SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies.

Structural variation (SV) is an important form of genomic variation that influences gene function and expression by altering the structure of the genome. Although long-read data have been proven to better characterize SVs, SVs detected from noisy long-read data still include a considerable portion of false-positive calls. To accurately detect SVs in long-read data, we present SVDF, a method that employs a learning-based noise filtering strategy and an SV signature-adaptive clustering algorithm, for effectively reducing the likelihood of false-positive events. Benchmarking results from multiple orthogonal experiments demonstrate that, across different sequencing platforms and depths, SVDF achieves higher calling accuracy for each sample compared to several existing general SV calling tools. We believe that, with its meticulous and sensitive SV detection capability, SVDF can bring new opportunities and advancements to cutting-edge genomic research.

The prognostic impact of pathogenic stromal cell-associated genes in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) stands as the most prevalent subtype among lung cancers. Interactions between stromal and cancer cells influence tumor growth, invasion, and metastasis. However, the regulatory mechanisms of stromal cells in the lung adenocarcinoma tumor microenvironment remain unclear. This study seeks to elucidate the regulatory connections among critical pathogenic genes and their associated expression variations within distinct stromal cell subtypes. METHOD: Analysis and investigation were conducted on a total of 114,019 single-cell RNA data and 346 The Cancer Genome Atlas (TCGA) LUAD-related samples using bioinformatics and statistical algorithms. Differential gene expression analysis was performed for tumor samples and controls, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential genes between stromal cells and other cell clusters were identified and intersected with the differential genes from TCGA. We employed a combination of LASSO regression and multivariable Cox regression to identify the ultimate set of pathogenic gene. Survival models were trained to predict the relationship between patient survival and these pathogenic genes. Analysis of transcription factor (TF) cell specificity and pseudotime trajectories within stromal cell subpopulations revealed that vascular endothelial cells (ECs) and matrix cancer-associated fibroblasts (CAFs) are key in regulation of the prognosis-associated genes CAV2, COL1A1, TIMP1, ETS2, AKAP12, ID1 and COL1A2. RESULTS: Seven pathogenic genes associated with LUAD in stromal cells were identified and used to develop a survival model. High expression of these genes is linked to a greater risk of poor survival. Stromal cells were categorized into eight subtypes and one unannotated cluster. Mesothelial cells, vascular endothelial cells (ECs), and matrix cancer-associated fibroblasts (CAFs) showed cell-specific regulation of the pathogenic genes. CONCLUSIONS: The seven disease-causing genes in vascular ECs and matrix CAFs can be used to detect the survival status of LUAD patients, providing new directions for future targeted drug design.

Therapeutic effects of quinine in a mouse model of atopic dermatitis.

Atopic dermatitis (AD) is a chronic inflammatory skin disease that seriously affects quality of life. Quinine is a bitter taste receptor agonist that exhibits antimalarial effects. The aim of the present study was to examine the therapeutic effects of quinine in AD­like mice. AD was induced with 2,4­dinitrochlorobenzene, and the mice were treated with 10 mg/kg quinine for 1, 4 and 7 days. A total of 60 BALB/c mice were divided into the following groups: Healthy, AD­like, AD­like + quinine and healthy + quinine, with 1, 4 and 7 days groups for each treatment. Blood was extracted from all mice and ELISA was performed to detect immunoglobulin E (IgE) levels. H&E­stained tissue sections were prepared from skin lesions on the backs of the mice and pathological changes were observed. Cytokines were detected via ELISA, and the filaggrin (FLG) and kallikrein­7 (KLK7) proteins were detected via western blotting and immunohistochemistry. IKKα and NF­κB mRNA were analyzed via reverse transcription­quantitative PCR. Quinine ameliorated skin damage in the AD­like mice, reduced IgE expression in the blood, inhibited expression of IKKα and NF­κB, reduced cytokine secretion, reduced KLK7 expression, reduced scratching frequency, increased FLG expression and repaired the skin barrier. These results suggested that quinine exhibited therapeutic effects in AD­like mice.

Machine learning for phytopathology: from the molecular scale towards the network scale.

With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant-pathogen interactions and discuss the applications and advances of machine learning in plant-pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein-protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology.

Single-cell RNA Sequencing Analysis Identifies Key Genes in Brain Metastasis from Lung Adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LADC) is the most common type of lung cancer and is a subtype of non-small-cell lung cancer (NSCLC). Approximately 40% of LADC patients experience brain metastases (BMs) during the course of the disease. In this study, integrated bioinformatics methods were applied to identify key genes related to brain metastasis in lung adenocarcinoma. METHODS: We derived and characterized genes differentially expressed between the primary tumour and brain metastases using tumour cells isolated from two lung cancer Patient-derived xenografts (PDX) cases (GSE 69405). Gene ontology (GO) and KEGG pathway enrichment analyses were applied, and protein-protein interaction (PPI) networks and Cytoscape software were utilized to identify key genes. RESULTS: Four key genes, including CKAP4 (Cytoskeleton Associated Protein 4), SERPINA1 (Serpin Family A Member 1), SDC2 (Syndecan 2) and GNG11 (G Protein Subunit Gamma 11) were identified for BM-LADC by the Venn diagram. CONCLUSION: We believe these key genes may be potential biomarkers for improved prognosis and treatment of lung adenocarcinoma.

LC-MS/MS-Based Quantitative Proteomics Analysis of Different Stages of Non-Small-Cell Lung Cancer.

Lung cancer has a higher incidence rate and mortality rate than all other cancers. Early diagnosis and treatment of lung cancer remain a major challenge, and the 5-year survival rate of its patients is only 15%. Basic and clinical research, especially the discovery of biomarkers, is crucial for improving the diagnosis and treatment of lung cancer patients. To identify novel biomarkers for lung cancer, we used the iTRAQ8-plex labeling technology combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze the serum and urine of patients with different stages of lung adenocarcinoma and healthy individuals. A total of 441 proteins were identified in the serum, and 1,161 proteins were identified in the urine. The levels of elongation factor 1-alpha 2, proteasome subunit alpha type, and spermatogenesis-associated protein increased significantly in the serum of patients with lung cancer compared with those in healthy controls. The levels of transmembrane protein 143, cadherin 5, fibronectin 1, and collectin-11 decreased significantly in the serum of patients with metastases compared with those of nonmetastatic lung cancer patients. In the urine of stage III and IV lung cancer patients, the prostate-specific antigen and prostatic acid phosphatase decreased significantly, whereas neutrophil defensin 1 increased significantly. The results of LC-MS/MS were confirmed by enzyme-linked immunosorbent assay (ELISA) for transmembrane protein 143, cadherin 5, fibronectin 1, and collectin-11 in the serum. These proteins may be a potential early diagnosis and metastasis biomarkers for lung adenocarcinoma. Furthermore, the relative content of these markers in the serum and urine could be used to determine the progression of lung adenocarcinoma and achieve accurate staging and diagnosis.