Glycolipids

So as to plan an appropriate vaccine candidate, it must have the ability to induce CTL and HTL immune response. In other words subunit vaccine must contain both CTL and HTL epitopes along with suitable linkers. Keeping in mind the end goal to effectively activate both innate and adaptive immune response, subunit vaccine must consist of a strong immunostimulatory adjuvant. In the previous decades, there is a huge headway in the adjuvant engineering, for instance, Toll-like receptor (TLR) agonists have made its contribution as a part of peptide-based subunit vaccine as a functional option for present-day immunotherapy^{45 (link)}. Recently, Junqueira et al. have shown that CpGs oligodeoxynucleotides (CpG ODNs) and Glycoinositolphospholipids (GIPL) gotten from Trypanosome cruzi having the ability to activate TLR-4 and TLR9 leads to actuate potent pro-inflammatory reaction^{46 (link)}. Secondly, proteo-glycolipid complex (P8GLC) derived from Leishmania parasite has shown its affinity for the TLR-4 receptor and recognized as ligand^{47 (link)}. Moreover, TLRs having the capability to recognize the Plasmodium ligands, for example, Plasmodium falciparum primes the human TLR-4 response towards high proinflammatory cytokine profile^{48 (link)}. Shanmugam A. et at. has reported that synthetic TLR-4 agonist namely RS-09 (Sequence: APPHALS) can be used as a novel class of adjuvant^{49 (link)}, therefore, it was added as an adjuvant and linked with epitopes (CTL and HTL) by using EAAAK linker^{50 (link)}. Linkers assume an imperative part in simulating the vaccine construct to work as an independent immunogen and producing higher antibody titer than that of single immunogen^{51 (link)}. Total three linkers namely EAAAK, AAY, and GPGPG, were used to construct the final vaccine. AAY and GPGPG linkers were added at the intra-epitope position to link the CTL and HTL epitopes, respectively.

The binding specificities of the his-tagged recombinant BKPyV VP1s were analyzed in the neoglycolipid (NGL)-based microarray system.³⁴ (link) Two versions of microarrays were used: (1) ganglioside-focused arrays featuring 25 glycolipid and NGL probes (Figure 2A), and (2) broad spectrum screening microarrays of 672 sequence-defined lipid-linked glycan probes, of mammalian and non-mammalian type essentially as previously described.³⁵ (link) The glycan probes included in the screening arrays and their sequences are given in Table S2. Details of the preparation of the glycan probes and the generation of the microarrays are in Supplementary Glycan Microarray Document (Table S3) in accordance with the MIRAGE (Minimum Information Required for A Glycomics Experiment) guidelines for reporting of glycan microarray-based data.³⁶ (link) The microarray analyses were performed essentially as described.³ (link)^,37 (link) In brief, after blocking the slides for 1h with HBS buffer (10 mM HEPES, pH 7.4, 150 mM NaCl) containing 0.33% (w/v) blocker Casein (Pierce), 0.3% (w/v) BSA (Sigma-Aldrich) and 5 mM CaCl₂, the microarrays were overlaid with the VP1 proteins for 90 min as protein-antibody complexes that were prepared by preincubating VP1 with mouse monoclonal anti-polyhistidine and biotinylated anti-mouse IgG antibodies (both from Sigma) at a ratio of 4:2:1 (by weight) and diluted in the blocking solution to provide a final VP1 concentration of 150 μg/mL. Binding was detected with Alexa Fluor-647-labelled streptavidin (Molecular Probes) at 1 μg/mL for 30 min. Unless otherwise specified, all steps were carried out at ambient temperature. Microarray imaging and data analysis are described in the Supplementary MIRAGE document (Table S3).

