Biology subjects

Rajasekharan, A.

Publications and source records attributed to Rajasekharan, A..

3 recordsLinked to original sources

bioRxiv2026

Inhibition of mucin-type O-glycosylation impairs melanogenesis, melanoma growth, and metastatic capacity

Tumor-associated glycans are critical regulators of immune evasion in melanoma. Despite the success of immune checkpoint blockade, resistance and relapse remain a challenge, highlighting the need to therapeutically target additional immunosuppressive axes. One such axis involves Siglec-sialoglycan interactions that facilitate tumor immune evasion. The melanoma-associated antigen Pmel17/gp100 is a melanosomal glycoprotein overexpressed in tumor cells and essential for melanosomal architecture. Pmel17/gp100 forms amyloid fibrils within melanosomes, and its extensive O-glycosylation contributes to melanin biosynthesis and melanoma progression. We show that pharmacological inhibition of O-glycosylation using peracetyl N-thioglycolyl-D-galactosamine (Ac5GalNTGc, 1a) decreases Pmel17/gp100 glycosylation and melanin synthesis in B16F10 melanoma cells, induces tumor surface hyposialylation, diminishes Siglec-E engagement, and disrupts the Siglec-sialoglycan immune checkpoint. In C57BL/6J mice, 1a significantly delayed tumor growth, prolonged survival, and reduced lung metastases relative to controls. These findings identify O-glycosylation as a dual-mode therapeutic vulnerability, linking melanosome dysfunction to attenuation of Siglec-mediated immune evasion in melanoma. SummaryMucin-type O-glycans on melanoma cell surfaces and the melanosomal protein, Pmel17/gp100, regulate melanogenesis and melanoma metastasis. The O-glycosylation inhibitor, peracetyl N-thioglycolyl-D-galactosamine (Ac5GalNTGc, 1a), suppressed melanin synthesis and induced hyposialylation, thereby disrupting Siglec-mediated immune evasion and retarding tumor growth and metastasis.

cancer biology↗

bioRxiv2019

A proximity biotinylation map of a human cell

Compartmentalization is a defining characteristic of eukaryotic cells, partitioning cellular processes into discrete subcellular locations. High throughput microscopy1 and biochemical fractionation coupled with mass spectrometry2-6 have helped to define the proteomes of a variety of organelles and macromolecular structures. However, many other intracellular compartments have remained refractory to such approaches, due for example to difficulty in purifying non-membrane bound structures. Proximity-dependent biotinylation techniques such as BioID provide an alternative approach for defining the composition of cellular compartments in living cells7-10. Here we present a BioID-based map of a human cell based on 192 markers from 32 different subcellular compartments, comprising 35,902 high confidence proximity interactions, and defining the intracellular locations of 4,145 unique proteins in HEK 293 cells. Our localization predictions meet or exceed previous-approaches, with higher specificity, and enabled the discovery of proteins at the mitochondrial outer membrane-endoplasmic reticulum (ER) interface that are critical for mitochondrial homeostasis. Based on this dataset, we have established humancellmap.org as a community resource that provides online tools for localization analysis of user BioID data, and demonstrate how this resource can be used to better understand BioID datasets.

molecular biology↗

bioRxiv2019

Recapitulation and Retrospective Prediction of Biomedical Associations Using Temporally-enabled Word Embeddings

The recent explosion of biomedical knowledge presents both a major opportunity and challenge for scientists tackling complex problems in healthcare. Here we present an approach for synthesizing biomedical knowledge based on a combination of word-embeddings and select cooccurrences. We evaluated our ability to recapitulate and retrospectively predict disease-gene associations from the Online Mendelian Inheritance in Man (OMIM) resource. Our metrics achieved an area under the curve (AUC) value of 0.981 at the recapitulation task for 2,400 disease-gene associations. At the most stringent cutoff, our metrics predicted 13.89% of these associations before their first cooccurrence in the literature, with a median time of 4 years between prediction and first cooccurrence. Finally, our literature metrics can be combined with human genetics data to retrospectively predict disease-gene associations, IL-6 and Giant Cell Arteritis provided as an example. We believe this framework can provide robust biomedical hypotheses at a much faster pace than current standard practices.

bioinformatics↗