BackgroundLung adenocarcinoma (LUAD) remains a leading cause of cancer mortality, with metabolic reprogramming, particularly dysregulated mitochondrial transport and glycolysis (MG), emerging as a hallmark of its progression. Despite the recognized importance of these pathways, an integrated, clinically actionable model that combines them for patient stratification and therapeutic targeting is lacking.ObjectiveThis study aims to decode integrated MG-associated molecular patterns in the progression of LUAD, providing novel clinical actionable target for LUAD patients.MethodsGSVA and WGCNA were applied to an integrated GEO cohort to identify MG-associated genes. LASSO-Cox and multivariable Cox analyses were used to construct and validate a prognostic model and prioritize CCNA2 as a prognostic candidate. Single-cell, spatial, cell-based, drug-sensitivity, and xenograft analyses were used for validation.ResultsThe MG-associated signature stratified overall survival, and CCNA2 was enriched in malignant cells. CCNA2 knockdown reduced A549 proliferation, wound closure, and invasion. GSCA analysis identified BHG712, IPA-3, and KIN001-260 as candidate compounds, and IPA-3 produced the largest reduction in xenograft tumor burden.ConclusionThe integrated analyses identify an MG-associated prognostic framework and prioritize CCNA2 for further validation in LUAD.
IntroductionHereditary spherocytosis (HS) is the most common inherited red cell membranopathy caused by defects in erythrocyte membrane and cytoskeletal proteins, including ankyrin, spectrin, band 3, and protein 4.2. Among these, mutations in SLC4A1, which encodes the erythrocyte anion exchanger band 3 (AE1), account for approximately 20–30% of HS cases and it is associated with distal renal tubular acidosis (dRTA), reflecting phenotypic and functional heterogeneity.MethodsIn this study, seven unrelated Indian patients with clinically suspected HS were investigated using detailed hematological, biochemical, and clinical evaluations along with eosin-5′-maleimide (EMA) binding assays. Molecular analysis was performed using targeted next-generation sequencing (t-NGS) covering 81 genes associated with red cell disorders, and the identified SLC4A1 variants were validated by Sanger sequencing. The structural and functional consequences of the variants were assessed through in silico tools including DynaMut, PolyPhen-2, and SIFT.ResultsSeven SLC4A1 variants were identified, including six previously reported variants and one novel variant, p.Phe702Ser, detected in heterozygous or compound heterozygous states. These variants were distributed across both cytoplasmic and transmembrane domains of the band 3 protein. Most patients presented with mild to moderate HS characterized by anemia, jaundice, splenomegaly, reticulocytosis, and reduced EMA fluorescence. One patient harboring compound heterozygous variants (p.Arg490His and p.Ala858Asp) exhibited HS associated with dRTA, highlighting the functional diversity of SLC4A1 mutations. The novel p.Phe702Ser variant, located in the transmembrane domain TM9, was predicted to destabilize AE1 structure and potentially impair anion transport.DiscussionMarked intrafamilial phenotypic variability was observed despite identical genotypes. These findings expand the mutational spectrum of SLC4A1-related HS in Indian patients.
BackgroundThe secondary injury cascade following spinal cord injury (SCI) involves substantial metabolic reprogramming and immune responses. However, whether transcriptional signatures related to lactylation, ferroptosis, and neuroinflammation show coordinated changes after SCI, and which cellular states are associated with these signatures, remain unclear.MethodsPublic bulk transcriptomic datasets (GSE47681 and GSE5296) and a single-cell RNA-seq dataset (GSE162610) were integrated. Bioinformatic analyses included single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network analysis (WGCNA), protein-protein interaction analysis, pseudotime trajectory inference, and in silico perturbation analysis. A C5 unilateral contusion SCI mouse model was used for transcriptomic validation, behavioral assessments, Western blotting, and immunofluorescence. Human peripheral blood RNA-seq data (GSE151371) were used as an exploratory external reference for systemic SCI-associated expression patterns.ResultsBulk transcriptomic analysis revealed coordinated elevation of lactylation-related, ferroptosis-related, and inflammation-related signature scores after SCI, with the highest scores observed at 3 days post-injury. Single-cell RNA-seq localized these signatures predominantly to expanded disease-associated microglia (DAM)-like states. Network screening prioritized vimentin (Vim) as a candidate hub gene associated with the three signatures, and Vim-high pathological microglia exhibited an activated inflammatory-metabolic transcriptional state. In the SCI mouse model, VIM, pan-lysine lactylation (pan-Kla), acyl-CoA synthetase long-chain family member 4 (ACSL4), and interleukin-1β (IL-1β) were upregulated in whole-lesion spinal cord tissue. Immunofluorescence further showed increased VIM, pan-Kla, and ACSL4 signals with spatial overlap within IBA1-positive cells after SCI.ConclusionPathological DAM-like microglia represent major microglial states exhibiting concurrent lactylation-related, ferroptosis-related, and inflammatory signatures after SCI. VIM was prioritized as a candidate hub associated with this pathological microglial program. These findings provide a cellular and molecular framework for future mechanistic studies of secondary SCI.
