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.

Understanding codon usage patterns and their shaping factors in members of the Solanaceae family may reflect important features of their evolutionary history. Here, we investigated codon usage bias (CUB) over the whole genome of tobacco, tomato and potato. Analyzing the effective number of codons (ENc) revealed a weak CUB over the whole genome of the three genomes. The correlation analysis between the ENc and the nucleotide content at the third codon position indicated that genes with high ENc have high A/T and low G/C contents. Analyzing nucleotide composition and the relative synonymous codon usage confirmed that the three genomes are AT-rich and favor codons ending with T and/or A. Comparing CUB in each of the three genomes revealed that approximately 50% and 45% of the genes were dominated by mutational and natural selection, respectively. The remaining 5% of the genes were affected by combined factors of mutation and translation selection. The first four axes of the correspondence analysis (CA) were the major contributors to the variation within the codon sequences (CDS) of the three genomes. Results of a multi-regression analysis between the 4 axes and the CUB indices verified the fact that natural selection and directional mutation for base composition along with other factors could describe the variations in the CUB within the CDS of the three genomes. In conclusion, the CUB of the three species showed the same weak trend and that mutation and natural selection were the main factors influencing the nuclear genes of the three genomes.

Human SARS-CoV-2 and avian infectious bronchitis virus (IBV) are highly contagious and deadly coronaviruses, causing devastating respiratory diseases in humans and chickens. The lack of effective therapeutics exacerbates the impact of outbreaks associated with SARS-CoV-2 and IBV infections. Thus, novel drugs or therapeutic agents are highly in demand for controlling viral transmission and disease progression. Mesenchymal stem cells (MSC) secreted factors (secretome) are safe and efficient alternatives to stem cells in MSC-based therapies. This study aimed to investigate the antiviral potentials of human Wharton’s jelly MSC secretome (hWJ-MSC-S) against SARS-CoV-2 and IBV infections in vitro and in ovo. The half-maximal inhibitory concentrations (IC₅₀), cytotoxic concentration (CC₅₀), and selective index (SI) values of hWJ-MSC-S were determined using Vero-E6 cells. The virucidal, anti-adsorption, and anti-replication antiviral mechanisms of hWJ-MSC-S were evaluated. The hWJ-MSC-S significantly inhibited infection of SARS-CoV-2 and IBV, without affecting the viability of cells and embryos. Interestingly, hWJ-MSC-S reduced viral infection by >90%, in vitro. The IC₅₀ and SI of hWJ-MSC secretome against SARS-CoV-2 were 166.6 and 235.29 µg/mL, respectively, while for IBV, IC₅₀ and SI were 439.9 and 89.11 µg/mL, respectively. The virucidal and anti-replication antiviral effects of hWJ-MSC-S were very prominent compared to the anti-adsorption effect. In the in ovo model, hWJ-MSC-S reduced IBV titer by >99%. Liquid chromatography-tandem mass spectrometry (LC/MS-MS) analysis of hWJ-MSC-S revealed a significant enrichment of immunomodulatory and antiviral proteins. Collectively, our results not only uncovered the antiviral potency of hWJ-MSC-S against SARS-CoV-2 and IBV, but also described the mechanism by which hWJ-MSC-S inhibits viral infection. These findings indicate that hWJ-MSC-S could be utilized in future pre-clinical and clinical studies to develop effective therapeutic approaches against human COVID-19 and avian IB respiratory diseases.

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.

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.