A Novel Non-invasive Method Allowing for Discovery of Pathologically Relevant Proteins From Small Airways

BackgroundThere is a lack of early and precise biomarkers for personalized respiratory medicine. Breath contains an aerosol of droplet particles, which are formed from the epithelial lining fluid when the small airways close and re-open during inhalation succeeding a full expiration. These particles can be collected by impaction using the PExA® method (Particles in Exhaled Air), and are derived from an area of high clinical interest previously difficult to access, making them a potential source of biomarkers reflecting pathological processes in the small airways.Research questionOur aim was to investigate if PExA method is useful for discovery of biomarkers that reflect pathology of small airways.Methods and analysis10 healthy controls and 20 subjects with asthma, of whom 10 with small airway involvement as indicated by a high lung clearance index (LCI ≥2.9 z-score), were examined in a cross-sectional design, using the PExA instrument. The samples were analysed with the SOMAscan proteomics platform (SomaLogic Inc). ResultsTwo hundred-seven proteins were detected in up to 80% of the samples. Nine proteins showed differential abundance in subjects with asthma and high LCI as compared to healthy controls. Two of these were less abundant (ALDOA4, C4), and seven more abundant (FIGF, SERPINA1, CD93, CCL18, F10, IgM, IL1RAP). sRAGE levels were lower in ex-smokers (n=14) than in never smokers (n=16). Gene Ontology (GO) annotation database analyses revealed that the PEx proteome is enriched in extracellular proteins associated with extracellular exosome-vesicles and innate immunity.ConclusionThe applied analytical method was reproducible and allowed identification of pathologically interesting proteins in PEx samples from asthmatic subjects with high LCI. The results suggest that PEx based proteomics is a novel and promising approach to study respiratory diseases with small airway involvement.

PlantGSAD: a comprehensive gene set annotation database for plant species

With the accumulation of massive data sets from high-throughput experiments and the rapid emergence of new types of omics data, gene sets have become more diverse and essential for the refinement of gene annotation at multidimensional levels. Accordingly, we collected and defined 236 007 gene sets across different categories for 44 plant species in the Plant Gene Set Annotation Database (PlantGSAD). These gene sets were divided into nine main categories covering many functional subcategories, such as trait ontology, co-expression modules, chromatin states, and liquid-liquid phase separation. The annotations from the collected gene sets covered all of the genes in the Brassicaceae species Arabidopsis and Poaceae species Oryza sativa. Several GSEA tools are implemented in PlantGSAD to improve the efficiency of the analysis, including custom SEA for a flexible strategy based on customized annotations, SEACOMPARE for the cross-comparison of SEA results, and integrated visualization features for ontological analysis that intuitively reflects their parent-child relationships. In summary, PlantGSAD provides numerous gene sets for multiple plant species and highly efficient analysis tools. We believe that PlantGSAD will become a multifunctional analysis platform that can be used to predict and elucidate the functions and mechanisms of genes of interest. PlantGSAD is publicly available at http://systemsbiology.cau.edu.cn/PlantGSEAv2/.

Network Pharmacology Analysis of the Identification of Phytochemicals and Therapeutic Mechanisms of Paeoniae Radix Alba for the Treatment of Asthma

Background. Paeoniae Radix Alba (PRA), the root of the plant Paeonia lactiflora Pall., has been suggested to play an important role for the treatment of asthma. A biochemical understanding of the clinical effects of Paeoniae Radix Alba is needed. Here, we explore the phytochemicals and therapeutic mechanisms via a systematic and comprehensive network pharmacology analysis. Methods. Through TCMSP, PubChem, GeneCards database, and SwissTargetPrediction online tools, potential targets of active ingredients from PRA for the treatment of asthma were obtained. Cytoscape 3.7.2 was used to determine the target of active ingredients of PRA. Target protein interaction (PPI) network was constructed through the STRING database. The Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genes (KEGG) pathway enrichment analysis were analyzed through the biological information annotation database (DAVID). Results. Our results indicate that PRA contains 21 candidate active ingredients with the potential to treat asthma. The enrichment analysis of GO and KEGG pathways found that the treatment of asthma by PRA may be related to the process of TNF (tumor necrosis factor) release, which can regulate and inhibit multiple signaling pathways such as ceramide signaling. Conclusions. Our work provides a phytochemical basis and therapeutic mechanisms of PRA for the treatment of asthma, which provides new insights on further research on PRA.

Four chromosome scale genomes and a pan-genome annotation to accelerate pecan tree breeding

Genome-enabled biotechnologies have the potential to accelerate breeding efforts in long-lived perennial crop species. Despite the transformative potential of molecular tools in pecan and other outcrossing tree species, highly heterozygous genomes, significant presence–absence gene content variation, and histories of interspecific hybridization have constrained breeding efforts. To overcome these challenges, here, we present diploid genome assemblies and annotations of four outbred pecan genotypes, including a PacBio HiFi chromosome-scale assembly of both haplotypes of the ‘Pawnee’ cultivar. Comparative analysis and pan-genome integration reveal substantial and likely adaptive interspecific genomic introgressions, including an over-retained haplotype introgressed from bitternut hickory into pecan breeding pedigrees. Further, by leveraging our pan-genome presence–absence and functional annotation database among genomes and within the two outbred haplotypes of the ‘Lakota’ genome, we identify candidate genes for pest and pathogen resistance. Combined, these analyses and resources highlight significant progress towards functional and quantitative genomics in highly diverse and outbred crops.

