Search Results (365)

Background/Objectives: There has been limited evidence on the long-term impacts of coffee intake on health. We aimed to investigate the association between coffee intake and the incidence of diseases and mortality risk over 20 years among community-dwelling Chinese adults. Methods: Participants were from the Hong Kong Osteoporosis Study who attended baseline assessments during 1995–2010. Coffee intake was self-reported through a food frequency questionnaire and was previously validated. Disease diagnoses, which were mapped into 1795 distinct phecodes, and mortality data were obtained from linkage with territory-wide electronic health records. Cox models were used to estimate the association between coffee intake and the incidence of each disease outcome and mortality among individuals without a history of the respective medical condition at baseline. All models were adjusted for age, sex, body mass index, smoking, alcohol drinking, and education. Results: Among the 7420 included participants (mean age 53.2 years, 72.2% women), 54.0% were non-coffee drinkers, and only 2.7% consumed more than one cup of coffee per day. Over a median follow-up of 20.0 years, any coffee intake was associated with a reduced risk of dementia, atrial fibrillation, painful respirations, infections, atopic dermatitis, and dizziness at a false discovery rate (FDR) of <0.05. Furthermore, any coffee intake was associated with an 18% reduced risk of all-cause mortality (95% confidence interval = 0.73–0.93). Conclusion: In a population with relatively low coffee consumption, any coffee intake is linked to a lower risk of several neurological, circulatory, and respiratory diseases and symptoms, as well as mortality. Full article

In the context of rapidly increasing population and diversified market demands, the steady improvement of yield and quality in horticultural crops has become an urgent challenge that modern breeding efforts must tackle. Heterosis, a pivotal theoretical foundation for plant breeding, facilitates the creation of superior hybrids through crossbreeding and selection among a variety of parents. However, the vast number of potential hybrids presents a significant challenge for breeders in efficiently predicting and selecting the most promising candidates. The development and refinement of effective hybrid prediction methods have long been central to research in this field. This article systematically reviews the advancements in hybrid prediction for horticultural crops, including the roles of marker-assisted breeding and genomic prediction in phenotypic forecasting. It also underscores the limitations of some predictors, like genetic distance, which do not consistently offer reliable hybrid predictions. Looking ahead, it explores the integration of phenomics with genomic prediction technologies as a means to elevate prediction accuracy within actual breeding programs. Full article

In the context of crop breeding, plant height (PH) plays a pivotal role in determining straw and grain yield. Although extensive research has explored the genetic control of PH in wheat, there remains an opportunity for further advancements by integrating genomics with growth-related phenomics. Our study utilizes the latest genome-wide association scan (GWAS) techniques to unravel the genetic basis of temporal variation in PH across 179 Bulgarian bread wheat accessions, including landraces, tall historical, and semi-dwarf modern varieties. A GWAS was performed with phenotypic data from three growing seasons, the calculated best linear unbiased estimators, and the leveraging genotypic information from the 25K Infinium iSelect array, using three statistical methods (MLM, FarmCPU, and BLINK). Twenty-five quantitative trait loci (QTL) associated with PH were identified across fourteen chromosomes, encompassing 21 environmentally stable quantitative trait nucleotides (QTNs), and four haplotype blocks. Certain loci (17) on chromosomes 1A, 1B, 1D, 2A, 2D, 3A, 3B, 4A, 5B, 5D, and 6A remain unlinked to any known Rht (Reduced height) genes, QTL, or GWAS loci associated with PH, and represent novel regions of potential breeding significance. Notably, these loci exhibit varying effects on PH, contribute significantly to natural variance, and are expressed during seedling to reproductive stages. The haplotype block on chromosome 6A contains five QTN loci associated with reduced height and two loci promoting height. This configuration suggests a substantial impact on natural variation and holds promise for accurate marker-assisted selection. The potentially novel genomic regions harbor putative candidate gene coding for glutamine synthetase, gibberellin 2-oxidase, auxin response factor, ethylene-responsive transcription factor, and nitric oxide synthase; cell cycle-related genes, encoding cyclin, regulator of chromosome condensation (RCC1) protein, katanin p60 ATPase-containing subunit, and expansins; genes implicated in stem mechanical strength and defense mechanisms, as well as gene regulators such as transcription factors and protein kinases. These findings enrich the pool of semi-dwarfing gene resources, providing the potential to further optimize PH, improve lodging resistance, and achieve higher grain yields in bread wheat. Full article

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean—encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience. Full article

