Evolutionary patterns of DNA base composition at polymorphic sites highlight the role of the environment in shaping barley and rice genomes.

Insights into changes in genome base composition underlying crop domestication can be gained by using comparative genomics. With this approach, previous studies have reported that crop genomes during domestication accumulate more nucleotides adenine (A) and thymine (T) (termed as [AT]-increase) across polymorphic sites. However, the potential influence of the environment or its factors, for example, solar ultraviolet (UV) radiation and temperature, on the [AT]-increase has not been well elucidated. Here, we investigated the [AT]-increase in barley (Hordeum vulgare L.) and rice (Oryza sativa L.) and the association with natural environments, where accessions are distributed. With 12,798,376 and 2,861,535 single-nucleotide polymorphisms from 368 barley and 1375 rice accessions, respectively, we discovered that [AT] increases from wild accessions to improved cultivars, and genomic regions with larger [AT]-increase tend to have higher UV-related motif frequencies, suggesting solar UV radiation as a potential factor in driving genome variation. To link [AT] change with the geographic distribution, we gathered georeferenced accessions and examined their local environments. Interestingly, negative correlations between [AT] and environmental factors were observed (r = -0.39 ∼ -0.75) and modern accessions with higher [AT] values, as compared with wild relatives, are from the environments with lower solar UV radiation or lower temperature. With [AT] and environmental factors as phenotypes, genome-wide association mapping identified three candidate genes that have the potential to contribute to [AT] variation under the effect of environmental conditions. Our findings provide genomic and environmental insights into evolutionary pattern of DNA base composition and underlying mechanisms.

Evolutionary genomics of climatic adaptation and resilience to climate change in alfalfa.

Given the escalating impact of climate change on agriculture and food security, gaining insights into the evolutionary dynamics of climatic adaptation and uncovering climate-adapted variation empower the breeding of climate-resilience crops to face future climate change. Alfalfa (Medicago sativa subsp. sativa), the queen of forages with remarkable adaptability across diverse global environments, is an excellent model for investigating species' responses to climate change. We conducted population genomic analyses to unravel alfalfa's climatic adaptation and genetic susceptibility to future climate change, utilizing genome resequencing data from 702 accessions of 24 Medicago species. We found that interspecific genetic exchange has fueled the gene pool of alfalfa, particularly enriching defense and stress response genes. Inter-subspecific introgression between Medicago sativa subsp. falcata (subsp. falcata) and alfalfa not only aids alfalfa's climatic adaptation but also introduces genetic burden. A total of 1671 genes were associated with climatic adaptation, and 5.7% of them were introgression from subsp. falcata. Integrating climate-associated variants and climate data, we identified vulnerable populations to future climate change, particularly in higher latitudes of the northern hemisphere, serving as a clarion call for targeted conservation initiatives and breeding efforts. Moreover, we unveil pre-adaptive populations demonstrating heightened resilience to climate fluctuations, illuminating a pathway for future breeding strategies. This study enhances our understanding of alfalfa's local adaptation and facilitates breeding of climate-resilient cultivars, contributing to effective agricultural strategies facing future climate change.

Novel allelic variations in Tannin1 and Tannin2 contribute to tannin absence in sorghum.

Sorghum is an important food crop commonly used for brewing, feed, and bioenergy. Certain genotypes of sorghum contain high concentrations of condensed tannins in seeds, which are beneficial, such as protecting grains from herbivore bird pests, but also impair grain quality and digestibility. Previously, we identified Tannin1 and Tannin2, each with three recessive causal alleles, regulate tannin absence in sorghum. In this study, via characterizing 421 sorghum accessions, we further identified three novel recessive alleles from these two genes. The tan1-d allele contains a 12-bp deletion at position 659 nt and the tan1-e allele contains a 10-bp deletion at position 771 nt in Tannin1. The tan2-d allele contains a C-to-T transition, which results in a premature stop codon before the bHLH domain in Tannin2, and was predominantly selected in China. We further developed KASP assays targeting these identified recessive alleles to efficiently genotype large populations. These studies provide new insights in sorghum domestication and convenient tools for breeding programs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01463-y.

Environmental context of phenotypic plasticity in flowering time in sorghum and rice.

Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. Both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.

Genetic mapping of dynamic control of leaf angle across multiple canopy levels in maize.

Optimizing leaf angle and other canopy architecture traits has helped modern maize (Zea mays L.) become adapted to higher planting densities over the last 60 years. Traditional investigations into genetic control of leaf angle have focused on one leaf or the average of multiple leaves; as a result, our understanding of genetic control across multiple canopy levels is still limited. To address this, genetic mapping across four canopy levels was conducted in the present study to investigate the genetic control of leaf angle across the canopy. We developed two populations of doubled haploid lines derived from three inbreds with distinct leaf angle phenotypes. These populations were genotyped with genotyping-by-sequencing and phenotyped for leaf angle at four different canopy levels over multiple years. To understand how leaf angle changes across the canopy, the four measurements were used to derive three additional traits. Composite interval mapping was conducted with the leaf-specific measurements and the derived traits. A set of 59 quantitative trait loci (QTLs) were uncovered for seven traits, and two genomic regions were consistently detected across multiple canopy levels. Additionally, seven genomic regions were found to contain consistent QTLs with either relatively stable or dynamic effects at different canopy levels. Prioritizing the selection of QTLs with dynamic effects across the canopy will aid breeders in selecting maize hybrids with the ideal canopy architecture that continues to maximize yield on a per area basis under increasing planting densities.

