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Spinal and bulbar muscular atrophy (SBMA) is a neuromuscular disorder caused by CAG trinucleotide expansion in the androgen receptor (AR) gene. To improve the quality of in vitro cell-based assays for the evaluation of potential drug candidates for SBMA, we developed a morphology-based phenotypic analysis for a muscle cell model of SBMA that involves multiparametric morphological profiling to quantitatively assess the therapeutic effects of drugs on muscle cell phenotype. The analysis was validated using dihydrotestosterone and pioglitazone, which have been shown to exacerbate and ameliorate the pathophysiology of SBMA, respectively. Gene expression analysis revealed activation of the JNK pathway in the SBMA cells compared to the control cells. Phenotypic analysis revealed the effect of naratriptan, a JNK inhibitor, on the phenotypic changes of SBMA cells, and the results were confirmed by LDH assays. We then trained a predictive machine learning model to classify the drug responses, and it successfully discriminated between pioglitazone-type and naratriptan-type morphological profiles based on their morphological characteristics. Our morphology-based phenotypic analysis provides a noninvasive and efficient screening method to accelerate the development of therapeutics for SBMA.

Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination- and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists’ scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.

The measure of the quality of a systems biology model is how well it can reproduce and predict the behaviors of a biological system such as a microbial cell. In recent years, these models have been built up in layers, and each layer has been growing in sophistication and accuracy in parallel with a global data set to challenge and validate the models in predicting the content or activities of genes (genomics), proteins (proteomics), metabolites (metabolomics), and ultimately cell phenotypes (phenomics). This review focuses on the latter, the phenotypes of microbial cells. The development of Phenotype MicroArrays, which attempt to give a global view of cellular phenotypes, is described. In addition to their use in fleshing out and validating systems biology models, there are many other uses of this global phenotyping technology in basic and applied microbiology research, which are also described.

Presence-absence variations (PAVs) significantly influence phenotypic diversity across and within species by modulating functional modules associated with stress responsiveness, adaptation, and developmental processes. This modulation ultimately contributes to genetic diversity at both inter- and intra-species levels. However, existing tools available for detecting PAVs in assembled genomes possess limitations that hinder comprehensive analyses. These limitations include the absence of scalable workflows for multi-genome analysis, the imposition of stringent parameters regarding coverage, PAV length, and sequence identity, as well as the frequent necessity for manual integration. To address these challenges, we developed XtractPAV, an end-to-end pipeline that automates the extraction, annotation, and interactive visualization of PAVs across assembled genomes. XtractPAV was evaluated using assembled genomes from both eukaryotic and prokaryotic organisms, including Pyrus communis , Arabidopsis thaliana , Mus musculus , and Salmonella enterica , to assess its capability to detect presence-absence variations across diverse species. The performance of XtractPAV was benchmarked against other established pipelines, demonstrating an optimized workflow that allows comprehensive extraction and annotation of PAVs. To further validate PAVs, representative XtractPAV-identified PAVs in A. thaliana and P. communis were independently confirmed using WGS paired-end read mapping, demonstrating consistent query-specific insertion and deletion signatures at predicted loci in the reference genome. Notably, the pipeline successfully identified PAVs from the reference set and also revealed novel PAV regions overlapping with genes. Furthermore, the automated report generation feature of XtractPAV produces publication-ready summaries of PAV distributions alongside diverse interactive figures. The XtractPAV webpage is available at its project page https://sherazahmadd.github.io/XtractPAV/ and its GitHub repository https://github.com/SherazAhmadd/XtractPAV .

Accurate identification of crop varieties is essential for plant breeding programs and the protection of Plant Breeders’ Rights (PBR), yet traditional morphological assessment methods remain subjective and time-consuming, particularly for species with complex morphological diversity such as Rubus crataegifolius . This study demonstrates that geometric morphometric techniques provide an objective, quantitative complementary approach for distinguishing Korean raspberry varieties, addressing the limitations of subjective visual assessment while remaining compatible with molecular marker analysis. We employed three complementary morphometric approaches: landmark-based analysis (19 anatomical points capturing vein junctions and leaf margins), Elliptic Fourier Descriptors (EFD) for outline contours, and a hybrid landmark-EFD dataset. Using these approaches, we analyzed primocane and floricane leaves from 10 accessions of R. crataegifolius comprising 8 varieties and 2 landraces and performed principal component analysis (PCA) and linear discriminant analysis (LDA) with leave-one-out cross-validation. As a result, among the three morphometric approaches applied to primocane and floricane leaves, landmark-based analysis of primocane leaves achieved the highest classification accuracy (87.2%), with an overall average accuracy of 72.0% (range: 51.1–87.2%) across all six analytical combinations. LDA visualization suggested the presence of four major morphological groups, and primocane leaves exhibited higher discriminatory power than floricane leaves, which may reflect greater morphological uniformity under normal growing conditions. Landmark analysis effectively detected subtle differences in leaf venation and leaflet architecture that are difficult to distinguish visually, highlighting the capacity of morphometrics for objective and multidimensional morphological analysis. These findings suggest that morphometric analysis provides a practical and cost-effective preliminary screening tool, complementary to molecular approaches, for supporting Distinctness, Uniformity, and Stability (DUS) examination in raspberry variety evaluation. This approach shows strong potential for offering a scalable solution for variety registration and protection and supporting sustainable horticultural development.

Saccharomyces cerevisiae FOSU-QQT (SC.QQT), isolated from pineapple pomace wine, exhibits favorable aroma-producing capabilities. In this study, we performed integrated genomic and phenotypic analyses to comprehensively evaluate its safety profile, probiotic potential, and aroma-producing characteristics. Whole-genome sequencing (WGS) assembly predicted a genome size of 30,256,254 bp, encompassing 12,899 genes with a total coding length of 22,062,659 bp and an average GC content of 37.30%. Preliminary safety assessments, including hemolysis tests, antibiotic susceptibility profiling, and antibacterial activity assays, were complemented by in silico screening for antibiotic resistance-associated genes. Functional tolerance assays, specifically resistance to simulated gastrointestinal fluid, acid stress, and bile salts, demonstrated that SC.QQT exhibited robust survival under physiologically relevant gastrointestinal conditions. Collectively, these findings support its potential as a promising probiotic candidate with notable resilience, although further in vivo validation is required to confirm its application value. Additionally, gas chromatography–mass spectrometry (GC–MS) analysis of volatile compounds in pineapple pomace wine indicated that the presence of aroma-related genes in SC.QQT may enhance overall flavor complexity and intensify fruity aromatic notes during fermentation, underscoring its distinctive utility in fruit wine bioprocessing.

Background Genomes can be sequenced with relative ease, but ascribing gene function remains a major challenge. Genetically tractable model systems are crucial to meet this challenge. One powerful model is the social amoeba Dictyostelium discoideum , a eukaryotic microbe widely used to study diverse questions in the cell, developmental and evolutionary biology. Results We describe REMI-seq, an adaptation of Tn-seq, which allows high throughput, en masse , and quantitative identification of the genomic site of insertion of a drug resistance marker after restriction enzyme-mediated integration. We use REMI-seq to develop tools which greatly enhance the efficiency with which the sequence, transcriptome or proteome variation can be linked to phenotype in D. discoideum . These comprise (1) a near genome-wide resource of individual mutants and (2) a defined pool of ‘barcoded’ mutants to allow large-scale parallel phenotypic analyses. These resources are freely available and easily accessible through the REMI-seq website that also provides comprehensive guidance and pipelines for data analysis. We demonstrate that integrating these resources allows novel regulators of cell migration, phagocytosis and macropinocytosis to be rapidly identified. Conclusions We present methods and resources, generated using REMI-seq, for high throughput gene function analysis in a key model system.

