Search Results (37,374)

In the study of eggplant (Solanum melongena L.), a cross between the green peel line 19143 and the white peel line 19147 produced E4957 F₁ hybrids with a purple–brown peel. Self-fertilization of the F₁ hybrids yielded E4957 F₂ offspring with a segregation ratio of 27:9:21:7 among individuals with purple–brown, purple–red, green, and white peel colors, respectively, which was consistent with a genetic model controlled by reciprocal recessive epistasis between D and P, and Gv1 likely acting as a modifying factor. The green peel line 19143 exhibited higher chlorophyll but lower anthocyanin levels than the white peel line 19147, which contained low levels of both pigments, while the E4957 F₁ hybrids had elevated levels of both pigments. Two epistatic genes, D and P, associated with anthocyanin synthesis, were mapped on chromosomes 10 and 8, respectively. The putative modifying locus Gf, involved in chlorophyll accumulation in the flesh, was mapped on chromosome 8, and the localization interval was close to the previously reported Gv1 locus associated with chlorophyll synthesis in the peel. DNA markers (InDel22522, InDel5531, InDel-APRR2) were developed to genotype 237 F₂ individuals and correlate genotypes with phenotypes. Sequence analysis revealed a 6 bp deletion in the SmMYB1 (D) gene and a large deletion in the SmAPRR2-Like (Gv1) gene in the white peel line 19147, as well as a T to A mutation in the SmANS (P) gene in the green line 19143. This study provided evidence for inheritance between loci involved in anthocyanin and chlorophyll pathways contributing to eggplant peel color variation and provides molecular markers that may facilitate the breeding of eggplant varieties with diverse peel colors. Full article

The discovery of apolipoprotein L1 (APOL1) risk polymorphisms has significantly changed our knowledge of kidney disease susceptibility and development in African American populations. Several non-diabetic kidney disorders, such as focal segmental glomerulosclerosis (FSGS), collapsing glomerulopathy, HIV-associated nephropathy (HIVAN), and accelerated chronic kidney disease (CKD) development, are significantly more likely to occur in people with two coding variations, G1 and G2. The significance of context-dependent pathogenic processes is highlighted by the poor penetrance and remarkable phenotypic variety of APOL1-associated kidney disease, despite its substantial impact. This review synthesizes current knowledge of APOL1 biology through a molecular framework, emphasizing gain-of-toxic-function effects of risk variants in podocytes, dysregulated ion fluxes, mitochondrial dysfunction, impaired proteostasis, and activation of innate immune and inflammatory signaling pathways. We describe how the well-recognized “second-hit” paradigm has a biological basis, driven by strong inducibility by interferons and immunological activation, as well as strict basal regulation of APOL1 expression. Lastly, we explore future approaches to precision nephrology and highlight translational advancements, such as APOL1 gene-silencing techniques. This review provides a mechanistic roadmap for translating APOL1 biology into targeted therapeutic strategies by integrating genetics, cell biology, immunology, and systems-level approaches. Full article

Background: Hypertrophic cardiomyopathy (HCM) is a common inherited disease and a leading known cause of sudden cardiac arrest in young adults and athletes. While genetic testing has advanced rapidly in the past decade, the yield of genetic testing remains low. The Clinical Genome Resource (ClinGen) initiative has become a leading resource for defining the clinical relevance of genetic variants with expert groups focusing on evaluating the strength of evidence for each HCM implicated gene. With the rise of large biobanks and population databases, genetic discovery has been significantly advanced. However, whether these databases can be used to validate gene–disease associations curated by ClinGen and provide evidence for novel gene–disease associations remains unclear. Objectives: Here, we utilized a publicly available database containing 748,879 individuals across three large biobanks (All of Us, UK biobank, Mass General Brigham biobank). Methods: We tested the association of rare coding variants in each gene in the HCM ClinGen panel with HCM. In total, 38 genes were tested, and Bonferroni correction was applied accordingly. Results: Of the 12 genes with definitive evidence for HCM (e.g., MYBPC3, MYH7, TNNT2, ALPK3), 8 (67%) demonstrated nominally significant association with HCM on a population level, and 5 (42%) remained significant after Bonferroni correction, further supporting the validity of these genes in HCM panels. Several definitive genes which are much less commonly affected in HCM (CSRP3, MYL3, ACTC1, TPM1, FHOD3, MYL2, and TNNC1) did not pass our Bonferroni corrected-significance threshold, but all had positively associated effect sizes with HCM. No genes deemed to have moderate or limited evidence had any significant associations with HCM even before Bonferroni correction. Conclusions: Altogether, we show that large biobanks and population databases generally recapitulate established gene–disease associations for HCM and support the ClinGen group’s gene curations. The utilization of such publicly accessible databases represents an additional tool for assessing gene validity in monogenic cardiac disorders with an established phenotype, although it may have limited sensitivity and should not be solely relied on. Full article

Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk assessment. Methods: This narrative review synthesizes artificial intelligence (AI)-driven approaches for predicting and detecting chronic obstructive pulmonary disease (COPD) exacerbations across electronic health records, wearable sensors, imaging, environmental data, and patient-reported outcomes, emphasizing novel discoveries and emerging relationships rather than predictive performance. Results: Three major discoveries have been made. First, measurable physiological and behavioral deterioration may precede symptom recognition by approximately 7–14 days, thereby establishing a potential intervention window for anticipatory care. Second, machine learning (ML) models integrating pollutant exposure, medication adherence, and clinical characteristics have identified phenotypes with differential environmental sensitivity, including unexpected exposure–adherence interactions. Third, deep neural network analysis of full spirometry curves has revealed structural phenotypes beyond traditional Forced Expiratory Volume (FEV₁)-based measures and novel imaging biomarkers. The predictive performance ranges from the Area Under the Curve (AUC) 0.72–0.95, with a pooled meta-analytic AUC of approximately 0.77. Conclusions: AI has uncovered hidden patterns in the progression of COPD, supporting a shift from reactive to anticipatory management. Translation to routine care requires prospective validation, improved interpretability, workflow integration, and generalizability and equity. Full article

Background/Objectives: Paediatric cataract is among the most common treatable causes of childhood blindness, caused by a genetically diverse disorder with variable clinical features. Although genetic factors significantly contribute to the development of paediatric cataracts, recent data on their genetic makeup and genotype–phenotype relationships in Saudi Arabia is limited. This study aims to investigate the genetic spectrum, inheritance patterns, and genotype–phenotype correlations of paediatric cataract in a Saudi population over twenty years. Methods: We conducted a retrospective cohort study of children diagnosed with congenital or juvenile cataracts between 2000 and 2019 at two major referral centres in Riyadh. Clinical, ocular, and systemic data were collected through multidisciplinary evaluations. Genetic analysis involved whole-exome and whole-genome sequencing performed at College of American Pathologists (CAP)-accredited laboratories. Variant interpretation was supported by bioinformatic and Artificial Intelligence (AI) prediction tools. Genotype–phenotype relationships were systematically analysed. Results: The study included 28 cases of genetically confirmed paediatric cataracts. Variants classified as pathogenic or likely pathogenic were identified in 13 genes. Autosomal recessive inheritance was predominant, with many patients exhibiting homozygous variants, often due to consanguinity. Two novel variants were identified in the Collagen Type XVIII Alpha 1 Chain (COL18A1) and the RAB3 GTPase-activating protein catalytic subunit 2 (RAB3GAP2) genes. Considerable phenotypic variability was observed, even among patients with the same mutation, particularly those with the recurrent CRYBB1 c.171del (p.Asn58fs) mutation. Syndromic cataracts were more frequently associated with loss-of-function variants and multisystem features. Conclusions: This study offers updated insights into the genetics and clinical presentation of paediatric cataract in Saudi Arabia. It highlights high genetic diversity, unique inheritance patterns, and notable genotype–phenotype variability, emphasising the importance of early genetic testing and multidisciplinary assessment for improved diagnosis, management, and counselling. Full article

Yeasts play a vital role in food fermentation processes, where their viability, stress tolerance, and metabolic performance directly influence product quality and process efficiency. Controlling and modulating yeast behavior represents a challenge in the food industry, particularly in non-thermal processing contexts. Ultraviolet (UV) technology has traditionally been applied as a microbial control tool; however, yeast response mechanisms to UV irradiation extend beyond simple inactivation. Depending on wavelength, dose, and treatment conditions, UV exposure can lead to complete inactivation, partial reduction in viability, or induce stable phenotypic changes associated with cellular stress responses and Deoxyribonucleic Acid (DNA) damage processing. This review examines current knowledge on yeast–UV interactions across different food matrices, highlighting how UV treatments influence yeast physiology and functionality. In addition, recent studies suggest that UV-induced genetic alterations, when properly controlled, may contribute to yeast diversification and functional modulation without the use of genetically modified organisms. The review discusses technological opportunities, practical limitations, and future research needs, emphasizing the dual role of UV technology as a tool for yeast control and as a potential driver of functional modulation. Full article

