Search Results (235)

Sugarcane (Saccharum spp. L.) is a globally vital sugar and energy crop whose genetic improvement has been constrained by its complex polyploid–allopolyploid genome. To address this limitation, we developed a practical, high-throughput single-nucleotide polymorphism (SNP) genotyping system. Using specific-locus amplified fragment sequencing (SLAF-seq) on 107 diverse accessions, we identified 2,420,550 high-quality SNPs anchored to the Saccharum officinarum LA-Purple genome. Stringent filtering yielded 55,750 SNPs for population analysis, which revealed three distinct genetic groups consistent with breeding history and adaptation. From these resources, 329 SNPs were converted into PCR-based ligase detection reaction (PCR-LDR) markers, resulting in a validated panel of 177 highly reliable SNPs (151 core and 26 extended) organized into an efficient multiplex typing system. The panel exhibited exceptional discriminatory power, successfully distinguishing all 303 tested sugarcane varieties and clearly resolving 186 individuals from three segregated hybrid populations. Compared to existing SSR and SNaPshot platforms, this SNP system offers superior experimental reproducibility, enhanced varietal clustering, and broader genome coverage. This work provides a robust, efficient genotyping tool to advance sugarcane variety identification, germplasm management, pedigree analysis, and marker-assisted breeding, with potential applicability to other complex polyploid crops. Full article

Background: Barnacles are important marine fouling organisms, and their complex life cycle involves key metamorphic nodes from nauplius to cyprid larvae and then to sessile adults. However, the molecular mechanisms underlying their larval development remain poorly understood. Metabolomics and transcriptomics are powerful tools for exploring biological development pathways and regulatory networks. Methods: We employed non-targeted metabolomics and transcriptomics to analyze three key developmental stages of embryonic stage, nauplius stage, and cyprid stage. Differential metabolites were screened using fold change (FC), p-value, and variable importance in projection (VIP) values, while DEGs were identified with adjusted p-value and |log₂(fold change)| criteria. WGCNA was used to construct gene co-expression networks, and qRT-PCR validated RNA-seq results. Results: A total of 3683 metabolites were identified, with the bile secretion pathway serving as a core regulatory pathway throughout early development. Transcriptomic analysis identified 7234 DEGs, which were clustered into four modules corresponding to different developmental stages. Key pathways such as chitin metabolism, and linoleic acid metabolism were significantly enriched, and qRT-PCR confirmed the reliability of RNA-seq data. Conclusions: This study reveals the metabolic and molecular regulatory mechanisms underlying the early development of M. volcano, highlighting stage-specific metabolic characteristics and core gene modules. The findings provide a theoretical basis for understanding barnacle developmental adaptation strategies and offer potential targets for the development of novel antifouling agents. Full article

Whole-genome duplication (WGD) is a powerful evolutionary force, yet the mechanisms by which neopolyploids achieve transcriptomic stability and phenotypic success remain poorly understood. This study investigated the phenotypic and transcriptomic consequences of ploidy changes in the flatworm Macrostomum lignano, a “successful” neopolyploid model. We exploited two established sublines derived from the inbred DV1 line: the euploid DV1_8 (hidden tetraploid, SSL₁L₂) and the aneuploid DV1_10 (hidden hexaploid, SSL₁L₁L₂L₂). By integrating whole-genome sequencing (WGS)-informed normalization with RNA-seq analysis, we differentiated true regulatory shifts from gene-dosage effects. We revealed that while most genes scale linearly with ploidy, 1308 genes exhibited a nonlinear aneuploidy-induced transcriptional response. The remarkable trans-acting effects were observed across subgenome S encoded by disomic small chromosomes. Differentially expressed genes (DEGs) were enriched in pathways essential for homeostasis and growth: mTOR signaling, ubiquitin-mediated proteolysis, and the Hippo/Wnt pathways. Phenotypes of the DV1_10 worms exhibited increased body size, enhanced cell proliferation, and higher viability in comparison to the DV1_8 worms (60.25% vs. 21.5%). These findings suggest that M. lignano possesses mechanisms for dosage compensation to mitigate the deleterious effects of aneuploidy. Ultimately, this study demonstrates how genomic plasticity and rewiring of the transcriptome may facilitate the evolutionary success of animal neopolyploids. Full article

