Comprehensive genomic resources related to domestication and crop improvement traits in Lima bean

AbstractLima bean (Phaseolus lunatus L.), one of the five domesticated Phaseolus bean crops, shows a wide range of ecological adaptations along its distribution range from Mexico to Argentina. These adaptations make it a promising crop for improving food security under predicted scenarios of climate change in Latin America and elsewhere. In this work, we combine long and short read sequencing technologies with a dense genetic map from a biparental population to obtain the chromosome-level genome assembly for Lima bean. Annotation of 28,326 gene models show high diversity among 1917 genes with conserved domains related to disease resistance. Structural comparison across 22,180 orthologs with common bean reveals high genome synteny and five large intrachromosomal rearrangements. Population genomic analyses show that wild Lima bean is organized into six clusters with mostly non-overlapping distributions and that Mesomerican landraces can be further subdivided into three subclusters. RNA-seq data reveal 4275 differentially expressed genes, which can be related to pod dehiscence and seed development. We expect the resources presented here to serve as a solid basis to achieve a comprehensive view of the degree of convergent evolution of Phaseolus species under domestication and provide tools and information for breeding for climate change resiliency.

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Comprehensive genomic resources related to domestication and crop improvement traits in Lima bean

10.21203/rs.3.rs-95762/v1 ◽

2020 ◽

Author(s):

Tatiana Garcia ◽

Jorge Duitama ◽

Stephanie Zullo ◽

Juanita Gil ◽

Andrea Ariani ◽

...

Keyword(s):

Climate Change ◽

Common Bean ◽

Convergent Evolution ◽

Developmental Stages ◽

Crop Improvement ◽

Genomic Analysis ◽

Lima Bean ◽

Phaseolus Lunatus ◽

Human Consumption ◽

Gene Pools

Abstract Lima bean (Phaseolus lunatus L.) is one of the five domesticated Phaseolus bean crops, which are essential sources of dietary proteins for human consumption. Compared to common bean (P. vulgaris), it shows a wider range of ecological adaptations along its distribution range from Mexico to Argentina. These adaptations and its phenotypic plasticity make Lima bean a promising crop for improving food security under predicted scenarios of climate change in Latin America and elsewhere. Lima bean is also an excellent model to study convergent evolution of the adaptive domestication syndrome due to its dual domestication in Mesoamerica and the Andes. Combining long and short read sequencing technologies with a dense genetic map from a biparental population, we obtained the first chromosome-level genome assembly for Lima bean. Annotation of 28,326 gene models showed high diversity among 1,917 genes with conserved domains related to disease resistance. Structural comparison across 21,180 orthologs with common bean revealed high genome synteny and two large intrachromosomal rearrangements. Speciation between P. lunatus and P. vulgaris occurred about six million years ago according to nucleotide evolution between these orthologs. Population genomic analysis of GBS data for 482 wild and domesticated accessions from the Mesoamerican and Andean gene pools provided novel evidence on population structure at a finer geographical scale. Results show that wild Lima bean is organized into six clusters with mostly non-overlapping distributions and that Mesomerican landraces can be further subdivided into three subclusters. A new wild cluster of diversity was found in the Colombian Andes and a separate genetic cluster was observed for Mesoamerican landraces of the Peninsula of Yucatan in Mexico. This study also documents genome wide patterns of selection and haplotype introgression events among gene pools. Analysis of RNA-seq data obtained from wild and domesticated accessions at two different pod developmental stages revealed 4,275 differentially expressed genes, which could be related to pod dehiscence and seed development. We expect that the present resources serve as a solid basis to achieve a comprehensive view of the degree of convergent evolution of Phaseolus species under domestication and provide new tools and information for breeding for climate change resiliency of different domesticated species.

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Magic-BLAST, an accurate DNA and RNA-seq aligner for long and short reads

10.1101/390013 ◽

2018 ◽

Cited By ~ 10

Author(s):

Grzegorz M Boratyn ◽

Jean Thierry-Mieg ◽

Danielle Thierry-Mieg ◽

Ben Busby ◽

Thomas L Madden

Keyword(s):

Data Sets ◽

Rna Seq ◽

Seed Selection ◽

Sequencing Technologies ◽

Dna And Rna ◽

Spliced Alignment ◽

Long Reads ◽

Wide Range ◽

Blast Database ◽

Innovative Techniques

ABSTRACTNext-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline. It uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.

