Journal: bioRxiv

Article Title: Analysis of circulating protein aggregates reveals pathological hallmarks of amyotrophic lateral sclerosis

doi: 10.1101/2020.04.30.070979

Figure Lengend Snippet: Molecular weight (MW) (A), isoelectric point (pI) (B) and hydrophobicity (GRAVY index) (C), known to affect aggregation propensity, are compared across proteins found to be expressed only in ALS and HC CPA (ALS and HC respectively), proteins shared between ALS and HC CPA datasets (Shared), proteins within brain aggregates (Brain) and in the entire human proteome. The distribution plots show the dispersion of the samples with relative frequency, while the violin plots show median and interquartile ranges. Statistical analysis was performed on ALS, HC, Shared and Human proteome with one-way ANOVA, Kruskal-Wallis test with Dunn’s multiple comparison as post-test for group analysis with * expressing the level of significance (*: p = 0.0251; ****: p < 0.0001).

Article Snippet: Non-parametric group analysis was performed using Kruskal–Wallis one-way test of variance on ranks with Dunn’s multiple comparison as post-test using GraphPad(v7).

Techniques: Molecular Weight, Dispersion, Comparison, Expressing

Journal: bioRxiv

Article Title: Analysis of circulating protein aggregates reveals pathological hallmarks of amyotrophic lateral sclerosis

doi: 10.1101/2020.04.30.070979

Figure Lengend Snippet: Molecular weight (MW) (A), isoelectric point (pI) (B) and hydrophobicity (GRAVY index) (C) were compared across TMT-CPA, BPA and Human proteome datasets. Statistical analysis was performed with one-way ANOVA, Kruskal-Wallis test with Dunn’s multiple comparison as post-test. The violin plots show median and interquartile ranges across the three datasets. The TMT-CPA dataset differ significantly compared to the Human proteome and BPA for MW and pI, while for hydrophobicity (GRAVY index), there is only a less significant difference with the BPA dataset. **** p < 0.0001. (A and B); * p = 0.0348, TMT-CPA versus BPA (C); * p = 0.0224, BPA versus Human proteome (C).

Article Snippet: Non-parametric group analysis was performed using Kruskal–Wallis one-way test of variance on ranks with Dunn’s multiple comparison as post-test using GraphPad(v7).

Techniques: Molecular Weight, Comparison

Journal: Frontiers in Immunology

Article Title: A Quantitative Approach to Unravel the Role of Host Genetics in IgG-FcγR Complex Formation After Vaccination

doi: 10.3389/fimmu.2022.820148

Figure Lengend Snippet: IgG1 allotype determines whether boosting IgG1 concentration or boosting IgG1 affinity (k on IgG1- FcγR) would be most effective for increasing complex formation. (A) Model predictions for complex formation of RV144 vaccinees (n=105) in two FcγRIIIa polymorphisms, FcγRIIIa-V 158 (light pink) and FcγRIIIa-F 158 (dark pink), and three IgG1 allotypes, G1m1,3 (original RV144 data), G1m1 and G1m-1,3. Polymorphisms were simulated by altering the binding affinities of each IgG subtype to FcγR as previously published and indicated in Figure 3A . Allotypes are simulated by multiplying each vaccinee’s IgG1, IgG2, IgG3 and IgG4 initial concentration by its respective conversion factor as previously published and indicated in Figure 4A (Friedman test with Dunn’s multiple comparisons test comparing the two polymorphisms within each allotype; ****p-value < 0.001). (B) Simulated IgG1 concentration boosting in each allotype (G1m1,3, white; G1m1, gray; G1m-1,3 black) and polymorphism (FcγRIIIa-V 158 , light pink; FcγRIIIa-F 158 , dark pink) combination. Boosts were calculated by multiplying the individual’s baseline initial IgG1 concentration value by the boost levels and then this was added on top of each individual’s baseline. (B) Color indicates median change in complex formation for each genetic background. (C) Simulated boosting of k on IgG1- FcγR in each allotype (G1m1,3, white; G1m1, gray; G1m-1,3 black) and polymorphism (FcγRIIIa-V 158 , light pink; FcγRIIIa-F 158 , dark pink) combination. Boosts were calculated by multiplying the individual’s baseline k on IgG1- FcγR value by the boost levels and then this was added on top of each individual’s baseline. Color indicates median change in complex formation for each genetic background and boost as indicated. (D) The ratio of median change in complex formation with a boost in IgG1 concentration over median change in complex formation with a boost in k on IgG1-FcγR (affinity) at each boosting level. This ratio shows which type of boost is most effective for increasing complex formation (IgG1 concentration, purple; k on IgG1-FcγR, green) and when both are equally beneficial (white).

Article Snippet: In order to evaluate affinity changes resulting from glycosylation, projected upon all vaccinees for each of the three allotypes, the IgG-FcR immune complex formation was simulated at baseline, and the difference between each individual’s complex formation at baseline and with glycosylation for each allotyped population and compared them with a Friedman test with Dunn’s multiple comparisons in GraphPad Prism.

Techniques: Concentration Assay, Binding Assay

Journal: Frontiers in Immunology

Article Title: A Quantitative Approach to Unravel the Role of Host Genetics in IgG-FcγR Complex Formation After Vaccination

doi: 10.3389/fimmu.2022.820148

Figure Lengend Snippet: Glycosylation differentially impacts IgG1 allotypes. (A) Expected IgG1, IgG2, IgG3, and IgG4 concentrations for G1m1,3 (white), G1m1 (gray), and G1m-1,3 (black) allotypes based on previously published work ( , ). (B) Model predictions for complex formation as IgG1 concentration and k on IgG1- FcγR are altered over physiological ranges ( Figure 2B ). Lines indicate IgG1 concentrations for three different IgG1 allotypes (G1m1,3 (white), G1m1 (gray), G1m-1,3 (black)), and the affinity change expected from an afucosylation glycosylation modification (purple) compared to baseline (light blue). (C) The difference ( Figure 2C ) between the combined parameter change surface ( Figure 2A ) and the additive surface ( Figure 2B ). Lines indicate IgG1 concentrations for three different IgG1 allotypes (G1m1,3 (white), G1m1 (gray), G1m-1,3 (black)), and the affinity change expected from an afucosylation glycosylation modification (dark blue) compared to baseline FcgRIIIaV158 (light blue). (D) Change in complex formation from baseline affinity to an afucosylated affinity in each allotype, G1m1,3 (white), G1m1 (gray), and G1m-1,3 (black) (Friedman test with Dunn’s multiple comparisons test; ****p-value < 0.001).

Article Snippet: In order to evaluate affinity changes resulting from glycosylation, projected upon all vaccinees for each of the three allotypes, the IgG-FcR immune complex formation was simulated at baseline, and the difference between each individual’s complex formation at baseline and with glycosylation for each allotyped population and compared them with a Friedman test with Dunn’s multiple comparisons in GraphPad Prism.

Techniques: Glycoproteomics, Concentration Assay, Modification