Latent autoimmune diabetes in adults (LADA) is characterized by the presence of glutamate decarboxylase autoantibodies (GADA). LADA has intermediate features between type 1 diabetes and type 2 diabetes. In addition, genetic risk factors for both types of diabetes are present in LADA. Nonetheless, evidence about the genetics of LADA in non-European populations is scarce. This study aims to perform a genome-wide association study with a phenome-wide association study of LADA in a southeastern Mexican population. We included 59 patients diagnosed with LADA from a previous study and 3121 individuals without diabetes from the MxGDAR/ENCODAT database. We utilized the GENESIS package in R to perform the genome-wide association study (GWAS) of LADA and PLINK for the phenome-wide association study (PheWAS) of LADA features. Nine polymorphisms reach the nominal association level (1 × 10⁻⁵) in the GWAS. The PheWAS showed that rs7305229 is genome-wide and associated with serum GADA levels in our sample (p = 1.84 × 10⁻⁸). rs7305229 is located downstream of the FAIM2 gene; previous reports associate FAIM2 variants with childhood obesity, body mass index, body adiposity measures, lymphocyte CD8+ activity, and anti-thyroid peroxidase antibodies. Our findings reveal that rs7305229 affects the GADA levels in patients with LADA from southeastern Mexico. More studies are needed to determine if this risk genotype exists in other populations with LADA. Full article

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model. Full article

Soybeans are an essential source of food, protein, and oil worldwide, and the nodules on their root systems play a critical role in nitrogen fixation and plant growth. In this study, we tackled the challenge of limited high-resolution image quantities and the constraints on model learning by innovatively employing image segmentation technology for an in-depth analysis of soybean nodule phenomics. Through a meticulously designed segmentation algorithm, we broke down large-resolution images into numerous smaller ones, effectively improving the model’s learning efficiency and significantly increasing the available data volume, thus laying a solid foundation for subsequent analysis. In terms of model selection and optimization, after several rounds of comparison and testing, YOLOX was identified as the optimal model, achieving an accuracy of 91.38% on the test set with an R² of up to 86%, fully demonstrating its efficiency and reliability in nodule counting tasks. Subsequently, we utilized YOLOV5 for instance segmentation, achieving a precision of 93.8% in quickly and accurately extracting key phenotypic indicators such as the area, circumference, length, and width of the nodules, and calculated the statistical properties of these indicators. This provided a wealth of quantitative data for the morphological study of soybean nodules. The research not only enhanced the efficiency and accuracy of obtaining nodule phenotypic data and reduced costs but also provided important scientific evidence for the selection and breeding of soybean materials, highlighting its potential application value in agricultural research and practical production. Full article

Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production and global food security. To combat these challenges, the integration of bioinformatics educational tools and AI applications provide a synergistic approach to identify and analyze stress-responsive genes, regulatory networks and molecular markers associated with stress tolerance. Bioinformatics educational tools offer a robust framework for data collection, storage and initial analysis, while AI applications enhance pattern recognition, predictive modeling and real-time data processing capabilities. This review uniquely integrates bioinformatics educational tools and AI applications, highlighting their combined role in managing abiotic stress in plants and crops. The novelty is demonstrated by the integration of multiomics data with AI algorithms, providing deeper insights into stress response pathways, biomarker discovery and pattern recognition. Key AI applications include predictive modeling of stress resistance genes, gene regulatory network inference, omics data integration and real-time plant monitoring through the fusion of remote sensing and AI-assisted phenomics. Challenges such as handling big omics data, model interpretability, overfitting and experimental validation remain there, but future prospects involve developing user-friendly bioinformatics educational platforms, establishing common data standards, interdisciplinary collaboration and harnessing AI for real-time stress mitigation strategies in plants and crops. Educational initiatives, interdisciplinary collaborations and trainings are essential to equip the next generation of researchers with the required skills to utilize these advanced tools effectively. The convergence of bioinformatics and AI holds vast prospects for accelerating the development of stress-resilient plants and crops, optimizing agricultural practices and ensuring global food security under increasing environmental pressures. Moreover, this integrated approach is crucial for advancing sustainable agriculture and ensuring global food security amidst growing environmental challenges. Full article

Background: The need for efficient and simplified techniques for seafood traceability is growing. This study proposes the Biolog EcoPlate assay as an innovative method for assessing wild and farmed Sparus aurata traceability, offering advantages over other molecular techniques in terms of technical simplicity. Methods: The Biolog EcoPlate assay, known for its high-throughput capabilities in microbial ecology, was utilized to evaluate the functional diversity of microbial communities from various organs of S. aurata (seabream) from the Mediterranean area. Samples were taken from the anterior and posterior gut, cloaca swabs and gills to distinguish between farmed and wild-caught individuals. The analysis focused on color development in OmniLog Units for specific carbon sources at 48 h. Results: Gills provided the most accurate clusterization of sample origin. The assay monitored the development of color for carbon sources such as α-cyclodextrin, D-cellobiose, glycogen, α-D-lactose, L-threonine and L-phenylalanine. A mock experiment using principal component analysis (PCA) successfully identified the origin of a blind sample. Shannon and Simpson indexes were used to statistically assess the diversity, reflecting the clusterization of different organ samples; Conclusions: The Biolog EcoPlate assay proves to be a quick, cost-effective method for discriminate S. aurata traceability (wild vs. farmed), demonstrating reliable reproducibility and effective differentiation between farmed and wild-caught seabream. Full article

