Aquatic Plant Genomics: Advances, Applications, and Prospects

Hu, Shiqi; Li, Gaojie; Yang, Jingjing; Hou, Hongwei

doi:10.1155/2017/6347874

Cited by 8 publications

(8 citation statements)

References 89 publications

(76 reference statements)

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“…Colocasia esculenta is found in tropical habitat and produces unisexual flowers, whereas the four species of subfamily Lemnoideae produce bisexual flowers and inhabit aquatic habitat ( Mayo et al, 1997 ; Cusimano et al, 2011 ). These species also demonstrated a different rate of mutations, which is consistent with the finding that aquatic and tropical plant have diverse mutation rates ( Abbasi et al, 2016 ; Hu et al, 2017 ; Hart et al, 2019 ; Wang et al, 2020 ). Sampling is therefore sparse in the previous study for a large and ancient monocot family like Araceae, which dates back to the Early Cretaceous period, and is divided into eight diverse subfamilies distributed across the multitude of ecological habitats ( Cusimano et al, 2011 ; Nauheimer et al, 2012 ; Henriquez et al, 2014 ).…”

Section: Introductionsupporting

confidence: 87%

“…Colocasia esculenta is tropical and belongs to the crown group, whereas the species of Lemnoideae are aquatic and belong to the basal group. Aquatic plants evolve faster as compared to non-aquatic, and tropical plants evolve faster as compared to temperate plants ( Abbasi et al, 2016 ; Hu et al, 2017 ; Hart et al, 2019 ; Wang et al, 2020 ). We found higher rates of mutation in terms of substitutions and InDels in the species of Lemnoideae as compared to other species ( Table 3 ).…”

Section: Discussionmentioning

confidence: 99%

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Mutational Dynamics of Aroid Chloroplast Genomes II

et al. 2021

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The co-occurrence among single nucleotide polymorphisms (SNPs), insertions-deletions (InDels), and oligonucleotide repeats has been reported in prokaryote, eukaryote, and chloroplast genomes. Correlations among SNPs, InDels, and repeats have been investigated in the plant family Araceae previously using pair-wise sequence alignments of the chloroplast genomes of two morphotypes of one species, Colocasia esculenta belonging to subfamily Aroideae (crown group), and four species from the subfamily Lemnoideae, a basal group. The family Araceae is a large family comprising 3,645 species in 144 genera, grouped into eight subfamilies. In the current study, we performed 34 comparisons using 27 species from 7 subfamilies of Araceae to determine correlation coefficients among the mutational events at the family, subfamily, and genus levels. We express strength of the correlations as: negligible or very weak (0.10–0.19), weak (0.20–0.29), moderate (0.30–0.39), strong (0.40–0.69), very strong (0.70–0.99), and perfect (1.00). We observed strong/very strong correlations in most comparisons, whereas a few comparisons showed moderate correlations. The average correlation coefficient was recorded as 0.66 between “SNPs and InDels,” 0.50 between “InDels and repeats,” and 0.42 between “SNPs and repeats.” In qualitative analyses, 95–100% of the repeats at family and sub-family level, while 36–86% of the repeats at genus level comparisons co-occurred with SNPs in the same bins. Our findings show that such correlations among mutational events exist throughout Araceae and support the hypothesis of distribution of oligonucleotide repeats as a proxy for mutational hotspots.

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Section: Introductionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Mutational Dynamics of Aroid Chloroplast Genomes II

et al. 2021

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“…This hinders our ability to establish a general picture of whether traits are conserved or not in aquatic macrophytes, and to compare patterns found with other organisms. A likely reason is that the rather phylogenetically-distant nature of aquatic macrophytes causes difficulties for phylogenetic studies (Hu et al, 2017), as these plants are evolutionarily highly dispersed across the Tree of Life (Du et al, 2016), with at least 50 independent origins from their closest terrestrial relatives (Cook, 1990).…”

