Characterization of Selected Microalgae Species as Potential Sources of Nutrients and Antioxidants.

Microalgae are exceptional organisms from a nutritional perspective, boasting an array of bioactive compounds that have long justified their incorporation into human diets. In this study, we explored the potential of five microalgae species: Nannochloropsis sp., Tetraselmis chuii, Chaetoceros muelleri, Thalassiosira weissflogii, and Tisochrysis lutea. We conducted comprehensive analyses of their nutritional profiles, encompassing protein content, individual amino acid composition, mineral and trace element levels, fatty acid profiles (including saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs)), polyphenol compositions, and vitamin B content. The antioxidant activity of the ethanolic extracts was evaluated using two methods: ABTS and DPPH radical scavenging assay. The total protein content of the microalgae ranged from 34.09 ± 0.39% to 42.45 ± 0.18%, with the highest concentration observed in T. weissflogii. Essential amino acids such as histidine, threonine, lysine, valine, isoleucine, leucine, phenylalanine, and methionine were present in concentrations ranging from 0.53 ± 0.02 to 12.55 ± 2.21 g/16 g N. Glutamic acid emerged as the most abundant amino acid, with concentrations ranging from 6.73 ± 0.82 to 12.55 ± 2.21 g/16 g N. Among the microalgae species, T. chuii exhibited the highest concentrations of calcium (Ca) and manganese (Mn), while C. muelleri showed prominence in magnesium (Mg), sodium (Na), and iron (Fe). T. weissflogii stood out for its potassium (K) content, and T. lutea contained notable amounts of copper (Cu), zinc (Zn), and lead (Pb). Regarding fatty acid profiles, Nannochloropsis sp. and T. chuii were predominantly composed of SFA, while C. muelleri and T. weissflogii were rich in MUFA. PUFAs dominated the fatty acid profile of T. lutea, which also exhibited the most diverse range of polyphenolic substances. We also analyzed the B vitamin content, with T. lutea displaying the highest concentrations of niacin (B3) and riboflavin (B2). Antioxidant activity was confirmed for all microalgae tested using DPPH and ABTS radical IC50 (mg/mL) converted to Trolox equivalent (TEAC). These findings underscore the substantial potential of the examined microalgae species as sources of biologically valuable substances characterized by rapid growth and relatively undemanding cultivation conditions.

Can Photoselective Nets' Influence Pollen Traits? A Case Study in 'Matua' and 'Tomuri' Kiwifruit Cultivars.

The increasing use of photoselective nets (PNs) raises the question of their influence on pollen traits. We aimed to evaluate the effect of PNs (yellow, pearl, and grey) on the pollen of 'Matua' and 'Tomuri' Actinidia deliciosa cultivars. The pollen size and the exine were studied with a light microscopy and a scanning electron microscopy, and the fertility was analysed by a viability assay and in vitro germination. The total soluble proteins (TSPs) and sugars (TSSs) were quantified by colorimetric assays. The molecular structure of the pollen grain's wall was analysed by a Raman spectroscopy. The pollen from the plants under the PNs had a larger width and area and a lower germination rate. No significant changes were observed in the exine's microperforations. The TSP and TSS contents were influenced by the cultivar and PNs (particularly the pearl PN). The Raman spectra of the pollen from the plants grown under the nets presented some bands that significantly shifted from their original position, indicating differences in the vibration modes of the molecules, but no overall changes at their structural or organisation level were found. Our study showed that the PNs could influence several pollen traits, with the pearl PN inducing greater modifications. Our results also support the idea that cultivars affect the outcome of some characteristics.

Integrating Experimental and Computational Analyses of Yeast Protein Profiles for Optimizing the Production of High-Quality Microbial Proteins.

