The molecular basis of phenotypic evolution: beyond the usual suspects

Lin, Rong-Chien; Ferreira, Bianca T.; Yuan, Yao-Wu

doi:10.1016/j.tig.2024.04.010

Cited by 2 publications

(1 citation statement)

References 84 publications

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“…The “312” or “213” motif architecture is the potentially conservative combination of functional motifs or even modular structural domains [ 80 , 81 , 82 , 83 ]. Taxon-specific coding and non-coding RNA genes have been found to possess a profound impact on lineage-specific phenotypic diversification and adaptation [ 36 , 84 ]. The identification of Oryza -specific lncRNAs provides a novel example for understanding the adaptation of rice to hyperosmolarity stress.…”

Section: Discussionmentioning

confidence: 99%

Long Noncoding RNAs in Response to Hyperosmolarity Stress, but Not Salt Stress, Were Mainly Enriched in the Rice Roots

Pang,

Zheng,

Min

et al. 2024

IJMS

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Due to their immobility and possession of underground parts, plants have evolved various mechanisms to endure and adapt to abiotic stresses such as extreme temperatures, drought, and salinity. However, the contribution of long noncoding RNAs (lncRNAs) to different abiotic stresses and distinct rice seedling parts remains largely uncharacterized beyond the protein-coding gene (PCG) layer. Using transcriptomics and bioinformatics methods, we systematically identified lncRNAs and characterized their expression patterns in the roots and shoots of wild type (WT) and ososca1.1 (reduced hyperosmolality-induced [Ca2+]i increase in rice) seedlings under hyperosmolarity and salt stresses. Here, 2937 candidate lncRNAs were identified in rice seedlings, with intergenic lncRNAs representing the largest category. Although the detectable sequence conservation of lncRNAs was low, we observed that lncRNAs had more orthologs within the Oryza. By comparing WT and ososca1.1, the transcription level of OsOSCA1.1-related lncRNAs in roots was greatly enhanced in the face of hyperosmolality stress. Regarding regulation mode, the co-expression network revealed connections between trans-regulated lncRNAs and their target PCGs related to OsOSCA1.1 and its mediation of hyperosmolality stress sensing. Interestingly, compared to PCGs, the expression of lncRNAs in roots was more sensitive to hyperosmolarity stress than to salt stress. Furthermore, OsOSCA1.1-related hyperosmolarity stress-responsive lncRNAs were enriched in roots, and their potential cis-regulated genes were associated with transcriptional regulation and signaling transduction. Not to be ignored, we identified a motif-conserved and hyperosmolarity stress-activated lncRNA gene (OSlncRNA), speculating on its origin and evolutionary history in Oryza. In summary, we provide a global perspective and a lncRNA resource to understand hyperosmolality stress sensing in rice roots, which helps to decode the complex molecular networks involved in plant sensing and adaptation to stressful environments.

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Section: Discussionmentioning

confidence: 99%

Long Noncoding RNAs in Response to Hyperosmolarity Stress, but Not Salt Stress, Were Mainly Enriched in the Rice Roots

Pang,

Zheng,

Min

et al. 2024

IJMS

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Population structure limits inferences from genomic prediction and genome-wide association studies in a forest tree

Slavov,

Macaya-Sanz,

DiFazio

et al. 2024

Preprint

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There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, assess risks of genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and a replicated common-garden experiment to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and practice assisted migration. We analyzed hierarchical population structure by partitioning phenotypic and genomic (SNP) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60-81%) occurred at the population level and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive ability r > 0.9) but almost as well using climate or geography (r > 0.8). In contrast, genomic prediction within populations was poor (r < 0.2). Similarly, we identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Our results highlight the difficulty of using GWAS to identify causal loci when there is population structure, and the limitations of using genomic information alone to guide assisted migration.

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The molecular basis of phenotypic evolution: beyond the usual suspects

Cited by 2 publications

References 84 publications

Long Noncoding RNAs in Response to Hyperosmolarity Stress, but Not Salt Stress, Were Mainly Enriched in the Rice Roots

Long Noncoding RNAs in Response to Hyperosmolarity Stress, but Not Salt Stress, Were Mainly Enriched in the Rice Roots

Population structure limits inferences from genomic prediction and genome-wide association studies in a forest tree

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