A Novel Approach of SWATH-Based Metabolomics Analysis Using the Human Metabolome Database Spectral Library.

Metabolomics is a potential approach to paving new avenues for clinical diagnosis, molecular medicine, and therapeutic drug monitoring and development. The conventional metabolomics analysis pipeline depends on the data-independent acquisition (DIA) technique. Although powerful, it still suffers from stochastic, non-reproducible ion selection across samples. Despite the presence of different metabolomics workbenches, metabolite identification remains a tedious and time-consuming task. Consequently, sequential windowed acquisition of all theoretical MS (SWATH) acquisition has attracted much attention to overcome this limitation. This article aims to develop a novel SWATH platform for data analysis with a generation of an accurate mass spectral library for metabolite identification using SWATH acquisition. The workflow was validated using inclusion/exclusion compound lists. The false-positive identification was 3.4% from the non-endogenous drugs with 96.6% specificity. The workflow has proven to overcome background noise despite the complexity of the SWATH sample. From the Human Metabolome Database (HMDB), 1282 compounds were tested in various biological samples to demonstrate the feasibility of the workflow. The current study identified 377 compounds in positive and 303 in negative modes with 392 unique non-redundant metabolites. Finally, a free software tool, SASA, was developed to analyze SWATH-acquired samples using the proposed pipeline.

Antitrypanosomal, Antitopoisomerase-I, and Cytotoxic Biological Evaluation of Some African Plants Belonging to Crassulaceae; Chemical Profiling of Extract Using UHPLC/QTOF-MS/MS.

In our continuous study for some African plants as a source for antitrypanosomally and cytotoxic active drugs, nine different plants belonging to the Crassulaceae family have been selected for the present study. Sedum sieboldii leaves extract showed an antitrypanosomal activity against Trypanosoma brucei with an IC50 value of 8.5 µg/mL. In addition, they have cytotoxic activities against (HCT-116), (HEPG-2) and (MCF-7), with IC50 values of 28.18 ± 0.24, 22.05 ± 0.66, and 26.47 ± 0.85 µg/mL, respectively. Furthermore, the extract displayed inhibition against Topoisomerase-1 with an IC50 value of 1.31 µg/mL. It showed the highest phenolics and flavonoids content among the other plants' extracts. In order to identify the secondary metabolites which may be responsible for such activities, profiling of the polar secondary metabolites of S. sieboldii extract via Ultra-Performance Liquid Chromatography coupled to High-Resolution QTOF-MS operated in negative and positive ionization modes, which revealed the presence of 46 metabolites, including flavonoids, phenolic acids, anthocyanidins, coumarin, and other metabolites.

Current Understanding of Human Metaproteome Association and Modulation.

During the last decade, metaproteomics has provided a better understanding and functional characterization of the microbiome. A large body of evidence now reveals interspecies, species of bacteria-host interactions, via the secreted modulatory microbial protein "metaproteome". Although high-throughput state-of-art mass spectrometry has recently empowered metaproteomics, its profile remains unclear, and, most importantly, the exact consequences and underlying mechanism of these protein molecules on the host are insufficiently understood. Here we address the current progress in the study of the human metaproteome, suggesting possible modulation, a metaproteome dysbiotic signature, challenges, and future perspectives.

gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.

The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values (S ij values) for codon-tRNA wobble interaction efficiencies. The initial implementation of the tAI had significant flaws. For instance, generated S ij weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. Consequently, a species-specific approach (stAI) was developed to overcome those limitations. However, the stAI method employed a hill climbing algorithm to optimize the S ij weights, which is not ideal for obtaining the best set of S ij weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. In addition, it did not perform well in computing the tAI of fungal genomes in comparison with the original implementation. We developed a novel approach named genetic tAI (gtAI) implemented as a Python package (https://github.com/AliYoussef96/gtAI), which employs a genetic algorithm to obtain the best set of S ij weights and follows a new codon usage-based workflow that better computes the tAI of genomes from the three domains of life. The gtAI has significantly improved the correlation with the codon adaptation index (CAI) and the prediction of protein abundance (empirical data) compared to the stAI.

Detection of early prognostic biomarkers for metastasis of Ewing's sarcoma in pediatric patients.

