Search Results (1,158)

Introduction: Aquatic contaminants can alter fish behavior before overt toxicity becomes evident, making neurobehavioral endpoints relevant for ecosystem protection and for hazard prioritization within a One Health framework. We recently developed a high-throughput visual-acoustic zebrafish larval thigmotaxis assay in which edge preference is interpreted as an anxiety-like behavioral endpoint, thereby adding spatial phenotyping beyond conventional locomotion metrics. Objective: To evaluate assay performance in a multisubstance screening challenge and determine whether it can discriminate distinct behavioral fingerprints without prior knowledge of chemical identity. Methodology: Zebrafish larvae were exposed for 1 h at 120 hpf. For each substance, 24 larvae were tested per condition, with six concentrations per substance, plus positive and negative controls. Larvae were challenged using alternating light/dark and tapping/quiet paradigms. The primary endpoint was the percentage of time spent at the edge as a proxy for anxiety-like behavior, while total distance and mean total velocity when moving were used as contextual locomotor metrics; edge distance and edge velocity were used as supportive spatial metrics. Data from 37 substances were analyzed through a standardized automated workflow. Results: Controls performed as expected and supported assay stability across runs. The chemical screening revealed heterogeneous but reproducible behavioral fingerprints. Seven substances produced weak/minimal acute responses, ten showed predominantly suppressive profiles, three predominantly activating profiles, nine showed prominent thigmotaxis-specific anxiety-like signals not explained by locomotion alone, and eight displayed mixed or stimulus-dependent patterns, including non-monotonic responses. Several substances altered edge preference while distance and velocity changed less, differently, or in the opposite direction, indicating behavioral reorganization rather than simple hypo- or hyperactivity. The multi-stimulus design was critical because some effects were evident only under specific sensory contexts. Conclusions: The multisubstance challenge supports the discriminatory capacity, robustness, and added value of the assay for high-throughput neurobehavioral screening. By capturing anxiety-like behavior through thigmotaxis and complementing it with locomotor context, the method improves phenotypic resolution for aquatic pollution assessment and offers a sensitive fish-based NAM to prioritize chemicals of concern for both environmental and human health-oriented testing strategies. Full article

Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in the identification of stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction-driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor–ligand recognition, protein–protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We further emphasize ROS as integrative redox switches that connect stress sensing, defense activation, senescence-related transitions, and recovery, and chromatin-associated mechanisms as regulators that may stabilize primed or memory-like adaptive states. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca²⁺, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture. Full article

The invasive tomato leafminer, Tuta absoluta, poses a severe global threat to solanaceous crops, necessitating sustainable biocontrol solutions. Through systematic bioprospecting across several Moroccan soils, we constructed a novel library of indigenous fungal isolates using complementary Tenebrio molitor baiting and selective media methods. High-throughput phenotyping identified 49 highly pathogenic isolates, which were characterized for conidial production, thermotolerance, and virulence against T. absoluta. We discovered two lead isolates, Beauveria bassiana UCA-350 and Metarhizium robertsii UCA-329, that demonstrated superior virulence, reducing median survival time and achieving lower LC₅₀ values than most commercial reference strains. Notably, virulence was positively correlated with in vitro conidial yield, revealing a key trait linkage for strain selection. Furthermore, genus-level divergence in thermotolerance was observed, with Beauveria isolates exhibiting significantly higher heat resilience. Our integrated multi-trait screening pipeline not only delivers two potent, regionally sourced biocontrol candidates but also establishes a phenotypic selection framework that prioritizes both efficacy and production scalability, advancing the rational development of next-generation mycoinsecticides. Full article

