Metabolomics and Lipidomics Screening Reveal Reprogrammed Signaling Pathways toward Cancer Development in Non-Alcoholic Steatohepatitis.

With the rising incidence of hepatocellular carcinoma (HCC) from non-alcoholic steatohepatitis (NASH), identifying new metabolic readouts that function in metabolic pathway perpetuation is still a demand. The study aimed to compare the metabolic signature between NASH and NASH-HCC patients to explore novel reprogrammed metabolic pathways that might modulate cancer progression in NASH patients. NASH and NASH-HCC patients were recruited and screened for metabolomics, and isotope-labeled lipidomics were targeted and profiled using the EXION-LCTM system equipped with a Triple-TOFTM 5600+ system. Results demonstrated significantly (p ≤ 0.05) higher levels of triacylglycerol, AFP, AST, and cancer antigen 19-9 in NASH-HCC than in NASH patients, while prothrombin time, platelet count, and total leukocyte count were decreased significantly (p ≤ 0.05). Serum metabolic profiling showed a panel of twenty metabolites with 10% FDR and p ≤ 0.05 in both targeted and non-targeted analysis that could segregate NASH-HCC from NASH patients. Pathway analysis revealed that the metabolites are implicated in the down-regulation of necroptosis, amino acid metabolism, and regulation of lipid metabolism by PPAR-α, biogenic amine synthesis, fatty acid metabolism, and the mTOR signaling pathway. Cholesterol metabolism, DNA repair, methylation pathway, bile acid, and salts metabolism were significantly upregulated in NASH-HCC compared to the NASH group. Metabolite-protein interactions network analysis clarified a set of well-known protein encoding genes that play crucial roles in cancer, including PEMT, IL4I1, BAAT, TAT, CDKAL1, NNMT, PNP, NOS1, and AHCYL. Taken together, reliable metabolite fingerprints are presented and illustrated in a detailed map for the most predominant reprogrammed metabolic pathways that target HCC development from NASH.

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.

Diabetic microenvironment deteriorates the regenerative capacities of adipose mesenchymal stromal cells.

BACKGROUND: Type 2 diabetes is an endocrine disorder characterized by compromised insulin sensitivity that eventually leads to overt disease. Adipose stem cells (ASCs) showed promising potency in improving type 2 diabetes and its complications through their immunomodulatory and differentiation capabilities. However, the hyperglycaemia of the diabetic microenvironment may exert a detrimental effect on the functionality of ASCs. Herein, we investigate ASC homeostasis and regenerative potential in the diabetic milieu. METHODS: We conducted data collection and functional enrichment analysis to investigate the differential gene expression profile of MSCs in the diabetic microenvironment. Next, ASCs were cultured in a medium containing diabetic serum (DS) or normal non-diabetic serum (NS) for six days and one-month periods. Proteomic analysis was carried out, and ASCs were then evaluated for apoptosis, changes in the expression of surface markers and DNA repair genes, intracellular oxidative stress, and differentiation capacity. The crosstalk between the ASCs and the diabetic microenvironment was determined by the expression of pro and anti-inflammatory cytokines and cytokine receptors. RESULTS: The enrichment of MSCs differentially expressed genes in diabetes points to an alteration in oxidative stress regulating pathways in MSCs. Next, proteomic analysis of ASCs in DS revealed differentially expressed proteins that are related to enhanced cellular apoptosis, DNA damage and oxidative stress, altered immunomodulatory and differentiation potential. Our experiments confirmed these data and showed that ASCs cultured in DS suffered apoptosis, intracellular oxidative stress, and defective DNA repair. Under diabetic conditions, ASCs also showed compromised osteogenic, adipogenic, and angiogenic differentiation capacities. Both pro- and anti-inflammatory cytokine expression were significantly altered by culture of ASCs in DS denoting defective immunomodulatory potential. Interestingly, ASCs showed induction of antioxidative stress genes and proteins such as SIRT1, TERF1, Clusterin and PKM2. CONCLUSION: We propose that this deterioration in the regenerative function of ASCs is partially mediated by the induced oxidative stress and the diabetic inflammatory milieu. The induction of antioxidative stress factors in ASCs may indicate an adaptation mechanism to the increased oxidative stress in the diabetic microenvironment.

The cross talk between type II diabetic microenvironment and the regenerative capacities of human adipose tissue-derived pericytes: a promising cell therapy.

