solanum tuberosum  (Vector Laboratories)

Journal: Blood

Article Title: Enhanced α2-3–linked sialylation determines the extended half-life of CHO-rVWF

doi: 10.1182/blood.2024027038

Figure Lengend Snippet: Differences in glycosylation and sialylation between CHO-rVWF and pdVWF. (A) Diagrams illustrate common N- and O-linked glycan structures expressed on human pdVWF and lectin affinities. (B) Lectin plate–binding assays were performed to compare glycans expressed on CHO-rVWF to pdVWF. Lectins used included SNA, MAA-II, WGA, RCA-I, and ECA. All enzyme-linked immunosorbent assays (ELISAs) were performed in triplicate and results expressed as a percentage of binding normalized to pdVWF. Data were analyzed for normality using the Shapiro-Wilk test and compared using the Student t test. Data are presented as mean ± standard error of the mean (SEM). (C) A, B, and H blood group carbohydrate determinants on pdVWF and CHO-rVWF were assessed using plate-binding assays. (D) LC-MS was used to analyze the N-glycans on CHO-rVWF compared to pdVWF; chromatograms of pdVWF (top) and CHO-rVWF (bottom). Peaks are annotated with the most abundant N-glycan indicated per peak. In pdVWF, H5N4F1S2, highlighted in pink, was present in α2,3 α2,3; α2,3 α2,6; and α2,6 α2,6 forms. In contrast, CHO-rVWF only displays a single linkage form (α2,3 α2,3). (E) To investigate the clearance of CHO-rVWF in mice, VWF –/– mice were infused with either pdVWF (blue) or CHO-rVWF (red), and blood was collected at 3, 30 minutes, and 1, 2, 3, 4, and 6 hours after infusion. At each time point, residual circulating VWF concentration was determined by VWF:Ag ELISA, and mean residence time was calculated. P value is the outcome of extra-sum-of-squares F test. (F) To study the importance of terminal sialylation in modulating the prolonged half-life of CHO-rVWF, in vivo studies were repeated in VWF –/– mice after the digestion of CHO-rVWF and pdVWF with α2-3,6,8,9 neuraminidase (asialo-CHO-rVWF and asialo-pdVWF respectively). Three to 5 mice were used per point time, and data are presented as mean ± SEM. MAA-II, M amurensis lectin II; SNA, S nigra agglutinin; WGA, wheat germ agglutinin.

Article Snippet: Biotinylated lectins used included Sambucus nigra agglutinin, Maackia amurensis agglutinin II, wheat germ agglutinin, Erythrina cristagalli agglutinin (ECA), Ricinus communis agglutinin I (RCA-I), Ulex europaeus agglutinin I, concanavalin A, and peanut agglutinin; Galanthus nivalis lectin; Solanum tuberosum lectin; and Lens culinaris agglutinin (Vector Laboratories).

Techniques: Glycoproteomics, Binding Assay, Liquid Chromatography with Mass Spectroscopy, Concentration Assay, Enzyme-linked Immunosorbent Assay, In Vivo

Journal: Gut microbes

Article Title: Single-cell microbiota phenotyping reveals distinct disease and therapy-associated signatures in Crohn's disease.

doi: 10.1080/19490976.2025.2452250

Figure Lengend Snippet: Figure 2. Microbiota phenotyping by flow cytometry and machine learning to identify specific biosignatures in human stool samples. (a) Human intestinal bacteria from stool samples were stained with monoclonal antibodies specific for the human immunoglobulins IgA1, IgA2, IgM and IgG and with the lectins peanut agglutinin (PNA), wheat germ agglutinin (WGA), Solanum tuberosum lectin (STL) and concanavalin a (ConA). Each staining panel also included the cell wall/membrane-permeable DNA dye hoechst 33,342. After data acquisition in a flow cytometer, the cells of each staining panel were clustered according to a previously defined self-organizing map (SOM) into 2025 clusters. The abundance of cells per cluster in the total of 4050 clusters represented the overall microbiota phenotype of a sample. (b) The clusters were filtered by Wilcoxon statistical evaluation and recursive feature elimination to select the significant and most relevant clusters defining the specific microbiota biosignature for random forest model-based classification of disease and comparison of samples by Bray-Curtis dissimilarity (β-diversity) projection. (c) Outline of all relevant data processing steps and used packages (R) for computing a microbiota phenotype.

Article Snippet: The lectins used for staining were 0.5 μg/test Peanut AgglutininCF®488 (PNA, Biotium Cat. No.29060), 0.5 μg/test Concanavalin A-CF®680 (Con A, Biotium Cat. No. 29020–1) and 0.25 μg/test Wheat Germ Agglutinin-CF®555 (WGA, Biotium Cat. No.29076–1); 0.5 μg/test of biotinylated Solanum Tuberosum Agglutinin (STL, Vector Laboratories/ Biozol Cat. No. B-1165) were shortly pre-incubated with 2 μL (1:50, v/v) anti-Biotin-PerCP antibody (clone: Bio3-18E7, Miltenyi Biotech Cat. No. 130- 133-293) before adding to the residual reagents.

Techniques: Flow Cytometry, Bacteria, Staining, Bioprocessing, Membrane, Comparison