Lipidic moieties were described by common Martini parameters. RLs CG topologies (not available in the Martini FF) were built starting from those of monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) in their anionic form (López et al., 2013 (link); van Eerden et al., 2015 (link)). Out of many different models available, differing in the mapping, Martini bead types, rhamnose/s bead bonds, dihedral angles as well as constraint and/or exclusion definitions, we selected the one shown in Figure 2 on the basis of the comparison of results obtained by all-atom (AA) and CG simulations. In particular, a RL (mono- or di-RL) was simulated in both water (10 ns) and POPC membrane (128 total lipids, 500 ns) using both C36 (2 fs time steps) and Martini 2 (20 fs time step) FFs.
First, CG distributions of bonds, angles and dihedral angles were compared to the atomistic ones (for the comparison we used the centre of mass of atoms corresponding to each CG bead). The choice of the best model required several tests until a good reproduction of CG bonds, angles and dihedral angles was achieved (see Supplementary Figures S1, S2 for the optimised mono- and di-RLs topologies, shown in Supplementary Tables S1, S2).
To ensure that the chosen CG topologies could also reproduce membrane properties, we monitored both area per lipid and membrane thickness of (a) RL-containing POPC membrane as well as for (b) a more complex model containing POPC/POPG/ergosterol (53/23/25) and 1 RL/25 lipids (40/60, mono- and di-RL) (see Supplementary Figure S3). Both models allowed us to compare the results with previous works from our group (Monnier et al., 2019 (link)). These latter models were run with 256 lipids, 4 mono-RLs and 6 di-RLs for 500 ns (AA) and with 864 lipids, 9 mono-RLs and 27 di-RLs for 10 µs (CG).
For model (a), membrane properties were nicely reproduced with stable trajectories (20 fs time step). Good results were also achieved for model (b) (see Supplementary Figure S3) when reducing the time step (14 fs), as previously reported for other complex glycolipids (López et al., 2013 (link)).
The final parameters used for mono- and di-RLs topologies are listed in Supplementary Tables S1, S2. They correspond to those of the model which gave the best results in terms of system stability, bond, angle and dihedral angle distributions (Supplementary Figure S1), and structural membrane properties (Supplementary Figure S3). Mappings and Martini bead assignments are shown in Figure 2A.

Galactolipids such as monogalactosyldiacylglycerols (MGDG) and digalactosyldiacylglycerols (DGDG) are crucial lipids involved in photosynthesis and metabolic regulation. They are affected by various stress conditions such as nutrient limitation. Often when galactolipids are quantified, it is only performed as a sum parameter using methods that do not differentiate between the individual MGDG and DGDG. However, to obtain an impression of the distribution and to evaluate whether the formation of individual MGDG and DGDG changes depending on sulfur nutrition, it was necessary to develop a method that simultaneously determines individual MGDG and DGDG. The development of the new LC-ESI-MS/MS method, as well as the method validation process, are based on procedures described by Fischer et al. [35 (link)] with slight modifications. The galactolipids MGDG and DGDG were analyzed on an Agilent 1260 Infinity Quarternary LC System (Agilent Technologies Deutschland GmbH, Waldbronn, Germany) coupled to a triple quadrupole API 4000 QTrap mass spectrometer (Sciex Germany GmbH, Darmstadt, Germany) equipped with a turbo spray source, which was operating in positive ion mode, with the following mass spectrometer settings: ion spray voltage: 4500 V; ion source heater  =  650 °C source gas 1: 50 psi; source gas 2: 0 psi; and curtain gas: 10 psi. The injection volume for all samples was 5 μL, the column oven temperature was set to 20 °C, and the autosampler was maintained at 4 °C. The separation of analytes was achieved using a Kinetex^® C18 column (2.6 μm, 150 mm × 2.1 mm), equipped with a Kinetex^® C18 security guard column (Phenomenex Inc., Torrance, CA, USA), using a constant flow rate of 300 μL min⁻¹. Eluent A was water with 2 mM ammonium acetate and eluent B was acetonitrile. The elution started with 55% eluent B for 2 min and linearly increased to 87.5% eluent B within 2 min, which was kept constant for 41 min. Then, the composition was readjusted to 55% eluent B within 1 min, followed by 4 min of re-equilibration. MRM transitions of the different galactolipids were obtained by direct flow injection of the MGDG and DGDG mix (in methanol with 2 mM ammonium acetate) into the ESI source in positive mode. Each glycolipid (MGDG, DGDG) precursor ion was determined based on three specific fragment ions (product ions). Table 1 and Table 2 summarize the precursor and fragment ions of all compounds tested in the present study. Mass analyzer settings were optimized for all analytes to maximize the transmission and sensitivity of each characteristic mass transition. These optimizations were acquired automatically by using autotune mode provided by the Analyst^® software 1.6.1 (AB Sciex Germany GmbH, Darmstadt, Germany).