PANI-Coated VOx Nanobelts with Core-Shell Architecture for Flexible All-Solid-State Supercapacitor.

As a typical pseudocapacitor material, VOx possesses mixed valence states, making it an ideal electrode material for symmetric screen-printed supercapacitors. However, its high internal resistance and low energy density are the main hurdles to its widespread application. In this study, a two-dimensional PANI@VOx nanobelt with a core-shell architecture was constructed via a two-step route. This strategy involves the preparation of VOx using a solvothermal method, and a subsequent in situ polymerization process of the PANI. By virtue of the synergistic effect between the VOx core and the PANI shell, the optimal VOx@PANI has an enhanced conductivity of 0.7 ± 0.04 S/Ω, which can deliver a high specific capacitance of 347.5 F/g at 0.5 A/g, a decent cycling life of ~72.0%, and an outstanding Coulomb efficiency of ~100% after 5000 cycles at 5 A/g. Moreover, a flexible all-solid-state symmetric supercapacitor (VOx@PANI SSC) with an in-planar interdigitated structure was screen-printed and assembled on a nickel current collector; it yielded a remarkable areal energy density of 115.17 µWh/cm2 at an areal power density of 0.39 mW/cm2, and possessed outstanding flexibility and mechanical performance. Notably, a "Xiaomi" hygrothermograph (3.0 V) was powered easily by tandem SSCs with an operating voltage of 3.1 V. Therefore, this advanced pseudocapacitor material with core-shell architecture opens novel ideas for flexible symmetric supercapacitors in powering portable/wearable products.

Generation of Sesame Mutant Population by Mutagenesis and Identification of High Oleate Mutants by GC Analysis.

Sesame is one of the important oilseed crops in the world. Natural genetic variation exists in the sesame germplasm collection. Mining and utilizing the genetic allele variation from the germplasm collection is an important approach for seed quality improvement. The sesame germplasm accession, PI 263470, which has a significantly higher level of oleic acid (54.0%) than the average (39.5%), was identified by screening the entire USDA germplasm collection. The seeds from this accession were planted in a greenhouse. Leaf tissues and seeds were harvested from individual plants. DNA sequencing of the coding region of the fatty acid desaturase gene (FAD2) confirmed that this accession contained a natural mutation of G425A which may correspond to the deduced amino acid substitution of R142H leading to the high level of oleic acid, but it was a mixed accession with three genotypes (G/G, G/A, and A/A at the position). The genotype with A/A was selected and self-crossed for three generations. The purified seeds were used for EMS-induced mutagenesis to further enhance the level of oleic acid. A total of 635 M2 plants were generated from mutagenesis. Some mutant plants had significant morphological changes including leafy flat stems and others. M3 seeds were used for fatty acid composition analysis by gas chromatography (GC). Several mutant lines were identified with high oleic acid (70%). Six M3 mutant lines plus one control line were advanced to M7 or M8 generations. Their high oleate traits from M7 or M8 seeds harvested from M6 or M7 plants were further confirmed. The level of oleic acid from one mutant line (M7 915-2) was over 75%. The coding region of FAD2 was sequenced from these six mutants, but no mutation was identified. Additional loci may contribute to the high level of oleic acid. The mutants identified in this study can be used as breeding materials for sesame improvement and as genetic materials for forward genetic studies.

Machine learning for predicting phenotype from genotype and environment.

Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.

Research on the control strategies of data flow transmission paths for MPTCP-based communication networks.

The performance of multipath transmission control protocol (MPTCP) subflow through the enhancement mechanism of the MPTCP communication is improved. When dealing with multiple MPTCP subflows occupying the same transmission path, critical issues such as selection and optimization of multipath, and efficient scheduling of available multiple tracks are effectively addressed by incorporating the technology called software defined network (SDN) that is constructed based on four key parameters, namely, network transmission bandwidth, transmission paths, path capacity, and network latency. Besides, critical equipment such as the network physical device layer and SDN controller are integrated with the four parameters. So, the network model defines the transmission control process and data information. Considering the predetermined total network bandwidth capacity to select multiple paths, the adequate bandwidth capacity is determined by defining the data transfer rate between MPTCP terminals and MPTCP servers. However, the processing latency of the OpenFlow switch and the SDN controller is excluded. The effective network transmission paths are calculated through two rounds of path selection algorithms. Moreover, according to the demand capacity of the data transmission and the supply capacity of the required occupied network resource, a supply and demand strategy is formulated by considering the bandwidth capacity of the total network and invalid network latency factors. Then, the available network transmission path from the valid network transmission path is calculated. The shortest path calculation problem, which is the calculation and sorting of the shortest path, is transformed into a clustering, Inter-Cluster Average Classification (ICA), problem. The instruction of the OpenFlow communication flow is designed to schedule MPTCP subflows. Thus, various validation objectives, including the network model, effective network latency, effective transmission paths, supply-demand strategies, ineffective transmission paths, shortest feasible paths, and communication rules are addressed by the proposed method whose reliability, stability, and data transmission performance are validated through comparative analysis with other conventional algorithms. Found that the network latency is around 20 s, the network transmission rate is approximately 10 Mbps, the network bandwidth capacity reaches around 25Mbps, the network resource utilization rate is about 75%, and the network swallowing volume is approximately 3 M/s.