Objective: TNFSF13B, which encodes B-cell-activating factor (BAFF) and peptidylarginine deiminase 4 (PADI4), plays crucial roles in the pathogenesis of ANCA-associated vasculitis (AAV). This study investigated the associations of single-nucleotide polymorphisms (SNPs) in TNFSF13B/BAFF and PADI4 genes with AAV susceptibility, clinical phenotypes, and disease activity in a Guangxi Chinese population. Methods: A case–control study included 324 AAV patients and 324 healthy controls. After propensity score matching (201 pairs), genomic DNA was genotyped for TNFSF13B/BAFF rs3759467 (formerly rs386492354) and rs1041569, and PADI4 rs11203366 and rs874881 using multiplex PCR and high-throughput sequencing. Genetic associations were analyzed via logistic regression, subgroup, haplotype, and clinical correlation analyses. For each of the four SNPs separately, machine learning models (logistic regression, SVM, Random Forest, XGBoost) were built and evaluated via 5-fold cross-validation. No formal adjustment for multiple comparisons was applied due to the exploratory nature of this study. Results: For TNFSF13B/BAFF, the rs3759467 C allele was protective (dominant model OR = 0.60, p = 0.011; log-additive OR = 0.71, p = 0.020; CA haplotype OR = 0.71, p = 0.019), while the rs1041569 T allele was a risk factor (dominant model OR = 1.70, p = 0.016). Subgroup analysis revealed stronger protective effects of rs3759467 in females, Han ethnicity, and MPA patients, and stronger risk effects of rs1041569 in Han ethnicity and MPA patients. Haplotype CA was protective (OR = 0.71, p = 0.019), and TT was risk-associated (OR = 1.55, p = 0.017). Both TNFSF13B/BAFF SNPs were associated with rash and hemoptysis incidence (p < 0.05). rs1041569 was also associated with RBC (red blood cell) count and HB (hemoglobin) levels (p < 0.05). For PADI4, rs11203366 and rs874881 showed no association with AAV susceptibility (all p > 0.05). However, their genotypes were associated with disease activity (BVAS, Birmingham Vasculitis Activity Score), RBC count, and HB levels (p < 0.05). Although machine learning was applied to explore predictive patterns, its performance was suboptimal (AUC < 0.6), indicating limited clinical applicability. Accordingly, the primary findings rely on the genetic model analysis, and the machine learning results should not be overinterpreted as clinically actionable. SHAP analysis indicated that risk-associated genotypes contributed most to model predictions. Conclusions:TNFSF13B/BAFF gene polymorphisms rs3759467 and rs1041569 were associated with AAV susceptibility in this Guangxi cohort, influencing clinical manifestations like rash, hemoptysis, and anemia severity. PADI4 polymorphisms rs11203366 and rs874881 are not associated with susceptibility but may correlate with disease activity and hematological parameters. These findings highlight the ethnic and clinical subtype specificity of genetic influences in AAV. Due to the lack of external validation, these findings are exploratory and require replication. Full article

As important silkworm varieties reared on artificial diet, Youshi No. 1 (YS) and Guangshi No. 1 (GS) showed remarkable differences in physiological characteristics. GS had significantly better performance than YS in body weight, cocooning ability, food intake, feed utilization efficiency, and digestive enzyme activities. We further performed metabolomics and 16S rRNA high-throughput sequencing to analyze their metabolic profiles and midgut microbiota. More than 40 differential metabolites were screened out, and four metabolic pathways related to feeding divergence were determined via KEGG enrichment, among which L-valine was enriched in multiple pathways. Significant structural differences were also observed in midgut microbiota, and Bacillus was positively correlated with pantothenic acid and valine metabolism. These correlational results disclosed that differential metabolites and gut microbiota might underlie the phenotypic variations between the two varieties. Integrated analysis combined with functional verification experiments demonstrated that supplementation of 1% L-valine or specific Bacillus strains in an artificial diet was associated with the improvement of the growth performance, cocoon quality, and feed utilization efficiency of the YS silkworm variety. Full article

Cisplatin is a widely used platinum-based chemotherapeutic agent that often causes irreversible hair cell loss, leading to hearing impairment. To date, effective strategies for preventing cisplatin-induced ototoxicity remain limited. Corchorus olitorius L. (COL) is rich in bioactive phytochemicals with antioxidant and anti-inflammatory properties; however, the protective role of COL stem against cisplatin-induced hearing loss has not been explored. This study aimed to determine whether COL stem extract treatment could mitigate cisplatin-induced hair cell damage in the lateral line system of zebrafish. Herein, we use 7-day post-fertilization (dpf) transgenic zebrafish larvae as a high-throughput screening platform to assessed COL stem extract against cisplatin-induced hair cell injury. Endpoints included mechanotransduction (MET) function, reactive oxygen species (ROS) production, apoptotic and inflammatory responses, and locomotor behavior. Antioxidant capacity and acute toxicity were also evaluated. Pretreatment with COL stem extract preserved hair cell viability, restored MET function, reduced ROS accumulation, upregulated Nrf-2-dependent cytoprotective genes, suppressed apoptosis, and attenuated macrophage infiltration. The recovery of swimming behavior correlated with hair cell protection, confirming the phenotypic relevance. This study demonstrates, for the first time, that COL stem exerts potent otoprotective effects through antioxidative, anti-apoptotic, and anti-inflammatory mechanisms, contributes to maintain mechanosensory function and swimming behavior. The findings support COL stem as a promising candidate for otoprotection and validate zebrafish-based high-throughput screening for novel therapeutic discovery. Full article