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Lima Bean Growth, Leaf Stomatal and Nonstomatal Limitations to Photosynthesis, and 13C Discrimination in Response to Saline Irrigation

Journal of the American Society for Horticultural Science ◽

10.21273/jashs04996-20 ◽

2021 ◽

pp. 1-13

Author(s):

Xuan Liu ◽

Donald L. Suarez

Keyword(s):

Salt Stress ◽

Gas Exchange ◽

Growth Stage ◽

Plant Biomass ◽

Leaf Gas Exchange ◽

Lima Bean ◽

Phaseolus Lunatus ◽

Stomatal Limitation ◽

Wide Range ◽

Bean Yield

Soil salinization is a widespread problem severely impacting crop production. Understanding how salt stress affects growth-controlling photosynthetic performance is essential for improving crop salt tolerance and alleviating the salt impact. Lima bean (Phaseolus lunatus) is an important crop, but little information is available on its growth and leaf gas exchange in relation to a wide range of salinity. In this study, the responses of leaf gas exchange and whole plant growth of lima bean (cv. Fordhook 242) to six salinities with electrical conductivity (EC) of 2.9 (control), 5.7, 7.8, 10.0, 13.0, and 15.5 dS·m−1 in irrigation waters were assessed. Significant linear reduction by increasing salinity was observed on plant biomass, bean yield, and leaf net carbon assimilation rate (A). As EC increased from the control to 15.5 dS·m−1, plant biomass and A decreased by 87% and 69%, respectively, at the vegetative growth stage, and by 96% and 83%, respectively, at the pod growth stage, and bean yield decreased by 98%. Judged by the linear relations, the reduction in A accounted for a large portion of the growth reduction and bean yield loss. Salinity also had a significantly negative and linear effect on leaf stomatal conductance (gS). Leaf intercellular CO2 concentration (Ci) and leaf C13 isotope discrimination (Δ13) declined in parallel significantly with increasing salinity. The A-Ci curve analysis revealed that stomatal limitation [Lg (percent)] to A increased significantly and linearly, from 18% to 78% and from 22% to 87% at the vegetative and pod-filling stages, respectively, as EC increased from the control to the highest level. Thus, relatively nonstomatal or biochemical limitation [Lm (percent), Lm = 100 − Lg] to A responded negatively to increasing salinity. This result is coincident with the observed Δ13 salt-response trend. Furthermore, leaf carboxylation efficiency and CO2-saturated photosynthetic capacity [maximum A (Amax)] were unaffected by increasing salinity. Our results strongly indicate that the reduction in lima bean A by salt stress was mainly due to stomatal limitation and biochemical properties for photosynthesis might not be impaired. Because stomatal limitation reduces A exactly from lowering CO2 availability to leaves, increasing CO2 supply with an elevated CO2 concentration may raise A of the salt-stressed lima bean leaves and alleviate the salt impact. This is supported by our finding that the external CO2 concentration for 50% of Amax increased significantly and linearly with increasing salinity at the both growth stages. Leaf water use efficiency showed an increasing trend and no evident decline in leaf chlorophyll soil plant analysis development (SPAD) readings was observed as salinity increased.

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Fine structure of the giant cells induced by Meloidogyne javanica in lima bean

Canadian Journal of Botany ◽

10.1139/b84-065 ◽

1984 ◽

Vol 62 (3) ◽

pp. 429-436 ◽

Cited By ~ 4

Author(s):

F. Fattah ◽

J. M. Webster

Keyword(s):

Electron Microscopy ◽

Fine Structure ◽

Light And Electron Microscopy ◽

Lima Bean ◽

Meloidogyne Javanica ◽

Giant Cells ◽

Egg Laying ◽

Phaseolus Lunatus ◽

Wide Range

Giant cells associated with egg-laying females of Meloidogyne javanica in lima bean, Phaseolus lunatus cv. L-136, were examined by light and electron microscopy. These giant cells have characteristics that are typical of nematode-induced giant cells in a wide range of hosts, but they differ in that they (i) are less closely associated with xylem vessels, (ii) contain a very large number of plastids which are devoid of starch grains, and (iii) contain several different forms of cytoplasmic crystals. One form of the crystal is associated with a large number of "spiny" vesicles. The possible role of these features, especially the crystals, in the giant cell response of lima bean is discussed.

