The transcriptome of the ovine choroid plexus is regulated by thyroid hormone but not by photoperiod.

Stability of feature attribution: Contrasting supervised and unsupervised selection for radiopathomics and TCGA outcomes.

Optimising genes selection with greedy heuristic fuzzy clustering for binary classification problems

Role of a ficolin in the innate immune mechanisms of Penaeus vannamei against Vibrio parahaemolyticus.

Single-cell sequencing enables comprehensive profiling of individual cells, revealing cellular heterogeneity and function with unprecedented resolution. However, current analysis frameworks lack the ability to simultaneously explore and visualize cellular hierarchies at multiple biological levels. To address these limitations, we present CellScope, a promising framework for constructing high-resolution cell atlases at multiple clustering levels. CellScope employs a two-stage manifold fitting process for gene selection and noise reduction, followed by agglomerative clustering, and integrates UMAP visualization with hierarchical clustering to intuitively represent cellular relationships simultaneously at multiple levels-such as cell lineage, cell type, and cell subtype levels. Compared to established pipelines such as Seurat and Scanpy, CellScope comprehensively improves clustering performance, visualization clarity, computational efficiency, and algorithm interpretability, while reducing dependence on hyperparameters across a multitude of single-cell datasets. Most importantly, it can reveal biological insights that other contemporary methods are unable to detect, thereby deepening our understanding of cellular heterogeneity and function, and potentially informing disease research.

Gene therapy targeting specific organelle genes crucial for cancer cell survival provides a promising strategy for hepatocellular carcinoma (HCC) therapy. The integration of metal-organic framework nanostructures with catalytic nucleic acids provides a promising route toward precision gene therapy. Herein, we report a dual-cascade DNAzyme nanoreactor constructed from the zeolitic imidazolate framework-90 (ZIF-90), which simultaneously serves as a protective host and a zinc ion reservoir. The framework encapsulates an EGR-1-targeting DNAzyme and is further engineered with lignin, N-acetylgalactosamine, and triphenylphosphonium ligands to achieve cascade targeting of HCC cells and their mitochondria. Within the mitochondrial microenvironment, characterized by mildly alkaline pH and elevated ATP concentration, the lignin shell and MOF scaffold undergo programmed disassembly. This cascade process triggers the release of DNAzyme together with Zn2+ cofactors, thereby initiating site-specific catalytic cleavage of target mRNA. Structural characterization confirmed the crystalline integrity, functionalization sequence, and stimuli-responsive degradation of the nanoreactor, while biological assays demonstrated efficient mitochondrial localization and selective gene silencing. In vivo studies further revealed pronounced tumor suppression with minimal systemic toxicity. This work highlights the potential of ZIF-90 as chemically programmable carriers that combine metal-organic framework with DNAzyme for organelle-level therapeutic intervention.

DNA microarray technology produces high-dimensional gene expression data, where many genes are irrelevant to disease. Effective feature selection is thus essential to mitigate the curse of dimensionality and enhance classification performance. This study introduces a multi-objective feature selection approach employing a Clustering-Based Binary Differential Evolution (CBDE) mutation to identify a compact set of disease-relevant genes. The proposed DeFs-CBDE algorithm was assessed on four gene expression datasets, i.e. brain, breast, lung, and central nervos system cancer by selecting informative feature subsets and evaluating them using five state-of-the-art classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors, Decision Tree (DT), and Random Forest. The DeFs-CBDE method achieved of 100 % accuracy on the brain dataset with three classifiers. On the lung dataset, DeFs-CBDE reached 97.56 % accuracy with SVM and DT. For the breast dataset, DeFs-CBDE attained 93.33 % accuracy very close to the highest score of 93.81 % accuracy. The CNS dataset proved the most challenging, where it achieved 91.67 % accuracy with SVM. Across all datasets, DeFs-CBDE consistently achieved high classification performance.

