Inactivated C3b

Read filtering, OTU clustering, and annotation were performed with the MASQUE pipeline (https://github.com/aghozlane/masque), as described by Quereda et al. (63 (link)). A total of 2851 OTUs were obtained at 97% sequence identity threshold. The statistical analyses were performed with SHAMAN (shaman.c3bi.pasteur.fr) based on R software (v3.1.1) and bioconductor packages (v2.14). Because bacterial communities were expected to differ substantially between mosquito midguts and water samples, the normalization of OTU counts was performed at the OTU level by sample type (midgut or water) using the DESeq2 normalization method. All samples including the negative controls and technical replicates were included in the normalization step. The technical replicates were removed from the data set before analysis. To account for possible contamination at various steps in the sample-processing pipeline, the OTU counts were corrected with the reads from the negative controls (see above). All OTUs found in the negative control samples were removed from the normalized OTU table unless the count in a real sample was >10 times higher than the mean OTU count in the negative controls. This operation was performed with a homemade script in R (64 (link)). This normalized OTU count table with the OTUs found in the negative controls removed (file S8) was used for the richness, Shannon index, Venn diagrams, abundance heat map, and NMDS analysis. Observed richness, Shannon index, and Bray-Curtis distances were calculated with the vegan package in R (65 ). The effects of sample types and ecotypes on the bacterial richness were tested by fitting a generalized linear model (GLM) with a Poisson distribution. The SEs were corrected for overdispersion using a quasi-GLM model where the variance is given by the mean multiplied by the dispersion parameter. A χ² test was applied to compare the significance of deviance shift after adding the covariates sequentially. The effects of sample type, ecotype, and their interaction on Shannon index were tested by fitting a linear model with a normal error distribution. The response variable was power-transformed to satisfy the model assumptions. The significance of each variable was tested with an analysis of variance (ANOVA) after adding the covariates sequentially. The results of the two models were confirmed by the convergence of backward and forward selection based on the Akaike information criterion. The Bray-Curtis distances were plotted with an NMDS method constrained in two dimensions. The Spearman correlation with real distances and stress value was estimated with the vegan R package (65 ). Effects of habitat and sample type on β diversity were tested with the betadisper and adonis permutational multivariate ANOVA methods from the vegan R package with 999 permutations of the Bray-Curtis distance matrix derived from OTU counts.
In SHAMAN, a GLM was fitted and vectors of contrasts were defined to determine the significance in abundance variation between sample types. The GLM included the main effect of habitat (sylvatic or domestic), the main effect of sample type (midgut or water), and their interaction. The resulting P values were adjusted for multiple testing according to the Benjamini and Hochberg procedure. All OTUs that were present in the negative controls were excluded from the final list of differentially abundant OTUs.
To confirm the OTU-based results with an OTU-independent method, a dissimilarity matrix was generated with the SIMKA software (66 ). Reads with a positive match against the sequences assembled from the negative controls were removed using Bowtie v2.2.9 (67 (link)). Then, k-mers of size 32 and occurring at least greater than two times were identified with SIMKA. Bray-Curtis dissimilarity was estimated between each sample.

Each experiment was performed in two technical replicates and three independent runs. The level of complement activation for each experiment is expressed as M ± σ_M, where M is the average of the mean values obtained in each independent run and σ_M the average standard deviation calculated by:
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$${{\rm{\sigma }}_{\rm{M}}}{\rm{ = }}\sqrt {{{{{\rm{\sigma }}_{\rm{1}}}{\rm{ + }}{{\rm{\sigma }}_{\rm{2}}}{\rm{ + }}{{\rm{\sigma }}_{\rm{3}}}} \over {\rm{3}}}} $$
\end{document}To calculate the activation of the complement system, the concentration of iC3b measured in the negative control (PBS-challenged sera) was subtracted from the concentration measured in the tested samples (cobra venom and liposomes-challenged sera). The standard variation of the subtraction was calculated as the square root of the sum of the quadrature of the standard deviation of the negative control plus the quadrature of the standard deviation of the test samples. Statistical comparison among the different groups was carried out by running a one-way analysis of variance (ANOVA) and Dunnett’s Multiple comparison test. Differences were considered as statistically significant among the selected groups when p < 0.05.

Before performing the complement activation assay, the levels of endotoxin contamination in the selected liposomes were quantified as presented in our recent work.19 (link) Briefly, the endotoxin levels were determined using the Limulus amoebocyte lysate (LAL) chromogenic methods by kinetic assay. The control standard curve was prepared with the standard endotoxin from the E. coli O111:B4 strain (cat. EC010, 10 ng/vial; Associates of Cape Code, Inc.). The sample readouts were carried out at 405 nm, according to manufacturer’s instruction. Results for each sample were taken into consideration only if the correlation coefficient of the calibration curve was ≥0.98.
A previously published protocol for the quantitative determination of complement activation in human plasma was further optimised as described in Figure 2.20
The liposome’s stock solutions were stored at 4 °C. Working solutions of 450 μg/mL and 150 μg/mL were prepared by diluting the liposomes in sterile PBS buffer (pH 7.4). The liposome solutions for in vitro testing were prepared at concentrations of 50 and 150 μg/mL by mixing the liposomes working solutions with PBS and human serum. In brief, the enzyme-linked immunosorbent assay (ELISA) was performed according to the MicroVue iC3b EIA Quidel manufacturer’s instructions. Serum samples were diluted in a U-shaped 96-well plate by 1:100 in the specimen diluent. Then the standards, the controls and the diluted serum samples were transferred to the ELISA plate and incubated at room temperature for 30 min. The ELISA plate was thoroughly washed with wash solution provided by the kit. The iC3b conjugate was added to the plate for 30 min and after washing steps, substrate solution was added to the plate for 30 min. Finally, the addition of stop solution blocked the enzymatic reaction and the iC3b levels were determined by absorbance reading at 405 nm with an EnSpire® Multimode plate reader (Perkin Elmer).

Pooled serum from healthy donors (cod A113, Quidel, Corporation, CA, USA) and individual serum samples derived from healthy donors as well as from patients suffering from diseases such as immunodeficiency, allergy and cancer (In.Vent, Germany) were aliquoted and stored at − 80°C until further analysis. Additional information on clinical parameters of human serum obtained from both healthy donors (n_healthy=32 donors) and patients with preconditions (n_allergy=26, n_{immunodeficinecy}=16; n_cancer=15) are presented in the Supplements Table S2. Heat Aggregated Gamma Globulin (HAGG; cat. A114) and Cobra Venom Factor (CVF; cat. A600) were purchased from Quidel (Santa Clara, CA, USA) while Zymosan A (CAS 58856–93-2) was purchased from Sigma Aldrich (Italy) to be used as positive controls for the activation of the complement system. The induction of iC3b levels were quantified with a commercially available Microvue Enzyme-linked Immunosorbent Assay kit (cat. A006, Quidel, Corporation, CA, USA) following manufacturer’s instructions.