Eosinophils are multifunctional granulocytes with central roles in the pathobiology of chronic airway diseases. While systemic eosinophilia (>300 cells/μL) is a well-established biomarker to guide therapeutic decision-making, accumulating evidence indicates that eosinophils are not a uniform population but instead exhibit substantial phenotypic and functional heterogeneity across biological compartments, inflammatory states, and disease contexts. In this review, we synthesize the current understanding of eosinophil heterogeneity in airway diseases and critically evaluate the strengths and limitations of surface marker-based approaches, with emphasis on CD62L/L-selectin-defined subpopulations. Although CD62L-based stratification has provided valuable insight into eosinophil activation and tissue localization, its limited specificity, inconsistent clinical associations, and reliance on murine models restrict its utility as a framework for eosinophil subtyping in humans. We highlight how transcriptomic and proteomic profiling has transformed the field by revealing that peripheral blood eosinophils are largely quiescent, whereas disease-relevant functional specialization is predominantly acquired within inflamed tissues in response to cues from the local microenvironment. These molecular studies support a model in which eosinophil heterogeneity represents a continuum of activation rather than discrete, fixed subsets. A refined, integrative approach to understanding eosinophil heterogeneity is critical for improving patient stratification, predicting therapeutic responsiveness, and optimizing precision medicine strategies in chronic airway diseases. Full article

Real-time measurement of tea bud phenotypes via mobile devices is constrained by model lightweighting challenges, and research on non-contact measurement of tea bud phenotypes based on key points remains largely unexplored. Information on the growth posture of tea buds is an important basis for determining tea maturity grades, quality monitoring, and tea breeding. Therefore, this work develops a deep learning-enabled YOLOv8p-Tea model to estimate key point information of tea bud posture and automatically obtain three-dimensional point cloud information of tea buds by integrating depth information, thereby achieving in situ measurement of tea bud phenotypic parameters. Meanwhile, the model is trained and validated using a tea bud (one-bud-three-leaf) image dataset, and its effectiveness is demonstrated through experiments. Compared to the YOLOv8p-pose model, the model achieves a mAP50 of 98.3%, a P of 97%, and parameters of 0.72 M, with mAP50 and P improved by 1.5% and 1.9%, respectively, and the parameter count is reduced by 25%. To validate the accuracy of phenotypic extraction, the model was deployed on edge devices, and 30 tea buds with one bud and three leaves were randomly selected in a tea garden. The final in situ measurement results showed an MRE of 6.63%. Experimental findings indicate that the developed method is capable of not only effectively estimate tea bud posture but also accurately achieves in situ measurement of tea bud phenotypes, which holds potential applications for meeting the construction needs of smart tea gardens and optimizing tea breeding. Full article

Background: Acute kidney injury (AKI) is a common and high-risk complication of hospitalization that frequently occurs in patients with chronic cardiometabolic disease. Although heart failure (HF) and diabetes mellitus (DM) are prevalent among hospitalized adults and may differentially modify AKI-associated outcomes, their joint impact on in-hospital risk profiles and cumulative burden remains incompletely characterized. Methods: We conducted a retrospective analysis of adult hospitalizations complicated by AKI using a nationally representative inpatient database. Hospitalizations were classified into four cardiorenal metabolic phenotypes: AKI alone, AKI with HF, AKI with DM, and AKI with both HF and DM. Primary outcomes included in-hospital mortality, dialysis initiation, and mechanical ventilation. Survey-weighted multivariable logistic regression models incorporating HF, DM, and their interaction were used to estimate adjusted associations and model-based predicted probabilities. Adjusted risks were visualized across outcomes, and a composite burden metric was constructed to summarize cumulative in-hospital adverse events. Results: AKI outcomes varied substantially across cardiorenal metabolic phenotypes. HF was consistently associated with higher adjusted mortality and mechanical ventilation risk, whereas DM alone was associated with lower adjusted mortality. A significant interaction between HF and DM was observed regarding dialysis initiation, with a disproportionately higher adjusted risk when both conditions coexisted. Integrated visualization across outcomes demonstrated distinct risk profiles by phenotype, with the combined HF and DM group exhibiting the highest cumulative burden of adverse in-hospital events. Conclusions: Among hospitalizations complicated by AKI, the underlying cardiorenal metabolic status is associated with marked heterogeneity in in-hospital outcomes. HF appears to be a dominant modifier of AKI-associated risk, while DM exerts outcome-specific effects and synergistically increases the risk of dialysis initiation when combined with HF. These findings highlight the importance of incorporating cardiometabolic context into AKI risk stratification approaches and underscore the value of multidimensional in-hospital assessments. Full article