Accurate identification of maize kernel morphology is crucial for breeding and quality improvement. Traditional manual methods are limited in dealing with complex structures and cannot fully capture kernel characteristics from a phenome perspective. To address this, our study aims to develop a high-throughput 3D phenotypic analysis method for maize kernels using Micro-CT-based point cloud data, thereby enhancing both accuracy and efficiency. We introduced new phenotypic indicators and developed a kernel phenome interaction network to better characterize the diversity and variability of kernel traits. Using a natural population of maize, high-resolution 2D slice data from Micro-CT scans were converted into 3D point cloud models for detailed analysis. This process led to the proposal of five new indicators, such as the endosperm density uniformity index (ENDUI) and endosperm integrity index (ENII), and the construction of their corresponding phenome interaction network. The study identified 27 3D morphological feature parameters, significantly improving the accuracy of kernel phenotypic analysis. These new indicators enable a more comprehensive evaluation of trait differences between subgroups. Results show that ENDUI and ENII are central to the phenome interaction networks, revealing synergistic relationships and environmental adaptation strategies during kernel growth. Additionally, it was found that length traits significantly impact the volumes of the embryo and endosperm, with linear regression coefficients of 0.599 and 0.502, respectively. This study not only advances maize kernel morphology research but also offers a novel method for phenotypic analysis. By enriching the phenotypic diversity of maize kernels, it contributes to breeding programs and grain processing improvements, ultimately enhancing the quality, and utilization value of maize kernels.

This study analyzed clinical data and hematological parameters from 3072 patients undergoing thalassemia screening at Guangdong Provincial People’s Hospital, investigating the correlation between non-thalassemic hematologic diseases and elevated fetal hemoglobin (HbF) in Guangdong. Results revealed a significant correlation and identified non-thalassemic hematologic diseases as an independent risk factor for HbF elevation (OR = 13.36, P  < 0.001), indicating a substantially higher risk than that of non-hematologic disease patients. Among patients with elevated HbF, significant parameter differences emerged: non-thalassemia hematologic disease patients differed from mild β-thalassemia patients with elevated HbF in mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), red cell distribution width–coefficient of variation (RDW-CV) ( P  < 0.05); Significant differences in MCV, MCH, RDW-CV, creatinine (CREA), ferritin (Ferr), total bilirubin (TBIL) and direct bilirubin (DBIL) were found between patients with non-thalassemic hematologic diseases and moderate-to-severe β-thalassemia with elevated HbF levels ( P  < 0.05). Furthermore, significant differences existed between mild and moderate-to-severe β-thalassemia patients with elevated HbF in MCV, MCH, Ferr, TBIL and DBIL ( P  < 0.05). This research delineates the epidemiological characteristics of hematologic disease-associated HbF elevation in Guangdong, addressing a regional knowledge gap and providing a foundation for clinical risk stratification. Analysis of these differential hematologic indicators deepens understanding of non-thalassemia disease progression, offers clinicians targeted diagnostic and therapeutic strategies, facilitates early detection and intervention, and contributes to broader hematologic research and discipline development.

Circulating melanoma cells (CMCs) are responsible for the hematogenous spread of melanoma and, ultimately, metastasis. However, their study has been limited by the low abundance in patient blood and the heterogeneous expression of surface markers. The FDA-approved CellSearch platform enriches CD146-positive CMCs, whose number correlates with progression-free survival and overall survival. However, a single marker may not be sufficient to identify them all. The Parsortix system allows enrichment of CMCs based on their size and deformability, keeping them viable and suitable for downstream molecular analyses. In this study, we tested the strengths, weaknesses and potential convergences of both platforms to integrate the counting of CMCs with a protocol for their genetic analysis. Samples run on Parsortix were labeled with a customized melanoma antibody cocktail, which efficiently labeled and distinguished CMCs from endothelial cells/leukocytes. The capture rate of CellSearch and Parsortix was comparable for cell lines, but Parsortix had a higher capture rate in real-life samples. Moreover, double enrichment with both CellSearch and Parsortix succeeded in removing most of the leukocyte contamination, resulting in an almost pure CMC sample suitable for genetic analysis. In this regard, a proof-of-concept analysis of CMCs from a paradigmatic case of a metastatic uveal melanoma patient led to the identification of multiple genetic alterations. In particular, the GNAQ p.Q209L was identified as homozygous, while a deletion in BAP1 exon 9 was found hemizygous. Moreover, an isochromosome 8 and a homozygous deletion of the CDKN2A gene were detected. In conclusion, we have optimized an approach to successfully enrich and retrieve viable CMCs from metastatic melanoma patients. Moreover, this study provides proof-of-principle for the feasibility of a marker-agnostic CMC enrichment followed by CMC phenotypic identification and genetic analysis.Kindly check and confirm the processed contributed equally is correctly identify We confirm