The carbohydrate specificity of antibodies obtained by immunizing laboratory animals with immunogens prepared from the Aspergillus fumigatuscell wall was analyzed using the library of biotinylated...

Specific monosaccharide residue, β-D-galactofuranose (Galf) featuring a five-membered ring structure, is found in the glycans of fungi and bacteria, but is normally absent in healthy mammals and humans. In this study, synthetic oligosaccharides mimicking bacterial and fungal glycans were investigated by SERS (Surface-Enhanced Raman Scattering) techniques for the first time to distinguish between different types of glycan chains. SERS spectra of oligosaccharides related to fungal α-(1→2)-mannan, β-(1→3)-glucan, β-(1→6)-glucan, galactomannan of Aspergillus, galactan I of Klebsiella pneumoniae, and diheteroglycan of Enterococcus faecalis were measured. To analyze the spectra, a number of machine learning methods were used that complemented each other: principal component analysis (PCA), confidence interval estimation (CIE), and logistic regression with L1 regularization. Each of the methods has shown own effectiveness in analyzing spectra. Namely, PCA allows the visualization of the divergence of spectra in the principal component space, CIE visualizes the degree of overlap of spectra through confidence interval analysis, and logistic regression allows researchers to build a model for determining the belonging of the analyte to a given class of carbohydrate structures. Additionally, the methods complement each other, allowing the determination of important features representing the main differences in the spectra containing and not containing Galf residue. The developed mathematical models enabled the reliable identification of Galf residues within glycan compositions. Given the high sensitivity of SERS, this spectroscopic technique serves as a promising basis for developing diagnostic test systems aimed at detecting biomarkers of fungal and bacterial infections.

Furanoside derivatives are broadly present in the antigenic structures of pathogenic microorganisms and play a key role in their recognition by the host immune system. Despite the high demand for vaccine and diagnostic development, their chemical synthesis remains challenging. During the development of a new methodology for the synthesis of galactofuranoside building blocks, we encountered an unexpected predominance of the furanoside form in the equilibrium mixture of benzoylated β-galactosides. Since the furanoside form is typically less stable and is usually present only in minor amounts, we turned to computational studies to elucidate the driving force of this pyranoside- into -furanoside isomerisation. The DFT B3LYP-D3 approach was employed for this task with additional validation of its results at DLPNO-CCSD(T) level for the lowest energy conformers. The results demonstrate that the van-der-Waals interactions between phenyl rings of the benzoate substituents are crucial for the stabilization of the furanoside isomer. This outcome could not be rationalized within the framework of conventional carbohydrate chemistry, as the key intramolecular interactions determining the equilibrium lie outside the carbohydrate ring system. Consideration of such effects is essential to rationalize the reactivity of structurally complex and densely protected carbohydrate compounds.

A water-soluble high-molecular neutral polysaccharide (NP) was isolated from the eggs of the sea urchin Tripneustes gratilla. The formation of glucose only upon the treatment of NP by amyloglucosidase and the value of its optical rotation [α]_D +233.5 (c 0.2, water) confirmed its belonging to the family of α-d-glucans. According to the results of NMR spectroscopy and methylation analysis, the chains of NP are built up of non-reducing terminal, 4-linked and 4,6-disubstituted glucose residues at a ratio of 1:8:1. A branched structure with an average linear chain length of about five glucose residues was calculated from the spectrum of iodine complex. Contrary to the previously published structure of branched α-d-glucan from the sea urchin Strongylocentrotus nudus bearing single glucose units as branches, the polysaccharide NP contains oligosaccharide branches at position 6, which was confirmed by NMR data. Hence, NP has a glycogen-like structure with a rather high degree of branching, which markedly exceeds that of usual mammalian or fungal glycogens.

In this study, we used the SERS method for the first time to measure the spectra of four polysaccharide markers of fungal infections: linear β-(1→3)- and β-(1→6)-linked D-glucans, branched mannan of Candida albicans and galactomannan of Aspergillus fumigatus. Aqueous solutions of the polysaccharides were studied in concentrations from 10 pg/mL to 100 μg/mL. The spectra were analyzed using machine learning methods: principal component analysis for data visualization and partial least squares with a ridge regularizer, which were used to construct metrics reflecting the accuracy of substance recognition relative to each other. The spectral changes with varying analyte concentration were observed and stable calibration has been achieved. Subsequent measurements of fungal polysaccharides in the presence of a physiological concentration of human serum albumin (45 mg/mL), used to model blood serum, enabled accurate analyte detection in a clinically relevant concentration range of 10 pg/mL to 100 ng/mL. In this case, the calibration dependence was calculated using the partial least squares method with the L1-regularizer. Blind testing was evaluated using a train-derived applicability-domain criterion based on the disagreement between the model prediction and an independent concentration estimate.