150 research outputs found
Effects of complete and partial loss of the 24S-hydroxycholesterol-generating enzyme
Brain cholesterol metabolic products include neurosteroids and oxysterols, which play important roles in cellular physiology. In neurons, the cholesterol oxidation product, 24S-hydroxycholesterol (24S-HC), is a regulator of signaling and transcription. Here, we examined the behavioral effects of 24S-HC loss, using global and cell-selective genetic deletion of the synthetic enzyme CYP46A1. Mice that are globally deficient in CYP46A1 exhibited hypoactivity at young ages and unexpected increases in conditioned fear memory. Despite strong reductions in hippocampal 24S-HC in mice with selective loss of CYP46A1 in VGLUT1-positive cells, behavioral effects were not recapitulated in these conditional knockout mice. Global knockout produced strong, developmentally dependent transcriptional effects on select cholesterol metabolism genes. These included paradoxical changes in Liver X Receptor targets. Again, conditional knockout was insufficient to recapitulate most changes. Overall, our results highlight the complex effects of 24S-HC in an in vivo setting that are not fully predicted by known mechanisms. The results also demonstrate that the complete inhibition of enzymatic activity may be needed for a detectable, therapeutically relevant impact on gene expression and behavior
Progress and Opportunities of Foundation Models in Bioinformatics
Bioinformatics has witnessed a paradigm shift with the increasing integration
of artificial intelligence (AI), particularly through the adoption of
foundation models (FMs). These AI techniques have rapidly advanced, addressing
historical challenges in bioinformatics such as the scarcity of annotated data
and the presence of data noise. FMs are particularly adept at handling
large-scale, unlabeled data, a common scenario in biological contexts due to
the time-consuming and costly nature of experimentally determining labeled
data. This characteristic has allowed FMs to excel and achieve notable results
in various downstream validation tasks, demonstrating their ability to
represent diverse biological entities effectively. Undoubtedly, FMs have
ushered in a new era in computational biology, especially in the realm of deep
learning. The primary goal of this survey is to conduct a systematic
investigation and summary of FMs in bioinformatics, tracing their evolution,
current research status, and the methodologies employed. Central to our focus
is the application of FMs to specific biological problems, aiming to guide the
research community in choosing appropriate FMs for their research needs. We
delve into the specifics of the problem at hand including sequence analysis,
structure prediction, function annotation, and multimodal integration,
comparing the structures and advancements against traditional methods.
Furthermore, the review analyses challenges and limitations faced by FMs in
biology, such as data noise, model explainability, and potential biases.
Finally, we outline potential development paths and strategies for FMs in
future biological research, setting the stage for continued innovation and
application in this rapidly evolving field. This comprehensive review serves
not only as an academic resource but also as a roadmap for future explorations
and applications of FMs in biology.Comment: 27 pages, 3 figures, 2 table
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Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast
Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes
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