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Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

ABSTRACT: Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

PROVIDER: PRJNA1123575 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR29385960.fastq.gz	Fastqsanger.gz
	SRR29385961.fastq.gz	Fastqsanger.gz
	SRR29385962.fastq.gz	Fastqsanger.gz
	SRR29385963.fastq.gz	Fastqsanger.gz
	SRR29385964.fastq.gz	Fastqsanger.gz

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Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Project description:Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and uncover novel gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.

2024-08-01 | GSE269734 | GEO

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes.

Project description:Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and helps uncover gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.

| S-EPMC11326257 | biostudies-literature

Epithelial-mesenchymal transition: an organizing principle of mammalian regeneration.

Project description:Introduction: The MRL mouse strain is one of the few examples of a mammal capable of healing appendage wounds by regeneration, a process that begins with the formation of a blastema, a structure containing de-differentiating mesenchymal cells. HIF-1α expression in the nascent MRL wound site blastema is one of the earliest identified events and is sufficient to initiate the complete regenerative program. However, HIF-1α regulates many cellular processes modulating the expression of hundreds of genes. A later signal event is the absence of a functional G1 checkpoint, leading to G2 cell cycle arrest with increased cellular DNA but little cell division observed in the blastema. This lack of mitosis in MRL blastema cells is also a hallmark of regeneration in classical invertebrate and vertebrate regenerators such as planaria, hydra, and newt. Results and discussion: Here, we explore the cellular events occurring between HIF-1α upregulation and its regulation of the genes involved in G2 arrest (EVI-5, γH3, Wnt5a, and ROR2), and identify epithelial-mesenchymal transition (EMT) (Twist and Slug) and chromatin remodeling (EZH-2 and H3K27me3) as key intermediary processes. The locus of these cellular events is highly regionalized within the blastema, occurring in the same cells as determined by double staining by immunohistochemistry and FACS analysis, and appears as EMT and chromatin remodeling, followed by G2 arrest determined by kinetic expression studies.

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An evolutionarily conserved function of the pre-mRNA splicing regulator TNPO3 for T cell development

Project description:All vertebrates possess lymphocytes, enabling the functional interrogation of this evolutionarily conserved organizing principle of adaptive immunity in non-mammalian species. A forward genetic screen in zebrafish indicated that a mutation of tnpo3, encoding a regulator of alternative splicing of pre-mRNAs, specifically impaired intrathymic T cell development. In mouse T cells, Tnpo3 deficiency predominantly disrupted the splicing of genes encoding RNA-binding motifs, and it specifically blocked the splicing of Va11, associated with reduced numbers of peripheral T cells and the complete loss of iNKT cells. Our results reveal an evolutionarily conserved function of a pre-mRNA processing factor for T cell development, uncovering a previously unappreciated checkpoint during the formation of mature mRNAs of the Tcra gene.

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A self-organizing principle for learning nonlinear manifolds.

Project description:Modern science confronts us with massive amounts of data: expression profiles of thousands of human genes, multimedia documents, subjective judgments on consumer products or political candidates, trade indices, global climate patterns, etc. These data are often highly structured, but that structure is hidden in a complex set of relationships or high-dimensional abstractions. Here we present a self-organizing algorithm for embedding a set of related observations into a low-dimensional space that preserves the intrinsic dimensionality and metric structure of the data. The embedding is carried out by using an iterative pairwise refinement strategy that attempts to preserve local geometry while maintaining a minimum separation between distant objects. In effect, the method views the proximities between remote objects as lower bounds of their true geodesic distances and uses them as a means to impose global structure. Unlike previous approaches, our method can reveal the underlying geometry of the manifold without intensive nearest-neighbor or shortest-path computations and can reproduce the true geodesic distances of the data points in the low-dimensional embedding without requiring that these distances be estimated from the data sample. More importantly, the method is found to scale linearly with the number of points and can be applied to very large data sets that are intractable by conventional embedding procedures.

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Project description:Neuronal circuitry is often considered a clean slate that can be dynamically and arbitrarily molded by experience. However, when we investigated synaptic connectivity in groups of pyramidal neurons in the neocortex, we found that both connectivity and synaptic weights were surprisingly predictable. Synaptic weights follow very closely the number of connections in a group of neurons, saturating after only 20% of possible connections are formed between neurons in a group. When we examined the network topology of connectivity between neurons, we found that the neurons cluster into small world networks that are not scale-free, with less than 2 degrees of separation. We found a simple clustering rule where connectivity is directly proportional to the number of common neighbors, which accounts for these small world networks and accurately predicts the connection probability between any two neurons. This pyramidal neuron network clusters into multiple groups of a few dozen neurons each. The neurons composing each group are surprisingly distributed, typically more than 100 μm apart, allowing for multiple groups to be interlaced in the same space. In summary, we discovered a synaptic organizing principle that groups neurons in a manner that is common across animals and hence, independent of individual experiences. We speculate that these elementary neuronal groups are prescribed Lego-like building blocks of perception and that acquired memory relies more on combining these elementary assemblies into higher-order constructs.

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Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency

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