Biology subjects

Pittala, S.

Publications and source records attributed to Pittala, S..

3 recordsLinked to original sources

bioRxiv2026

P2Y14 receptor agonist impairs, and antagonist improves whole-body glucose homeostasis and liver steatosis

Knockout of the UDP-glucose-activated P2Y14 receptor (P2Y14R) in adipocytes or whole-body has been reported to provide metabolic benefits in obese mice. We hypothesized that selective P2Y14R activation would lead to metabolic impairments, whereas pharmacological antagonism would improve metabolic deficits in diet-induced obese (DIO) mice. Here, we investigated the metabolic effects of a synthetic P2Y14R agonist, UDP-like methylene-bridged MRS2905. Acute P2Y14R activation with MRS2905 triggered a robust and prolonged hyperglycemic effect in lean and obese mice with impaired glucose homeostasis. Moreover, MRS2905 treatment of obese mice lowered fasting plasma insulin and increased glucagon levels, along with upregulation of liver JNK phosphorylation and the expression of rate-limiting gluconeogenic genes. The MRS2905-induced hyperglycemic effect was blunted in whole-body P2Y14R knockout mice compared with wild-type control. In contrast, a potent P2Y14R antagonist mono-ester prodrug (MRS4779) partially reversed the agonist-induced hyperglycemia and restored proper glucose homeostasis after acute treatment. Chronic MRS4779 administration in DIO mice reduced fat mass and improved various metabolic parameters including liver steatosis. Additionally, we examined the roles of P2Y14R in hepatocytes of DIO mice. Here, we report that P2Y14R was upregulated in liver and hepatocytes from obese mice compared to lean mice. Overnight fasting also upregulated hepatic P2Y14R expression. P2Y14R deletion from hepatocytes in DIO mice improved fasting blood glucose level and lipid metabolism without improving glucose homeostasis. These results suggest a novel P2Y14R function in hepatic lipid metabolism, and P2Y14R antagonists may prove useful for the treatment of obesity and obesity-related metabolic disorders.

physiology↗

bioRxiv2019

Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces

Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the \"ground truth\" of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. In order to increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens. The framework leverages three key aspects of antibody-antigen interactions in order to learn predictive structural representations: (1) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (2) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (3) since more data is available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction.

bioinformatics↗

bioRxiv2019

Mixture of Experts for Predicting Antibody-Antigen Binding Affinity from Antigen Sequence

Antibodies provide a key mode of defense employed by the immune system to fight disease, so eliciting potent antibodies is one of the main goals in vaccine development. Antibodies are also being conceived as powerful therapeutic agents, so engineering potent antibodies is one of the main goals in biologic drug development. The power of antibodies lies in their affinity and specificity in recognizing their cognate antigens. Unfortunately, experimental techniques to determine antibody-antigen binding affinities are difficult to scale up to large sets of new antibodies and antigens. Though computational methods are suitable for large-scale prediction, current methods lack sufficient accuracy. Here, we address the problem of predicting the binding affinity of an antibody against an antigen variant based on the amino acid sequence of that antigen variant. We develop a mixture of experts approach that learns models for individual antibodies against some antigen variants, and then combines information across the antibodies in order to make accurate predictions for a wide range of new variants. In evaluation on a dataset consisting of 52 antibodies and 608 strains of HIV, the predictive accuracy of our approach is demonstrated to be significantly better than that of an existing approach. Our method provides a fast and accurate way to predict antibody-antigen binding affinity, which has the potential to expedite the study of antibody-antigen interactions for vaccine design and therapy.

immunology↗