Microbial proteins represent a promising solution to address the escalating global demand for protein, particularly in regions with limited arable land. Yeasts, such as Saccharomyces cerevisiae, are robust and safe protein-producing strains. However, the utilization of non-conventional yeast strains for microbial protein production has been hindered, partly due to a lack of comprehensive understanding of protein production traits. In this study, we conducted experimental analyses focusing on the growth, protein content, and amino acid composition of nine yeast strains, including one S. cerevisiae strain, three Yarrowia lipolytica strains, and five Pichia spp. strains. We identified that, though Y. lipolytica and Pichia spp. strains consumed glucose at a slower rate compared to S. cerevisiae, Pichia spp. strains showed a higher cellular protein content, and Y. lipolytica strains showed a higher glucose-to-biomass/protein yield and methionine content. We further applied computational approaches to explain that metabolism economy was the main underlying factor for the limited amount of scarce/carbon-inefficient amino acids (such as methionine) within yeast cell proteins. We additionally verified that the specialized metabolism was a key reason for the high methionine content in Y. lipolytica strains, and proposed Y. lipolytica strain as a potential producer of high-quality single-cell protein rich in scarce amino acids. Through experimental evaluation, we identified Pichia jadinii CICC 1258 as a potential strain for high-quality protein production under unfavorable pH/temperature conditions. Our work suggests a promising avenue for optimizing microbial protein production, identifying the factors influencing amino acid composition, and paving the way for the use of unconventional yeast strains to meet the growing protein demands.

Nutrient requirements determined by grain yield and protein content to optimize N, P, and K fertilizer management in China.

Nutrient requirement for crop growth, defined as the amount of nutrient that crops take up from soil to produce a specific grain yield, is a key parameter in determining fertilizer application rate. However, existing studies primarily focus on identifying nitrogen (N), phosphorus (P), and potassium (K) requirements solely in relation to grain yield, neglecting grain protein content, a crucial index for wheat grain quality. Addressing this gap, we conducted multi-site, multi-cultivar, and multi-year field trials across three ecological regions of China from 2016 to 2020 to elucidate variations in nutrient requirements for grain yield and grain protein. The research findings revealed that wheat grain yield ranged from 4.1 to 9.3 Mg ha-1 (average 6.9 Mg ha-1) and grain protein content ranged from 98 to 157 g kg-1 (average 127 g kg-1) across the three regions. Notably, the N requirement exhibited a nonlinear correlation with the wheat grain yield but a linear increase with increasing grain protein, while the P and K requirements positively correlated with grain yield and protein content. Regression models were formulated to determine the nutrient requirements (MENR), enabling the prediction of N, P, and K requirements for leading cultivars with varying grain yields and protein contents. Implementing nutrient requirements based on MENR projections resulted in substantial reductions in fertilizer rates: 22.0 kg ha-1 N (10.7 %), 9.9 kg ha-1 P (20.2 %), and 8.1 kg ha-1 K (16.3 %). This translated to potential savings of 0.4 Mt. N, 0.23 Mt. P, and 0.17 Mt. K, consequently mitigating 5.5 Mt. CO2 greenhouse-gas emission and yielding an economic benefit of 0.8 billion US$ annually in China. These findings underscore the significance of considering grain yield and protein content in estimating nutrient requirements for fertilizer recommendations to realize high-yielding, high-protein wheat production, and minimize overfertilization and associated environmental risks.

Integrating NDVI, SPAD, and Canopy Temperature for Strategic Nitrogen and Seeding Rate Management to Enhance Yield, Quality, and Sustainability in Wheat Cultivation.

