common progenitor pool towards several different mature cell types exist. In this regard the StemID algorithm was developed to infer multiple branching points of cellular differentiation and to predict a potential stem cell phenotype in the dataset. The lineage tree inference of StemID is guided by a previously derived topology of cell types and cell states using a clustering inferred by RaceID3.2 Medoids of clusters are connected amongst each other, representing potential lineage trajectories and cells are mapped onto the link that best represents its state of differentiation. This is done by projecting the vectors that connect the medoid and the cells in the same cluster, onto the links to all other cluster medoids. In order to find the most likely position of a cell on one of the links, all the link lengths between medoids are normalized to one and the longest projection is chosen. Moreover, to determine whether a link could indeed represent a potential differentiation trajectory and not transcriptional noise, every link is scored according to how densely it is packed. To achieve this, a link score is defined as one minus the maximum difference of the distance between two neighbouring cells on a link after normalizing the link length to one. A score of one would indicate a link that is densely populated with cells with small gaps, whereas a score close to zero would represent cells that are close to the cluster centres of a given link. Apart from lineage tree inference, StemID also aims to predict multipotent cells, namely stem cells, within the data. To this end the concept of entropy is used as a measure of transcriptional uniformity. In general, stem cells tend to express many different transcripts, whereas differentiated cells often express a smaller number of genes at high levels representing their specialized phenotype. This can be modelled using Shannon’s entropy where a multipotent progenitor cell with a uniform transcriptome would show high entropy values, whereas a differentiated cell and its specialized transcriptome would have low entropy values. Finally, StemID uses the product of both the number of outgoing branching links and the entropy values of a given cell type to calculate a score reflecting the likelihood of a given cell type to be a stem cell. Despite being useful in inferring lineage trajectories and predicting stem cells, StemID is dependent on a pre-inferred topology.
Regulation of cytochrome P450 expression by microRNAs and long noncoding RNAs: Epigenetic mechanisms in environmental toxicology and carcinogenesis
Published in Journal of Environmental Science and Health, Part C, 2019
Dongying Li, William H. Tolleson, Dianke Yu, Si Chen, Lei Guo, Wenming Xiao, Weida Tong, Baitang Ning
lncRNAs are also recognized as important epigenetic regulatory RNA species and potential biomarkers for human diseases.42 Advancements in high-throughput technologies, such microarray and NGS, have enabled unprecedented detection of novel transcripts, leading to an explosion of data related to lncRNAs. The NONCODE database (version 2016), an online repository of the most complete collection of lncRNAs, has identified 167,150 lncRNAs in humans that are transcribed from 101,700 lncRNA genes.142 Despite the growing amount of data becoming available concerning lncRNAs, much less is known about the properties, functions, and biological significance of individual lncRNAs. Initially considered merely transcriptional noise, lncRNAs were later found to be associated with many human diseases including cancer, metabolic diseases, cardiovascular diseases, autoimmune diseases, and Alzheimer’s disease.42,143–146 Mechanistic studies have demonstrated that lncRNAs can influence gene expression 143 that affects developmental147 (e.g. genomic imprinting), physiological148 (e.g. metabolism), and pathological149 (e.g. cancer development and progression) processes. Not only do lncRNAs impact these fundamental processes, they also respond to external stimuli. Indeed, an increasing number of effects of environmental chemicals on lncRNA expression are being reported.46,150,151