Accurate normalization of gene expression data in real-time quantitative polymerase chain reaction (RT-qPCR) relies on the identification of stably expressed reference genes, which remains uncharacterized in Pieris melete Ménétriés, a cruciferous crop pest with agricultural significance. This study systematically evaluated eight candidate reference genes (GAPDH, α-tub, β-actin, 18S, β-tub, EF1α, RPL27, RPS15) across four experimental conditions: developmental stages, tissues, temperature stresses, and diapause stages. Stability rankings were determined using four algorithms (geNorm, NormFinder, BestKeeper, ΔCt method) integrated via RefFinder. Results revealed that at different condition-specific stability patterns, RPL27 and EF1α were optimal for developmental stages, RPL27 paired with 18S were suitable for tissue analyses; EF1α and α-tub were stable at different temperature stresses, and RPL27 combined with RPS15 were stably expressed during diapause. Pairwise variation analysis confirmed that dual reference genes sufficiently enhanced normalization accuracy. This work provides the validated reference genes panel for P. melete, addressing a critical gap in molecular studies of this pest and ensuring robust gene expression analyses for future research on diapause regulation and pest control strategies.

Aggressive behavior is a complex and multifactorial trait influenced by several genes and shaped by societal and cultural constraints. To trace adaptation signals and identify potential new genes related to aggressive behavior, we explored variations in nine genes previously linked to aggressive behavior, as well as their 74 interacting genes retrieved from the STRING database. We identified 15 SNPs under positive selection in four genes (SEC24B, NCOA2, CTNNA1, and ALDH3A2), with selection consistently confirmed by both iHS and xp-EHH analyses. Among these, 15 SNPs showed high pairwise FST values and pronounced allele frequency differences between populations, suggesting their potential role in the local adaptation of the studied populations. The functional importance of these SNPs was confirmed by ten acting as eQTLs and five located in transcription factor binding sequences. The observed selection signatures may reflect adaptation in diverse biological processes, including protein trafficking and signal transduction, cell proliferation and differentiation, endocrine regulation, and lipid and aldehyde detoxification. Although these processes are not directly linked to aggression, they may have downstream effects on neurodevelopmental and hormonal regulation that could indirectly influence behavioral phenotypes. Experimental validation is required to confirm these signals and to clarify their functional and biological significance.

BackgroundHigh-dimensional omics data often contain more variables than observations, which can lead to overfitting and negatively impact the results of classical data analysis methods. To address the issue, supervised variable selection methods are often used, incorporating penalty terms into the model. While effective for selecting task-specific variables, this approach may not preserve the overall dataset structure for multiple downstream analyses. This study aims to evaluate unsupervised variable selection approaches and introduce a novel tool that improves data interpretability while maintaining biological information.ResultsWe assessed multiple unsupervised variable selection techniques to identify a representative subset of the original dataset. Based on this evaluation, we developed TRIM-IT, a computational tool that integrates unsupervised variable selection, clustering, survival analysis, and differential gene expression analysis. TRIM-IT was applied to glioblastoma transcriptomics data, uncovering three distinct patient clusters. These clusters correlated with tumor histology, exhibited significantly different survival outcomes, and revealed molecular profiles that suggest potential biomarker candidates.ConclusionTRIM-IT provides a novel approach for analyzing high-dimensional omics data while preserving key biological insights. Its ability to identify meaningful patient subgroups and molecular signatures highlights its applicability across various biomedical research contexts. The tool is implemented in R and the code is publicly available for reproduction and adaptation to other studies.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13040-025-00500-6.

Arctic and alpine insects experience extreme environmental stressors, yet the genomic basis of their adaptation is poorly understood. Diamesa midges (Diptera: Chironomidae) are cold-adapted insects inhabiting glacial and high-altitude freshwater ecosystems, but no chromosome-level genomes have been available to date. We present the first haplotype-resolved, chromosome-level genomes for four Diamesa species (D. hyperborea, D. lindrothi, D. serratosioi and D. tonsa), assembled using PacBio HiFi sequencing and Hi-C scaffolding. The assemblies show high completeness and k-mer representation. Phylogenomic analyses place Diamesinae as sister to other Chironomidae except Podonominae, and comparisons suggest introgression between the distinct species D. hyperborea and D. tonsa. Comparative genomic analyses across 20 Diptera species identified significant gene family contractions in Diamesa related to oxygen transport and metabolism, consistent with adaptation to high-altitude, low-oxygen environments. Expansions were observed in histone-related and Toll-like receptor gene families, suggesting roles in chromatin remodeling and immune regulation under cold stress. A glucose dehydrogenase gene family was significantly expanded across all cold-adapted species studied, implicating it in cryoprotectant synthesis and oxidative stress mitigation. Diamesa exhibited the largest gene family contraction at any phylogenetic node, with limited overlap in expansions with other cold-adapted Diptera, indicating lineage-specific adaptation. Our findings support the hypothesis that genome size condensation and selective gene family changes underpin survival in cold environments. These new genome assemblies provide a valuable resource for studying adaptation, speciation, and conservation in cold-specialist insects. Future integration of gene expression and population genomics will further clarify the evolutionary resilience of Diamesa in a warming world.