In recent years, plant genotyping has been shifting from the accumulation of whole-genome data toward their effective use in breeding programs This review examines key genotyping platforms, including single-nucleotide polymorphism (SNP) arrays, reduced-representation sequencing methods such as genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), targeted genotyping approaches, and whole-genome sequencing (WGS), analyzing their informativeness, cost, and computational limitations. The transition to pangenome-based genotyping and graph genomes is discussed, as these approaches reduce reference bias and increase sensitivity for detecting structural variants, introgressions, and rare alleles that are important for adaptation and breeding. The growing role of AI/ML is highlighted in modeling complex genotype–phenotype relationships, integrating genomic and phenotypic data, and improving the accuracy and interpretability of genomic predictions. Full article

Barley remains the fourth most cultivated cereal crop worldwide and is valued for its versatility in malting and brewing, animal feed, human nutrition, and dietary supplements. The identification of genotypes suitable for breeding or specific end-use applications requires multi-environment testing to evaluate agronomic performance, grain quality, and trait stability. In this study, a panel of 50 winter barley genotypes (two-row and six-row) originating from diverse genetic backgrounds was evaluated over three growing seasons (2021–2023) under the environmental conditions of southeastern Romania. Seven traits were analyzed, including three phenological traits (heading time, flowering time and plant height), grain yield, and three quality parameters (thousand-grain weight, protein content, and starch content). Environmental conditions had a strong influence on phenological development and grain yield, whereas grain quality traits showed relatively greater stability, indicating a stronger genetic control. Multivariate analyses (Principal Component Analysis (PCA) and Genotype plus Genotype-by-Environment interaction biplot (GGE biplots)) revealed clear relationships among traits and highlighted contrasting adaptive strategies between the two barley types. In two-row barley, genotypes such as Idra and Sandra combined favorable yield performance with stable grain quality traits and therefore represent promising candidates for breeding programs and large-scale cultivation. In six-row barley, SU-Ellen and LG Zebra showed high productivity and strong starch accumulation, making them valuable genetic resources for yield-oriented breeding, although further improvement in nitrogen use efficiency may be beneficial. The 2022–2023 growing season represented the most restrictive environment, emphasizing the importance of stability under stress conditions. Genotypes located close to the Average Environment Coordination axis (AEC axis) during that season, such as Ametist (six-row) and Lardeya (two-row), may represent promising material for breeding programs targeting drought resilience. Overall, the results expand the phenotypic characterization of winter barley germplasm and identify valuable genetic resources that can support pre-breeding efforts and the development of climate-resilient barley cultivars. Full article

Cell-based immuno-biosensors are novel platforms for studying immune responses of biological cells, with real-time insights more similar to physiological and pathological conditions. These systems utilize living immune cells as their main components, enabling them to detect disease-related biomarkers and cellular traits in a way that is often highly sensitive and label-free. Integration with microfluidics and organ-on-chip technologies has facilitated precise manipulational control over the cellular microenvironment. Not only has this resulted in high-throughput screening, but it also enabled smaller, more portable systems which can be used at the point of care. In this work, we review the recent advance in microfluidic cell-based immuno-biosensing associated with immune cells such as neutrophils, macrophages, T cell and dendrite cells. Some of the exciting developments include fusion with methods such as advanced imaging, electrical impedance sensing and application of machine learning to phenotyping. We will also elaborate on the issues related to the standardization of these systems, cell heterogeneity, and the challenges for translating these technologies for clinical application. Taken together, such integrated platforms have potential to fill the gap left in-between cellular immunology with biosensor engineering. Full article