Glioblastoma (GBM) remains one of the most aggressive malignancies, characterized by profound heterogeneity and a dismal prognosis. While genomic and transcriptomic profiling have provided structural insights, they often fail to capture the dynamic interactions within the tumor microenvironment (TME). Secretome analysis—the study of proteins actively secreted by tumor cells—offers a functional readout of these interactions and a reservoir for potential biomarkers. In this review, we critically evaluate the role of MALDI-TOF Mass Spectrometry as a strategic tool for GBM secretome profiling. While Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) remains the gold standard for deep protein discovery, we argue that MALDI-TOF’s speed, cost-effectiveness, and high-throughput capabilities position it as an ideal platform for clinical screening and “spectral phenotyping.” We discuss the technical hurdles, such as ion suppression and the “leakome” (intracellular contamination), and highlight how integrating MALDI with Extracellular Vesicle (EV) enrichment and Artificial Intelligence (AI) can bridge the gap between in vitro discovery and clinical application. Full article

Tomato (Solanum lycopersicum L.) is a globally important vegetable crop and a major dietary source of bioactive compounds, including lycopene, ascorbic acid, phenolics, and minerals. Modern tomato breeding has substantially improved yield, uniformity, and postharvest performance; however, these gains have often been accompanied by reduced flavor quality, lower nutritional value, and narrowing of the genetic base. This review synthesizes available evidence on Lithuanian tomato germplasm and evaluates its relevance for future breeding strategies aimed at enhancing genetic gain under Northern European conditions. The review integrates published data on genetic diversity, molecular characterization, morphological traits, fruit quality parameters, and yield performance of Lithuanian cultivars and hybrids developed in Lithuania. SSR-based studies indicate moderate genetic diversity, with mean expected heterozygosity of approximately 0.51 and mean PIC values of 0.47 in cultivars and 0.45 in hybrids, while also confirming a relatively narrow breeding pool. Lithuanian cultivars display substantial variation in fruit morphology, dry matter, soluble solids, firmness, lycopene, ascorbic acid, and yield. Traditional cultivars such as ‘Svara’, ‘Milžinai’, ‘Slapukai’, and ‘Balčiai’ show valuable nutritional and technological traits, whereas hybrids such as ‘Auksiai H’, ‘Adas H’, and ‘Ainiai H’ demonstrate improved productivity and firmness. The available evidence suggests persistent yield–quality trade-offs, particularly between productivity, soluble solids content, antioxidant accumulation, and postharvest performance. Although Lithuanian germplasm does not represent exceptionally broad genetic diversity, it contains regionally adapted material with stabilized trait combinations useful for breeding resilience, nutritional quality, and adaptation to temperate environments. Future progress will require broadening the genetic base and integrating traditional breeding with marker-assisted selection, genomic selection, GWAS, genome editing, multi-omics, and pangenomic approaches. Overall, Lithuanian tomato germplasm represents a locally adapted regional resource for translating genetic diversity into genetic gain in modern tomato breeding. Full article

Colorectal cancer (CRC) is the third most common malignancy worldwide. Detailed characterization of cell surface proteins (CSPs) is essential for the identification of prognostic biomarkers and the development of novel therapeutic strategies. Cancer progression and epithelial cell polarity influence the expression levels and subcellular localization of these proteins. However, quantitative information on the distribution of CSPs between the apical and basolateral membranes remains limited, particularly in CRC cells. Here, we developed a rapid, high-throughput method based on the enrichment of biotinylated peptides and proteins from the apical and basolateral surfaces of polarized CRC epithelial cells (HT29 and HCT116), followed by LC-MS/MS analysis. This approach enables the simultaneous identification of the side-specific distribution of ~1200 CSPs. In addition, almost 500 potential N-glycosylation sites with the canonical consensus sequence of these proteins were identified, which may serve as targets for future site-specific glycosylation analyses. To evaluate the sensitivity of the method, we altered the surface proteome by generating TKS4-knockout cells and identified several surface markers whose expression levels differed significantly from those of wild-type cells. Overall, our findings provide new insights into the role of CSPs in CRC cells and gene-edited models, particularly in the context of TKS4-dependent epithelial-to-mesenchymal transition (EMT)-like phenotypes that model cancer metastasis. Full article

