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1. Beyond the reference: gene expression variation and transcriptional response to RNA interference in Caenorhabditis elegans

   https://doi.org/10.1093/g3journal/jkad112  

   Bell, Avery Davis; Chou, Han Ting; Valencia, Francisco; Paaby, Annalise B (May 2023, G3: Genes, Genomes, Genetics)

   Kim, J (Ed.)

   Abstract Though natural systems harbor genetic and phenotypic variation, research in model organisms is often restricted to a reference strain. Focusing on a reference strain yields a great depth of knowledge but potentially at the cost of breadth of understanding. Furthermore, tools developed in the reference context may introduce bias when applied to other strains, posing challenges to defining the scope of variation within model systems. Here, we evaluate how genetic differences among 5 wild Caenorhabditis elegans strains affect gene expression and its quantification, in general and after induction of the RNA interference (RNAi) response. Across strains, 34% of genes were differentially expressed in the control condition, including 411 genes that were not expressed at all in at least 1 strain; 49 of these were unexpressed in reference strain N2. Reference genome mapping bias caused limited concern: despite hyperdiverse hotspots throughout the genome, 92% of variably expressed genes were robust to mapping issues. The transcriptional response to RNAi was highly strain- and target-gene-specific and did not correlate with RNAi efficiency, as the 2 RNAi-insensitive strains showed more differentially expressed genes following RNAi treatment than the RNAi-sensitive reference strain. We conclude that gene expression, generally and in response to RNAi, differs across C. elegans strains such that the choice of strain may meaningfully influence scientific inferences. Finally, we introduce a resource for querying gene expression variation in this dataset at https://wildworm.biosci.gatech.edu/rnai/. 

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2. Genetic Manipulation of Ticks: A Paradigm Shift in Tick and Tick-Borne Diseases Research

   https://doi.org/10.3389/fcimb.2021.678037  

   Nuss, Andrew; Sharma, Arvind; Gulia-Nuss, Monika (May 2021, Frontiers in Cellular and Infection Microbiology)

   Ticks are obligate hematophagous arthropods that are distributed worldwide and are one of the most important vectors of pathogens affecting humans and animals. Despite the growing burden of tick-borne diseases, research on ticks has lagged behind other arthropod vectors, such as mosquitoes. This is largely because of challenges in applying functional genomics and genetic tools to the idiosyncrasies unique to tick biology, particularly techniques for stable genetic transformations. CRISPR-Cas9 is transforming non-model organism research; however, successful germline editing has yet to be accomplished in ticks. Here, we review the ancillary methods needed for transgenic tick development and the use of CRISPR/Cas9, the most promising gene-editing approach, for tick genetic transformation. 

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3. Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network

   https://doi.org/10.1093/bioinformatics/btab812  

   Song, Tianci; Markham, Kathleen K; Li, Zhuliu; Muller, Kristen E; Greenham, Kathleen; Kuang, Rui (December 2021, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Abstract Motivation Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression co-localizations in tissue for detecting spatial expression patterns or functional relationships among the genes for biological interpretation in the spatial context. In this article, we present a convolutional neural network (CNN) regularized by the graph of protein–protein interaction (PPI) network to cluster spatially resolved gene expression. This method improves the coherence of spatial patterns and provides biological interpretation of the gene clusters in the spatial context by exploiting the spatial localization by convolution and gene functional relationships by graph-Laplacian regularization. Results In this study, we tested clustering the spatially variable genes or all expressed genes in the transcriptome in 22 Visium spatial transcriptomics datasets of different tissue sections publicly available from 10× Genomics and spatialLIBD. The results demonstrate that the PPI-regularized CNN constantly detects gene clusters with coherent spatial patterns and significantly enriched by gene functions with the state-of-the-art performance. Additional case studies on mouse kidney tissue and human breast cancer tissue suggest that the PPI-regularized CNN also detects spatially co-expressed genes to define the corresponding morphological context in the tissue with valuable insights. Availability and implementation Source code is available at https://github.com/kuanglab/CNN-PReg. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. Tomato fruit as a model for tissue-specific gene silencing in crop plants

   https://doi.org/10.1038/s41438-020-00363-4  

   Feder, Ari; Jensen, Sarah; Wang, Anquan; Courtney, Lance; Middleton, Lesley; Van Eck, Joyce; Liu, Yongsheng; Giovannoni, James J. (September 2020, Horticulture Research)

   Abstract Use of CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-associated 9)-mediated genome editing has proliferated for use in numerous plant species to modify gene function and expression, usually in the context of either transient or stably inherited genetic alternations. While extremely useful in many applications, modification of some loci yields outcomes detrimental to further experimental evaluation or viability of the target organism. Expression of Cas9 under a promoter conferring gene knockouts in a tissue-specific subset of genomes has been demonstrated in insect and animal models, and recently inArabidopsis. We developed an in planta GFP (green fluorescent protein) assay system to demonstrate fruit-specific gene editing in tomato using aphosphoenolpyruvate carboxylase 2gene promoter. We then targeted a SET-domain containing polycomb protein, SlEZ2, previously shown to yield pleiotropic phenotypes when targeted via³⁵S-driven RNA interference and we were able to characterize fruit phenotypes absent additional developmental perturbations. Tissue-specific gene editing will have applications in assessing function of essential genes otherwise difficult to study via germline modifications and will provide routes to edited genomes in tissues that could not otherwise be recovered when their germline modification perturbs their normal development. 

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5. Recent advances in gene function prediction using context-specific coexpression networks in plants

   https://doi.org/10.12688/f1000research.17207.1  

   Gupta, Chirag; Pereira, Andy (January 2019, F1000Research)

   Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks—created by integrating multiple expression datasets—connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional “global” to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks. 

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