Ribosome-omics of the human ribosome

Gupta, Varun; Warner, Jonathan R.

doi:10.1261/rna.043653.113

Cited by 79 publications

(99 citation statements)

References 66 publications

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“…As hinted in previous reports [16, 50], we found that human tissues display a notable level of RP expression heterogeneity (Fig. 2), with around one quarter of all RP genes exhibiting tissue-specific expression.…”

Section: Discussionsupporting

confidence: 86%

Patterns of ribosomal protein expression specify normal and malignant human cells

2016

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BackgroundRibosomes are highly conserved molecular machines whose core composition has traditionally been regarded as invariant. However, recent studies have reported intriguing differences in the expression of some ribosomal proteins (RPs) across tissues and highly specific effects on the translation of individual mRNAs.ResultsTo determine whether RPs are more generally linked to cell identity, we analyze the heterogeneity of RP expression in a large set of human tissues, primary cells, and tumors. We find that about a quarter of human RPs exhibit tissue-specific expression and that primary hematopoietic cells display the most complex patterns of RP expression, likely shaped by context-restricted transcriptional regulators. Strikingly, we uncover patterns of dysregulated expression of individual RPs across cancer types that arise through copy number variations and are predictive for disease progression.ConclusionsOur study reveals an unanticipated plasticity of RP expression across normal and malignant human cell types and provides a foundation for future characterization of cellular behaviors that are orchestrated by specific RPs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1104-z) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 86%

Patterns of ribosomal protein expression specify normal and malignant human cells

2016

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“…It has been shown that several ribosomal proteins are expressed in a tissue-specific manner [107]. Moreover, loss of certain ribosomal protein(s) only impairs specific tissue(s) [50]. These observations lead to the speculation that the structure/composition of ribosome varies from one tissue to another [50].…”

Section: Motivation Behind This Thesismentioning

confidence: 99%

“…Moreover, loss of certain ribosomal protein(s) only impairs specific tissue(s) [50]. These observations lead to the speculation that the structure/composition of ribosome varies from one tissue to another [50]. Therefore the interaction networks of ribosomal proteins should also vary in a cell/tissue-specific manner.…”

Section: Motivation Behind This Thesismentioning

confidence: 99%

“…Specifically, Eif3k, Eif3i, Eif3g, and Eif3e are constituents of the eukaryotic initiation factor 3 (eIF-3) complex, which plays important roles in the initiation of protein synthesis by binding to the 40S ribosome [137], whereas Eef1a1 is a subunit of the eukaryotic translation elongation factor-1 complex, which is responsible for the correct placement of certain tRNAs with respect to the ribosome [46]. These findings are interesting because experimental biologists have already speculated that the structure of the ribosome varies from one tissue to another [50,107]. Table 4.4: The most specific GO terms (T ) that contain two or more genes in a (β, σ)-UC in MouseMap dataset, the number of genes common between such a GO term and a (β, σ)-UC (#common), and the number (#(β, σ)-UC ) of (β, σ)-UCs that share this many (i.e., the number in #common) genes with the term T , as computed by UCMiner or ClustMiner.…”

Section: Analysis Of Mousemap Datasetmentioning

confidence: 99%

“…Moreover, loss of certain ribosomal proteins impairs only specific tissue(s) [50]. These observations lead to the speculation that the structure of the ribosome varies from one tissue to another [50,107].…”

Section: Introductionmentioning

confidence: 99%

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Unstable Communities in Network Ensembles

Rahman

Jan

Kim³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Ensembles of graphs arise naturally in many applications, for example, the temporal evolution of social contacts or computer communications, tissue-specific protein interaction networks, annual citation or co-authorship networks in a field, or a family of high-likelihood Bayesian networks inferred from systems biology data. Several techniques have been developed to analyze such ensembles. A canonical problem is that of computing communities that are persistent across the ensemble. This problem is usually formulated as one of computing dense subgraphs (communities) that are frequent, i.e., appear in many graphs in the ensemble.In this thesis, we seek to find "unstable communities," which are the antithesis of frequent, dense subgraphs. Informally, an unstable community is a set of nodes that induces highly-varying subgraphs in the ensemble. In other words, the graphs in the ensemble disagree about the precise pairwise connections among these nodes. The primary contribution of this dissertation is to introduce the concept of unstable communities as a novel problem in the field of graph mining. Specifically, it presents three approaches to mathematically formulate the concept of unstable communities, devises algorithms for computing such communities in a given ensemble of networks, and shows the usefulness of this concept in a variety of settings.Our first definition of unstable community relies on two parameters: the first ensures that a node set induces several different subgraphs in the ensemble and the second guarantees that each of these subgraphs occurs in a large number of graphs in the ensemble. We present two algorithms to enumerate unstable communities that match this definition. The first approach, ClustMiner, is a heuristic that transforms the problem into one of computing dense subgraphs in a single graph that summarizes the ensemble. The second approach, UCMiner, is guaranteed to enumerate all maximal unstable communities correctly. We apply both approaches to systems biology datasets to demonstrate that UCMiner is superior to ClustMiner in the sense that ClustMiner's output contains node sets that are not unstable while also missing several communities computed by UCMiner. We find several node sets that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns.Our second and third definitions of unstable community rely on a novel concept of (scaled) subgraph divergence, a formulation that uses the concept of relative entropy to measure the instability of a community. We propose another algorithm, SDMiner, that can exactly enumerate all maximal unstable communities with small (scaled) subgraph divergence. We perform extensive experiments on social network datasets to show that we can discover UCs that capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.Dedicated to the memory of my spiritual ...

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Autophagy‐dependent alternative splicing of ribosomal protein S24 produces a more stable isoform that aids in hypoxic cell survival

Kerry,

Specker,

Mizzoni

et al. 2024

FEBS Letters

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Cells remodel splicing and translation machineries to mount specialized gene expression responses to stress. Here, we show that hypoxic human cells in 2D and 3D culture models increase the relative abundance of a longer mRNA variant of ribosomal protein S24 (RPS24L) compared to a shorter mRNA variant (RPS24S) by favoring the inclusion of a 22 bp cassette exon. Mechanistically, RPS24L and RPS24S are induced and repressed, respectively, by distinct pathways in hypoxia: RPS24L is induced in an autophagy‐dependent manner, while RPS24S is reduced by mTORC1 repression in a hypoxia‐inducible factor‐dependent manner. RPS24L produces a more stable protein isoform that aids in hypoxic cell survival and growth, which could be exploited by cancer cells in the tumor microenvironment.

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Ribosome-omics of the human ribosome

Cited by 79 publications

References 66 publications

Patterns of ribosomal protein expression specify normal and malignant human cells

Patterns of ribosomal protein expression specify normal and malignant human cells

Unstable Communities in Network Ensembles

Autophagy‐dependent alternative splicing of ribosomal protein S24 produces a more stable isoform that aids in hypoxic cell survival

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