Mechanised non-contact, non-destructive imaging methodologies have revolutionised plant phenotyping, increasing throughput well beyond what was possible using traditional manual methods. Quantifying the variation in post-harvest material such as seeds and fruits, usually the economically important part of the crop, can be critical for commercial quality assessment as well as breeding and research. Therefore, reliable methods that gather metrics of interest, quickly and efficiently, are of widespread interest across sectors. This study focuses on evaluating the phenotyping capabilities of the Cgrain Value™, a novel grain imaging machine designed for quality and purity assessment and used primarily in commercial cereal production and processing. The performance of the Cgrain Value™ in its generation of high-throughput quantitative phenotypic data is compared with a well-established machine, MARVIN, assessing repeatability and reproducibility across a range of metrics. The findings highlight the potential of the Cgrain Value™, and some shortcomings, to provide detailed three-dimensional size, shape, and colour information rapidly, offering insights into oat grain morphology that could enhance genome-wide association studies and inform the breeding efforts in oat improvement programmes. Full article

Breeding for nitrogen use efficiency (NUE) is becoming more important as global uncertainty makes the production and application of nitrogen (N) fertilizers more expensive and environmentally unfriendly. Despite this, most cereal breeding programs still use yield-related components as proxies for NUE, likely due to the prohibitive cost and time of collecting and analyzing samples through traditional lab-based methods. Drone-based NUE phenotyping provides a viable and scalable alternative as it is quicker, non-destructive, and consistent. Here, we present a study that utilized financially accessible cost-effective drones mounted with red-green-blue (RGB) image sensors coupled with the open-source AirMeasurer platform and advanced statistical analysis to exclude low-NUE lines in multi-seasonal field experiments. The method helped us to identify high N agronomic use efficiency lines but was less effective with a high N recovery efficiency line. We found that the drone-powered approach was very effective at 180 kg N per hectare (N180, an optimized N-rate) as it completely removed low-NUE wheat lines in the trial, which would facilitate breeders to quickly reduce the number of lines taken through multi-year breeding programs. Hence, this encouraging and scalable approach demonstrates its ability to conduct NUE phenotyping in wheat. With continuous refinements in field experiments, this method would be employable as an openly accessible platform to identify NUE lines at different N-rates for breeding and resource use efficiency studies in wheat. Full article

The dimensions of organs such as flowers, leaves, and seeds are governed by processes of cellular proliferation and expansion. In soybeans, the dimensions of these organs exhibit a strong correlation with crop yield, quality, and other phenotypic traits. Nevertheless, there exists a scarcity of research concerning the regulatory genes influencing flower size, particularly within the soybean species. In this study, 309 samples of 3 soybean types (123 cultivar, 90 landrace, and 96 wild) were re-sequenced. The microscopic phenotype of soybean flower organs was photographed using a three-eye microscope, and the phenotypic data were extracted by means of computer vision. Pearson correlation analysis was employed to assess the relationship between petal and seed phenotypes, revealing a strong correlation between the sizes of these two organs. Through GWASs, SNP loci significantly associated with flower organ size were identified. Subsequently, haplotype analysis was conducted to screen for upstream and downstream genes of these loci, thereby identifying potential candidate genes. In total, 77 significant SNPs associated with vexil petals, 562 significant SNPs associated with wing petals, and 34 significant SNPs associated with keel petals were found. Candidate genes were screened by candidate sites, and haplotype analysis was performed on the candidate genes. Finally, the present investigation yielded 25 and 10 genes of notable significance through haplotype analysis in the vexil and wing regions, respectively. Notably, Glyma.07G234200, previously documented for its high expression across various plant organs, including flowers, pods, leaves, roots, and seeds, was among these identified genes. The research contributes novel insights to soybean breeding endeavors, particularly in the exploration of genes governing organ development, the selection of field materials, and the enhancement of crop yield. It played a role in the process of material selection during the growth period and further accelerated the process of soybean breeding material selection. Full article