This study explores the interplay between nitrogen doses and seeding rates on wheat yield, biomass, and protein content. Utilizing tools such as the Normalized Difference Vegetation Index (NDVI), Soil Plant Analysis Development (SPAD) measurements, and canopy temperature (CT), we conducted experiments over five growing seasons. The treatments included three nitrogen levels (0, 60, 120 kg/ha) and three seeding rates (300, 400, 500 seeds/m2) in a split-plot design with 90 plots and two replications. Our results show that an intermediate nitrogen dose (60 kg/ha) combined with a moderate seed rate (400 seeds/m2) enhances wheat yield by 22.95%. Reduced nitrogen levels increased protein content, demonstrating wheat's adaptive mechanisms under nitrogen constraints. NDVI analysis highlighted significant growth during the tillering phase with high nitrogen, emphasizing early-stage nutrient management. SPAD measurements showed that early nitrogen applications boost chlorophyll content, essential for vigorous early growth, while CT data indicate that optimal nitrogen and seed rates can effectively modulate plant stress responses. As crops mature, the predictive capacity of NDVI declines, indicating the need for adjusted nitrogen strategies. Collectively, these findings advocate for refined management of nitrogen and seeding rates, integrating NDVI, SPAD, and CT assessments to enhance yields and promote sustainable agricultural practices while minimizing environmental impacts.

Culinary treatments impact the digestibility and protein quality of edible insects: a case study with Tenebrio molitor and Gryllus assimilis.

The escalating global population is anticipated to intensify the demand for high-quality proteins, necessitating the exploration of alternative protein sources. Edible insects are a promising solution, owing to their nutritional richness and sustainability. However, their digestibility and protein quality, particularly after culinary treatment, remains underexplored. In the present study, we investigated the effects of various culinary treatments on the protein digestibility of two insect species, Tenebrio molitor and Gryllus assimilis. Our findings revealed that culinary treatments such as boiling, roasting, drying, and microwave heating significantly influenced the digestibility of both insect species. Notably, drying emerged as the most effective method, leading to a substantial increase in digestibility. Furthermore, we assessed protein quality using the digestible indispensable amino acid score (DIAAS) and found that the choice of the calculation method significantly influenced the evaluation of protein quality. By including the sum of the anhydrous amino acids, we eliminated the potential overestimation of protein content and obtained a more reliable assessment of protein quality. Our results underscore the importance of culinary treatments and calculation methods in determining the suitability of insects as protein sources for human nutrition.

Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.

Impact of foliar application of iron and zinc fertilizers on grain iron, zinc, and protein contents in bread wheat (Triticum aestivum L.).

Introduction: Micronutrient deficiencies, particularly iron (Fe) and zinc (Zn), are prevalent in a large part of the human population across the world, especially in children below 5 years of age and pregnant women in developing countries. Since wheat constitutes a significant proportion of the human diet, improving grain Fe and Zn content in wheat has become important in improving human health. Objective: This study aimed to quantify the effect of foliar application of iron sulfate heptahydrate (FeSO4.7H2O) and zinc sulfate heptahydrate (ZnSO4.7H2O) and their combination on grain Fe and Zn concentrations, as well as grain protein content (GPC). The study also aimed to assess the utility of these applications in large field conditions. Methods: To address this issue, field experiments were conducted using 10 wheat cultivars and applying a foliar spray of FeSO4.7H2O (0.25%) and ZnSO4.7H2O (0.50%) separately (@400 L of solution in water per hectare during each spray) and in combination at two different crop growth stages (flowering and milking) for three consecutive crop seasons (2017-2020). The study used a split-plot design with two replications to assess the impact of foliar application on GFeC, GZnC, and GPC. In addition, an experiment was also conducted to assess the effect of soil (basal) @ 25 kg/ha ZnSO4, foliar @ 2 kg/ha, ZnSO4.7H2O (0.50%), and the combination of basal + foliar application of ZnSO4 on the grain micronutrient content of wheat cultivar WB 02 under large field conditions. Results: GFeC increased by 5.1, 6.1, and 5.9% with foliar applications of FeSO4, ZnSO4, and their combination, respectively. GZnC increased by 5.2, 39.6, and 43.8% with foliar applications of FeSO4, ZnSO4, and their combination, respectively. DBW 173 recorded the highest increase in GZnC at 56.9% with the combined foliar application of FeSO4 and ZnSO4, followed closely by HPBW 01 at 53.0% with the ZnSO4 foliar application, compared to the control. The GPC increased by 6.8, 4.9, and 3.3% with foliar applications of FeSO4, ZnSO4, and their combination, respectively. Large-plot experiments also exhibited a significant positive effect of ZnSO4 not only on grain Zn (40.3%, p ≤ 0.001) and protein content (p ≤ 0.05) but also on grain yield (p ≤ 0.05) and hectoliter weight (p ≤ 0.01), indicating the suitability of the technology in large field conditions. Conclusion: Cultivars exhibited a slight increase in GFeC with solitary foliar applications of FeSO4, ZnSO4, and their combination. In contrast, a significant increase in GZnC was observed with the foliar application of ZnSO4 and the combined application of FeSO4 and ZnSO4. In terms of GPC, the most significant enhancement occurred with the foliar application of FeSO4, followed by ZnSO4 and their combination. Data demonstrated the significant effect of foliar application of ZnSO4 on enhancing GZnC by 39.6%. Large plot experiments also exhibited an increase of 40.3% in GZnC through the foliar application of ZnSO4, indicating the effectiveness of the technology to be adopted in the farmer's field.