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Resilience Capacity Assessment of the Traditional Lima Bean (Phaseolus lunatus L.) Landraces Facing Climate Change

Agronomy ◽

10.3390/agronomy10060758 ◽

2020 ◽

Vol 10 (6) ◽

pp. 758 ◽

Cited By ~ 1

Author(s):

María Isabel Martínez-Nieto ◽

Elena Estrelles ◽

Josefa Prieto-Mossi ◽

Josep Roselló ◽

Pilar Soriano

Keyword(s):

Climate Change ◽

Phylogenetic Analyses ◽

Lima Bean ◽

Phaseolus Lunatus ◽

Time Base ◽

Base Temperature ◽

Seed Traits ◽

South American Species ◽

Commercial Cultivar ◽

Water Stress Tolerance

Agriculture is highly exposed to climate warming, and promoting traditional cultivars constitutes an adaptive farming mechanism from climate change impacts. This study compared seed traits and adaptability in the germinative process, through temperature and drought response, between a commercial cultivar and Mediterranean Phaseolus lunatus L. landraces. Genetic and phylogenetic analyses were conducted to characterize local cultivars. Optimal germination temperature, and water stress tolerance, with increasing polyethylene glycol (PEG) concentrations, were initially evaluated. Base temperature, thermal time, base potential and hydrotime were calculated to compare the thermal and hydric responses and competitiveness among cultivars. Eight molecular markers were analyzed to calculate polymorphism and divergence parameters, of which three, together with South American species accessions, were used to construct a Bayesian phylogeny. No major differences were found in seed traits, rather different bicolored patterns. A preference for high temperatures and fast germination were observed. The ‘Pintat’ landrace showed marked competitiveness compared to the commercial cultivar when faced with temperature and drought tolerance. No genetic differences were found among the Valencian landraces and the phylogeny confirmed their Andean origin. Promoting landraces for their greater resilience is a tool to help overcome the worldwide challenge deriving from climate change and loss of agrobiodiversity.

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A MODELING SYSTEM FOR CLIMATE CHANGE RISK ASSESSMENT, MANAGEMENT AND HEDGING IN COASTAL AREAS

Proceedings of International Conference "Managinag risks to coastal regions and communities in a changinag world" (EMECS'11 - SeaCoasts XXVI) ◽

10.31519/conferencearticle_5b1b9398d1adf1.08545898 ◽

2017 ◽

Author(s):

Sergei Soldatenko ◽

Genrikh Alekseev ◽

Alexander Danilov ◽

...

Keyword(s):

Climate Change ◽

Risk Assessment ◽

Coastal Areas ◽

Mitigation Strategies ◽

System Structure ◽

The Arctic ◽

Risk Modeling ◽

Wide Range ◽

Adverse Weather ◽

The Impact

Every aspect of human operations faces a wide range of risks, some of which can cause serious consequences. By the start of 21st century, mankind has recognized a new class of risks posed by climate change. It is obvious, that the global climate is changing, and will continue to change, in ways that affect the planning and day to day operations of businesses, government agencies and other organizations and institutions. The manifestations of climate change include but not limited to rising sea levels, increasing temperature, flooding, melting polar sea ice, adverse weather events (e.g. heatwaves, drought, and storms) and a rise in related problems (e.g. health and environmental). Assessing and managing climate risks represent one of the most challenging issues of today and for the future. The purpose of the risk modeling system discussed in this paper is to provide a framework and methodology to quantify risks caused by climate change, to facilitate estimates of the impact of climate change on various spheres of human activities and to compare eventual adaptation and risk mitigation strategies. The system integrates both physical climate system and economic models together with knowledge-based subsystem, which can help support proactive risk management. System structure and its main components are considered. Special attention is paid to climate risk assessment, management and hedging in the Arctic coastal areas.

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The Biology of Mediterranean-Type Ecosystems

10.1093/oso/9780198739135.001.0001 ◽

2018 ◽

Cited By ~ 7

Author(s):

Karen J. Esler ◽

Anna L. Jacobsen ◽

R. Brandon Pratt

Keyword(s):

