[Analysis of enzyme activity and substrate specificity of dolichyl-phosphate ß-glucosyltransferase].

Protein folding and quality control processes primarily occur in the endoplasmic reticulum (ER). ER-resident molecular chaperones play a crucial role in guiding nascent polypeptides towards their correct tertiary structures. Some of these chaperones specifically recognize glucosylated N-glycan moieties on peptide. It is of great significance to study the N-glycan biosynthetic pathway and glycoprotein quality control system by analyzing the sugar donor of ER luminal glucosyltransferases, known as dolichol phosphate glucose (Dol-P-Glc), or its analogues in vitro. In this study, we investigated a range of dolichol analogues to synthesize lipid phosphate glucose, which served as substrates for dolichyl-phosphate ß-glucosyltransferase E (Alg5E) derived from Trichomonas vaginalis. The results demonstrated that the recombinant Alg5E, expressed in Escherichia coli, exhibited strong catalytic activity and the ability to recognize lipid phosphate glucose with varying chain lengths. Interestingly, the enzyme's catalytic reaction was found to be faster with longer carbon chains in the substrate. Additionally, Alg5E showed a preference for branched chain methyl groups in the lipid structure. Furthermore, our study confirmed the importance of divalent metal ions in the binding of the crucial DXD motif, which is essential for the enzyme's catalytic function. These findings lay the groundwork for future research on glucosyltransferases Alg6, Alg8, and Alg10 in the synthesis pathway of dolichol-linked oligosaccharide (DLO).

Plasma-Free Blood as a Potential Alternative to Whole Blood for Transcriptomic Analysis.

RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics, and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples that are generally considered less useful byproducts during the processes of plasma and serum separation, respectively. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the suitability of employing PFB or SFB samples as an alternative RNA source in transcriptomic analysis. We performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples than SFB samples in terms of protein-coding gene expression patterns, detection of differentially expressed genes, and immunological characterizations, suggesting that PFB can serve as a viable alternative to WB for transcriptomic analysis. Our study contributes to the optimization of blood sample utilization and the advancement of precision medicine research. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00121-1.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Overexpression of CD99 is associated with tumor adaptiveness and indicates the tumor recurrence and therapeutic responses in gliomas.

Glioma undergoes adaptive changes, leading to poor prognosis and resistance to treatment. CD99 influences the migration and invasion of glioma cells and plays an oncogene role. However, whether CD99 can affect the adaptiveness of gliomas is still lacking in research, making its clinical value underestimated. Here, we enrolled our in-house and public multiomics datasets for bioinformatic analysis and conducted immunohistochemistry staining to investigate the role of CD99 in glioma adaptive response and its clinical implications. CD99 is expressed in more adaptative glioma subtypes and cell states. Under hypoxic conditions, CD99 is upregulated in glioma cells and is associated with angiogenesis and metabolic adaptations. Gliomas with over-expressed CD99 also increased the immunosuppressive tumor-associated macrophages. The relevance with tumor adaptiveness of CD99 presented clinical significance. We discovered that CD99 overexpression is associated with short-time recurrence and validated its prognostic value. Additionally, Glioma patients with high expression of CD99 were resistant to chemotherapy and radiotherapy. The CD99 expression was also related to anti-angiogenic and immune checkpoint inhibitor therapy response. Inhibitors of the PI3K-AKT pathway have therapeutic potential against CD99-overexpressing gliomas. Our study identified CD99 as a biomarker characterizing the adaptive response in glioma. Gliomas with high CD99 expression are highly tolerant to stress conditions such as hypoxia and antitumor immunity, making treatment responses dimmer and tumor progression. Therefore, for patients with CD99-overexpressing gliomas, tumor adaptiveness should be fully considered during treatment to avoid drug resistance, and closer clinical monitoring should be carried out to improve the prognosis.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.