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1. A cowpea severe mosaic virus-based vector simplifies virus-induced gene silencing and foreign protein expression in soybean

   https://doi.org/10.1186/s13007-022-00950-7  

   Zaulda, Fides Angeli; Yang, Seung Hyun; Han, Junping; Mlotshwa, Sizolwenkosi; Dorrance, Anne; Qu, Feng (December 2022, Plant Methods)

   Abstract BackgroundSoybean gene functions cannot be easily interrogated through transgenic disruption (knock-out) of genes-of-interest, or transgenic overexpression of proteins-of-interest, because soybean transformation is time-consuming and technically challenging. An attractive alternative is to administer transient gene silencing or overexpression with a plant virus-based vector. However, existing virus-induced gene silencing (VIGS) and/or overexpression vectors suitable for soybean have various drawbacks that hinder their widespread adoption. ResultsWe describe the development of a new vector based on cowpea severe mosaic virus (CPSMV), a plus-strand RNA virus with its genome divided into two RNA segments, RNA1 and RNA2. This vector, designated FZ, incorporates a cloning site in the RNA2 cDNA, permitting insertion of nonviral sequences. When paired with an optimized RNA1 construct, FZ readily infects bothNicotiana benthamianaand soybean. As a result, FZ constructs destined for soybean can be first delivered toN. benthamianain order to propagate the modified viruses to high titers. FZ-based silencing constructs induced robust silencing of phytoene desaturase genes inN. benthamiana, multiple soybean accessions, and cowpea. Meanwhile, FZ supported systemic expression of fluorescent proteins mNeonGreen and mCherry inN. benthamianaand soybean. Finally, FZ-mediated expression of the Arabidopsis transcription factor MYB75 causedN. benthamianato bear brown leaves and purple, twisted flowers, indicating that MYB75 retained the function of activating anthocyanin synthesis pathways in a different plant. ConclusionsThe new CPSMV-derived FZ vector provides a convenient and versatile soybean functional genomics tool that is expected to accelerate the characterization of soybean genes controlling crucial productivity traits. 

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2. Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize

   https://doi.org/10.1186/s13059-022-02747-2  

   Ramstein, Guillaume_P; Buckler, Edward_S (September 2022, Genome Biology)

   Abstract BackgroundCrop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations. ResultsUsing only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants. ConclusionsOur results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (https://doi.org/10.25739/hybz-2957). 

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3. Regulatory controls of duplicated gene expression during fiber development in allotetraploid cotton

   https://doi.org/10.1038/s41588-023-01530-8  

   You, Jiaqi; Liu, Zhenping; Qi, Zhengyang; Ma, Yizan; Sun, Mengling; Su, Ling; Niu, Hao; Peng, Yabing; Luo, Xuanxuan; Zhu, Mengmeng; et al (November 2023, Nature Genetics)

   Abstract Polyploidy complicates transcriptional regulation and increases phenotypic diversity in organisms. The dynamics of genetic regulation of gene expression between coresident subgenomes in polyploids remains to be understood. Here we document the genetic regulation of fiber development in allotetraploid cottonGossypium hirsutumby sequencing 376 genomes and 2,215 time-series transcriptomes. We characterize 1,258 genes comprising 36 genetic modules that control staged fiber development and uncover genetic components governing their partitioned expression relative to subgenomic duplicated genes (homoeologs). Only about 30% of fiber quality-related homoeologs show phenotypically favorable allele aggregation in cultivars, highlighting the potential for subgenome additivity in fiber improvement. We envision a genome-enabled breeding strategy, with particular attention to 48 favorable alleles related to fiber phenotypes that have been subjected to purifying selection during domestication. Our work delineates the dynamics of gene regulation during fiber development and highlights the potential of subgenomic coordination underpinning phenotypes in polyploid plants. 

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4. Few Fixed Variants between Trophic Specialist Pupfish Species Reveal Candidate Cis -Regulatory Alleles Underlying Rapid Craniofacial Divergence

   https://doi.org/10.1093/molbev/msaa218  

   McGirr, Joseph A; Martin, Christopher H (September 2020, Molecular Biology and Evolution)

   Wittkopp, Patricia (Ed.)

   Abstract Investigating closely related species that rapidly evolved divergent feeding morphology is a powerful approach to identify genetic variation underlying variation in complex traits. This can also lead to the discovery of novel candidate genes influencing natural and clinical variation in human craniofacial phenotypes. We combined whole-genome resequencing of 258 individuals with 50 transcriptomes to identify candidate cis-acting genetic variation underlying rapidly evolving craniofacial phenotypes within an adaptive radiation of Cyprinodon pupfishes. This radiation consists of a dietary generalist species and two derived trophic niche specialists—a molluscivore and a scale-eating species. Despite extensive morphological divergence, these species only diverged 10 kya and produce fertile hybrids in the laboratory. Out of 9.3 million genome-wide SNPs and 80,012 structural variants, we found very few alleles fixed between species—only 157 SNPs and 87 deletions. Comparing gene expression across 38 purebred F1 offspring sampled at three early developmental stages, we identified 17 fixed variants within 10 kb of 12 genes that were highly differentially expressed between species. By measuring allele-specific expression in F1 hybrids from multiple crosses, we found that the majority of expression divergence between species was explained by trans-regulatory mechanisms. We also found strong evidence for two cis-regulatory alleles affecting expression divergence of two genes with putative effects on skeletal development (dync2li1 and pycr3). These results suggest that SNPs and structural variants contribute to the evolution of novel traits and highlight the utility of the San Salvador Island pupfish system as an evolutionary model for craniofacial development. 

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5. Discovering genotype–phenotype relationships with machine learning and the Visual Physiology Opsin Database ( VPOD )

   https://doi.org/10.1093/gigascience/giae073  

   Frazer, Seth_A; Baghbanzadeh, Mahdi; Rahnavard, Ali; Crandall, Keith_A; Oakley, Todd_H (October 2024, GigaScience)

   Abstract BackgroundPredicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families, including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax—the wavelength of maximum absorbance—which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype. ResultsHere, we report a newly compiled database of all heterologously expressed opsin genes with λmax phenotypes that we call the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains 864 unique opsin genotypes and corresponding λmax phenotypes collected across all animals from 73 separate publications. We use VPOD data and deepBreaks to show regression-based machine learning (ML) models often reliably predict λmax, account for nonadditive effects of mutations on function, and identify functionally critical amino acid sites. ConclusionThe ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism’s ecological niche, and may be used more broadly for de novo protein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes. 

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