Uncovering the Activity of Alkaline Earth Metal Hydrogenation Catalysis Through Molecular Volcano Plots

Recent advances in alkaline earth (Ae) metal hydrogenation catalysis have broadened the spectrum of potential catalysts to include candidates from the main group, providing a sustainable alternative to the commonly used transition metals. Although Ae-amides have already been demonstrated to catalyze hydrogenation of imines and alkenes, a lucid understanding of how different metal/ligand combinations influence the catalytic activity is yet to be established. In this article, we use linear scaling relationships and molecular volcano plots to assess the potential of the Ae metal-based catalysts for the hydrogenation of alkenes. By analyzing combinations of eight metals (mono-, bi-, tri-, and tetravalent) and seven ligands, we delineate the impact of metal-ligand interplay on the hydrogenation activity. Our findings highlight that the catalytic activity is majorly determined by the charge and the size of the metal ions. While bivalent Ae metal cations delicately regulate the binding and the release of the reactants and the products, respectively, providing the right balance for this reaction, ligands play only a minor role in determining their catalytic activity. We show how volcano plots can be utilized for the rapid screening of prospective Ae catalysts to establish a guideline to achieve maximum activity in facilitating the hydrogenation process.

POS0881 DETECTION OF THE GENE EXPRESSIONS IN PERIPHERAL BLOOD INVOLVED IN THE PROGRESSION OF PULMONARY VESSEL DISEASE AT THE SUBCLINICAL STAGE OF PULMONARY HYPERTENSION ASSOCIATED WITH SYSTEMIC SCLEROSIS

Background:Pulmonary hypertension (PH) is prominent as a vascular involvement of systemic sclerosis (SSc), which remains a leading cause of death in spite of current best treatments. Recently, hemodynamic definition of PH was updated from mPAP≥25mmHg to mPAP>20mmHg and PVR≥3WU. Although new definition may improve the prognosis of PH associated with SSc by giving a chance to start management early, it may be insufficient as more than 2/3 of the pulmonary circulation is already impaired by the time of meeting the definition. Therefore, the ideal therapeutic intervention should be started at the subclinical stage of PH in SSc patients, but little is known about underlying pathological mechanisms at the stage. In this study, we investigate progression to exercise-induced PH (exPH)1), which is considered subclinical PH, in the prospective registry of high-risk population for developing PH associated with SSc.Objectives:To detect the gene expressions in peripheral blood involved in the progression of pulmonary vessel disease (PVD) at the subclinical stage of PH associated with SSc.Methods:Total of 180 patients who had not met PH criteria with Raynaud phenomenon, skin sclerosis or SSc-related autoantibody was registered. To detect the early PVD, exercise Doppler echocardiography (exDE) was carried out every 6 or 12 months for up to 6 years. The definition of exPH was maximum sPAP>40mmHg or increase in sPAP>20mmHg estimated by exDE during exercise. For gene expression analysis, total RNAs from whole peripheral blood cells were extracted by PAXgene system, and then multiplex sequencing was done. To identify candidate genes involved in the progression to exPH, random forest machine learning method was employed. Volcano plots, a scatter plots to visualize fold-changes and p-values of differentially expressed genes (DEGs) between exPH and others (exN), were also used for seeking the important genes for disease progression.Results:At the time of registration, 34.4% of patients met exPH criteria, and 15.6% of patients developed exPH during follow-up period (35.0±18.1 months). Expression of TNF gene was selected as the most useful genes to predict progression to exPH by random forest, and the accuracy of the model was about 87%. Volcano plots indicated that expressions of TMEM176A and TMEM176B were prominent (fold-change >2.4 and -log10 p-value >3.5) in exPH patients. The accuracy was improved to 90% if the expression of TNF and TMEMA/B were used for the prediction of progression to exPH. We found that statistically significant increase in the expression of TNF was eliminated at the time of fulfilling the exPH criteria, while increase in expressions of TMEM A/B were still kept.Conclusion:It was reported that TNFα drives pulmonary arterial hypertension by suppressing the BMP type-II receptor and altering NOTCH signalling2). Our findings suggest that TNFα plays important role only in the period of pre-exPH. On the other hand, increase in expressions of TMEM A/B were observed through the period of pre-exPH to post-exPH. It suggests that there are multiple phases before developing PH associated with SSc. It is very important to understand the phases for the precise treatment to arrest the progression of PVD.References:[1]R. Naeije et al., Am J resp and critical care med 187, 576-583 (2013). 2) LA. Hurst et al., Nat Commun. 13;8:14079 (2017).Disclosure of Interests:Yoshinobu Koyama Speakers bureau: Asahikasei, Ayumi, BMS, Mitsubishi Tanabe, Shin-nihon, Paid instructor for: Asahikasei, Asteras, BMS, Grant/research support from: Eli-Lilly, Yoshiharu Sato: None declared, Tatsuma Shoji: None declared, Soichiro Fuke: None declared, Takatsune Umayahara: None declared, Moe Sakamoto: None declared