Background/Objectives: Chimeric antigen receptor (CAR) T cells are a powerful cancer therapy, but their function depends heavily on internal signaling domains and metabolic adaptability. Most studies evaluate CAR behavior upon antigen exposure, yet intrinsic signaling properties may pre-program CAR T cell states even in the absence of stimulation. This study investigates how CAR design and metabolic support shape baseline transcriptional programs, focusing on tonic signaling and NF-κB-related pathways. Methods: We engineered CAR T cells targeting HER2 or GPC3 antigens, incorporating either 4-1BB or CD28 co-stimulatory domains, respectively. A subset of cells was further modified with adenosine deaminase 1 (ADA1) and CD26 to degrade extracellular adenosine and supply inosine, a metabolic strategy termed metabolic refueling (MR). Bulk RNA-seq was performed on resting T cells without antigen stimulation. We analyzed differential gene expression, gene set enrichment (GO, KEGG, Hallmarks), and transcription factor activity (DoRothEA) to assess the impact of CAR design and MR on T cell programming. Results: All CAR T cells exhibited activation of NF-κB–centered inflammatory programs at baseline, indicating tonic signaling. GPC3 CAR T cells showed stronger baseline activation than HER2 CAR T cells. Metabolic refueling amplified these programs without altering their directionality, enhancing inflammatory, survival, and effector modules. Transcription factor activity scores mirrored these trends, highlighting RELA, FOS, and STATs as key regulatory nodes. Conclusions: CAR-intrinsic features, notably co-stimulatory domain choice, define the tonic NF-κB activation tone in resting CAR T cells. Metabolic refueling boosts these baseline states without overstimulation, suggesting it may be especially valuable for weaker CAR constructs. These findings provide a framework for tuning CAR T cell function through combinatorial design strategies targeting signaling and metabolism. Full article

Egyptian rice landraces represent a unique genetic reservoir shaped by arid environments, yet their genomic and transcriptional response to salt stress remains largely unexplored. Here, we integrated genomic, transcriptomic, and population genetic analyses to systematically unravel the mechanisms of salt tolerance in this vital germplasm. Resequencing 56 Egyptian accessions uncovered a treasure trove of genetic variation, including 18,204 novel SNPs. An expanded GWAS on 258 accessions discovered 17 novel loci for salt tolerance. Parallel RNA-Seq analysis of a salt-tolerant-susceptible pair (Giza 176 vs. 9311) under stress delineated a defense network centered on phenylpropanoid and lipid metabolic pathways in the tolerant genotype. The power of our integrated approach was exemplified by the convergent identification of ONAC063, where GWAS loci, transcriptional responsiveness, and haplotype-phenotype association collectively validated its role. Furthermore, selection sweep analysis highlighted 62 candidate genes under divergent selection. Our study not only positions Egyptian rice as a key resource for allele mining but also establishes a robust multi-omics pipeline for bridging genetic diversity with complex traits, accelerating the discovery of functional genes for breeding climate-resilient crops. Full article

Background/Objectives: The pharyngeal microbiota plays a critical role in respiratory health by supporting immune modulation, colonization resistance, and metabolic functions. Disruptions in this microbial ecosystem are associated with respiratory diseases; however, standard diagnostics often target individual pathogens, overlooking overall microbial dynamics. This study investigates the composition and diversity of the pharyngeal microbiota in three populations: individuals with pre-COPD (with and without concurrent acute respiratory infection [ARI]) and those with stable COPD. Methods: Pharyngeal swabs were analyzed using 16S rDNA sequencing on the Illumina MiSeq platform. Taxonomic and functional profiles were generated with MicrobAT^®, while microbial diversity was evaluated using the Shannon index and PERMANOVA. Group differences in microbiota composition were assessed via Kruskal–Wallis tests and robust PCA. The sample size was estimated at 8 subjects per group to detect significant differences (α = 0.05, 80% power, SD ≈ 20). Results: Twenty-nine swabs were collected: 11 from pre-COPD subjects (PC), 9 from ARI patients receiving antibiotics, and 9 from individuals with stable severe COPD. Microbial diversity was preserved in the PC group (100%) but markedly reduced in ARI (25%) and COPD (15%). Microbiota composition differed significantly across groups (R² = 0.371, p = 0.001), particularly at the phylum level. Functional analysis revealed minimal deficits in PC (<10%) but major impairments in ARI (81%) and COPD (56%), indicating reduced microbial functional capacity. Conclusions: Broad-spectrum microbial analysis highlights the importance of assessing pharyngeal microbiota beyond traditional pathogen detection, offering potential for innovative diagnostic and therapeutic approaches. Full article

