Ordered Hierarchical Porous Structure of PtSn/3DOMM-Al2O3 Catalyst for Promoting Propane Non-Oxidative Dehydrogenation.

Herein, the hierarchical porous catalyst of 3-dimensional ordered macro-mesoporous (3DOMM) Al2O3 supported active PtSn nanoparticles (NPs) was prepared by the combined synthesized path of evaporation-induced self-assembly with colloid crystal template (EISA-CCT) methods. The hierarchical macro-mesoporous composite structure can markedly increase the specific surface area, accommodate the diffusion of propene, and decrease the number of surface acid sites. In addition, the special surface property and pore structure of 3DOMM-Al2O3 can modify the interaction between metals and substrates, as well as stabilize the metal nanoparticle, which promotes the formation of a highly active and stable PtSn phase. The PtSn/3DOMM-Al2O3 catalyst exhibits higher productivity and stability than PtSn/Al2O3 catalysts with macropore and mesopore structures. The PtSn/3DOMM-Al2O3 catalyst displays the best catalytic performance with propylene selectivity over 95% at a propane conversion of 33.9%. The study of the ordered hierarchical porous structure of PtSn/3DOMM-Al2O3 catalysts can contribute to obtaining improved catalysts in industrial processes.

Genetic screening of MMP1 as a potential pathogenic gene in chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Airway inflammation and remodeling are the two key processes involved in COPD pathogenesis. However, the key pathogenic genes driving COPD development have not been revealed. This study aims to identify and validate hub gene(s) underlying COPD development through bioinformatics analysis and experimental validation. METHODS: Three lung tissue sequencing datasets of the COPD (including GSE38974, GSE103174, and GSE106986) were analyzed. Further, differentially expressed genes (DEGs) were used to compare patients with COPD with non-COPD individuals, and the Robust Rank Aggregation (RRA) analysis was also performed. Results revealed a series of potential pathogenic genes of COPD. DEGs were subjected to KEGG, GO, and GSEA analyses. The scRNA dataset of human lung tissues (Human Lung Cell Atlas), and human primary airway epithelial cells (GSE134147) were used to identify the cell subtype localization. The qRT-PCR assay was performed in the human lung tissues, COPD mice model, and primary bronchial epithelial cells at the air-liquid interface (ALI) under cigarette smoke extract (CSE) stimulation to verify the expression of the hub genes. LASSO and GLM analysis with the hub genes were performed to identify the most critical gene. RNA-seq was performed after knocking down the critical gene using siRNA in HBECs at ALI. The potential role of the critical gene was confirmed through qRT-PCR, Western blot, and Immunofluorescence (IF) assays. RESULTS: A total of 98 genes were significantly and differently expressed in 3 GEO datasets. The KEGG and GO analyses showed that most of these genes are responsible for inflammation, immunity, and cell proliferation. The core gene set including 15 genes was screened out and consequently, the MMP1 was the most likely responsible for the progression of COPD. Moreover, we confirmed that MMP1 is significantly related to inflammatory effects and cilia function in human bronchial epithelial cells cultured at the air-liquid interface (ALI). CONCLUSION: In summary, we confirmed that inflammation and cell proliferation are potentially critical processes in COPD occurrence and development. A total of 15 potential hub genes were identified among which MMP1 was the most likely gene responsible for the development of COPD. Therefore, MMP1 is a potential molecular target of COPD therapy.

GSA Central-A web platform to perform, learn, and discuss gene set analysis.

Gene Set Analysis (GSA) is one of the most commonly used strategies to analyze omics data. Hundreds of GSA-related papers have been published, giving birth to a GSA field in Bioinformatics studies. However, as the field grows, it is becoming more difficult to obtain a clear view of all available methods, resources, and their quality. In this paper, we introduce a web platform called "GSA Central" which, as its name indicates, acts as a focal point to centralize GSA information and tools useful to beginners, average users, and experts in the GSA field. "GSA Central" contains five different resources: A Galaxy instance containing GSA tools ("Galaxy-GSA"), a portal to educational material ("GSA Classroom"), a comprehensive database of articles ("GSARefDB"), a set of benchmarking tools ("GSA BenchmarKING"), and a blog ("GSA Blog"). We expect that "GSA Central" will become a useful resource for users looking for introductory learning, state-of-the-art updates, method/tool selection guidelines and insights, tool usage, tool integration under a Galaxy environment, tool design, and tool validation/benchmarking. Moreover, we expect this kind of platform to become an example of a "thematic platform" containing all the resources that people in the field might need, an approach that could be extended to other bioinformatics topics or scientific fields.

Popularity and performance of bioinformatics software: the case of gene set analysis.

BACKGROUND: Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. RESULTS: Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods ("GSA-BenchmarKING"). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. CONCLUSIONS: The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

GREG-studying transcriptional regulation using integrative graph databases.

A gene regulatory process is the result of the concerted action of transcription factors, co-factors, regulatory non-coding RNAs (ncRNAs) and chromatin interactions. Therefore, the combination of protein-DNA, protein-protein, ncRNA-DNA, ncRNA-protein and DNA-DNA data in a single graph database offers new possibilities regarding generation of biological hypotheses. GREG (The Gene Regulation Graph Database) is an integrative database and web resource that allows the user to visualize and explore the network of all above-mentioned interactions for a query transcription factor, long non-coding RNA, genomic range or DNA annotation, as well as extracting node and interaction information, identifying connected nodes and performing advanced graphical queries directly on the regulatory network, in a simple and efficient way. In this article, we introduce GREG together with some application examples (including exploratory research of Nanog's regulatory landscape and the etiology of chronic obstructive pulmonary disease), which we use as a demonstration of the advantages of using graph databases in biomedical research. Database URL: https://mora-lab.github.io/projects/greg.html, www.moralab.science/GREG/.