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Trends in Genetics : TIG Jul 2018There is abundant variation in gene expression between individuals, populations, and species. The evolution of gene regulation and expression within and between species... (Review)
Review
There is abundant variation in gene expression between individuals, populations, and species. The evolution of gene regulation and expression within and between species is thought to frequently contribute to adaptation. Yet considerable evidence suggests that the primary evolutionary force acting on variation in gene expression is stabilizing selection. We review here the results of recent studies characterizing the evolution of gene expression occurring in cis (via linked polymorphisms) or in trans (through diffusible products of other genes) and their contribution to adaptation and response to the environment. We review the evidence for buffering of variation in gene expression at the level of both transcription and translation, and the possible mechanisms for this buffering. Lastly, we summarize unresolved questions about the evolution of gene regulation.
Topics: Animals; Evolution, Molecular; Gene Expression; Gene Expression Regulation; Humans; Polymorphism, Genetic
PubMed: 29680748
DOI: 10.1016/j.tig.2018.03.007 -
Cell May 2021Genetic studies have revealed many variant loci that are associated with immune-mediated diseases. To elucidate the disease pathogenesis, it is essential to understand...
Genetic studies have revealed many variant loci that are associated with immune-mediated diseases. To elucidate the disease pathogenesis, it is essential to understand the function of these variants, especially under disease-associated conditions. Here, we performed a large-scale immune cell gene-expression analysis, together with whole-genome sequence analysis. Our dataset consists of 28 distinct immune cell subsets from 337 patients diagnosed with 10 categories of immune-mediated diseases and 79 healthy volunteers. Our dataset captured distinctive gene-expression profiles across immune cell types and diseases. Expression quantitative trait loci (eQTL) analysis revealed dynamic variations of eQTL effects in the context of immunological conditions, as well as cell types. These cell-type-specific and context-dependent eQTLs showed significant enrichment in immune disease-associated genetic variants, and they implicated the disease-relevant cell types, genes, and environment. This atlas deepens our understanding of the immunogenetic functions of disease-associated variants under in vivo disease conditions.
Topics: Adult; Female; Gene Expression; Gene Expression Regulation; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Immune System; Immune System Diseases; Male; Middle Aged; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Transcriptome; Whole Genome Sequencing
PubMed: 33930287
DOI: 10.1016/j.cell.2021.03.056 -
Cell Oct 2008Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels.... (Review)
Review
Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
Topics: Animals; Bacteria; Eukaryotic Cells; Gene Expression; Humans; Stochastic Processes; Transcription, Genetic
PubMed: 18957198
DOI: 10.1016/j.cell.2008.09.050 -
Current Opinion in Chemical Biology Aug 2019Visualization of transcription in living cells has taught us that genes are often transcribed in bursts, with periods of gene activity interspersed by periods of... (Review)
Review
Visualization of transcription in living cells has taught us that genes are often transcribed in bursts, with periods of gene activity interspersed by periods of inactivity. Recently, technological advances in live-cell imaging have provided a more detailed picture of the characteristics of transcriptional bursts, and have allowed direct visualization of the upstream regulatory steps of bursting at single-molecule resolution. In this review, we highlight the latest insights into transcription dynamics and we discuss recent developments in understanding the regulation of transcriptional bursting through the binding kinetics of transcription factors, enhancer-promoter interactions and clustering/phase separation of the transcriptional machinery.
Topics: Gene Expression; Transcription Factors; Transcription, Genetic
PubMed: 31284216
DOI: 10.1016/j.cbpa.2019.05.031 -
STAR Protocols Dec 2023This overview guides both novices and experienced researchers facing challenging targets to select the most appropriate gene expression system for producing a particular... (Review)
Review
This overview guides both novices and experienced researchers facing challenging targets to select the most appropriate gene expression system for producing a particular protein. By answering four key questions, readers can determine the most suitable gene expression system following a decision scheme. This guide addresses the most commonly used and accessible systems and provides brief descriptions of the main gene expression systems' key characteristics to assist decision making. Additionally, information has been included for selected less frequently used "exotic" gene expression systems.
Topics: Ligands; Databases, Pharmaceutical; Recombinant Proteins; Gene Expression
PubMed: 37917580
DOI: 10.1016/j.xpro.2023.102572 -
Cell Jan 2019Transcriptional regulation in metazoans occurs through long-range genomic contacts between enhancers and promoters, and most genes are transcribed in episodic "bursts"...