Background: Chemotherapy, targeted therapy and radiotherapy are the cornerstones of cancer treatment. However, therapeutic resistance—not only to these classic modalities but also to novel therapeutics like immune checkpoint inhibitors (ICIs) and antibody-drug conjugates—remains a major hurdle. Resistance significantly limits efficacy and increases recurrence rates. A deep understanding of the molecular mechanisms driving this resistance is critical for developing personalized therapeutic strategies and improving patient outcomes. Recent Advances: Patient-derived cancer organoids have emerged as a powerful preclinical platform that faithfully recapitulates the genetic, phenotypic, and histological characteristics of original tumors. Consequently, PDOs are being widely utilized to evaluate drug responses, investigate resistance mechanisms, and discover novel therapeutic targets for a range of therapies. Limitations: While organoid models have been instrumental in studying resistance, significant limitations persist. First, standard organoid-only models lack key tumor microenvironment components, such as immune cells, limiting immunotherapy research. Second, there is a significant lack of research on acquired resistance, particularly in lung cancer. This gap is largely driven by the clinical infeasibility of rebiopsy in patients with progressive diseases. Third, the absence of standardized protocols for generating and validating resistance models hinders reproducibility and complicates clinical translation. Conclusions: This review summarizes recent advances in using organoid models to study resistance to chemotherapy, radiotherapy, and novel therapeutics (ICIs and ADCs). We emphasize the critical need for standardization in resistance organoid research. We also propose future directions to overcome existing challenges, including the integration of co-culture systems (to include the TME) and advanced technologies (e.g., scRNA-seq, Spatial Transcriptomics). Our specific focus is on advancing lung cancer resistance modeling to enable functional precision medicine. Full article

Understanding connectivity between populations is key to identifying hotspots of diversity, dispersal sinks and sources, and effective management units for natural resources. Multi-species connectivity seeks to overcome species-specific idiosyncrasies to identify shared patterns that are most critical to spatial management. The linear Hawaiian archipelago provides an excellent platform to assess multi-species connectivity patterns, with shared boundaries to gene flow identified across a majority of the 41 coral reef species surveyed to date. Here, we evaluate genome-scale data by comparing consistency and resolution to previous connectivity studies using far fewer loci. We used pool-seq to genotype 22,503–232,730 single nucleotide polymorphisms per species (625,215 SNPs total) from the same individuals published in previous studies of two fishes, two corals, and two lobsters. Additionally, one coral species (Pocillopora meandrina) without previous archipelago-wide population genetic data was included. With greater statistical power, most genetic differences between pairwise comparisons of islands were significant (250 of 308), consistent with the most recent larval dispersal models for the Hawaiian Archipelago. These data reveal significant differentiation at a finer scale than previously reported using single-marker studies, yet did not overturn any of the conclusions or management implications drawn from previous studies. We confirm that population genomic datasets are consistent with previously reported patterns of multispecies connectivity but add a finer layer of population resolution that is pertinent to management. Full article

Background/Objectives: To investigate visual acuity, refractive outcomes and the predictive accuracy of modern intraocular lens (IOL) power calculation formulas in eyes implanted with two presbyopia-correcting IOLs: trifocal Zeiss AT LISA TRI and the nondiffractive EDOF Teleon Comfort. Methods: This retrospective consecutive chart review included 115 patients who underwent uncomplicated bilateral cataract surgery and received either the LISA TRI (n = 56) or Comfort (n = 59). Biometric measurements were obtained preoperatively, and refractive outcomes were assessed 1, 3, and 6 months postoperatively. Postoperative spherical equivalent (SEQ) was compared to predicted SEQ using the ESCRS calculator and IOLCON platform. Outcome measures included mean prediction error and mean absolute error (MAE). Distance-corrected visual acuity (VA), uncorrected VA, defocus curves, preferred viewing distances, contrast sensitivity, and photopic reading speed were also analyzed. Results: All formulas performed better in the LISA TRI group, with significantly lower MAE and higher proportions of eyes within ±0.50 diopters (D). Systematic prediction error offsets were observed for three formulas (K6, Castrop, Hoffer QST) in the LISA TRI group and for all five formulas in the MF15 group. Refractive stability was achieved by 3 months for the LISA TRI, while 20% of Comfort eyes continued to show SEQ shifts > 0.50 D at 3 months. Defocus equivalent (DEQ) had lower proportions of eyes within ±0.50 D than SEQ. Conclusions: The LISA TRI demonstrated superior predictive accuracy, faster refractive stabilization, and stronger near performance than the Comfort. These findings support the importance of IOL-specific constant optimization and highlight the need for incorporating DEQ into routine refractive outcome evaluation. Full article