IntroductionThis study aims to evaluate the predictive performance of different machine learning algorithms and to identify the key factors influencing eggshell breaking resistance in laying hens.MethodsThree machine learning methods, C5.0 decision tree, Random Forest (RF), and Support Vector Regression (SVR), were applied to egg classification and prediction tasks. Eggshell strength (ER) was predicted using egg weight (EW), shell weight (SW), shell thickness (ST), and shape index (SI).ResultsThe C5.0 decision tree, trained on 464 eggs, achieved an overall classification accuracy of 65.1%, with a tendency to misclassify brown eggs as white, suggesting potential feature overlap or class imbalance. Among the regression models, RF outperformed SVR, yielding higher R2 (0.852 vs. 0.553) and adjusted R2 (0.835 vs. 0.548) values, along with lower error metrics (MSE, RMSE, and MAPE). In addition to superior predictive accuracy, the RF model provided insights into the relative importance of egg quality traits affecting eggshell breaking resistance.DiscussionOverall, the findings indicate that ensemble-based machine learning methods are effective tools for both accurate prediction and identification of influential factors related to eggshell strength.
BackgroundAccurate determination of tandem thymine-adenine (TA) repeat numbers in the UGT1A1 promoter region (rs3064744) is essential for diagnosing Gilbert’s syndrome and personalizing therapy with toxic agents like irinotecan and atazanavir. However, traditional polymerase chain reaction (PCR) assays face severe limitations due to the AT-rich sequence and overlapping melting temperatures (Tm) of the highly homologous 7TA and 8TA alleles. In this context, melting curve analysis (MCA) employing fluorophore-quencher systems has emerged as a promising alternative. The purpose of this study was to develop a novel genotyping approach combining optimized aPCR-MCA analysis with an automated classifier to overcome the limitations posed by the differentiation of highly homologous alleles and to demonstrate its practical application, providing the distribution of rs3064744 genotypes across four regional cohorts of the Russian population.MethodsA specialized Dual Head 1D-convolutional neural network (1D-CNN) ensemble with Test-Time Augmentation (TTA) was developed. The model was trained and internally validated on 1,620 engineered plasmid samples, and independently evaluated on an external clinical test set of 440 unique patient genomic DNA specimens. Real-time PCR was performed on CFX96 and DTprime platforms. Additionally, population-wide screening was conducted on 997 archival clinical samples from Moscow, Sakha (Yakutia), Dagestan, and Rostov regions.ResultsWhile 5TA and 6TA alleles were easily separated, absolute Tm distributions of 7TA and 8TA alleles overlapped significantly, and non-uniform Tm shifts of 0.8 °C–1.4 °C occurred across platforms. Conventional absolute Tm thresholding was therefore inadequate. By assessing relative morphological curve divergence against co-amplified 7TA/7TA and 7TA/8TA reference anchors, the 1D-CNN ensemble neutralized instrument noise. It achieved 100% accuracy on internal validation and 100% concordance (440/440) with clinical reference pyrosequencing. Population screening revealed that Dagestan, Yakutia, and Rostov cohorts closely align with the European population. Rare 5TA and 8TA alleles were detected at low frequencies in Yakutia and Moscow.ConclusionCombining LNA-modified aPCR-MCA with a comparative 1D-CNN model successfully circumvents thermodynamic limitations and eliminates human operator bias. This integrated system offers an accessible, high-throughput, and clinically valid solution for routine UGT1A1 pharmacogenetic testing.
