Glycan engineering using synthetic ManNAc analogues enhances sialyl-Lewis-X expression and adhesion of leukocytes
Tasneem, A.; Parashar, S.; Jain, T.; Aittan, S.; Rautela, J.; Raza, K.; Sampathkumar, S.-G.
Show abstract
Sialyl-Lewis-X (sLeX/CD15s) epitopes regulate cell adhesion via interactions with selectins. High levels of sLeX are associated with chronic inflammation. Intense efforts have been devoted for the development of sLeX mimetics for the prevention of atherosclerosis. By contrast, low levels of sLeX are associated with leukocyte adhesion deficiency (LAD) disorders. In this context, we employed metabolic glycan engineering to alter the fine structures of sialoglycans. Treatment of HL-60 (human acute myeloid leukemia) cells with peracetyl N-cyclobutanoyl-D-mannosamine (Ac4ManNCb) resulted in a four-fold increase in both sLeX levels and adhesion to E-selectin-Fc chimera. Enhanced sLeX levels on CD162/PSGL-1, CD43, and CD44 were observed through immunoprecipitation. Molecular dynamics (MD) simulations on interactions with E-selectin revealed dramatic differentials in the conformational dynamics of sLeX-Cb compared to sLeX, highlighting the significance of remote N-acyl side chains of sialic acids in facilitating bio-active conformations. These results provide opportunities for pharmacological interventions in both LAD and chronic inflammation. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/473788v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@172ab54org.highwire.dtl.DTLVardef@14e45acorg.highwire.dtl.DTLVardef@1c05c65org.highwire.dtl.DTLVardef@1f2056e_HPS_FORMAT_FIGEXP M_FIG C_FIG Remote substituents on the tetrasaccharide sialyl-Lewis X (sLeX / CD15s) influence ensembles of bio-active conformations and alter biological outcomes. Treatment with peracetyl N-cyclobutanoyl-D-mannosamine (Ac4ManNCb) results in four-fold enhancement in the expression of sialyl-Lewis-X (sLeX / CD15s) and adhesion to E-selectin (CD62E). Molecular dynamics simulations show that the sLeX-Cb prefers a flatter bio-active conformation (gray) compared to wild-type sLeX (cyan). Institute and/or researcher Twitter usernames: @NImmunology @Gops_GlycoIndia
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Attenuation of polysialic acid biosynthesis in cells by the small molecule inhibitor 8-keto-sialic acid 94%
- Overcoming Ligand Discovery Challenges: Developing Peptide-Based Tracers for SPSB2 93%
- A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities 92%
Similar papers in this journal
- Selective Display of a Chemoattractant Agonist on Cancer Cells Activates the Formyl Peptide Receptor 1 on Immune Cells 93%
- Tuning the transglycosylation reaction of a GH11 xylanase by a delicate enhancement of its thumb flexibility 92%
- Efficient chemical and enzymatic syntheses of FAD nucleobase analogues and their analysis as enzyme cofactors 92%
Similar papers in this journal
Similar papers in this journal
- Hijacking SARS-Cov-2/ACE2 receptor interaction by natural and semi-synthetic steroidal agents acting on functional pockets on receptor binding region 93%
- Mucin-type O-glycosylation Landscapes of SARS-CoV-2 Spike Proteins 90%
- Hybrid dynamic pharmacophore models as effective tools to identify novel chemotypes for anti-TB inhibitor design: A case study with Mtb-DapB 90%
Similar papers in this journal
- Structural remodeling of SARS-CoV-2 spike protein glycans reveals the regulatory roles in receptor binding affinity 92%
- Simple and practical sialoglycan encoding system reveals vast diversity in nature and identifies a universal sialoglycan-recognizing probe derived from AB5 toxin B subunits 92%
- Insight into glycosphingolipid crypticity: Crystal structure of the anti-tumor antibody 14F7 and recognition of NeuGc GM3 ganglioside 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.