Integrated proteome and phosphoproteome analysis of interscapular brown adipose and subcutaneous white adipose tissues upon high fat diet feeding in mouse

A "One-Step" Strategy for the Global Characterization of Core-Fucosylated Glycoproteome

Core fucosylation, a special type of N-linked glycosylation, is important in tumor proliferation, invasion, metastatic potential, and therapy resistance. However, the core-fucosylated glycoproteome has not been extensively profiled due to the low abundance and poor ionization efficiency of glycosylated peptides. Here, a "one-step" strategy has been described for protein core-fucosylation characterization in biological samples. Core-fucosylated peptides can be selectively labeled with a glycosylated probe, which is linked with a temperature-sensitive poly(N-isopropylacrylamide) (PNIPAM) polymer, by mutant endoglycosidase (EndoF3-D165A). The labeled probe can be further removed by wild-type endoglycosidase (EndoF3) in a traceless manner for mass spectrometry (MS) analysis. The feasibility and effectiveness of the "one-step" strategy are evaluated in bovine serum albumin (BSA) spiked with standard core-fucosylated peptides, H1299, and Jurkat cell lines. The "one-step" strategy is then employed to characterize core-fucosylated sites in human lung adenocarcinoma, resulting in the identification of 2494 core-fucosylated sites distributed on 1176 glycoproteins. Further data analysis reveals that 196 core-fucosylated sites are significantly upregulated in tumors, which may serve as potential drug development targets or diagnostic biomarkers. Together, this "one-step" strategy has great potential for use in global and in-depth analysis of the core-fucosylated glycoproteome to promote its mechanism research.

A dataset resource for clinically associated phosphosites in hepatocellular carcinoma

Phosphorylation is one of the most common post-translational modifications (PTMs) and is closely related to protein activity and function, playing a critical role during cancer development. Quantitative phosphoproteomic strategies have been widely used to study the underlying mechanisms of cancer progression or drug resistance. In this report, we analyzed the association of phosphosite levels originated from our previously reported proteogenomic study in hepatocellular carcinoma (HCC) with clinical parameters, including prognosis, recurrence, and Tumor-Node-Metastasis (TNM) stages. By using both the log-rank test and univariate Cox proportional hazards regression analysis, we found that the abundance levels of 1712 phosphosites were associated with prognosis and those of 393 phosphosites associated with recurrence. Besides, 692 phosphosites had different abundance levels among TNM stages (I, II, III+IV) by Analysis of Variance (ANOVA) test. Gene ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using proteins with these statistically significant phosphosites. In conclusion, we provided a dataset resource for clinically associated phosphosites in HCC, which may be beneficial to liver cancer related basic research.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.