Over the years, comprehensive explorations of the model organisms Caenorhabditis elegans (elegant worm) and Drosophila melanogaster (vinegar fly) have contributed substantially to our understanding of complex biological processes and pathways in multicellular organisms generally. Extensive functional genomic–phenomic, genomic, transcriptomic, and proteomic data sets have enabled the discovery and characterisation of genes that are crucial for life, called ‘essential genes’. Recently, we investigated the feasibility of inferring essential genes from such data sets using advanced bioinformatics and showed that a machine learning (ML)-based workflow could be used to extract or engineer features from DNA, RNA, protein, and/or cellular data/information to underpin the reliable prediction of essential genes both within and between C. elegans and D. melanogaster. As these are two distantly related species within the Ecdysozoa, we proposed that this ML approach would be particularly well suited for species that are within the same phylum or evolutionary clade. In the present study, we cross-predicted essential genes within the phylum Nematoda (evolutionary clade V)—between C. elegans and the pathogenic parasitic nematode H. contortus—and then ranked and prioritised H. contortus proteins encoded by these genes as intervention (e.g., drug) target candidates. Using strong, validated predictors, we inferred essential genes of H. contortus that are involved predominantly in crucial biological processes/pathways including ribosome biogenesis, translation, RNA binding/processing, and signalling and which are highly transcribed in the germline, somatic gonad precursors, sex myoblasts, vulva cell precursors, various nerve cells, glia, or hypodermis. The findings indicate that this in silico workflow provides a promising avenue to identify and prioritise panels/groups of drug target candidates in parasitic nematodes for experimental validation in vitro and/or in vivo. Full article

Histone acetyltransferases (HATs) modify the amino-terminal tails of the core histone proteins via acetylation, regulating chromatin structure and transcription. GENERAL CONTROL NON-DEREPRESSIBLE 5 (GCN5) is a HAT that specifically acetylates H3K14 residues. GCN5 has been associated with cell division and differentiation, meristem function, root, stem, foliar, and floral development, and plant environmental response. The flowers of gcn5 plants display a reduced stamen length and exhibit male sterility relative to the wild-type plants. We show that these effects may arise from gibberellin (GA)-signaling defects. The signaling pathway of bioactive GAs depends on the proteolysis of their repressors, DELLA proteins. The repressor GA (RGA) DELLA protein represses plant growth, inflorescence, and flower and seed development. Our molecular data indicate that GCN5 is required for the activation and H3K14 acetylation of genes involved in the late stages of GA biosynthesis and catabolism. We studied the genetic interaction of the RGA and GCN5; the RGA can partially suppress GCN5 action during the whole plant life cycle. The reduced elongation of the stamen filament of gcn5–6 mutants is reversed in the rga–t2;gcn5–6 double mutants. RGAs suppress the GCN5 effect on the gene expression and histone acetylation of GA catabolism and GA signaling. Interestingly, the RGA and RGL2 do not suppress ADA2b function, suggesting that ADA2b acts downstream of GA signaling and is distinct from GCN5 activity. In conclusion, we propose that the action of GCN5 on stamen elongation is partially mediated by RGA and GA signaling. Full article

Millions of patients suffer from type 1 diabetes (T1D) and its associated complications. Nevertheless, the pursuit of a cure for T1D has encountered significant challenges, with a crucial impediment being the lack of biomarkers that can accurately predict the progression of T1D and reliable therapeutic targets for T1D. Hence, there is an urgent need to discover novel protein biomarkers and therapeutic targets, which holds promise for targeted therapy for T1D. In this study, we extracted summary-level data on 4907 plasma proteins from 35,559 Icelanders and 2923 plasma proteins from 54,219 UK participants as exposures. The genome-wide association study (GWAS) summary statistics on T1D and T1D with complications were obtained from the R9 release results from the FinnGen consortium. Summary-data-based Mendelian randomization (SMR) analysis was employed to evaluate the causal associations between the genetically predicted levels of plasma proteins and T1D-associated outcomes. Colocalization analysis was utilized to investigate the shared genetic variants between the exposure and outcome. Moreover, transcriptome analysis and a protein–protein interaction (PPI) network further illustrated the expression patterns of the identified protein targets and their interactions with the established targets of T1D. Finally, a Mendelian randomization phenome-wide association study evaluated the potential side effects of the identified core protein targets. In the primary SMR analysis, we identified 72 potential protein targets for T1D and its complications, and nine of them were considered crucial protein targets. Within the group were five risk targets and four protective targets. Backed by evidence from the colocalization analysis, the protein targets were classified into four tiers, with MANSC4, CTRB1, SIGLEC5 and MST1 being categorized as tier 1 targets. Delving into the DrugBank database, we retrieved 11 existing medications for T1D along with their therapeutic targets. The PPI network clarified the interactions among the identified potential protein targets and established ones. Finally, the Mendelian randomization phenome-wide association study corroborated MANSC4 as a reliable target capable of mitigating the risk of various forms of diabetes, and it revealed the absence of adverse effects linked to CTRB1, SIGLEC5 and MST1. This study unveiled many protein biomarkers and therapeutic targets for T1D and its complications. Such advancements hold great promise for the progression of drug development and targeted therapy for T1D. Full article