Sepsis is a life-threatening condition driven by a dysregulated immune response, leading to systemic inflammation and multi-organ failure. Among the key molecular regulators, S100A8/A9 has emerged as a critical damage-associated molecular pattern (DAMP) protein, amplifying pro-inflammatory signaling via the Toll-like receptor 4 (TLR4) and receptor for advanced glycation end products (RAGE) pathways. Elevated S100A8/A9 levels correlate with disease severity, making it a promising biomarker and therapeutic target. To unravel the role of S100A8/A9 in sepsis, we integrate scRNA-seq and RNA-seq approaches. scRNA-seq enables cell-type-specific resolution of immune responses, uncovering cellular heterogeneity, state transitions, and inflammatory pathways at the single-cell level. In contrast, RNA-seq provides a comprehensive view of global transcriptomic alterations, allowing robust statistical analysis of differentially expressed genes. The integration of both approaches enables precise deconvolution of immune cell contributions, validation of cell-specific markers, and identification of potential therapeutic targets. Our findings highlight the S100A8/A9-driven inflammatory cascade, its impact on immune cell interactions, and its potential as a diagnostic and prognostic biomarker in sepsis. Eight protein targets resulted from the integrative transcriptomics studies (corresponding to S100A8, S100A9, S100A6, NAMPT, FTH1, B2M, KLF6 and SRGN) have been used to predict interaction affinities with 2958 ChEMBL approved drugs, by using a pre-trained AI models (PLAPT) in order to point directions on drug repurposing in sepsis. The strongest predicted interactions have been confirmed with molecular docking and molecular dynamics analysis. This study underscores the power of combining high-throughput transcriptomics to advance our understanding of sepsis pathophysiology and develop precision medicine strategies. Full article

Understanding the genetic diversity and population structure of sugarcane germplasm banks is essential for generating progenies with maximum variability. In this study, 350 accessions from the EEAOC germplasm bank were genotyped using DArT-seq markers. Genetic diversity, population structure, and variability were assessed through Bayesian analysis, principal coordinate analysis (PCoA), and analysis of molecular variance (AMOVA). Additionally, different sizes of core collections were evaluated. After filtering, 74,969 high-quality SNPs were retained, and two outlier genotypes were excluded. The mean observed heterozygosity (HO) was 0.28, while the mean expected heterozygosity (HE) was 0.3. Polymorphic information content (PIC) values ranged from 0 to 0.38 (mean 0.22), and the mean discrimination power (Dj) was 0.28. Structure and PCoA analyses consistently revealed three genetic clusters. AMOVA indicated that most of the genetic variation was found within subpopulations, while 10.25% was attributable to differences among them (p < 0.0001), where ΦFST suggested moderate genetic differentiation. Core collection analysis showed that a subset of 35 genotypes (10%) captured nearly 96% of the total genetic diversity, while a 30% core captured over 98%. These results provide valuable information for the effective management and utilization of sugarcane genetic resources and support the design of breeding strategies to develop superior cultivars. Full article

Listeria monocytogenes and Salmonella enterica Typhimurium are leading causes of foodborne illness in the United States and frequently implicated in produce outbreaks. Conventional decontamination methods, such as cold-water washes with chlorine, have limited antibacterial efficacy and environmental sustainability. Power ultrasound has emerged as a promising non-thermal alternative, but the molecular mechanisms remain insufficiently elucidated. This study evaluated transcriptomic responses of L. monocytogenes and S. enterica Typhimurium to (i) ultrasound (20 kHz), (ii) chlorine (50 ppm), and (iii) combined ultrasound + chlorine treatments. RNA-seq analysis identified differentially expressed genes, as well as enriched Gene Ontology and KEGG terms. Results showed that ultrasound and chlorine triggered distinct transcriptomic responses. L. monocytogenes exhibited broad transcriptional shifts under ultrasound, including significant upregulation of phosphotransferase system components and central metabolism. Chlorine alone induced a narrower response, with fewer differentially expressed genes clustering into limited functional categories. In contrast, the combined ultrasound + chlorine treatment elicited the strongest response in S. enterica Typhimurium, with enrichment of multiple energy- and metabolism-related pathways, including the citrate cycle, carbon metabolism, and microbial metabolism in diverse environments. These findings provide new insights into ultrasound-triggered responses in foodborne pathogens and may inform development of optimized ultrasound-based hurdle sanitization strategies for produce safety. Full article