Chromophobe renal cell carcinoma (chRCC) is a distinct subtype of non–clear cell renal cell carcinoma (ncRCC), arising from intercalated cells of the distal nephron collecting ducts. No standard treatments are specifically approved for chRCC, which is further hindered by lack of a universally accepted grading system. This study sought to find molecular drivers that may aid in the diagnosis or development of treatments for chRCC. A retrospective analysis of chRCC was conducted using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) repository, accessed through cBioPortal (version 17.0-public) on 21 July 2025. The study examined recurrent somatic mutations and assessed co-occurrence with Benjamini–Hochberg False Discovery Rate (FDR) correction. Additional analyses evaluated mutation by sex and race, with significance set at p < 0.05. The cohort included 180 tumor samples from 170 chRCC patients. Most patients were adults (n = 167, 98.2%) and White (n = 115, 67.6%). Recurrent alterations occurred in genes part of the p53, PI3K/mTOR, Hippo, and NOTCH signaling pathway. Exploratory demographic analyses identified isolated single-patient mutations in select genes across sex and race; however, these rare events are not interpretable as population-level differences. This study provides a comprehensive genomic profile of chRCC across multiple demographic categories.

The gold standard for assessing expression of miRNAs, small molecules involved in numerous biological processes, is reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The reliability of RT-qPCR analysis results largely depends on accurate data normalization and the selection of an appropriate reference gene. This study evaluated the stability of five candidate reference genes—miR-525, miR-520c, SNORD48, miR-135b, and miR-143—in human placental samples. GeNorm, NormFinder, BestKeeper, and the delta Ct-method were used to evaluate gene expression stability. The effect of reference gene selection for normalization of target miRNAs (miR-1537, miR-190b, miR-16, miR-21, and miR-146a) expression in term placental samples from smokers and non-smokers was also investigated. All statistical tools identified miR-525, miR-520c, and SNORD48 as the three most stable reference genes, except for GeNorm, which recommends the combination of the first two genes. Normalization using SNORD48 and miR-525 produced comparable results for miR-21 expression in the placental samples, both in smokers and non-smokers, whereas normalization with miR-143 yielded markedly different outcomes compared to SNORD48 and miR-525. These findings highlight the considerable impact of reference gene selection on RT-qPCR results, emphasizing the importance of careful validation to avoid misinterpretation of gene expression data.

Loropetalum chinense var. rubrum is a highly favored ornamental plant in landscaping applications, where its vibrant purplish-red foliage and dense clusters of purplish-red flowers create a striking contrast to the green leaves and white blossoms of its parent species, L. chinense. Despite extensive horticultural utilization, chloroplast genome divergence between L. chinense and its red-leaved variety is poorly characterized. Here, we sequenced and analyzed complete chloroplast genomes of L. chinense and other two horticultural selections of L. chinense var. rubrum exhibiting distinct flower colors. Through comparative genomics, we aimed to identify sequence-level variations and detect potential selection signatures in chloroplast genes. The results showed that the chloroplast genomes ranged from 159,425 to 159,452bp, with variations primarily driven by insertions and deletions (INDELs) within single-copy regions, while inverted repeat (IR) regions were highly conserved. Three hypervariable regions (trnH-GUG-psbA, ndhD, ycf1 ) enabled the differentiation between L. chinense and its horticultural variety rubrum, and six other regions (e.g., psbA, rps16-trnQ-UUG, atpF) exhibited intraspecific variation unique to the latter. Furthermore, we detected signals of positive selection in the rpoB gene, which contains a micro-inversion unique to the light red-flowered individual. This inversion resulted in five amino acid substitutions, suggesting potential functional consequences for the gene product. These findings enhance our understanding of chloroplast genome evolution in L. chinense and provide valuable genetic tools for phylogenetic and population genomic studies.