Interspecific variation in Rubisco CO2/O2 specificity along the leaf economic spectrum across 23 woody angiosperm plants in the Pacific islands.

The coordinated interspecific variation in leaf traits and leaf lifespan is known as the leaf economic spectrum (LES). The limitation of CO2 diffusion to chloroplasts within the lamina is significant in C3 photosynthesis, resulting in a shortage of CO2 for Rubisco. Although Rubisco CO2/O2 specificity (SC/O) should be adaptively adjusted in response to the interspecific variation in CO2 concentrations [CO2] associated with Rubisco, SC/O variations across species along the LES remain unknown. We investigated the coordination among leaf traits, including SC/O, CO2 conductance, leaf protein content, and leaf mass area, across 23 woody C3 species coexisting on an oceanic island through phylogenetic correlation analyses. A high SC/O indicates a high CO2 specificity of Rubisco. SC/O was negatively correlated with [CO2] at Rubisco and total CO2 conductance within lamina, while it was positively correlated with leaf protein across species, regardless of phylogenetic constraint. A simulation analysis shows that the optimal SC/O for maximizing photosynthesis depends on both [CO2] at Rubisco sites and leaf protein per unit leaf area. SC/O is a key parameter along the LES axis and is crucial for maximizing photosynthesis across species and the adaptation of woody plants.

Moderate Salinity Stress Affects Rice Quality by Influencing Expression of Amylose- and Protein-Content-Associated Genes.

Salinity is an environmental stress that severely impacts rice grain yield and quality. However, limited information is available on the molecular mechanism by which salinity reduces grain quality. In this study, we investigated the milling, appearance, eating and cooking, and nutritional quality among three japonica rice cultivars grown either under moderate salinity with an electrical conductivity of 4 dS/m or under non-saline conditions in a paddy field in Dongying, Shandong, China. Moderate salinity affected rice appearance quality predominantly by increasing chalkiness rate and chalkiness degree and affected rice eating and cooking and nutritional quality predominantly by decreasing amylose content and increasing protein content. We compared the expression levels of genes determining grain chalkiness, amylose content, and protein content in developing seeds (0, 5, 10, 15, and 20 days after flowering) of plants grown under saline or non-saline conditions. The chalkiness-related gene Chalk5 was up-regulated and WHITE-CORE RATE 1 was repressed. The genes Nuclear factor Y and Wx, which determine amylose content, were downregulated, while protein-content-associated genes OsAAP6 and OsGluA2 were upregulated by salinity in the developing seeds. These findings suggest some target genes that may be utilized to improve the grain quality under salinity stress conditions via gene-pyramiding breeding approaches.

Investigation of the growth of Listeria in plant-based beverages.