Melanoma is an aggressive skin cancer with high metastatic potential and poor long-term survival, highlighting the need for new therapeutic targets. Although microRNAs are critical regulators of tumor progression, the function of miR-374b-5p in melanoma remains poorly understood. Here, we identify miR-374b-5p as a tumor suppressor in melanoma cells. We show that miR-374b-5p directly targets vascular endothelial growth factor C (Vegfc) and is associated with changes in mitogen-activated protein kinase (MAPK) signaling, accompanied by reduced levels of phosphorylated extracellular signal-regulated kinase (pERK) and tyrosinase (TYR). Consistent with these observations, miR-374b-5p overexpression suppresses melanoma cell proliferation, migration, and invasion in vitro. Conditioned media from miR-374b-5p-overexpressing melanoma cells is also associated with changes in macrophage-related inflammatory markers, suggesting that these alterations are consistent with a shift toward a more pro-inflammatory macrophage phenotype. In a mouse model, miR-374b-5p overexpression significantly reduced tumor growth and angiogenesis, and downregulated the lymphangiogenic factor VEGFC. Together, these findings identify miR-374b-5p as a novel regulator of melanoma progression that acts through VEGFC-associated MAPK signaling and tumor microenvironment reprogramming, identifying miR-374b-5p as a promising therapeutic candidate for melanoma. Full article

Cheliped regeneration in the E. sinensis is a tightly regulated physiological process, yet the molecular regulatory mechanisms underlying sexual dimorphism during regeneration remain unclear. In this study, we combined morphological observation with transcriptomic analysis to systematically investigate the regenerative stage characteristics and sex-related differences. The papilla stage 4 dpa was identified as a pivotal transitional stage, bridging initial wound healing and cellular dedifferentiation (2 dpa) with subsequent redifferentiation and morphogenesis (7 dpa). Morphological sex-based differences characterized by larger regenerating chelipeds in males became prominent by the late stage (28 dpa). Notably, the molecular foundation of sexual dimorphism was found to be established at 4 dpa, significantly preceding the emergence of phenotypic differences. This early divergence was driven by sex-dimorphic endocrine networks: males exhibited preferential expression of genes such as Fem-1c-like, Cyp2L1-like, CpAMP1A-like and Nedd4-like, while females showed enrichment in elevated aromatase activity. Weighted gene co-expression network analysis (WGCNA) identified the Hox gene Antp as a core hub regulator, exhibiting high co-expression with key epidermal-related genes such as Cht6, Cht2-like and more. Its suppressed expression at 2 dpa aligned with the requirements for dedifferentiation, whereas its peak at 4 dpa indicated a crucial role in orchestrating appendage patterning and exoskeleton assembly. RNA interference (RNAi) knockdown of Antp resulted in obscured differentiation between the propodus and carpus in both sexes and confirmed its regulatory control over downstream targets including Ubx, Bmp2-like, and CpAMP1A-like. This study suggests a putative hierarchical regulatory model in which systemic hormonal signals may integrate Antp and other sex-biased regulators to potentially facilitate structured limb regeneration. These findings offer tentative novel insights into the interplay between developmental plasticity and sex-based regulatory divergence in decapod crustaceans. Full article

Aging and stress-related factors affect sperm DNA methylation in regions associated with genes responsible for embryonic development. The stochastic epigenetic variation hypothesis holds potential to explain these patterns, proposing that, in response to stressors, naturally variable methylation regions (VMRs) associated with morphogenetic genes exhibit increased methylation variation to diversify phenotypes and improve the chances of survival of the genetic lineage. Here, we test predictions from this hypothesis using mouse and rat sperm DNA methylation data from publicly available sources. Specifically, we identify VMRs and analyze their overlap with regions differentially methylated (DMRs) in response to aging, stressors, and with various genomic elements. We demonstrate that the nature of the DNA regions, rather than the nature of the stressor, determines the response of the sperm methylome to aging and stress, and propose a model that explains shifts in methylation within VMRs through stochastic changes, whereby initially hypermethylated regions lose methylation and initially hypomethylated regions gain methylation. VMRs are depleted of open chromatin regions and histones in male germ cells and are enriched for a binding motif for ZFP42, an epigenetic remodeler. This knowledge may open opportunities for the development of interventions to control epigenetic information transfer via germ cells. Full article