AIMS: Ewing's Sarcoma is an extremely aggressive tumor in children. The disease is associated with highly metastatic rate, especially at the time of diagnosis, contributing to a lower survival rate and poor prognosis. The study aimed to identify predictive biomarkers for metastatic Ewing's sarcoma through in-depth analysis of the plasma proteome profile of pediatric Ewing's sarcoma patients. MAIN METHODS: Plasma samples from Ewing's sarcoma patients and control individuals were profiled using both shotgun and dimethyl-labeled proteomics analysis. Subsequently, Ewing's sarcoma patients were further stratified according to their metastatic state and chemotherapy response. Western blot was used for validation. Univariate and multivariate analyses were performed to determine proteome metastasis predictors. Receiver operating characteristic (ROC) analysis was done to assess the diagnostic significance of the potential plasma Ewing's sarcoma biomarkers. KEY FINDINGS: Our results revealed a set of proteins significantly associated with the metastatic Ewing's sarcoma disease profile. These proteins include ceruloplasmin and several immunoglobulins. Additionally, our study disclosed significant differentially expressed proteins in pediatric Ewing's sarcoma, including CD5 antigen-like, clusterin, and dermcidin. Stable isotope dimethyl labeling and western blot further confirmed our results, strengthening the impact of such proteins in disease development. Furthermore, an unbiased ROC curve evaluated and confirmed the predictive power of these biomarker candidates. SIGNIFICANCE: This study presented potential empirical predictive circulating biomarkers for determining the disease status of pediatric Ewing's sarcoma, which is vital for early prediction.

Integrated multiomics analysis to infer COVID-19 biological insights.

Three years after the pandemic, we still have an imprecise comprehension of the pathogen landscape and we are left with an urgent need for early detection methods and effective therapy for severe COVID-19 patients. The implications of infection go beyond pulmonary damage since the virus hijacks the host's cellular machinery and consumes its resources. Here, we profiled the plasma proteome and metabolome of a cohort of 57 control and severe COVID-19 cases using high-resolution mass spectrometry. We analyzed their proteome and metabolome profiles with multiple depths and methodologies as conventional single omics analysis and other multi-omics integrative methods to obtain the most comprehensive method that portrays an in-depth molecular landscape of the disease. Our findings revealed that integrating the knowledge-based and statistical-based techniques (knowledge-statistical network) outperformed other methods not only on the pathway detection level but even on the number of features detected within pathways. The versatile usage of this approach could provide us with a better understanding of the molecular mechanisms behind any biological system and provide multi-dimensional therapeutic solutions by simultaneously targeting more than one pathogenic factor.

Xconnector: Retrieving and visualizing metabolites and pathways information from various database resources.

Metabolomics databases contain crucial information collected from various biological systems and experiments. Developers and scientists performed massive efforts to make the database public and accessible. The diversity of the metabolomics databases arises from the different data types included within the database originating from various sources and experiments can be confusing for biologists and researchers who need further manual investigation for the retrieved data. Xconnector is a software package designed to easily retrieve and visualize metabolomics data from different databases. Xconnector can parse information from Human Metabolome Database (HMDB), Livestock Metabolome Database (LMDB), Yeast Metabolome Database (YMDB), Toxin and Toxin Target Database (T3DB), ReSpect Phytochemicals Database (ReSpectDB), The Blood Exposome Database, Phenol-Explorer Database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Small Molecule Pathway Database (SMPDB). Using Python language, Xconnector connects the targeted databases, recover requested metabolites from single or different database sources, reformat and repack the data to generate a single Excel CSV file containing all information from the databases, in an application programming interface (API)/ Python dependent manner seamlessly. In addition, Xconnector automatically generates graphical outputs in a time-saving approach ready for publication. SIGNIFICANCE: The powerful ability of Xconnector to summarize metabolomics information from different sources would enable researchers to get a closer glimpse on the nature of potential molecules of interest toward medical diagnostics, better biomarker discovery, and personalized medicine. The software is available as an executable application and as a python package compatible for different operating systems.

UniprotR: Retrieving and visualizing protein sequence and functional information from Universal Protein Resource (UniProt knowledgebase).

UniprotR is a software package designed to easily retrieve, cluster and visualize protein data from UniProt knowledgebase (UniProtKB) using R language. The package is implemented mainly to process, parse and illustrate proteomics data in a handy and time-saving approach allowing researchers to summarize all required protein information available at UniProtKB in a readable data frame, Excel CSV file, and/or graphical output. UniprotR generates a set of graphics including gene ontology, chromosomal location, protein scoring and status, protein networking, sequence phylogenetic tree, and physicochemical properties. In addition, the package supports clustering of proteins based on primary gene name or chromosomal location, facilitating additional downstream analysis. SIGNIFICANCE: In this work, we implemented a robust package for retrieving and visualizing information from multiple sources such UniProtKB, SWISS-MODEL, and STRING. UniprotR Contains functions that enable retrieving and cluster data in a handy way and visualize data in publishable graphs to facilitate researcher's work and fulfill their needs. UniprotR will aid in saving time for downstream data analysis instead of manual time consuming data analysis. AVAILABILITY AND IMPLEMENTATION: UniprotR released as free open source code under the license of GPLv3, and available in CRAN (The Comprehensive R Archive Network) and GitHub. (https://cran.r-project.org/web/packages/UniprotR/index.html). (https://github.com/Proteomicslab57357/UniprotR).