Section: Functional and Phylogenetic Perspectivesmentioning

confidence: 99%

“…To further understand how evolutionary history shapes the geographical distribution of aquatic macrophytes, we need accurate information on the phylogenetic relationships between plant species. To date, this has been performed using several methods of varying complexity and reliability, but the implementation of this new era of 'ecophylogenetics' (Mouquet et al, 2012) to the macroecology of aquatic macrophytes is still facing a number of methodological challenges (Hu et al, 2017). As a first step, some studies have used taxonomic classification as a surrogate for evolutionary relatedness, as implemented recently by Alahuhta et al (2017c) and García-Girón et al (2019c), in order to develop proxies for aquatic macrophyte phylogenetic diversity.…”

Section: Functional and Phylogenetic Perspectivesmentioning

confidence: 99%

Macroecology of macrophytes in the freshwater realm: Patterns, mechanisms and implications

et al. 2021

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Broad-scale studies of species distributions and diversity have contributed to the emergence of general macroecological rules. These rules are typically founded on research using well-known terrestrial taxa as models and it is thus uncertain whether aquatic macrophytes follow these macroecological rules. Our purpose is to draw together available information from broad-scale research on aquatic macrophytes growing in lakes, ponds, wetlands, rivers and streams. We summarize how different macroecological rules fit the patterns shown by freshwater plants at various spatial scales. Finally, we outline future actions which should be taken to advance macroecological research on freshwater plants. Our review suggested that some macroecological patterns are relatively well-evidenced for aquatic macrophytes, whereas little information exists for others. We found, for example, that the species richness-latitude relationship follows a unimodal pattern, and species turnover prevails over species nestedness, whereas higher nestedness-related richness differences are found in low beta diversity regions. Contrary to terrestrial plants, climate or history seem not to be dominant determinants explaining these broad-scale patterns; instead local explanatory variables (e.g., water quality, such as alkalinity and nutrients, and hydromorphology) are often important for freshwater plants. We identified several knowledge gaps related, for example, to a smaller number of studies in lotic habitats, compared with lentic habitats, lack of spatiallyadequate aquatic plant studies, deficiency of comprehensive species traits databases for aquatic macrophytes, and absence of a true phylogeny comprising most freshwater plant lineages. We hope this review will encourage the undertaking of additional macroecological investigations on freshwater plants across broad spatial and temporal scales.

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“…Genome sequencing was made possible with the advent of sequencing technologies. 8,9 Complete sequencing of a plant genome was first demonstrated for a model plant named Arabidopsis (Arabidopsis thaliana) 10,11 and afterward for rice (Oryza sativa). Subsequently, the whole genome of over 250 species in the plant kingdom have been sequenced: bryophytes, pteridophytes, gymnosperms, and angiosperms 12,13 (Figure 1).…”

Section: Plant Genome Sequences and Bioinformatics Resourcesmentioning

confidence: 99%

Machine learning algorithms: their applications in plant omics and agronomic traits’ improvement

et al. 2022

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Agronomic traits of plants especially those of economic or aesthetic importance are threatened by climatic and environmental factors such as climate change, biotic, and abiotic stresses. These threats are now being mitigated through the analyses of omics data like genomics, transcriptomics, proteomics, metabolomics, and phenomics. The emergence of high-throughput omics technology has led to an avalanche of plant omics data. Plant research demands novel analytical paradigms to extract and harness large plant omics data for plant improvement effectively and efficiently. Machine learning algorithms are well-suited analytical and computational approaches for the integrative analysis of large unstructured, heterogeneous datasets. This study presents an overview of omics approaches to improve plant agronomic traits and crucial curated plant genomic data sources. Furthermore, we summarize machine learning algorithms and software tools/programming packages used in plant omics research. Lastly, we discuss advancements in machine learning algorithms' applications in improving agronomic traits of economically important plants. Extensive application of machine learning would advance plant omics studies. These advancements would consequently help agricultural scientists improve economically important plants’ quality, yield, and tolerance against abiotic and biotic stresses and other plant health-threatening issues.

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Aquatic Plant Genomics: Advances, Applications, and Prospects

Cited by 8 publications

References 89 publications

Mutational Dynamics of Aroid Chloroplast Genomes II

Mutational Dynamics of Aroid Chloroplast Genomes II

Macroecology of macrophytes in the freshwater realm: Patterns, mechanisms and implications

Machine learning algorithms: their applications in plant omics and agronomic traits’ improvement

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