Climate Change ◽

South Africa ◽

Invasive Species ◽

Habitat Loss ◽

Endemic Species ◽

Geographic Area ◽

Cutting Edge ◽

Wide Range ◽

The Mediterranean ◽

Mediterranean Type Ecosystems

The world’s mediterranean-type climate regions (including areas within the Mediterranean, South Africa, Australia, California, and Chile) have long been of interest to biologists by virtue of their extraordinary biodiversity and the appearance of evolutionary convergence between these disparate regions. Comparisons between mediterranean-type climate regions have provided important insights into questions at the cutting edge of ecological, ecophysiological and evolutionary research. These regions, dominated by evergreen shrubland communities, contain many rare and endemic species. Their mild climate makes them appealing places to live and visit and this has resulted in numerous threats to the species and communities that occupy them. Threats include a wide range of factors such as habitat loss due to development and agriculture, disturbance, invasive species, and climate change. As a result, they continue to attract far more attention than their limited geographic area might suggest. This book provides a concise but comprehensive introduction to mediterranean-type ecosystems. As with other books in the Biology of Habitats Series, the emphasis in this book is on the organisms that dominate these regions although their management, conservation, and restoration are also considered.

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Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03812-3 ◽

2021 ◽

Author(s):

Mark Cooper ◽

Kai P. Voss-Fels ◽

Carlos D. Messina ◽

Tom Tang ◽

Graeme L. Hammer

Keyword(s):

Climate Change ◽

Crop Improvement ◽

Target Population ◽

Crop Productivity ◽

Climate Change Scenarios ◽

Global Food Security ◽

Genotype By Environment ◽

Improvement Strategies ◽

Yield Gaps ◽

On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

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Stomatal conductance limited the CO2 response of grassland in the last century

BMC Biology ◽

10.1186/s12915-021-00988-4 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Juan C. Baca Cabrera ◽

Regina T. Hirl ◽

Rudi Schäufele ◽

Andy Macdonald ◽

Hans Schnyder

Keyword(s):

Climate Change ◽

Stomatal Conductance ◽

Nitrogen Nutrition ◽

Physiological Control ◽

Co2 Response ◽

Nitrogen Acquisition ◽

Wide Range ◽

Park Grass Experiment ◽

Conductance Changes ◽

Canopy Stomatal Conductance

Abstract Background The anthropogenic increase of atmospheric CO2 concentration (ca) is impacting carbon (C), water, and nitrogen (N) cycles in grassland and other terrestrial biomes. Plant canopy stomatal conductance is a key player in these coupled cycles: it is a physiological control of vegetation water use efficiency (the ratio of C gain by photosynthesis to water loss by transpiration), and it responds to photosynthetic activity, which is influenced by vegetation N status. It is unknown if the ca-increase and climate change over the last century have already affected canopy stomatal conductance and its links with C and N processes in grassland. Results Here, we assessed two independent proxies of (growing season-integrating canopy-scale) stomatal conductance changes over the last century: trends of δ18O in cellulose (δ18Ocellulose) in archived herbage from a wide range of grassland communities on the Park Grass Experiment at Rothamsted (U.K.) and changes of the ratio of yields to the CO2 concentration gradient between the atmosphere and the leaf internal gas space (ca – ci). The two proxies correlated closely (R2 = 0.70), in agreement with the hypothesis. In addition, the sensitivity of δ18Ocellulose changes to estimated stomatal conductance changes agreed broadly with published sensitivities across a range of contemporary field and controlled environment studies, further supporting the utility of δ18Ocellulose changes for historical reconstruction of stomatal conductance changes at Park Grass. Trends of δ18Ocellulose differed strongly between plots and indicated much greater reductions of stomatal conductance in grass-rich than dicot-rich communities. Reductions of stomatal conductance were connected with reductions of yield trends, nitrogen acquisition, and nitrogen nutrition index. Although all plots were nitrogen-limited or phosphorus- and nitrogen-co-limited to different degrees, long-term reductions of stomatal conductance were largely independent of fertilizer regimes and soil pH, except for nitrogen fertilizer supply which promoted the abundance of grasses. Conclusions Our data indicate that some types of temperate grassland may have attained saturation of C sink activity more than one century ago. Increasing N fertilizer supply may not be an effective climate change mitigation strategy in many grasslands, as it promotes the expansion of grasses at the disadvantage of the more CO2 responsive forbs and N-fixing legumes.

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Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽

10.3390/genes12020311 ◽

2021 ◽

Vol 12 (2) ◽

pp. 311

Author(s):

Zhenqiu Liu

Keyword(s):

Single Cell ◽

Free Parameter ◽

Graphical Model ◽

Expression Patterns ◽

Information Criterion ◽

Log P ◽

Rna Seq ◽

Clustering Methods ◽

Wide Range ◽

Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

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