The objective of the present study was to evaluate whether the content of sugar, protein, fat, or fibre in commercially available and specially formulated plant-based beverages (oat, soya and pea) influences the growth rates of Listeria. Beverages were inoculated with a strain cocktail of Listeria (approximately 1 × 103 CFU/mL), and the data demonstrated that Listeria could proliferate in all tested beverages. Moreover, varying concentrations of naturally occurring or added sugar (0-3.3%), protein (3.3-5%), fat (1.1-3.5%) and added fibre (0-1.5%) did not have a statistically significant (p > 0.05) impact on the growth rates of Listeria in the tested plant-based beverages. These data suggest that the wide variety of commercial plant-based beverages serve as an ideal medium for the growth of Listeria irrespective of product composition. All the various products tested provided sufficient nutrients to support at least a 2.6-log increase of Listeria within 16 h at room temperature, with some beverages supporting a 3-log increase. Therefore, these data highlight the importance of careful storage and handling of these increasingly varied and popular products.

Presence of humic acid in the environment holds promise as a potential mitigating factor for the joint toxicity of polystyrene nanoplastics and herbicide atrazine to Chlorella vulgaris: 96-H acute toxicity.

Increasing amounts of amino-functionalized polystyrene nanoplastics (PS-NH2) are entering aquatic ecosystems, raising concerns. Hence, this study investigated 96-h acute toxicity of PS-NH2 and its combination with the pesticide atrazine (ATZ) in the absence/presence of humic acid (HA) on the microalgae Chlorella vulgaris (C. vulgaris). Results showed that both PS-NH2 and PS-NH2+ATZ reduced algal growth, photosynthetic pigments, protein content, and antioxidant capacity, while increasing enzymatic activities. Gene expression related to oxidative stress was altered in C. vulgaris exposed to these treatments. Morphological and intracellular changes were also observed. The combined toxicity of PS-NH2+ATZ demonstrated a synergistic effect, but the addition of environmentally relevant concentration of HA significantly alleviated its toxicity to C. vulgaris, indicating an antagonistic effect due to the emergence of an eco-corona, and entrapment and sedimentation of PS-NH2+ATZ particles by HA. This study firstly highlights the role of HA in mitigating the toxicity of PS-NH2 when combined with other harmful compounds, enhancing our understanding of HA's presence in the environment.

MicroRNA164e suppresses NAC100 transcription factor-mediated synthesis of seed storage proteins in chickpea.

Development of protein-enriched chickpea varieties necessitates an understanding of specific genes and key regulatory circuits that govern the synthesis of seed storage proteins (SSPs). Here, we demonstrated the novel involvement of Ca-miR164e-CaNAC100 in regulating SSP synthesis in chickpea. Ca-miRNA164e was significantly decreased during seed maturation, especially in high-protein accessions. The miRNA was found to directly target the transactivation conferring C-terminal region of a nuclear-localized transcription factor, CaNAC100 as revealed using RNA ligase-mediated-rapid amplification of cDNA ends and target mimic assays. The functional role of CaNAC100 was demonstrated through seed-specific overexpression (NACOE) resulting in significantly augmented seed protein content (SPC) consequential to increased SSP transcription. Further, NACOE lines displayed conspicuously enhanced seed weight but reduced numbers and yield. Conversely, a downregulation of CaNAC100 and SSP transcripts was evident in seed-specific overexpression lines of Ca-miR164e that culminated in significantly lowered SPC. CaNAC100 was additionally demonstrated to transactivate the SSP-encoding genes by directly binding to their promoters as demonstrated using electrophoretic mobility shift and dual-luciferase reporter assays. Taken together, our study for the first time established a distinct role of CaNAC100 in positively influencing SSP synthesis and its critical regulation by CamiR164e, thereby serving as an understanding that can be utilized for developing SPC-rich chickpea varieties.

