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1. De Novo Gene Birth, Horizontal Gene Transfer, and Gene Duplication as Sources of New Gene Families Associated with the Origin of Symbiosis in Amanita

   https://doi.org/10.1093/gbe/evaa193  

   Wang, Yen-Wen ; Hess, Jaqueline ; Slot, Jason C ; Pringle, Anne ( November 2020 , Genome Biology and Evolution)

   Ma, Li-Jun (Ed.)

   Abstract By introducing novel capacities and functions, new genes and gene families may play a crucial role in ecological transitions. Mechanisms generating new gene families include de novo gene birth, horizontal gene transfer, and neofunctionalization following a duplication event. The ectomycorrhizal (ECM) symbiosis is a ubiquitous mutualism and the association has evolved repeatedly and independently many times among the fungi, but the evolutionary dynamics enabling its emergence remain elusive. We developed a phylogenetic workflow to first understand if gene families unique to ECM Amanita fungi and absent from closely related asymbiotic species are functionally relevant to the symbiosis, and then to systematically infer their origins. We identified 109 gene families unique to ECM Amanita species. Genes belonging to unique gene families are under strong purifying selection and are upregulated during symbiosis, compared with genes of conserved or orphan gene families. The origins of seven of the unique gene families are strongly supported as either de novo gene birth (two gene families), horizontal gene transfer (four), or gene duplication (one). An additional 34 families appear new because of their selective retention within symbiotic species. Among the 109 unique gene families, the most upregulated gene in symbiotic cultures encodes a 1-aminocyclopropane-1-carboxylate deaminase, an enzyme capable of downregulating the synthesis of the plant hormone ethylene, a common negative regulator of plant-microbial mutualisms. 

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2. Insight into the bZIP Gene Family in Solanum tuberosum: Genome and Transcriptome Analysis to Understand the Roles of Gene Diversification in Spatiotemporal Gene Expression and Function

   https://doi.org/10.3390/ijms22010253  

   Herath, Venura ; Verchot, Jeanmarie ( January 2021 , International Journal of Molecular Sciences)

   null (Ed.)

   The basic region-leucine zipper (bZIP) transcription factors (TFs) form homodimers and heterodimers via the coil–coil region. The bZIP dimerization network influences gene expression across plant development and in response to a range of environmental stresses. The recent release of the most comprehensive potato reference genome was used to identify 80 StbZIP genes and to characterize their gene structure, phylogenetic relationships, and gene expression profiles. The StbZIP genes have undergone 22 segmental and one tandem duplication events. Ka/Ks analysis suggested that most duplications experienced purifying selection. Amino acid sequence alignments and phylogenetic comparisons made with the Arabidopsis bZIP family were used to assign the StbZIP genes to functional groups based on the Arabidopsis orthologs. The patterns of introns and exons were conserved within the assigned functional groups which are supportive of the phylogeny and evidence of a common progenitor. Inspection of the leucine repeat heptads within the bZIP domains identified a pattern of attractive pairs favoring homodimerization, and repulsive pairs favoring heterodimerization. These patterns of attractive and repulsive heptads were similar within each functional group for Arabidopsis and S. tuberosum orthologs. High-throughput RNA-seq data indicated the most highly expressed and repressed genes that might play significant roles in tissue growth and development, abiotic stress response, and response to pathogens including Potato virus X. These data provide useful information for further functional analysis of the StbZIP gene family and their potential applications in crop improvement. 

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3. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

   https://doi.org/10.1093/bioinformatics/btab273  

   Song, Dongyuan ; Li, Kexin ; Hemminger, Zachary ; Wollman, Roy ; Li, Jingyi Jessica ( July 2021 , Bioinformatics)

   ABSTRACT: Motivation Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Results Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data. Availability and implementation The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks

   https://doi.org/10.1186/s12918-018-0549-y  

   Karbalayghareh, Alireza ; Braga-Neto, Ulisses ; Dougherty, Edward R. ( March 2018 , BMC Systems Biology)
5. Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development

   https://doi.org/10.3389/fgene.2019.01387  

   Gallivan, Cameron P. ; Ren, Honglei ; Read, Elizabeth L. ( January 2020 , Frontiers in Genetics)