VolcaNoseR is a web app for creating, exploring, labeling and sharing volcano plots

Comparative genome- and proteome-wide screens yield large amounts of data. To efficiently present such datasets and to simplify the identification of hits, the results are often presented in a type of scatterplot known as a volcano plot, which shows a measure of effect size versus a measure of significance. The data points with the largest effect size and a statistical significance beyond a user-defined threshold are considered as hits. Such hits are usually annotated in the plot by a label with their name. Volcano plots can represent ten thousands of data points, of which typically only a handful is annotated. The information of data that is not annotated is hardly or not accessible. To simplify access to the data and enable its re-use, we have developed an open source and online web tool with R/Shiny. The web app is named VolcaNoseR and it can be used to create, explore, label and share volcano plots (https://huygens.science.uva.nl/VolcaNoseR). When the data is stored in an online data repository, the web app can retrieve that data together with user-defined settings to generate a customized, interactive volcano plot. Users can interact with the data, adjust the plot and share their modified plot together with the underlying data. Therefore, VolcaNoseR increases the transparency and re-use of large comparative genome- and proteome-wide datasets.

HD-eXplosion: visualization of hydrogen–deuterium exchange data as chiclet and volcano plots with statistical filtering

Summary Hydrogen–Deuterium eXchange coupled to mass spectrometry is a powerful tool for the analysis of protein dynamics and interactions. Bottom-up experiments looking at deuterium uptake differences between various conditions are the most common. These produce multi-dimensional data that can be challenging to depict in a single visual format. Each user must also set significance thresholds to define meaningful differences and make these apparent in data presentation. To assist in this process, we have created HD-eXplosion, an open-source, web-based application for the generation of chiclet and volcano plots with statistical filters. HD-eXplosion fills a void in available software packages and produces customizable plots that are publication quality. Availability and implementation The HD-eXplosion application is available at http://hd-explosion.utdallas.edu. The source code can be found at https://github.com/HD-Explosion.

VolcaNoseR – a web app for creating, exploring, labeling and sharing volcano plots

Comparative genome- and proteome-wide screens yield large amounts of data. To efficiently present such datasets and to simplify the identification of hits, the results are often presented in a type of scatterplot known as a volcano plot, which shows a measure of effect size versus a measure of significance. The data points with the largest effect size and a statistical significance beyond a user-defined threshold are considered as hits. Such hits are usually annotated in the plot by a label with their name. Volcano plots can represent ten thousands of data points, of which typically only a handful is annotated. The information of data that is not annotated is hardly or not accessible. To simplify access to the data and enable its re-use, we have developed an open source and online web tool with R/shiny. The web app is named VolcaNoseR and it can be used to create, explore, label and share volcano plots (https://huygens.science.uva.nl/VolcaNoseR). When the data is stored in an online data repository, the web app can retrieve that data together with user-defined settings to generate a customized, interactive volcano plot. Users can interact with the data, adjust the plot and share their modified plot together with the underlying data. Therefore, VolcaNoseR increases the transparency and re-use of large comparative genome- and proteome-wide datasets.