Lara K. Mahal

The paper evaluates how glycan-binding proteins and immune cells recognize mirror-image glycans, finding widespread binding and donor-specific recognition of L-glycans.

This study identifies miRNAs that bidirectionally regulate FUT1, affecting α-1,2-fucosylation and cancer biology through non-canonical binding mechanisms.

The paper shows that the Q226H mutation in H5N1 hemagglutinin allows binding to human-type a2-6 sialic acid receptors, suggesting a path towards human adaptation.

The paper shows that extended O-glycans are present on intranuclear proteins and demonstrate their role in RNA processing through specific examples.

This study investigates Siglec-6's binding mechanism to gangliosides, showing it relies on both conserved and membrane-associated residues for effective recognition.

This paper develops MCNet, a machine learning model that predicts protein-glycan binding strength using atom-level representations, enabling cross-chiral recognition and unifying diverse glycobiology datasets.

This study uses lectin microarrays and machine learning to identify conserved and species-specific glycan signatures in synovial fluid that distinguish healthy from osteoarthritic joints in humans, dogs, and horses.

This study shows that aging reduces 2,6-linked sialic acids on T cells, leading to exhaustion and impaired immune function.

The paper identifies miRNA regulators of OGT, showing evolutionary conservation of upregulatory miR-148/152 and downregulatory let-7 miRNA binding sites.

This study identifies miRNAs that regulate CMAS expression, impacting sialylation levels and showing bidirectional miRNA regulation in pancreatic cancer.

The paper identifies miRNAs that upregulate CD98hc and ST3GAL1/ST3GAL2 in melanoma, revealing a bidirectional regulatory mechanism in miRNA networks.

The paper uses CRISPR screens and lectin microarrays to identify TM9SF3 and the CCC complex as novel regulators of high mannose N-glycan synthesis.

This paper develops a phage-based liquid lectin array to detect subtle glycocalyx differences by displaying multivalent lectins on DNA-barcoded M13 phage.

The paper analyzes baseline multi-omic data from influenza vaccine recipients to identify pre-immune states predictive of vaccination responses.

The paper uses deep learning to predict branched glycan phenotypes from single-cell transcriptomic data, identifying key genes influencing glycan biology.

The paper shows that N-glycan modifications regulate endothelial-to-hematopoietic transition by affecting glycosylation enzymes and HSPC production.