Alveolar macrophages (AMs) serve as a first line of defense against respiratory pathogens, including Cryptococcus neoformans , the primary causative agent of cryptococcosis, a deadly pulmonary mycosis which commonly afflicts immunocompromised individuals. While these innate immune cells are thought to play a pivotal role in controlling the outcome of C. neoformans infections, this critical host-pathogen interaction is more commonly studied in vitro using bone marrow-derived macrophages (BMDM) or immortalized macrophage cell lines that differ in ontogeny and phenotype from AMs. In this work, we characterized fetal liver-derived alveolar-like macrophages (FLAMs) as an alternate model to study the earliest stages of C. neoformans infection. Here, we show that the FLAM steady state transcriptome is more similar to primary AMs than peritoneal macrophages and the macrophage cell lines, RAW264.7 and J774, and that FLAMs exhibit distinct transcriptional responses to IFNγ stimulation and C. neoformans infection compared to J774 cells. Specifically, transcriptome profiling and gene ontology analysis indicate that C. neoformans infection of FLAMs, but not J774 cells, increases the expression of canonical glycolytic genes, including Slc2a1, Pgk1, and Ldha , which is accompanied by a metabolic shift favoring glycolysis. Furthermore, activation or inhibition of hypoxia inducible factor 1 (HIF1) activity utilizing dimethyloxalylglycine (DMOG) and echinomycin, respectively, indicates that the expression of select glycolytic genes in C. neoformans -infected FLAMs is HIF1-dependent. Collectively, our results suggest that FLAMs serve as an appropriate tool for modeling AM: C. neoformans interactions and investigating the effects of this pathogen on host AM immunometabolism.

BackgroundAlzheimer’s disease (AD) affects 1 in 9 individuals aged 65+ in the U.S., with females twice as likely as males. As AD pathology begins decades before symptoms appear, early detection and intervention are critical.MethodI developed NeuroPlasmaNet, an AI system that identifies early‐stage blood‐based biomarkers using graph neural networks (GNNs) to simulate the sex‐specific brain‐blood multi‐omics gene networks guided by neural pathology. Since AD is defined in the brain and detected in blood, I first constructed biotype‐stratified brain gene graphs based on cell types and layers, controlling for confounding biases such as age, APOE genotype, and education. I deployed reinforcement fine‐tuning with the cosine‐based similarity reward function to optimize each learning iteration and align predicted genes with AD biological relevancy. This approach revealed distinct male vs. female molecular signatures. I then refined non‐invasive blood‐based gene selection and identified NeuroPlasma12 gene panel. This brain‐first analysis grounded blood markers in AD neural pathology, avoiding systemic noise. I introduced the NeuroPlasma Score (NPS) to quantify the gene panel profile.ResultNeuroPlasmaNet achieved 92.03% accuracy (AUC=0.9410, p < 0.001) for early AD detection using NeuroPlasma12 gene panel (e.g., C1QB, TXNIP, TREM2, GFAP, PLCG2, CD163, CAMK1D, LRP10). I also identified potential therapeutic targets (e.g., GRIN2B, PLCG2, GRM5, CAMK1D), some novel and others published in Nature and JAMA. Beyond gene markers, the results highlight modifiable cognitive risk factors, such as substance use, infections, circadian disruption, and nutrition deficiencies, and promote personalized, preventive brain health strategies to the largely overlooked preclinical population.ConclusionMy study connects brain‐blood through sex‐specific gene graph networks, improving the blood markers precision by grounding it in neural pathology and avoiding noise from whole‐body blood circulation. Using NeuroPlasmaNet, an AI system incorporating cell type/layer stratification and biologically guided reinforcement learning, I identified NeuroPlasma12 gene panel in blood transcriptomics, validating my hypothesis that blood markers can detect early AD signals and support preventive brain health to serve the 75% overlooked population.

Identification and selection of reference genes for real-time quantitative PCR in Dianthus spiculifolius subjected to five abiotic stresses

Integrating WGCNA and machine learning to distinguish active pulmonary tuberculosis from latent tuberculosis infection based on neutrophil extracellular trap-related genes.

Whole-genome sequencing identifies genetic diversity and adaptive signatures of hypoxia and ultraviolet radiation in Chinese chickens.

Evaluating selection at intermediate scales within genes provides robust identification of genes under positive selection in M. tuberculosis clinical isolates.