Network Pharmacology Study of The Mechanism of Anti-Pneumonia Action Mechanism of Gentiana Rhodantha Franch

Background: Serum dragon bile is a Chinese medicine used to treat pneumonia, but its mechanism of action is not clear. Meanwhile, due to the development of microarray and RNA‐sequencing technology, high-throughput sequencing analysis is being used increasingly, and it has been applied as an indispensable tool in many medical fields. Therefore, in this article, we want to employed the bioinformatics approach to explore the relevant pharmacological mechanism of dragon serum bile in the treatment of pneumonia through network pharmacology.Methods: In this paper, the active chemical composition and action target of serum dragon bile are obtained through the pharmacology database (TCMSP) of Chinese medicine system and the literature, and the data set of the intersection of active ingredient and disease target is established, and the protein interoperability network of serum gallbladder action target and pneumonia action target is analyzed by using protein interaction network (PPI). Using the Biological Information Annotation Database (DAVID) for gene ontology (GO) functional richness analysis and based on kyoto Gene and Genomics Encyclopedia (KEGG) pathogenic rich analysis, to predict the mechanism of the role of seroclon bile against pneumonia. Results: Through the network pharmacological prediction, it is shown that the main chemical components of serum dragon bile are quercetin, isoorientin, luteolin, Stigmasterol, vanillic acid, etc, all of which have anti-pneumonia effects. The anti-pneumonia effect of serum dragon bile is mainly regulated by pathways in cancer, Bradder cancer, TNF signaling pathway, Hepatitis B and Non-small cell lung cancer, among which the TNF signaling pathway is more associated with pneumonia. Conclusions: It is concluded from the network pharmacological prediction that serum dragon bile may play an anti-pneumonia role by promoting apoptosis, survival, immunity, etc. Its anti-pneumonia path is closely related to key targets IL6, FOS, CASP3 and AKT1. This study provides theoretical support for the follow-up study of the anti-pneumonia mechanism of serum gentian bile.

Identification of Novel Genomic-Variant Patterns of OR56A5, OR52L1, and CTSD in Retinitis Pigmentosa Patients by Whole-Exome Sequencing

Inherited retinal dystrophies (IRDs) are rare but highly heterogeneous genetic disorders that affect individuals and families worldwide. However, given its wide variability, its analysis of the driver genes for over 50% of the cases remains unexplored. The present study aims to identify novel driver genes, disease-causing variants, and retinitis pigmentosa (RP)-associated pathways. Using family-based whole-exome sequencing (WES) to identify putative RP-causing rare variants, we identified a total of five potentially pathogenic variants located in genes OR56A5, OR52L1, CTSD, PRF1, KBTBD13, and ATP2B4. Of the variants present in all affected individuals, genes OR56A5, OR52L1, CTSD, KBTBD13, and ATP2B4 present as missense mutations, while PRF1 and CTSD present as frameshift variants. Sanger sequencing confirmed the presence of the novel pathogenic variant PRF1 (c.124_128del) that has not been reported previously. More causal-effect or evidence-based studies will be required to elucidate the precise roles of these SNPs in the RP pathogenesis. Taken together, our findings may allow us to explore the risk variants based on the sequencing data and upgrade the existing variant annotation database in Taiwan. It may help detect specific eye diseases such as retinitis pigmentosa in East Asia.

Early Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks

Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.

A novel non-invasive method allowing for discovery of pathologically relevant proteins from small airways

BackgroundBreath contains an aerosol of droplet particles, which are formed from the epithelial lining fluid when the small airways close and re-open during inhalation succeeding a full expiration. These particles can be collected by impaction using the PExA® method (Particles in Exhaled Air), and constitute a potential source of biomarkers reflecting pathological processes in the small airways.ObjectiveOur aim was to investigate if PExA method may be useful for discovery of biomarkers that reflect pathology of small airways.Methods10 healthy controls and 20 subjects with asthma, of whom 10 with small airway involvement as indicated by a high lung clearance index (LCI ≥2.9 z-score), were examined using the PExA instrument. The samples were analysed with the SOMAscan proteomics platform (SomaLogic Inc).ResultsTwo hundred-seven proteins were detected in up to 80% of the samples. Nine proteins showed differential abundance in subjects with asthma and high LCI as compared to healthy controls. Two of these were less abundant (ALDOA4, C4), and seven more abundant (FIGF, SERPINA1, CD93, CCL18, F10, IgM, IL1RAP). sRAGE levels were lower in ex-smokers (n=14) than in never smokers (n=16). Gene Ontology (GO) annotation database analyses revealed that the PEx proteome is enriched in extracellular proteins associated with extracellular exosome-vesicles and innate immunity.ConclusionThe applied analytical method was reproducible and allowed identification of pathologically interesting proteins in PEx samples from asthmatic subjects with high LCI. The results suggest that PEx based proteomics is an novel and promising approach to study respiratory diseases with small airway involvement.