Gene regulatory networks (GRN) govern cellular identity and function through precise control of gene transcription. Single-cell technologies have provided powerful means to dissect regulatory mechanisms within specific cellular states. However, existing computational approaches for modeling single-cell RNA sequencing (scRNA-seq) data often infer local regulatory interactions independently, which limits their ability to resolve regulatory mechanisms from a global perspective. Here, we propose a deep learning framework (Planet) based on diffusion models for constructing cell-specific GRN, thereby providing a systems-level view of how protein regulators orchestrate transcriptional programs. Planet jointly optimizes local network structures in conjunction with gene expression profiles, thereby enhancing the structural consistency of the resulting networks at the global level. Specifically, Planet decomposes GRN generation into a series of Markovian evolution steps and introduces a Triple Hybrid-Attention Transformer to capture long-range regulatory dependencies across diffusion time-steps. Benchmarks on multiple scRNA-seq datasets demonstrate that Planet achieves competitive performance against state-of-the-art methods and yields only a slight improvement over DigNet under comparable conditions. Compared with conventional diffusion models that rely on fixed sampling schedules, Planet employs a fast-sampling strategy that accelerates inference with only minimal accuracy trade-off. When applied to mouse-lung Cd8⁺Gzmk⁺ T cells, Planet successfully reconstructs a cell-type-specific GRN, recovers both established and previously uncharacterized regulators, and delineates the dynamic immunoregulatory changes that accompany ageing. Overall, Planet provides a practical framework for constructing cell-specific GRNs with improved global consistency, offering a complementary perspective to existing methods and new insights into regulatory dynamics in health and disease. Full article

Single-cell RNA sequencing (scRNA-seq) has revolutionized genomic investigations by enabling the exploration of gene expression heterogeneity at the individual cell level. However, the complexity of scRNA-seq data analysis remains a challenge for many researchers. Here, we present OmniCellX, a browser-based tool designed to simplify and streamline scRNA-seq data analysis while addressing key challenges in accessibility, scalability, and usability. OmniCellX features a Docker-based installation, minimizing technical barriers and ensuring rapid deployment on local machines or clusters. Its dual-mode operation (analysis and visualization) integrates a comprehensive suite of analytical tools for tasks such as preprocessing, dimensionality reduction, clustering, differential expression, functional enrichment, cell–cell communication, and trajectory inference on raw data while enabling alternative interactive and publication-quality visualizations on pre-analyzed data. Supporting multiple input formats and leveraging the memory-efficient data structure for scalability, OmniCellX can efficiently handle datasets spanning millions of cells. The platform emphasizes user flexibility, offering adjustable parameters for real-time fine-tuning, alongside extensive documentation to guide users at even beginner levels. OmniCellX combines an intuitive interface with robust analytical power to perform single-cell data analysis and empower researchers to uncover biological insights with ease. Its scalability and versatility make it a valuable tool for advancing discoveries in cellular heterogeneity and biomedical research. Full article

We investigated the repeatability of the MS-39 in determining power vector components—the spherical equivalent (SEQ) and astigmatic powers (C0 and C45) and asphericity (Q)—of corneal epithelium, stroma, and endothelium in a large patient cohort. In this retrospective cross-sectional single-centre study, we evaluated a dataset containing 600 MS-39 anterior segment tomography measurements from 200 eyes (three repeat measurements each) taken prior to cataract surgery. The exported measurements included height map data for the epithelium, stroma, and endothelium surface. Model surfaces (spherocylinder (SphCyl), cylindrical conoid (CylConoid), and biconic (Biconic), all in the 3/6 mm zone) were fitted using nonlinear iterative optimisation, minimising the height difference between the measurement and model. The mean (MEAN) and standard deviation (SD) for each sequence of measurements were derived and analysed. In the 3 mm and 6 mm zone, the MEAN SEQ was 53.47/53.56/53.57 and 53.21/53.54/53.54 D for SphCyl/CylConoid/Biconic for the epithelium, −4.47/−4.51/−4.51 and −4.45/−4.50/−4.50 D for the stroma, and −6.23/−6.26/−6.26 and −6.18/−6.29/−6.30 D for the endothelium. With the three surface models and the 3/6 mm zone, the SD for SEQ/C0/C45 was in the range of 0.04 to 0.11/0.05 to 0.13/0.04 to 0.11 D for epithelium; 0.01 to 0.02/0.01 to 0.05/0.01 to 0.06 D for stroma; and 0.01 to 0.02/0.02 to 0.07/0.03 to 0.07 D for endothelium. Fitting floating model surfaces with astigmatism to map data of the corneal epithelium, stroma, and endothelium seems to be a robust and reliable method for extracting equivalent power and astigmatism using all the datapoints within a region of interest. Full article