Melanoma is the most aggressive type of skin cancer, driven by early invasion, phenotypic plasticity, and frequent resistance to targeted therapies. Although genomic profiling informs treatment selection, genotype alone often fails to predict therapeutic response, underscoring the need for rapid and physiologically relevant functional testing platforms. Here, we present a three-dimensional melanoma–skin organoid (mSO) model that integrates primary skin cells with melanoma cell lines in a self-assembling, high-throughput format. The spherical mSOs recapitulate native human skin architecture, including a stratified epidermis and a dermal–hypodermal core, while supporting melanoma growth within an appropriate tissue microenvironment. In this niche, melanoma cells display epidermal spreading in radial growth-like patterns, outward invasion, and transcriptional shifts toward a pro-invasive phenotype. Using live confocal imaging coupled with a custom automated image analysis pipeline, we quantitatively measured tumor growth, migration beyond the organoid boundary, and interactions between melanoma cells and normal melanocytes. The mSOs also captured genotype-specific drug responses: BRAF-mutant melanoma cells were sensitive to BRAF and MEK inhibition, whereas NRAS-mutant, BRAF–wild-type cells were resistant to BRAF inhibition but remained responsive to MEK inhibition. Altogether, our mSO platform combines architectural and functional complexity with experimental scalability, providing a robust framework for modeling melanoma progression and evaluating targeted therapeutic responses within a relevant skin microenvironment. In the future, adaptation of this system to include patient-derived tumor cells could support personalized therapeutic decision-making in melanoma. Full article

High-throughput and non-destructive phenotyping approaches are increasingly needed to support precision agriculture and plant breeding. This study evaluates the use of unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning to estimate anthocyanin content in red lettuce genotypes under field conditions. High-resolution RGB and multispectral images were acquired using a low-cost UAV platform, and vegetation indices sensitive to pigment variation were extracted at the plot scale. Ridge regression, decision tree, and random forest models were trained using 80% of the dataset and validated with the remaining 20%. Random forest achieved the highest performance for anthocyanin estimation, with coefficients of determination reaching R² = 0.84 and lower prediction errors than linear approaches. Overall, the results demonstrate that UAV-based multispectral sensing integrated with machine learning provides a robust, scalable, and cost-effective solution for non-destructive pigment phenotyping, with direct applications in biofortification-oriented breeding and precision agriculture. Full article

Understanding how observed phenotype frequencies relate to underlying genetic variation remains a central challenge in population genetics. Traditional approaches are primarily statistical or based on machine learning and often lack a unified analytical framework that explicitly characterizes the space of genotype distributions compatible with observed phenotypes. Here, we present an algebraic framework based on linear algebra that analytically relates phenotypic frequencies to compatible genotypic and allelic frequencies. In cases of complete penetrance, the proposed relations between phenotypic and genotypic frequencies are analytical for any possible sample realization whereas in the case of partial penetrance the same relations hold for the average frequency values and become exact as the size of the sample tends to infinity. Using the Moore–Penrose pseudoinverse and a constrained inference strategy, we express all genotypic frequency distributions consistent with observed phenotype data and a given genotype–phenotype mapping. We further introduce a method: Constrained Observation and Null Space-based Inference (CONSPIN), for reconstructing genotype–phenotype relationships from samples that share identical phenotype distributions. Implemented in Python 3.8.18, this approach enables systematic analysis of allele frequencies, genotype–phenotype mappings, and dominance relations, providing a powerful tool for interpreting genetic datasets, including high-throughput sequencing data and complex trait analyses. By explicitly characterizing the constraints imposed by phenotype frequencies on genotype space, this framework offers a new perspective on genetic variation and has potential applications in population genetics, complex trait analysis, and data-driven modeling of biological systems. Full article

