Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

Identifying Conserved Functional Gene Modules Underlying the Dynamic Regulation of Tea Plant Development and Secondary Metabolism.

Tea plants adjust development and metabolism by integrating environmental and endogenous signals in complex but poorly defined gene networks. Here, we present an integrative analysis framework for the identification of conserved modules controlling important agronomic traits using a comprehensive collection of RNA-seq datasets in Camellia plants including 189 samples. In total, 212 secondary metabolism-, 182 stress response-, and 182 tissue development-related coexpressed modules were revealed. Functional modules (e.g., drought response, theobromine biosynthesis, and new shoot development-related modules) and potential regulators that were highly conserved across diverse genetic backgrounds and/or environmental conditions were then identified by cross-experiment comparisons and consensus clustering. Moreover, we investigate the preservation of gene networks between Camellia sinensis and other Camellia species. This revealed that the coexpression patterns of several recently evolved modules related to secondary metabolism and environmental adaptation were rewired and showed higher connectivity in tea plants. These conserved modules are excellent candidates for modeling the core mechanism of tea plant development and secondary metabolism and should serve as a great resource for hypothesis generation and tea quality improvement.

Exaptation at the molecular genetic level.

The realization that body parts of animals and plants can be recruited or coopted for novel functions dates back to, or even predates the observations of Darwin. S.J. Gould and E.S. Vrba recognized a mode of evolution of characters that differs from adaptation. The umbrella term aptation was supplemented with the concept of exaptation. Unlike adaptations, which are restricted to features built by selection for their current role, exaptations are features that currently enhance fitness, even though their present role was not a result of natural selection. Exaptations can also arise from nonaptations; these are characters which had previously been evolving neutrally. All nonaptations are potential exaptations. The concept of exaptation was expanded to the molecular genetic level which aided greatly in understanding the enormous potential of neutrally evolving repetitive DNA-including transposed elements, formerly considered junk DNA-for the evolution of genes and genomes. The distinction between adaptations and exaptations is outlined in this review and examples are given. Also elaborated on is the fact that such distinctions are sometimes more difficult to determine; this is a widespread phenomenon in biology, where continua abound and clear borders between states and definitions are rare.

Origins and evolution of viruses of eukaryotes: The ultimate modularity.

Viruses and other selfish genetic elements are dominant entities in the biosphere, with respect to both physical abundance and genetic diversity. Various selfish elements parasitize on all cellular life forms. The relative abundances of different classes of viruses are dramatically different between prokaryotes and eukaryotes. In prokaryotes, the great majority of viruses possess double-stranded (ds) DNA genomes, with a substantial minority of single-stranded (ss) DNA viruses and only limited presence of RNA viruses. In contrast, in eukaryotes, RNA viruses account for the majority of the virome diversity although ssDNA and dsDNA viruses are common as well. Phylogenomic analysis yields tangible clues for the origins of major classes of eukaryotic viruses and in particular their likely roots in prokaryotes. Specifically, the ancestral genome of positive-strand RNA viruses of eukaryotes might have been assembled de novo from genes derived from prokaryotic retroelements and bacteria although a primordial origin of this class of viruses cannot be ruled out. Different groups of double-stranded RNA viruses derive either from dsRNA bacteriophages or from positive-strand RNA viruses. The eukaryotic ssDNA viruses apparently evolved via a fusion of genes from prokaryotic rolling circle-replicating plasmids and positive-strand RNA viruses. Different families of eukaryotic dsDNA viruses appear to have originated from specific groups of bacteriophages on at least two independent occasions. Polintons, the largest known eukaryotic transposons, predicted to also form virus particles, most likely, were the evolutionary intermediates between bacterial tectiviruses and several groups of eukaryotic dsDNA viruses including the proposed order "Megavirales" that unites diverse families of large and giant viruses. Strikingly, evolution of all classes of eukaryotic viruses appears to have involved fusion between structural and replicative gene modules derived from different sources along with additional acquisitions of diverse genes.