Transcriptional regulation in metazoans occurs through long-range genomic contacts between enhancers and promoters, and most genes are transcribed in episodic "bursts" of RNA synthesis. To understand the relationship between these two phenomena and the dynamic regulation of genes in response to upstream signals, we describe the use of live-cell RNA imaging coupled with Hi-C measurements and dissect the endogenous regulation of the estrogen-responsive TFF1 gene. Although TFF1 is highly induced, we observe short active periods and variable inactive periods ranging from minutes to days. The heterogeneity in inactive times gives rise to the widely observed "noise" in human gene expression and explains the distribution of protein levels in human tissue. We derive a mathematical model of regulation that relates transcription, chromosome structure, and the cell's ability to sense changes in estrogen and predicts that hypervariability is largely dynamic and does not reflect a stable biological state.
Topics: Estrogen Receptor alpha; Estrogens; Gene Expression; Gene Expression Regulation; Humans; Models, Theoretical; Promoter Regions, Genetic; RNA, Messenger; Single-Cell Analysis; Transcription, Genetic; Transcriptional Activation; Trefoil Factor-1
PubMed: 30554876
DOI: 10.1016/j.cell.2018.11.026 -
RNA (New York, N.Y.) Aug 2020In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only... (Review)
Review
In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.
Topics: Gene Expression; Gene Expression Profiling; High-Throughput Nucleotide Sequencing; Humans; RNA; Sequence Analysis, RNA
PubMed: 32284352
DOI: 10.1261/rna.074922.120 -
Cell Metabolism May 2014For many years, mitochondria were viewed as semiautonomous organelles, required only for cellular energetics. This view has been largely supplanted by the concept that... (Review)
Review
For many years, mitochondria were viewed as semiautonomous organelles, required only for cellular energetics. This view has been largely supplanted by the concept that mitochondria are fully integrated into the cell and that mitochondrial stresses rapidly activate cytosolic signaling pathways that ultimately alter nuclear gene expression. Remarkably, this coordinated response to mild mitochondrial stress appears to leave the cell less susceptible to subsequent perturbations. This response, termed mitohormesis, is being rapidly dissected in many model organisms. A fuller understanding of mitohormesis promises to provide insight into our susceptibility for disease and potentially provide a unifying hypothesis for why we age.
Topics: Animals; Gene Expression; Humans; Mitochondria; Signal Transduction; Stress, Physiological
PubMed: 24561260
DOI: 10.1016/j.cmet.2014.01.011 -
Development (Cambridge, England) Aug 2021The spinal cord receives input from peripheral sensory neurons and controls motor output by regulating muscle innervating motor neurons. These functions are carried out...
The spinal cord receives input from peripheral sensory neurons and controls motor output by regulating muscle innervating motor neurons. These functions are carried out by neural circuits comprising molecularly distinct neuronal subtypes generated in a characteristic spatiotemporal arrangement from progenitors in the embryonic neural tube. To gain insight into the diversity and complexity of cells in the developing human neural tube, we used single-cell mRNA sequencing to profile cervical and thoracic regions in four human embryos of Carnegie stages (CS) CS12, CS14, CS17 and CS19 from gestational weeks 4-7. Analysis of progenitor and neuronal populations from the neural tube and dorsal root ganglia identified dozens of distinct cell types and facilitated the reconstruction of the differentiation pathways of specific neuronal subtypes. Comparison with mouse revealed overall similarity of mammalian neural tube development while highlighting some human-specific features. These data provide a catalogue of gene expression and cell type identity in the human neural tube that will support future studies of sensory and motor control systems. The data can be explored at https://shiny.crick.ac.uk/scviewer/neuraltube/.
Topics: Animals; Cell Differentiation; Embryo, Mammalian; Ganglia, Spinal; Gene Expression; Gene Expression Profiling; Humans; Mice; Motor Neurons; Neural Tube; Sensory Receptor Cells; Spinal Cord; Thorax; Transcriptome
PubMed: 34351410
DOI: 10.1242/dev.199711 -
Genes Apr 2021We investigate the model of gene expression in the form of Iterated Function System (IFS), where the probability of choice of any iterated map depends on the state of...
We investigate the model of gene expression in the form of Iterated Function System (IFS), where the probability of choice of any iterated map depends on the state of the phase space. Random jump times of the process mark activation periods of the gene when pre-mRNA molecules are produced before mRNA and protein processing phases occur. The main idea is inspired by the continuous-time piecewise deterministic Markov process describing stochastic gene expression. We show that for our system there exists a unique invariant limit measure. We provide full probabilistic description of the process with a comparison of our results to those obtained for the model with continuous time.
Topics: Gene Expression Regulation; Models, Theoretical; Probability; Protein Biosynthesis; Stochastic Processes; Transcription, Genetic
PubMed: 33926131
DOI: 10.3390/genes12050648