Sonin, A. | Search bioRxiv

bioRxiv2023

Identifying the core genome of the nucleus-forming bacteriophage family and characterization of Erwinia phage RAY

We recently discovered that some bacteriophages establish a nucleus-like replication compartment (phage nucleus), but the core genes that define nucleus-based phage replication and their phylogenetic distribution were unknown. By studying phages that encode the major phage nucleus protein chimallin, including previously sequenced yet uncharacterized phages, we discovered that chimallin-encoding phages share a set of 72 highly conserved genes encoded within seven distinct gene blocks. Of these, 21 core genes are unique to this group, and all but one of these unique genes encode proteins of unknown function. We propose that phages with this core genome comprise a novel viral family we term Chimalliviridae. Fluorescence microscopy and cryo-electron tomography studies of Erwinia phage vB_EamM_RAY confirm that many of the key steps of nucleus-based replication encoded in the core genome are conserved among diverse chimalliviruses, and reveal that non-core components can confer intriguing variations on this replication mechanism. For instance, unlike previously studied nucleus-forming phages, RAY doesnt degrade the host genome, and its PhuZ homolog appears to form a five-stranded filament with a lumen. This work expands our understanding of phage nucleus and PhuZ spindle diversity and function, providing a roadmap for identifying key mechanisms underlying nucleus-based phage replication.

microbiology↗

bioRxiv2020

Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer

Single cell RNA sequencing (scRNA-seq) can be used to infer a temporal ordering of dynamic cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. This scenario is especially pronounced in dynamic immune responses of innate and adaptive immune cells. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNA-seq data into a low-dimensional cellular state space. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results reveal an ordering of key transcription factors regulating CSR, including the concomitant induction of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors point to the existence of a regulatory mechanism that reinforces IL-4 signaling to direct CSR to the IgG1 isotype.

bioinformatics↗