A conserved nuclear factor YC subunit, NF-YC3, is essential for arbuscule development.

Establishing reciprocal symbiosis with arbuscular mycorrhizal (AM) fungi is an important evolutionary strategy of most terrestrial plants to adapt to environmental stresses, especially phosphate (Pi) deficiencies. Identifying the key genes essential for AM symbiosis in plants and dissecting their functional mechanisms will be helpful for the breeding of new crop varieties with enhanced nutrient uptake efficiency. Here, we report a nuclear factor YC subunit-encoding gene, OsNF-YC3, whose expression is specifically induced in arbuscule-containing cells, plays an essential role in AM symbiosis.

A Mycorrhiza-Induced UDP-Glucosyl Transferase Negatively Regulates the Arbuscular Mycorrhizal Symbiosis.

Most terrestrial plants can establish a reciprocal symbiosis with arbuscular mycorrhizal (AM) fungi to cope with adverse environmental stresses. The development of AM symbiosis is energetically costly and needs to be dynamically controlled by plants to maintain the association at mutual beneficial levels. Multiple components involved in the autoregulation of mycorrhiza (AOM) have been recently identified from several plant species; however, the mechanisms underlying the feedback regulation of AM symbiosis remain largely unknown.

A best-match approach for gene set analyses in embedding spaces.

Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks.

A cell autonomous regulator of neuronal excitability modulates tau in Alzheimer's disease vulnerable neurons.

Neurons from layer II of the entorhinal cortex (ECII) are the first to accumulate tau protein aggregates and degenerate during prodromal Alzheimer's disease. Gaining insight into the molecular mechanisms underlying this vulnerability will help reveal genes and pathways at play during incipient stages of the disease. Here, we use a data-driven functional genomics approach to model ECII neurons in silico and identify the proto-oncogene DEK as a regulator of tau pathology.

Joint embedding of biological networks for cross-species functional alignment.

Motivation: Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.

Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction.

We introduce the CPFNN (Correlation Pre-Filtering Neural Network) for biological age prediction based on blood DNA methylation data. The model is built on 20,000 top correlated DNA methylation features and trained by 1810 healthy samples from GEO database. The input data format and the instructions for parser and CPFNN model are detailed in this chapter.

Age Prediction by DNA Methylation in Neural Networks.

Aging is traditionally thought to be caused by complex and interacting factors such as DNA methylation. The traditional formula of DNA methylation aging is based on linear models and little work has explored the effectiveness of neural networks, which can learn non-linear relationships. DNA methylation data typically consists of hundreds of thousands of feature space and a much less number of biological samples.