A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities.

Glycans are critical to every facet of biology and medicine, from viral infections to embryogenesis. Tools to study glycans are rapidly evolving; however, the majority of our knowledge is deeply dependent on binding by glycan binding proteins (e.g., lectins). The specificities of lectins, which are often naturally isolated proteins, have not been well-defined, making it difficult to leverage their full potential for glycan analysis. Herein, we use a combination of machine learning algorithms and expert annotation to define lectin specificity for this important probe set. Our analysis uses comprehensive glycan microarray analysis of commercially available lectins we obtained using version 5.0 of the Consortium for Functional Glycomics glycan microarray (CFGv5). This data set was made public in 2011. We report the creation of this data set and its use in large-scale evaluation of lectin-glycan binding behaviors. Our motif analysis was performed by integrating 68 manually defined glycan features with systematic probing of computational rules for significant binding motifs using mono- and disaccharides and linkages. Combining machine learning with manual annotation, we create a detailed interpretation of glycan-binding specificity for 57 unique lectins, categorized by their major binding motifs: mannose, complex-type N-glycan, O-glycan, fucose, sialic acid and sulfate, GlcNAc and chitin, Gal and LacNAc, and GalNAc. Our work provides fresh insights into the complex binding features of commercially available lectins in current use, providing a critical guide to these important reagents.

Glycomic analysis of host response reveals high mannose as a key mediator of influenza severity.

Influenza virus infections cause a wide variety of outcomes, from mild disease to 3 to 5 million cases of severe illness and ∼290,000 to 645,000 deaths annually worldwide. The molecular mechanisms underlying these disparate outcomes are currently unknown. Glycosylation within the human host plays a critical role in influenza virus biology. However, the impact these modifications have on the severity of influenza disease has not been examined. Herein, we profile the glycomic host responses to influenza virus infection as a function of disease severity using a ferret model and our lectin microarray technology. We identify the glycan epitope high mannose as a marker of influenza virus-induced pathogenesis and severity of disease outcome. Induction of high mannose is dependent upon the unfolded protein response (UPR) pathway, a pathway previously shown to associate with lung damage and severity of influenza virus infection. Also, the mannan-binding lectin (MBL2), an innate immune lectin that negatively impacts influenza outcomes, recognizes influenza virus-infected cells in a high mannose-dependent manner. Together, our data argue that the high mannose motif is an infection-associated molecular pattern on host cells that may guide immune responses leading to the concomitant damage associated with severity.

Combining 3D structure with glycan array data provides insight into the origin of glycan specificity.

Defining how a glycan-binding protein (GBP) specifically selects its cognate glycan from among the ensemble of glycans within the cellular glycome is an area of intense study. Powerful insight into recognition mechanisms can be gained from 3D structures of GBPs complexed to glycans; however, such structures remain difficult to obtain experimentally. Here an automated 3D structure generation technique, called computational carbohydrate grafting, is combined with the wealth of specificity information available from glycan array screening. Integration of the array data with modeling and crystallography allows generation of putative co-complex structures that can be objectively assessed and iteratively altered until a high level of agreement with experiment is achieved. Given an accurate model of the co-complexes, grafting is also able to discern which binding determinants are active when multiple potential determinants are present within a glycan. In some cases, induced fit in the protein or glycan was necessary to explain the observed specificity, while in other examples a revised definition of the minimal binding determinants was required. When applied to a collection of 10 GBP-glycan complexes, for which crystallographic and array data have been reported, grafting provided a structural rationalization for the binding specificity of >90% of 1223 arrayed glycans. A webtool that enables researchers to perform computational carbohydrate grafting is available at www.glycam.org/gr (accessed 03 March 2016).

Human Milk Bacterial and Glycosylation Patterns Differ by Delivery Mode.

Mammals have evolved to nourish their offspring exclusively with maternal milk for around half of the lactation period, a crucial developmental window. In view of oral-breast contact during lactation and the differences in oral microbiota between cesarean section (C-section) and vaginally delivered infants, we expected differences in milk composition by delivery mode. We performed a cross-sectional study of banked human milk and found changes related to time since delivery in bacterial abundance and glycosylation patterns only in milk from women who delivered vaginally. The results warrant further research into the effects of delivery mode on milk microbes, milk glycosylation, and postpartum infant development.