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#geneexpression

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Alo Japan

alojapan.com/1279056/the-impac The impact of public insurance on RRSO for HBOC in Japan: a nationwide data study #CancerGenetics #GeneExpression #GeneFunction #GeneTherapy #GynaecologicalCancer #HumanGenetics #Japan #JapanNews #JapanTopics #MolecularMedicine #news JOHBOC Registration Committee: Masami Arai (Department of Clinical Genetics, Juntendo University, Graduate School of Medicine, Tokyo, Japan), Seigo Nakamura (Division of Breast Surgical Oncology, Department of Su…

The impact of public insurance on RRSO for HBOC in Japan: a nationwide data study
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Public
Andrew Millar

3/8 🧬📊 For the first time (or do update us), we've modelled dynamic, plant gene circuits with absolute units for both RNA and protein copies per cell.

You can compare our mathematical models directly to your biochemical data. Over time, both approaches should benefit! #SystemsBiology

4/8 🤖🧪 Those absolute units mean that we can predict the DNA-binding dissociation constants (Kd) of our clock transcription factors in vivo, for example.

After some tricky calibration, we estimated comparable Kd values in vitro from our earlier data and the literature, extending the method to any promoter sequence (more on this later).

So, did they match?

#Bioinformatics#GeneExpression
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Daniel E. Weeks

“we introduce a concept called the gene dosage response curve (GDRC) that relates changes in gene expression to expected changes in phenotype. We show that, for many traits, GDRCs are systematically biased in one trait direction relative to the other and, surprisingly, that as many as 40% of GDRCs are non-monotone, with large increases and decreases in expression affecting the trait in the same direction. “

#genetics#GeneExpression
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Lukas VFN 🇪🇺

Rhythmic #GeneExpression in #plants is crucial for #symbiosis with nutrient-providing bacteria
phys.org/news/2024-07-rhythmic

Periodic #cytokinin responses in Lotus japonicus #rhizobium infection and nodule development science.org/doi/10.1126/scienc

"#Legumes thrive in low-nitrogen environments by partnering with #rhizobia, #soil #bacteria that convert atmospheric #nitrogen into #ammonium, a usable form for the plants. These beneficial bacteria are housed in root nodules formed on legume roots."

Graphical abstract summarizing the study
Public
Pierre Barraud

🧬 Important pre-print about RNA degradation! by Cosmin Saveanu's lab at Pasteur Institute (@cosminribo) 📚 Check out the concise and compelling article: "Deadenylation rate is not a major determinant of RNA degradation in yeast." 🤔 Contrary to the conventional view, it challenges the role of deadenylation rates, showing that mRNA stability hinges more on variations in decapping speed. 🚀 #RNA #Yeast #GeneExpression #PrePrint

biorxiv.org/content/10.1101/20

bioRxiv · Deadenylation rate is not a major determinant of RNA degradation in yeastGene expression and its regulation depend on mRNA degradation. In eukaryotes, degradation is controlled by deadenylation rates, since a short poly(A) tail is considered to be the signal that activates decapping and triggers mRNA degradation. In contrast to this view, we show that global stability of mRNAs can be explained by variations in decapping speed alone. Rapid decapping of unstable mRNAs, for example, allows little time for deadenylation, which explains their longer than average poly(A) tails. As predicted by modeling of RNA degradation kinetics, mRNA stabilization in the absence of decapping led to a decrease in the length of the poly(A) tail, while depletion of deadenylases only increased the tail length. Our results suggest that decapping activation dictates mRNA stability independent of the deadenylation speed. One-Sentence Summary Unstable mRNAs are characterized by rapid 5’ cap removal, independent of a prior shortening of the poly(A) tail. ### Competing Interest Statement The authors have declared no competing interest.
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SIB Swiss Bioinformatics

We are pleased to announce more SIB Resources recognized as crucial for the international community by the 🌍 Global Biodata Coalition and ELIXIR 🇪🇺.

🌟 Congratulations to SIB’s teams behind @SWISS_MODEL, ModelArchive, @bgeedb, and @cellosaurus.🌟

This is testament to our commitment to providing high-quality resources for the global life sciences community.

Find out more about how we do this: tinyurl.com/3yccpb6k

#geneexpression#celllines#proteomics
Public
Marc Robinson-Rechavi

Interesting study of the performance of phylogenetic models for gene expression evolution, although they seem to have only investigated single-optimum OU models, which IMO limits conclusions on datasets with orthologs and paralogs. (Indeed they find poor performance for our 2016 dataset testing the ortholog conjucture.)
biorxiv.org/content/10.1101/20
#phylogeny #GeneExpression #MolecularEvolution #bioinformatics #phylogenetics

bioRxiv · Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression EvolutionPhylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models. ### Competing Interest Statement The authors have declared no competing interest.
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