Indophenol Blue Colorimetric Method to Determine Grain Protein Content of Cereal Plants.

Here, we propose a method to convert the organic nitrogen in maize kernels into ammonia in solution and then chlorinate it to prepare monochloride salts, which can form an oxidatively coupled blue-green mixture with sodium salicylate and sodium dichloroisocyanurate. The concentration of ammonium ions in the blue-green mixture can then be determined in the solution, and finally the protein content in maize kernels can be calculated from the nitrogen content.

GWAS in a Collection of Bulgarian Old and Modern Bread Wheat Accessions Uncovers Novel Genomic Loci for Grain Protein Content and Thousand Kernel Weight.

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits-grain protein content (GPC) and thousand kernel weight (TKW)-across 179 Bulgarian contemporary and historic varieties and landraces across three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, and genotypic data from the 25K Infinium iSelect array. As a result, we detected 16 quantitative trait nucleotides (QTNs) associated with GPC and 15 associated with TKW, all of which passed the false discovery rate threshold. Seven loci favorably influenced GPC, resulting in an increase of 1.4% to 8.1%, while four loci had a positive impact on TKW with increases ranging from 1.9% to 8.4%. While some loci confirmed previously published associations, four QTNs linked to GPC on chromosomes 2A, 7A, and 7B, as well as two QTNs related to TKW on chromosomes 1B and 6A, may represent novel associations. Annotations for proteins involved in the senescence-associated nutrient remobilization and in the following buildup of resources required for seed germination have been found for selected putative candidate genes. These include genes coding for storage proteins, cysteine proteases, cellulose-synthase, alpha-amylase, transcriptional regulators, and F-box and RWP-RK family proteins. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

Integrating Genome-Wide Association Study, Transcriptome and Metabolome Reveal Novel QTL and Candidate Genes That Control Protein Content in Soybean.

Protein content (PC) is crucial to the nutritional quality of soybean [Glycine max (L.) Merrill]. In this study, a total of 266 accessions were used to perform a genome-wide association study (GWAS) in three tested environments. A total of 23,131 high-quality SNP markers (MAF ≥ 0.02, missing data ≤ 10%) were identified. A total of 40 association signals were significantly associated with PC. Among them, five novel quantitative trait nucleotides (QTNs) were discovered, and another 32 QTNs were found to be overlapping with the genomic regions of known quantitative trait loci (QTL) related to soybean PC. Combined with GWAS, metabolome and transcriptome sequencing, 59 differentially expressed genes (DEGs) that might control the change in protein content were identified. Meantime, four commonly upregulated differentially abundant metabolites (DAMs) and 29 commonly downregulated DAMs were found. Remarkably, the soybean gene Glyma.08G136900, which is homologous with Arabidopsis hydroxyproline-rich glycoproteins (HRGPs), may play an important role in improving the PC. Additionally, Glyma.08G136900 was divided into two main haplotype in the tested accessions. The PC of haplotype 1 was significantly lower than that of haplotype 2. The results of this study provided insights into the genetic mechanisms regulating protein content in soybean.

De novo genome assembly of a high-protein soybean variety HJ117.

OBJECTIVES: Soybean is an important feed and oil crop in the world due to its high protein and oil content. China has a collection of more than 43,000 soybean germplasm resources, which provides a rich genetic diversity for soybean breeding. However, the rich genetic diversity poses great challenges to the genetic improvement of soybean. This study reports on the de novo genome assembly of HJ117, a soybean variety with high protein content of 52.99%. These data will prove to be valuable resources for further soybean quality improvement research, and will aid in the elucidation of regulatory mechanisms underlying soybean protein content. DATA DESCRIPTION: We generated a contiguous reference genome of 1041.94 Mb for HJ117 using a combination of Illumina short reads (23.38 Gb) and PacBio long reads (25.58 Gb), with high-quality sequence coverage of approximately 22.44× and 24.55×, respectively. HJ117 was developed through backcross breeding, using Jidou 12 as the recurrent parent and Chamoshidou as the donor parent. The assembly was further assisted by 114.5 Gb Hi-C data (109.9×), resulting in a contig N50 of 19.32 Mb and scaffold N50 of 51.43 Mb. Notably, Core Eukaryotic Genes Mapping Approach (CEGMA) assessment and Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment results indicated that most core eukaryotic genes (97.18%) and genes in the BUSCO dataset (99.4%) were identified, and 96.44% of the genomic sequences were anchored onto twenty pseudochromosomes.

Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms.

The protein content (PC) and wet gluten content (WGC) are crucial indicators determining the quality of wheat, playing a pivotal role in evaluating processing and baking performance. Original reflectance (OR), wavelet feature (WF), and color index (CI) were extracted from hyperspectral and RGB sensors. Combining Pearson-competitive adaptive reweighted sampling (CARs)-variance inflation factor (VIF) with four machine learning (ML) algorithms were used to model accuracy of PC and WGC. As a result, three CIs, six ORs, and twelve WFs were selected for PC and WGC datasets. For single-modal data, the back-propagation neural network exhibited superior accuracy, with estimation accuracies (WF > OR > CI). For multi-modal data, the random forest regression paired with OR + WF + CI showed the highest validation accuracy. Utilizing the Gini impurity, WF outweighed OR and CI in the PC and WGC models. The amalgamation of MLs with multimodal data harnessed the synergies among various remote sensing sources, substantially augmenting model precision and stability.

Identification of Quantitative Trait Loci (QTL) for Sucrose and Protein Content in Soybean Seed.

Protein and sugar content are important seed quality traits in soybean because they improve the value and sustainability of soy food and feed products. Thus, identifying Quantitative Trait Loci (QTL) for soybean seed protein and sugar content can benefit plant breeders and the soybean market by accelerating the breeding process via marker-assisted selection. For this study, a population of recombinant inbred lines (RILs) was developed from a cross between R08-3221 (high protein and low sucrose) and R07-2000 (high sucrose and low protein). Phenotypic data for protein content were taken from the F2:4 and F2:5 generations. The DA7250 NIR analyzer and HPLC instruments were used to analyze total seed protein and sucrose content. Genotypic data were generated using analysis via the SoySNP6k chip. A total of four QTLs were identified in this study. Two QTLs for protein content were located on chromosomes 11 and 20, and two QTLs associated with sucrose content were located on chromosomes 14 and. 11, the latter of which co-localized with detected QTLs for protein, explaining 10% of the phenotypic variation for protein and sucrose content in soybean seed within the study population. Soybean breeding programs can use the results to improve soybean seed quality.

Microproteomic-Based Analysis of the Goat Milk Protein Synthesis Network and Casein Production Evaluation.

Goat milk has been consumed by humans since ancient times and is highly nutritious. Its quality is mainly determined by its casein content. Milk protein synthesis is controlled by a complex network with many signal pathways. Therefore, the aim of our study is to clearly depict the signal pathways involved in milk protein synthesis in goat mammary epithelial cells (GMECs) using state-of-the-art microproteomic techniques and to identify the key genes involved in the signal pathway. The microproteomic analysis identified more than 2253 proteins, with 323 pathways annotated from the identified proteins. Knockdown of IRS1 expression significantly influenced goat casein composition (α, ß, and κ); therefore, this study also examined the insulin receptor substrate 1 (IRS1) gene more closely. A total of 12 differential expression proteins (DEPs) were characterized as upregulated or downregulated in the IRS1-silenced sample compared to the negative control. The enrichment and signal pathways of these DEPs in GMECs were identified using GO annotation and KEGG, as well as KOG analysis. Our findings expand our understanding of the functional genes involved in milk protein synthesis in goats, paving the way for new approaches for modifying casein content for the dairy goat industry and milk product development.