The high-precision instance segmentation of tree saplings is a fundamental prerequisite for the high-throughput phenotypic analysis of individual seedlings in intelligent tree breeding and precision silviculture. However, sapling segmentation remains challenging because of blurred boundaries, object adhesion, missed detections, and inaccurate mask delineation in field environments. To improve sapling segmentation performance and address these challenges, this study proposes a multimodal Mask R-CNN framework in which RGB imagery was paired with one multispectral-derived vegetation index at a time to construct separate RGB-VI input combinations, taking ginkgo saplings as a representative case. A dataset of 400 saplings was constructed using a high-throughput field phenotyping platform. The backbone network was extended with an independent vegetation index branch, and three fusion strategies (early, multi-step, and late fusion) were designed within a feature pyramid network to enable multi-scale multimodal feature integration. The results showed that all multimodal models outperformed unimodal baselines in terms of segmentation accuracy and recall. Among them, the multi-step fusion strategy achieved the best performance, while the RGB-EVI multi-step fusion model achieved the highest strict-matching precision (AP@75 = 87.7%) and recall (71.3%), with superior performance in dense sapling delineation and background suppression. These findings indicate that multimodal feature fusion can effectively improve sapling instance segmentation and provide methodological support for high-throughput plant phenotyping. Full article

Soil cadmium (Cd) pollution has emerged as one of the key environmental issues threatening the safety of agricultural products worldwide, yet clean and low-cost intervention strategies that reduce Cd accumulation in edible crops without disrupting agricultural production remain scarce. Grafting onto tolerant rootstocks represents an emerging clean agronomic technology that achieves in situ Cd risk reduction within a single growing season. However, the molecular mechanisms by which rootstocks regulate scion phenotypes remain poorly understood. MicroRNAs (miRNAs) act as critical long-distance signals in plants, yet their roles in rootstock-mediated growth promotion and Cd reduction remain largely unclear. In this study, we used Solanum torvum as rootstock and purple eggplant (Solanum melongena) as scion to investigate growth, fruit quality, Cd accumulation, and miRNA-mediated regulatory mechanisms. Grafting significantly increased plant height (by 18%), stem diameter (by 12%), and yield without obvious effects on fruit quality. Under Cd stress, the Cd content in grafted eggplant fruits was reduced by 76%, whereas leaf potassium (K), calcium (Ca), and magnesium (Mg) contents were elevated by 21%, 17%, and 10%, respectively. High-throughput sequencing and quantitative real-time polymerase chain reaction identified five key differentially expressed miRNAs, including miR164a and miR166b, four of which were related to Cd stress. Gene Ontology (GO) enrichment analyzes that their target genes were mainly involved in hormone signal transduction and ion transport. Further validation suggested that grafting improved growth and reduced Cd accumulation by regulating genes of the NAC, SPL, and HD-ZIP III families. These results suggested that suitable rootstocks can enhance crop productivity and reduce toxic metal accumulation in edible parts through miRNA-mediated regulation. Full article

Northern corn leaf blight (NCLB), caused by Setosphaeria turcica, is a major foliar disease of maize. To dissect the genetic basis of NCLB resistance in breeding-relevant germplasm, we evaluated NCLB severity in a panel of 500 commercial maize hybrids under natural disease pressure over a single growing season. High-throughput genotyping generated densely distributed SNP markers. The stratification of the panel into four major genetic subgroups, together with the relatively rapid LD decay (approximately 84.9 kb), indicated abundant genetic variation and favorable mapping resolution in the hybrid population. Phenotypic analysis revealed that most hybrids showed resistance or moderate resistance to NCLB. The broad-sense heritability of NCLB severity was estimated at 0.6588. Genome-wide association studies using the FarmCPU, BLINK, and MLMM models identified 17 consensus association signals, and four candidate genes were prioritized, including ZmCW1, CALS2, ZmCW2, and NCED8. A candidate gene-centered network identified 27 direct connections related to ZmCW1, CALS2, and ZmCW2. GO enrichment analysis showed that genes in these networks may regulate NCLB resistance through oxidative stress and redox-related processes. These results unravel the genetic architecture underlying NCLB resistance in commercial maize hybrids and nominate target loci for maize resistance breeding. Full article