Genomic prediction of tocochromanols in exotic-derived maize.

Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic-derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain.

With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.

Template-Free Preparation of α-Ni(OH)2 Nanosphere as High-Performance Electrode Material for Advanced Supercapacitor.

Although it is one of the promising candidates for pseudocapacitance materials, Ni(OH)2 is confronted with poor specific capacitance and inferior cycling stability. The design and construction of three-dimensional (3D) nanosphere structures turns out to be a valid strategy to combat these disadvantages and has attracted tremendous attention. In this paper, a 3D α-Ni(OH)2 nanosphere is prepared via a facile and template-free dynamic refluxing approach. Significantly, the α-Ni(OH)2 nanosphere possesses a high specific surface area (119.4 m2/g) and an abundant porous structure. In addition, the as-obtained α-Ni(OH)2 electrodes are investigated by electrochemical measurements, which exhibit a high specific capacitance of 1243 F/g at 1 A/g in 6 M KOH electrolyte and an acceptable capacitive retention of 40.0% after 1500 charge/discharge cycles at 10 A/g, which can be attributed to the sphere's unique nanostructure. Furthermore, the as-assembled Ni(OH)2-36//AC asymmetric supercapacitor (ASC) yields a remarkable energy density of 26.50 Wh/kg, with a power density of 0.82 kW/kg. Notably, two ASCs in series can light a 2.5 V red lamp sustainably for more than 60 min, as well as power an LED band with a rated power of 25 W. Hence, this 3D α-Ni(OH)2 nanosphere may raise great potential applications for next-generation energy storage devices.

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain.

Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of ∼1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including 5 that had not been previously associated with maize grain tocochromanols: 4 biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for colocalized eQTL and GWAS association signals. Our multiomics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.

Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range.

Phenotypic plasticity is observed widely in plants and often studied with reaction norms for adult plant or end-of-season traits. Uncovering genetic, environmental and developmental patterns behind the observed phenotypic variation under natural field conditions is needed. Using a sorghum (Sorghum bicolor) genetic population evaluated for plant height in seven natural field conditions, we investigated the major pattern that differentiated these environments. We then examined the physiological relevance of the identified environmental index by investigating the developmental trajectory of the population with multistage height measurements in four additional environments and conducting crop growth modelling. We found that diurnal temperature range (DTR) during the rapid growth period of sorghum development was an effective environmental index. Three genetic loci (Dw1, Dw3 and qHT7.1) were consistently detected for individual environments, reaction-norm parameters across environments and growth-curve parameters through the season. Their genetic effects changed dynamically along the environmental gradient and the developmental stage. A conceptual model with three-dimensional reaction norms was proposed to showcase the interconnecting components: genotype, environment and development. Beyond genomic and environmental analyses, further integration of development and physiology at the whole-plant and molecular levels into complex trait dissection would enhance our understanding of mechanisms underlying phenotypic variation.

Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize.

Efficiently exploiting natural genetic diversity captured by accessions stored in genebanks is crucial to genetic improvement of major crops. Selecting accessions of interest from genebanks has traditionally required information from extensive and expensive evaluation; however, low-cost genotyping combined with genomic prediction have enabled us to generate predicted genetic merits for the entire set with targeted phenotypic evaluation of representative subsets. To explore this general approach, analytical assessment and empirical validation of the maize (Zea mays L.) association population (MAP) as a training population were conducted in the present study. Cross-validation within the MAP revealed mostly modest to strong predictive ability for 36 traits, generally in parallel with the square root of heritability. The MAP was then used to train the prediction models to generate genomic estimated breeding values (GEBVs) for the Ames Diversity Panel. Empirical validation conducted for nine traits across two validation populations confirmed the accuracy level indicated by the cross-validation of the training population. An upper bound for reliability (U value) was calculated for the accessions in the prediction population using genotypic data. The group of accessions with high U values generally had high predictive ability, even though the range of observed trait values was similar to the group of accessions with low U values. Our comprehensive analysis validated the general approach of turbocharging genebanks with genomics and genomic prediction. In addition, breeders and researchers can consider both GEBVs and U values to balance the needs of improving specific traits and broadening genetic diversity when selecting accessions from genebanks.

Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping.

Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes.

We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation.