Proteome Sci. 2025 May 26;23(1):5. doi: 10.1186/s12953-025-00241-8.

ABSTRACT

BACKGROUND: Metabolomics, a burgeoning field within systems biology, focuses on the comprehensive study of small molecules present in biological systems. Mass spectrometry (MS) has emerged as a powerful tool for metabolomic analysis due to its high sensitivity, resolution, and ability to characterize a wide range of metabolites thus offering deep insights into the metabolic profiles of living systems.

AIM OF REVIEW: This review provides an overview of the methodologies, workflows, strategies, data analysis techniques, and applications associated with mass spectrometry-based metabolomics.

KEY SCIENTIFIC CONCEPTS OF REVIEW: We discuss workflows, key strategies, experimental procedures, data analysis techniques, and diverse applications of metabolomics in various research domains. Nuances of sample preparation, metabolite extraction, separation using chromatographic techniques, mass spectrometry analysis, and data processing are elaborated. Moreover, standards, quality controls, metabolite annotation, software for statistical and pathway analysis are also covered. In conclusion, this review aims to facilitate the understanding and adoption of mass spectrometry-based metabolomics by newcomers and researchers alike by providing a foundational understanding and insights into the current state and future directions of this dynamic field.

PMID:40420110 | DOI:10.1186/s12953-025-00241-8

BMC Genomics. 2025 May 26;26(1):527. doi: 10.1186/s12864-025-11703-w.

ABSTRACT

BACKGROUND: House dust mites (HDMs) such as Dermatophagoides pteronyssinus are major allergy elicitors worldwide, yet their gene expression across developmental stages remains underexplored. Herein, we report a comprehensive RNAseq analysis of larvae, nymphs, and adult males and females, mapped to a recently published high-quality genome with extended functional annotations.

RESULTS: Analysis of differentially expressed genes (DEG) revealed that female-biased expression was the most prevalent profile (16% of genes), while males exhibited the highest fold-change differences. DEG data, combined with network clustering and functional enrichment analysis, highlighted distinct genes and biological processes for each stage and sex: females showed upregulation of genes related to cell division and oogenesis, with vitellogenins among the most abundant transcripts; males exhibited increased expression of genes encoding putative seminal fluid proteins (e.g. endopeptidases, serpins, antimicrobial peptides), and those involved in reproductive regulation (e.g. testis-specific serine kinases); while juveniles displayed enhanced expression of genes related to energy metabolism and growth. Further analysis of endocrine pathways revealed non-canonic mechanisms compared to insect models, particularly in ecdysteroid and sesquiterpenoid biosynthesis and regulation. Expression patterns in genes involved in cuticle formation were also identified, reflecting their role in developmental transitions and sexual differentiation. Allergen and allergen-related gene expression showed an overall increase in feeding juveniles, as well as sex-biased expression, with Der p 27 upregulated in females. These findings provide insight into the physiological roles of allergens in digestion, immunity, and muscle formation, among other functions. Additionally, seven new horizontally transferred genes, including a DNA-repair photolyase linked to females, and novel multigene families (e.g. 119 male-specific beta-propeller proteins, 70 hypothetical cuticular proteins, 23 tetraspanin-like proteins, 5 female-associated putative odorant-binding proteins) were identified.

CONCLUSIONS: This study provides the first genome-wide transcriptomic analysis of a HDM across life stages and sexes, expanding our understanding of the molecular mechanisms underlying mite development, sexual reproduction, and allergen expression. The generated data, fully available via supplementary spreadsheet and the ORCAE online platform, provide a valuable foundation for future allergy research and the development of new mite control strategies.

PMID:40419976 | DOI:10.1186/s12864-025-11703-w

BMC Bioinformatics. 2025 May 26;26(1):137. doi: 10.1186/s12859-025-06162-9.

ABSTRACT

Understanding the molecular mechanisms underlying diseases is crucial for more precise, personalized medicine. Pathway-level differential co-expression analysis, a powerful approach for transcriptomics, identifies condition-specific changes in gene-gene interaction networks, offering targeted insights. However, a key challenge is the lack of robust methods and benchmarks specifically for evaluating algorithms' ability to identify disrupted gene-gene associations across conditions. We introduce DRaCOoN (Differential Regulatory and Co-expression Networks), a Python package and web tool for pathway-level differential co-expression analysis. DRaCOoN uniquely integrates multiple association and differential metrics, with a novel, computationally efficient permutation test for significance assessment. Crucially, DRaCOoN also provides a benchmarking framework for comprehensive method evaluation. Extensive benchmarking on simulated data and three real-world datasets (bone healing, colorectal cancer, and head/neck carcinoma) showed that DRaCOoN, particularly with an entropy-based association measure and the s differential metric, consistently outperforms eight other methods. It remains highly accurate in balanced datasets, robust to varying gene perturbation levels, and identifies biologically relevant regulatory changes. Furthermore, DRaCOoN serves as both a powerful tool and a benchmarking framework for elucidating disease mechanisms from transcriptomics data, advancing precision medicine by uncovering critical gene regulatory alterations.

PMID:40419963 | DOI:10.1186/s12859-025-06162-9

J Headache Pain. 2025 May 26;26(1):130. doi: 10.1186/s10194-025-02058-4.

ABSTRACT

BACKGROUND: Migraine, characterized by recurrent episodes of severe headache, remains mechanistically enigmatic. While traditional theories emphasize trigeminovascular activation, the role of meningeal stromal-immune crosstalk in disease chronicity is poorly understood.

METHODS: A migraine-related chronic hypersensitivity model was utilized via intermittent intraperitoneal nitroglycerin (NTG, 10 mg/kg, every other day for 9 days) and peripheral mechanical hypersensitivity was assessed using von Frey filaments. Single-cell RNA sequencing (scRNA-seq) was performed on dura tissues to construct a cellular atlas of NTG-induced remodeling. These data were then integrated with migraine genome-wide association study (GWAS) risk genes, cell-cell interaction networks, and transcriptional regulation analysis to dissect NTG-driven meningeal remodeling.

RESULTS: The NTG-induced migraine-related chronic hypersensitivity model demonstrated sustained mechanical allodynia, as evidenced by significantly decreased paw withdrawal thresholds (p < 0.0001). Single-cell profiling of the dura mater revealed a 2.4-fold expansion of a pro-inflammatory fibroblast subpopulation (Fibro_c5: 1.9% in Vehicle vs. 4.6% in NTG group), which exhibited marked activation of TNF-α/NF-κB signaling pathways (normalized enrichment score [NES] = 1.83). Concomitantly, we observed an 82% increase in meningeal monocytes (5.7-10.4%) that showed preferential interaction with Fibro_c5 fibroblasts through Angptl1-mediated stromal-immune crosstalk (log2 fold change = 1.41). Regulatory network analysis identified Mafk as the upstream transcriptional regulator orchestrating Angptl1 expression in this pathological communication axis.

CONCLUSION: Our study reveals that NTG reprograms meningeal fibroblasts to expand a pro-inflammatory fibroblast subtype, which drives migraine-related chronic hypersensitivity through TNF-α/NF-κB signaling and Angptl1-mediated monocyte crosstalk. The identified Mafk-Angptl1 axis presents a potential therapeutic target, though human validation remains essential.

PMID:40419944 | DOI:10.1186/s10194-025-02058-4

Sci Rep. 2025 May 26;15(1):18388. doi: 10.1038/s41598-025-94530-z.

ABSTRACT

Breast cancer recurrence presents a significant global health challenge, and accurate prediction is crucial for effective patient management and improved outcomes. Reliable predictive tools can help tailor therapeutic approaches, provide personalized care, and enhance patient outcomes. In light of the current lack of such tools in clinical practice, our study aimed to develop predictive models for breast cancer recurrence within three years of treatment. We analyzed data from 408 breast cancer patients at the King Fahd Specialist Hospital in Dammam, Saudi Arabia and divided them into training (n = 285) and test (n = 123) cohorts. Using multivariable penalized logistic regression combined with a nested cross-validation framework and multivariate Cox regression analysis to determine time-dependent risk factors for breast cancer recurrence, we developed prognostic models that incorporated age, stage, tumor size, and treatment type. We evaluated the performance of the models using both the area under the receiver operating characteristic curve for multivariate logistic regression and C-index for multivariate Cox regression. The multivariate logistic regression model achieved an area under the curve (AUC) of 76% (95% confidence interval [CI]: 72-81%) for the training set and 76% (95% CI: 66-87%) for the test set. The Cox regression analysis yielded a C-index of 0.81 for the training set (95% CI: 0.73-0.84) and 0.84 for the test set (95% CI: 0.76-0.89). Chemotherapy was found to decrease recurrence odds by 86% (adjusted odds ratio [AOR]: 0.143, 95% CI: 0.089-0.218, p < 0.0001), and surgery resulted in a 99% reduction in recurrence probability (AOR: 0.009, 95% CI: 0.005-0.014, p < 0.0001). Increased tumor size improved the recurrence odds by 48.5% (AOR: 1.485, 95% CI: 1.128-1.918, p = 0.0043), while age did not significantly predict recurrence (AOR: 0.841, 95% CI: 0.657-1.061, p = 0.1398). The newly developed, routinely collected baseline clinical features to predict breast cancer recurrence may be a valuable tool for clinical decision-making and is freely available online. The tool can be accessed through the following link: https://iv3p9h-nurudeen-adegoke.shinyapps.io/breast_cancer .

PMID:40419677 | DOI:10.1038/s41598-025-94530-z

Nat Methods. 2025 May 26. doi: 10.1038/s41592-025-02700-8. Online ahead of print.

ABSTRACT

Enhancers and transcription factors (TFs) are crucial in regulating cellular processes. Current multiomic technologies to study these elements in gene regulatory mechanisms lack multiplexing capability and scalability. Here we present single-cell ultra-high-throughput multiplexed sequencing (SUM-seq) for co-assaying chromatin accessibility and gene expression in single nuclei. SUM-seq enables profiling hundreds of samples at the million cell scale and outperforms current high-throughput single-cell methods. We demonstrate the capability of SUM-seq to (1) resolve temporal gene regulation of macrophage M1 and M2 polarization to bridge TF regulatory networks and immune disease genetic variants, (2) define the regulatory landscape of primary T helper cell subsets and (3) dissect the effect of perturbing lineage TFs via arrayed CRISPR screens in spontaneously differentiating human induced pluripotent stem cells. SUM-seq offers a cost-effective, scalable solution for ultra-high-throughput single-cell multiomic sequencing, accelerating the unraveling of complex gene regulatory networks in cell differentiation, responses to perturbations and disease studies.

PMID:40419657 | DOI:10.1038/s41592-025-02700-8

Nat Methods. 2025 May 26. doi: 10.1038/s41592-025-02657-8. Online ahead of print.

NO ABSTRACT

PMID:40419656 | DOI:10.1038/s41592-025-02657-8

Nat Commun. 2025 May 26;16(1):4878. doi: 10.1038/s41467-025-60004-z.

ABSTRACT

Bacterial colonies growing on solid surfaces can exhibit robust expansion kinetics, with constant radial growth and saturating vertical expansion, suggesting a common developmental program. Here, we study this process for Escherichia coli cells using a combination of modeling and experiments. We show that linear radial colony expansion is set by the verticalization of interior cells due to mechanical constraints rather than radial nutrient gradients as commonly assumed. In contrast, vertical expansion slows down from an initial linear regime even while radial expansion continues linearly. This vertical slowdown is due to limitation of cell growth caused by vertical nutrient gradients, exacerbated by concurrent oxygen depletion. Starvation in the colony interior results in a distinct death zone which sets in as vertical expansion slows down, with the death zone increasing in size along with the expanding colony. Thus, our study reveals complex heterogeneity within simple monoclonal bacterial colonies, especially along the vertical dimension. The intricate dynamics of such emergent behavior can be understood quantitatively from an interplay of mechanical constraints and nutrient gradients arising from obligatory metabolic processes.

PMID:40419492 | DOI:10.1038/s41467-025-60004-z

Toxicol In Vitro. 2025 May 24:106086. doi: 10.1016/j.tiv.2025.106086. Online ahead of print.

ABSTRACT

Irradiation-induced immune impairment has been linked to human immune diseases, such as myelodysplastic syndromes (MDS) and leukemia. Global molecular responses to genome instability in immune cells can be identified by using transcriptomics. However, it is hard to link the molecular mechanism to the disease outcomes in the previous mechanistic studies. Here, transcriptomic dose-responses in human CD4+ T lymphocytes exposed to ultraviolet and heavy ion radiation were revealed by identification of the gene expression patterns of differential expression genes (DEGs) and calculating the point of departure (POD) of each DEG and molecular pathway, which provided an opportunity for quantitively illustrating the biological process of irradiation-induced immune impairments. Two potential adverse outcome pathways (AOPs) to irradiation-related leukemia were identified by mapping the molecular pathways into the biological event cascades, which provided phenotypic anchoring for the toxicological mechanisms. In addition, this study also revealed that NOP14/ NOP14-AS1 could be potential biomarkers of irradiation-induced immune impairment. Our works strengthen the use of AOP network in the next-generation risk assessment of irradiation-related diseases.

PMID:40419229 | DOI:10.1016/j.tiv.2025.106086

Biosystems. 2025 May 24:105501. doi: 10.1016/j.biosystems.2025.105501. Online ahead of print.

ABSTRACT

We explore here the fundamental and striking paradigmatic shifts between 'Domain of Laws' and 'Domain of No Laws', where the former is an apt encapsulation of our remarkably successful but orthodox science world view (including classical physics and quantum mechanics) with well- defined and stable configuration spaces having deterministic or stochastic evolution. The latter is a radically new Domain of No Law with evolving configuration spaces, non-deducible information creation, genuine novelties, and an unprestatable Adjacent Possible. We explore the features of these two distinct domains asking what can be defined with respect to work, energy, entropy, and agency. We offer a reconstruction of quantum mechanics to reframe traditional assumptions and address lingering questions concerning the nature of living, complex adaptive systems. We propose that a genuine responsible free will and a central role of agency are essential features of an evolving Biosphere. Here we extend this theme to call for a radically new and comprehensive view of science itself.

PMID:40419105 | DOI:10.1016/j.biosystems.2025.105501

ALTEX. 2025 May 26. doi: 10.14573/altex.2501291. Online ahead of print.

ABSTRACT

Chemical risk assessment is evolving from traditional deterministic approaches to embrace probabilistic methodologies, where risk of hazard manifestation is understood as a more or less probable event depending on exposure, individual factors, and stochastic processes. This is driven by advancements in human stem cells, complex tissue engineering, high-performance computing, and cheminformatics, and is more recently facilitated by large-scale artificial intelligence models. These innovations enable a more nuanced understanding of chemical hazards, capturing the complexity of biological responses and variability within populations. However, each technology comes with its own uncertainties impacting on the estimation of hazard probabilities. This shift addresses the limitations of point estimates and thresholds that oversimplify hazard assessment, allowing for the integration of kinetic variability and uncertainty metrics into risk models. By leveraging modern technologies and expansive toxicological data, probabilistic approaches offer a comprehensive evaluation of chemical safety. This paper summarizes a workshop held in 2023 and discusses the technological and data-driven enablers, and the challenges faced in their implementation, with particular focus on perturbation of biology as the basis of hazard estimates. The future of toxicological risk assessment lies in the successful integration of these probabilistic models, promising more accurate and holistic hazard evaluations.

PMID:40418784 | DOI:10.14573/altex.2501291

J Am Soc Nephrol. 2025 May 26. doi: 10.1681/ASN.0000000706. Online ahead of print.

ABSTRACT

BACKGROUND: Alterations in pre-mRNA splicing are crucial to the pathophysiology of various diseases. However, the effects of alternative splicing of mRNA on podocytes in hypertensive nephropathy are still unknown. The Sys_CARE project aimed to identify alternative splicing events involved in the development and progression of glomerular hypertension.

METHODS: Murine podocytes were exposed to mechanical stretch, after which proteins and mRNA were analyzed by proteomics, RNA sequencing and several bioinformatic alternative splicing tools.

RESULTS: Using transcriptomic and proteomic analysis, we identified significant changes in gene expression and protein abundance due to mechanical stretch. RNA-Seq identified over 3,000 alternative spliced genes after mechanical stretch, including all types of alternative splicing events. Among these, 17 genes exhibited an alternative splicing event across four different splicing analysis tools. From this group, we focused on Myl6, a component of the myosin protein complex, and Shroom3, an actin-binding protein essential for podocyte function. We identified two Shroom3 isoforms with significant expression changes under mechanical stretch, which was validated by qRT-PCR and in situ hybridization. Additionally, we observed an expression switch of two Myl6 isoforms after mechanical stretch, accompanied by an alteration in the C-terminal amino acid sequence.

CONCLUSIONS: A comprehensive RNA-Seq analysis of mechanically stretched podocytes identified novel potential podocyte-specific biomarkers and highlighted significant alternative splicing events, notably in the mRNA of Shroom3 and Myl6.

PMID:40418580 | DOI:10.1681/ASN.0000000706

Am J Physiol Renal Physiol. 2025 May 26. doi: 10.1152/ajprenal.00362.2024. Online ahead of print.

ABSTRACT

Diabetic kidney disease is a complication in one out of three patients with diabetes. Aberrant glucose metabolism in diabetes leads to structural and functional damage in glomerular tissue and a systemic inflammatory immune response. Complex cellular signaling is at the core of metabolic and functional derangement. Unfortunately, the mechanism underlying the role of inflammation in glomerular endothelial cell dysfunction during diabetic kidney disease is not fully understood. Mathematical models in systems biology allow the integration of experimental evidence and cellular signaling networks to understand mechanisms involved in disease progression. This study developed a logic-based ordinary differential equations model to study inflammatory crosstalk between macrophages and glomerular endothelial cells during diabetic kidney disease progression using a protein signaling network stimulated with glucose and lipopolysaccharide. This modeling approach reduced the biological parameters needed to study signaling networks. The model was fitted to and validated against available biochemical data from in vitro experiments. The model identified mechanisms for dysregulated signaling in macrophages and glomerular endothelial cells during diabetic kidney disease. In addition, the influence of signaling interactions on glomerular endothelial cell morphology through selective knockdown and downregulation was investigated. Simulation results showed that partial knockdown of VEGF receptor 1, PLC-γ, adherens junction proteins, and calcium partially improved intercellular junction integrity between glomerular endothelial cells. These findings contribute to understanding signaling and molecular perturbations that affect the glomerular endothelial cells in the early stage of diabetic kidney disease.

PMID:40418541 | DOI:10.1152/ajprenal.00362.2024

Methods Mol Biol. 2025;2923:163-180. doi: 10.1007/978-1-0716-4522-2_10.

ABSTRACT

High-throughput RNA sequencing (RNA-seq) is commonly used to quantify gene expression transcriptome-wide. While usually paired with polyadenylate (poly(A)) selection to enrich for messenger RNA (mRNA) to the exclusion of highly abundant ribosomal RNA (rRNA) in the cell, this strategy will under-quantify mRNA with short or absent poly(A) tails and can conflate changes in poly(A) tail length with changes in RNA level. This is notably an issue during early development, when cytoplasmic polyadenylation of maternal mRNA over time can be mistaken for genome activation in poly(A) + RNA-seq time courses. Here, we present a method to perform total RNA-seq using a streamlined rRNA depletion strategy customizable to any taxon. Antisense DNA oligos are designed with the aid of our Oligo-ASST web tool to sparsely tile the length of the rRNA, which are used with thermostable RNaseH to digest rRNA from a total RNA sample. After column cleanup, the mRNA-enriched sample is ready for sequencing library construction.

PMID:40418449 | DOI:10.1007/978-1-0716-4522-2_10

Methods Mol Biol. 2025;2923:89-117. doi: 10.1007/978-1-0716-4522-2_7.

ABSTRACT

The transcriptional machinery of a cell is often not homogenously distributed but rather forms clusters in the nucleus. These clusters are important for gene expression, but how they form and function is often not clear. The zebrafish embryo provides an excellent system to study these clusters of transcriptional machinery, because embryos are transparent and develop rapidly, making it easy to track proteins as they cluster and perform their function. Here, we provide a protocol for how to image nuclear clusters in living zebrafish embryos. The protocol includes information on the selection and encoding of proteins and fluorophores, embryo-embedding for live-cell microscopy, the use of a spinning disk microscope, staging of embryos post image acquisition, and image analysis. While the protocol is written in the context of our work with early zebrafish embryos, many of the tools will be useful in other contexts.

PMID:40418446 | DOI:10.1007/978-1-0716-4522-2_7

Adv Clin Exp Med. 2025 May 26. doi: 10.17219/acem/205024. Online ahead of print.

ABSTRACT

Cancer progression and therapeutic resistance are propelled by the remarkable plasticity of signaling networks, which dynamically rewire under selective pressures to maintain proliferation, enable immune evasion and promote metastasis. Despite advances in precision oncology, the dynamic crosstalk between tumor cells, non-coding genomes and the microenvironment continues to undermine treatment efficacy. This call for submissions, Revolutionizing Cancer Treatment: Navigating the Intricate Landscape of Cellular Signaling Networks, seeks cutting-edge research that dissects these adaptive mechanisms through innovative technologies - from single-cell multi-omics and spatial transcriptomics to AI-powered network modeling. We welcome studies leveraging physiomimetic models (e.g., organoids, 3D-bioprinted ecosystems) to decode tumor heterogeneity, as well as translational work targeting emergent vulnerabilities at the intersection of epigenetics, metabolic reprogramming and stromal interactions. By integrating systems biology with computational and experimental approaches, this collection aims to catalyze the design of adaptive therapies that outmaneuver cancer's evolutionary resilience.

PMID:40418208 | DOI:10.17219/acem/205024

Ecol Evol. 2025 May 23;15(5):e71363. doi: 10.1002/ece3.71363. eCollection 2025 May.

ABSTRACT

The form of arbitrary sexual signals may be driven by the need to be detectable against the background or, alternatively, by selection for efficient processing by the nervous system. This latter alternative is a prediction of the sensory drive hypothesis extended to include efficient coding as a driver of the form of sexual signals. This hypothesis posits that animal visual systems are adapted to process the visual statistics of natural scenes, and that easily processed stimuli induce a sensation of pleasure in the viewer. In support of this, natural scene statistics have been found to be preferred not only by humans, but by the peacock spider Maratus spicatus. Here we test if male peacock spiders of the highly sexually dimorphic Maratus genus generally (a) evolve colour patterns with image statistics that contrast with the natural background or (b) exploit a potential processing bias by evolving colour patterns with visual statistics similar to those of natural scenes. We analyse and compare multispectral images of male and female spiders of 21 Maratus species and of natural scenes similar to the spiders' habitat. We find that the image statistics of male patterns diverge from those of natural scenes, whereas the statistics of female patterns do not. Our results support the idea that colour patterns evolve contrasting image statistics to increase conspicuousness and matching image statistics to be camouflaged. Any processing bias for natural scene image statistics in Maratus thus appears to play little role in the evolution of their sexual signals.

PMID:40416758 | PMC:PMC12101072 | DOI:10.1002/ece3.71363

Front Plant Sci. 2025 May 6;16:1543956. doi: 10.3389/fpls.2025.1543956. eCollection 2025.

