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Cell Oct 2020Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes...
Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.
Topics: Gene Expression; Gene Expression Profiling; Humans; Proteome; Proteomics; RNA; RNA, Messenger; Transcriptome
PubMed: 32916130
DOI: 10.1016/j.cell.2020.08.036 -
Cell Apr 2016The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been... (Review)
Review
The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.
Topics: Animals; Gene Expression Regulation; Humans; Protein Biosynthesis; Protein Processing, Post-Translational; Proteins; Proteomics; RNA, Messenger; Transcription, Genetic
PubMed: 27104977
DOI: 10.1016/j.cell.2016.03.014 -
Cell Apr 2019G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond to specific cues in their environment. However, the relationship between...
G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond to specific cues in their environment. However, the relationship between stimulus and response for each GPCR is difficult to predict due to diversity in natural signal transduction architecture and expression. Using genome engineering in yeast, we constructed an insulated, modular GPCR signal transduction system to study how the response to stimuli can be predictably tuned using synthetic tools. We delineated the contributions of a minimal set of key components via computational and experimental refactoring, identifying simple design principles for rationally tuning the dose response. Using five different GPCRs, we demonstrate how this enables cells and consortia to be engineered to respond to desired concentrations of peptides, metabolites, and hormones relevant to human health. This work enables rational tuning of cell sensing while providing a framework to guide reprogramming of GPCR-based signaling in other systems.
Topics: Gene Expression; Genetic Engineering; Humans; Pheromones; Receptors, G-Protein-Coupled; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins; Signal Transduction; Transcription Factors
PubMed: 30955892
DOI: 10.1016/j.cell.2019.02.023 -
Nucleic Acids Research May 2023In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems....
In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context.
Topics: Animals; Mice; Alternative Splicing; Sequence Analysis, RNA; Protein Isoforms; Gene Expression Profiling; Gene Expression; Transcriptome
PubMed: 36928528
DOI: 10.1093/nar/gkad169 -
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 -
Biological Psychiatry Mar 2021Major depression (MD) is determined by a multitude of factors including genetic risk variants that regulate gene expression. We examined the genetic component of gene...
BACKGROUND
Major depression (MD) is determined by a multitude of factors including genetic risk variants that regulate gene expression. We examined the genetic component of gene expression in MD by performing a transcriptome-wide association study (TWAS), inferring gene expression-trait relationships from genetic, transcriptomic, and phenotypic information.
METHODS
Genes differentially expressed in depression were identified with the TWAS FUSION method, based on summary statistics from the largest genome-wide association analysis of MD (n = 135,458 cases, n = 344,901 controls) and gene expression levels from 21 tissue datasets (brain; blood; thyroid, adrenal, and pituitary glands). Follow-up analyses were performed to extensively characterize the identified associations: colocalization, conditional, and fine-mapping analyses together with TWAS-based pathway investigations.
RESULTS
Transcriptome-wide significant differences between cases and controls were found at 94 genes, approximately half of which were novel. Of the 94 significant genes, 6 represented strong, colocalized, and potentially causal associations with depression. Such high-confidence associations include NEGR1, CTC-467M3.3, TMEM106B, LRFN5, ESR2, and PROX2. Lastly, TWAS-based enrichment analysis highlighted dysregulation of gene sets for, among others, neuronal and synaptic processes.
CONCLUSIONS
This study sheds further light on the genetic component of gene expression in depression by characterizing the identified associations, unraveling novel risk genes, and determining which associations are congruent with a causal model. These findings can be used as a resource for prioritizing and designing subsequent functional studies of MD.
Topics: Depression; Depressive Disorder, Major; Gene Expression Profiling; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Membrane Proteins; Nerve Tissue Proteins; Transcriptome
PubMed: 33279206
DOI: 10.1016/j.biopsych.2020.09.010 -
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 -
Proceedings of the National Academy of... May 2021Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity,...
Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.
Topics: Cell Line; Chromatin Immunoprecipitation Sequencing; Computational Biology; Gene Expression; Gene Expression Profiling; Humans; Myocytes, Cardiac; Protein Conformation; Proteins; RNA, Messenger; RNA-Seq; Reproducibility of Results; Transcriptome
PubMed: 33941686
DOI: 10.1073/pnas.2014866118 -
Experimental Cell Research Oct 2022Skeletal muscle development and regeneration is governed by the combined action of Myf5, MyoD, Mrf4 and MyoG, also known as the myogenic regulatory factors (MRFs). These... (Review)
Review
Skeletal muscle development and regeneration is governed by the combined action of Myf5, MyoD, Mrf4 and MyoG, also known as the myogenic regulatory factors (MRFs). These transcription factors are expressed in a highly spatio-temporal restricted manner, ensuring the significant functional and metabolic diversity observed between the different muscle groups. In this review, we will discuss the multiple layers of regulation that contribute to the control of the exquisite expression patterns of the MRFs in particular, and of myogenic genes in general. We will highlight all major regulatory processes that play a role in myogenesis: from those that modulate chromatin status and transcription competence, such as DNA methylation, histone modification, chromatin remodeling, or non-coding RNAs, to those that control transcript and protein processing and modification, such as alternative splicing, polyadenylation, other mRNA modifications, or post-translational protein modifications. All these processes are exquisitely and tightly coordinated to ensure the proper activation, maintenance and termination of the myogenic process.
Topics: Chromatin Assembly and Disassembly; Gene Expression; Gene Expression Regulation; Muscle Development; Muscle, Skeletal; Myogenic Regulatory Factors; Transcription Factors
PubMed: 35926660
DOI: 10.1016/j.yexcr.2022.113299 -
Gene Aug 2018The PAX3 gene encodes a member of the PAX family of transcription factors that is characterized by a highly conserved paired box motif. The PAX3 protein is a... (Review)
Review
The PAX3 gene encodes a member of the PAX family of transcription factors that is characterized by a highly conserved paired box motif. The PAX3 protein is a transcription factor consisting of an N-terminal DNA binding domain (containing a paired box and homeodomain) and a C-terminal transcriptional activation domain. This protein is expressed during development of skeletal muscle, central nervous system and neural crest derivatives, and regulates expression of target genes that impact on proliferation, survival, differentiation and motility in these lineages. Germline mutations of the murine Pax3 and human PAX3 genes cause deficiencies in these developmental lineages and result in the Splotch phenotype and Waardenburg syndrome, respectively. Somatic genetic rearrangements that juxtapose the PAX3 DNA binding domain to the transcriptional activation domain of other transcription factors deregulate PAX3 function and contribute to the pathogenesis of the soft tissue cancers alveolar rhabdomyosarcoma and biphenotypic sinonasal sarcoma. The wild-type PAX3 protein is also expressed in other cancers related to developmental lineages that normally express this protein and exerts phenotypic effects related to its normal developmental role.
Topics: Animals; Gene Expression; Gene Expression Regulation, Developmental; Gene Expression Regulation, Neoplastic; Humans; Mutation; PAX3 Transcription Factor; Sarcoma; Waardenburg Syndrome
PubMed: 29730428
DOI: 10.1016/j.gene.2018.04.087