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1. Genetic dissection of cis -regulatory control of ZmWUSCHEL1 expression by type B RESPONSE REGULATORS

   https://doi.org/10.1093/plphys/kiad652  

   Chen, Zongliang; Cortes, Liz; Gallavotti, Andrea (December 2023, Plant Physiology)

   Abstract Mutations in cis-regulatory regions play an important role in the domestication and improvement of crops by altering gene expression. However, assessing the in vivo impact of cis-regulatory elements (CREs) on transcriptional regulation and phenotypic outcomes remains challenging. Previously, we showed that the dominant Barren inflorescence3 (Bif3) mutant of maize (Zea mays) contains a duplicated copy of the homeobox transcription factor gene ZmWUSCHEL1 (ZmWUS1), named ZmWUS1-B. ZmWUS1-B is controlled by a spontaneously generated novel promoter region that dramatically increases its expression and alters patterning and development of young ears. Overexpression of ZmWUS1-B is caused by a unique enhancer region containing multimerized binding sites for type B RESPONSE REGULATORs (RRs), key transcription factors in cytokinin signaling. To better understand how the enhancer increases the expression of ZmWUS1 in vivo, we specifically targeted the ZmWUS1-B enhancer region by CRISPR-Cas9-mediated editing. A series of deletion events with different numbers of type B RR DNA binding motifs (AGATAT) enabled us to determine how the number of AGATAT motifs impacts in vivo expression of ZmWUS1-B and consequently ear development. In combination with dual-luciferase assays in maize protoplasts, our analysis reveals that AGATAT motifs have an additive effect on ZmWUS1-B expression, while the distance separating AGATAT motifs does not appear to have a meaningful impact, indicating that the enhancer activity derives from the sum of individual CREs. These results also suggest that in maize inflorescence development, there is a threshold of buffering capacity for ZmWUS1 overexpression. 

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2. The genetic and ecophysiological diversity of Microcystis

   https://doi.org/10.1111/1462-2920.15615  

   Dick, Gregory_J; Duhaime, Melissa_B; Evans, Jacob_T; Errera, Reagan_M; Godwin, Casey_M; Kharbush, Jenan_J; Nitschky, Helena_S; Powers, McKenzie_A; Vanderploeg, Henry_A; Schmidt, Kathryn_C; et al (June 2021, Environmental Microbiology)

   Summary Microcystisis a cyanobacterium that forms toxic blooms in freshwater ecosystems around the world. Biological variation among taxa within the genus is apparent through genetic and phenotypic differences between strains and via the spatial and temporal distribution of strains in the environment, and this fine‐scale diversity exerts strong influence over bloom toxicity. Yet we do not know how varying traits ofMicrocystisstrains govern their environmental distribution, the tradeoffs and links between these traits, or how they are encoded at the genomic level. Here we synthesize current knowledge on the importance of diversity withinMicrocystisand on the genes and traits that likely underpin ecological differentiation of taxa. We briefly review spatial and environmental patterns ofMicrocystisdiversity in the field and genetic evidence for cohesive groups withinMicrocystis. We then compile data on strain‐level diversity regarding growth responses to environmental conditions and explore evidence for variation of community interactions acrossMicrocystisstrains. Potential links and tradeoffs between traits are identified and discussed. The resulting picture, while incomplete, highlights key knowledge gaps that need to be filled to enable new models for predicting strain‐level dynamics, which influence the development, toxicity and cosmopolitan nature ofMicrocystisblooms. 

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3. Predictive models of genetic redundancy in Arabidopsis thaliana

   https://doi.org/10.1093/molbev/msab111  

   Cusack, Siobhan A; Wang, Peipei; Lotreck, Serena G; Moore, Bethany M; Meng, Fanrui; Conner, Jeffrey K; Krysan, Patrick J; Lehti-Shiu, Melissa D; Shiu, Shin-Han (April 2021, Molecular Biology and Evolution)

   de Meaux, Juliette (Ed.)

   Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including post-translational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on hold-out, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies. 

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4. Toolbox of Characterized Genetic Parts for Staphylococcus aureus

   https://doi.org/10.1021/acssynbio.3c00325  

   Rondthaler, Stephen_N; Sarker, Biprodev; Howitz, Nathaniel; Shah, Ishita; Andrews, Lauren_B (December 2023, ACS Synthetic Biology)
5. The genetic architecture of larval aggregation behavior in Drosophila

   https://doi.org/10.1080/01677063.2021.1887174  

   McKinney, Ross M.; Valdez, Ryan; Ben-Shahar, Yehuda (February 2021, Journal of Neurogenetics)

   null (Ed.)