BACKGROUND: Pericytes (PCs) are multipotent contractile cells that wrap around the endothelial cells (ECs) to maintain the blood vessel's functionality and integrity. The hyperglycemia associated with Type 2 diabetes mellitus (T2DM) was shown to impair the function of PCs and increase the risk of diabetes complications. In this study, we aimed to investigate the deleterious effect of the diabetic microenvironment on the regenerative capacities of human PCs. METHODS: PCs isolated from human adipose tissue were cultured in the presence or absence of serum collected from diabetic patients. The functionality of PCs was analyzed after 6, 14, and 30 days. RESULTS: Microscopic examination of PCs cultured in DS (DS-PCs) showed increased aggregate formation and altered surface topography with hyperbolic invaginations. Compared to PCs cultured in normal serum (NS-PCs), DS-PCs showed more fragmented mitochondria and thicker nuclear membrane. DS caused impaired angiogenic differentiation of PCs as confirmed by tube formation, decreased VEGF-A and IGF-1 gene expression, upregulated TSP1, PF4, actin-related protein 2/3 complex, and downregulated COL21A1 protein expression. These cells suffered more pronounced apoptosis and showed higher expression of Clic4, apoptosis facilitator BCl-2-like protein, serine/threonine protein phosphatase, and caspase-7 proteins. DS-PCs showed dysregulated DNA repair genes CDKN1A, SIRT1, XRCC5 TERF2, and upregulation of the pro-inflammatory genes ICAM1, IL-6, and TNF-α. Further, DS-treated cells also showed disruption in the expression of the focal adhesion and binding proteins TSP1, TGF-ß, fibronectin, and PCDH7. Interestingly, DS-PCs showed resistance mechanisms upon exposure to diabetic microenvironment by maintaining the intracellular reactive oxygen species (ROS) level and upregulation of extracellular matrix (ECM) organizing proteins as vinculin, IQGAP1, and tubulin beta chain. CONCLUSION: These data showed that the diabetic microenvironment exert a deleterious effect on the regenerative capacities of human adipose tissue-derived PCs, and may thus have possible implications on the vascular complications of T2DM. Nevertheless, PCs have shown remarkable protective mechanisms when initially exposed to DS and thus they could provide a promising cellular therapy for T2DM.

parazitCUB: An R package to streamline the process of investigating the adaptations of parasites' codon usage bias.

Examining the intricate association between parasites and their hosts, particularly at the codon level, assumes paramount importance in comprehending evolutionary processes and forecasting the characteristics of novel parasites. While diverse metrics and statistical analyses are available to explore codon usage bias (CUB), there presently exists no dedicated tool for examining the co-adaptation of codon usage between parasites and hosts. Therefore, we introduce the parazitCUB R package to address this challenge in a scalable and efficient manner, as it is capable of handling extensive datasets and simultaneously analyzing of multiple parasites with optimized performance. parazitCUB enables the elucidation of parasite-host interactions and the evolutionary patterns of parasites through the implementation of various indices, cluster analysis, multivariate analysis, and data visualization techniques. The tool can be accessed at the following location: https://github.com/AliYoussef96/parazitCUB.

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.

Comparative Shotgun Proteomics Reveals the Characteristic Protein Signature of Osteosarcoma Subtypes.

Osteosarcoma is a primary malignant bone tumor affecting adolescents and young adults. This study aimed to identify proteomic signatures that distinguish between different osteosarcoma subtypes, providing insights into their molecular heterogeneity and potential implications for personalized treatment approaches. Using advanced proteomic techniques, we analyzed FFPE tumor samples from a cohort of pediatric osteosarcoma patients representing four various subtypes. Differential expression analysis revealed a significant proteomic signature that discriminated between these subtypes, highlighting distinct molecular profiles associated with different tumor characteristics. In contrast, clinical determinants did not correlate with the proteome signature of pediatric osteosarcoma. The identified proteomics signature encompassed a diverse array of proteins involved in focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathways, and proteoglycans in cancer, among the top enriched pathways. These findings underscore the importance of considering the molecular heterogeneity of osteosarcoma during diagnosis or even when developing personalized treatment strategies. By identifying subtype-specific proteomics signatures, clinicians may be able to tailor therapy regimens to individual patients, optimizing treatment efficacy and minimizing adverse effects.

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.

Insights into The Codon Usage Bias of 13 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Isolates from Different Geo-locations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of Coronavirus disease 2019 (COVID-19) which is an infectious disease that spread throughout the world and was declared as a pandemic by the World Health Organization (WHO). In this study, we performed a genome-wide analysis on the codon usage bias (CUB) of 13 SARS-CoV-2 isolates from different geo-locations (countries) in an attempt to characterize it, unravel the main force shaping its pattern, and understand its adaptation to Homo sapiens . Overall results revealed that, SARS-CoV-2 codon usage is slightly biased similarly to other RNA viruses. Nucleotide and dinucleotide compositions displayed a bias toward A/U content in all codon positions and CpU-ended codons preference, respectively. Eight common putative preferred codons were identified, and all of them were A/U-ended (U-ended: 7, A-ended: 1). In addition, natural selection was found to be the main force structuring the codon usage pattern of SARS-CoV-2. However, mutation pressure and other factors such as compositional constraints and hydrophobicity had an undeniable contribution. Two adaptation indices were utilized and indicated that SARS-CoV-2 is moderately adapted to Homo sapiens compared to other human viruses. The outcome of this study may help in understanding the underlying factors involved in the evolution of SARS-CoV-2 and may aid in vaccine design strategies.

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).

vhcub: Virus-host codon usage co-adaptation analysis.

Viruses show noticeable evolution to adapt and reproduce within their hosts. Theoretically, patterns and factors that affect the codon usage of viruses should reflect evolutionary changes that allow them to optimize their codon usage to their hosts. Some software tools can analyze the codon usage of organisms; however, their performance has room for improvement, as these tools do not focus on examining the codon usage co-adaptation between viruses and their hosts. This paper describes the vhcub R package, which is a crucial tool used to analyze the co-adaptation of codon usage between a virus and its host, with several implementations of indices and plots. The tool is available from: https://cran.r-project.org/web/packages/vhcub/.