ABSTRACT

Plant genetic evaluation often faces challenges due to complex genetic structures. Ryegrass (Lolium), a valuable species for pasture-based agriculture, exhibits heterogeneous genetic diversities among base breeding populations. Partially overlapping datasets from incompatible studies and commercial restrictions further impede outcome integration across studies, complicating the evaluation of key agricultural traits such as dry matter yield (DMY). To address these challenges: (1) we implemented a population genotyping approach to capture the genetic diversity in ryegrass base cultivars; (2) we introduced a machine learning-based strategy to integrate genetic distance matrices (GDMs) from incompatible genotyping approaches, including alignments using multidimensional scaling (MDS) and Procrustes transformation, as well as a novel evaluation strategy (BESMI) for the imputation of structural missing data. Endophytes complicate genetic evaluation by introducing additional variation in phenotypic expression. (3) We modelled the impacts of nine commercial endophytes on ryegrass DMY, enabling a more balanced estimation of untested cultivar-endophyte combinations. (4) Phylogenetic analysis provided a pseudo-pedigree relationship of the 113 ryegrass populations and revealed its associations with DMY variations. Overall, this research offers practical insights for integrating partially overlapping GDMs with structural missing data patterns and facilitates the identification of high-performing ryegrass clades. The methodological advancements-including population sequencing, MDS alignment via Procrustes transformation, and BESMI-extend beyond ryegrass applications.

PMID:40416085 | PMC:PMC12100933 | DOI:10.3389/fpls.2025.1543956

Ann Entomol Soc Am. 2025 Feb 22;118(3):229-236. doi: 10.1093/aesa/saaf009. eCollection 2025 May.

ABSTRACT

Targeting the distribution of germ-cell markers is a widely used strategy for investigating germline development in animals. Among these markers, the vasa (vas) orthologues, which encode ATP-dependent RNA helicases, are highly conserved. Previous studies have examined asexual (parthenogenetic) and viviparous embryos of the pea aphid Acyrthosiphon pisum using a cross-reacting Vas antibody. This study utilized a specific antibody against Ap-Vas1, a Vas orthologue in the pea aphid, to gain new insights into germline development. The Ap-Vas1-specific antibody facilitates earlier detection of germ-plasm assembly at the oocyte posterior, challenging the previous assumption that germ-plasm assembly begins only at the onset of embryogenesis. Treatment of oocytes and early embryos with cytoskeleton inhibitors suggests that germ-plasm assembly primarily depends on actin, in contrast to the fly Drosophila melanogaster, where both actin and microtubules are essential. Since pea aphids lack an orthologue of osk, which encodes the protein Osk responsible for anchoring Vas to the germ plasm in Drosophila, this suggests that pea aphids employ distinct mechanisms for osk- and microtubule-independent formation of the germ plasm. Moreover, the clustering of germ cells into germarium-like structures in the extraembryonic region before entering the embryos suggests a gonad formation process different from that in Drosophila, where germ cells begin to cluster into germaria after settling within the embryonic gonads. Therefore, the analysis of the Ap-Vas1 distribution provides a deeper understanding of germline development in asexual pea aphids, uncovering novel aspects of parthenogenetic and viviparous reproduction in insects.

PMID:40415969 | PMC:PMC12095909 | DOI:10.1093/aesa/saaf009

New Phytol. 2025 May 25. doi: 10.1111/nph.70232. Online ahead of print.

ABSTRACT

With the intensification of global warming, there is an urgent need to develop crops with enhanced heat tolerance. Pearl millet, as a typical C4 heat-tolerant crop, has mechanisms of heat tolerance at the cellular level which remain unclear. Constructed single-cell transcriptomic landscape of pearl millet leaves under heat stress and normal conditions, comprising 20 589 high-quality cells classified into five cell types. Vascular tissue cells were identified as the most critical cell type under heat stress, characterized by the highest number of differentially expressed genes and heat stress memory genes. Through single-cell WGCNA analysis combined with phenotypic and physiological analysis of heat stress memory gene UGT73C3 mutants and overexpression lines, we revealed the important role of heat stress memory genes in enhancing heat tolerance by promoting the clearance of reactive oxygen species accumulation. Our study provides a heat-tolerant crop leaf atlas revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.

PMID:40415399 | DOI:10.1111/nph.70232