Biology subjects

Nandakumar, R.

Publications and source records attributed to Nandakumar, R..

4 recordsLinked to original sources

bioRxiv2025

Targeting Monoallelic CREBBP/EP300 Mutations in Germinal Center-Derived B-Cell Lymphoma with a First-in-Class Histone Acetyltransferase Activator

Germinal center B-cell lymphomas frequently exhibit monoallelic loss-of-function mutations in the histone acetyltransferases CREBBP and p300, which contribute to lymphoma development, disrupt normal germinal center biology, and promote immune evasion. This study evaluates YF2, a histone acetyltransferase activator that increases CREBBP/p300 activity, as a potential therapeutic approach for B-cell lymphoma. YF2 binds the bromodomain of CREBBP/p300, increasing their auto-acetylation and enzymatic activity. It also induces cytotoxicity in B-cell lymphoma cell lines, with a stronger response in those carrying CREBBP/EP300 mutations. Additionally, YF2 increases the acetylation of key CREBBP/p300 substrates, including H3K27, p53, and BCL6, leading to enhanced apoptosis and altered B-cell diTerentiation. YF2 is well tolerated in vivo and extends survival in both cell line- and patient-derived xenograft mouse models. Moreover, YF2 exposure upregulates antigen-presentation markers and reshapes the immune microenvironment, strengthening responses to immune checkpoint blockade. Taken together, these findings support pharmacologic activation of CREBBP/p300 with YF2 as a compelling therapeutic strategy for B-cell lymphoma. SignificanceYF2 stimulates CREBBP/p300 activity to normalize B-cell diTerentiation and enhance immune recognition, oTering a potential approach to limit lymphoma progression and strengthen immunotherapy responses.

cancer biology↗

bioRxiv2024

Early prediction of preeclampsia using the first trimester vaginal microbiome

Preeclampsia is a severe obstetrical syndrome which contributes to 10-15% of all maternal deaths. Although the mechanisms underlying systemic damage in preeclampsia--such as impaired placentation, endothelial dysfunction, and immune dysregulation--are well studied, the initial triggers of the condition remain largely unknown. Furthermore, although the pathogenesis of preeclampsia begins early in pregnancy, there are no early diagnostics for this life-threatening syndrome, which is typically diagnosed much later, after systemic damage has already manifested. Here, we performed deep metagenomic sequencing and multiplex immunoassays of vaginal samples collected during the first trimester from 124 pregnant individuals, including 62 who developed preeclampsia with severe features. We identified multiple significant associations between vaginal immune factors, microbes, clinical factors, and the early pathogenesis of preeclampsia. These associations vary with BMI, and stratification revealed strong associations between preeclampsia and Bifidobacterium spp., Prevotella timonensis, and Sneathia vaginalis. Finally, we developed machine learning models that predict the development of preeclampsia using this first trimester data, collected ~5.7 months prior to clinical diagnosis, with an auROC of 0.78. We validated our models using data from an independent cohort (MOMS-PI), achieving an auROC of 0.80. Our findings highlight robust associations among the vaginal microbiome, local host immunity, and early pathogenic processes of preeclampsia, paving the way for early detection, prevention and intervention for this devastating condition.

microbiology↗

bioRxiv2024

Joint exome and metabolome analysis in individuals with dyslexia: Evidence for associated dysregulations of olfactory perception and autoimmune functions

Dyslexia is a learning disability that negatively affects reading, writing, and spelling development at the word level in 5%-9% of children. The phenotype is variable and complex, involving several potential cognitive and physical concomitants such as sensory dysregulation and immunodeficiencies. The biological pathogenesis is not well-understood. Toward a better understanding of the biological drivers of dyslexia, we conducted the first joint exome and metabolome investigation in a pilot sample of 30 participants with dyslexia and 13 controls. In this analysis, eight metabolites of interest emerged (pyridoxine, kynurenic acid, citraconic acid, phosphocreatine, hippuric acid, xylitol, 2-deoxyuridine, and acetylcysteine). A metabolite-metabolite interaction analysis identified Krebs cycle intermediates that may be implicated in the development of dyslexia. Gene ontology analysis based on exome variants resulted in several pathways of interest, including the sensory perception of smell (olfactory) and immune system-related responses. In the joint exome and metabolite analysis, the olfactory transduction pathway emerged as the primary pathway of interest. Although the olfactory transduction and Krebs cycle pathways have not previously been described in dyslexia literature, these pathways have been implicated in other neurodevelopmental disorders including autism spectrum disorder and obsessive-compulsive disorder, suggesting the possibility of these pathways playing a role in dyslexia as well. Immune system response pathways, on the other hand, have been implicated in both dyslexia and other neurodevelopmental disorders.

bioinformatics↗

bioRxiv2023

A data-fusion approach to identifying developmental dyslexia from multi-omics datasets

This exploratory study tested and validated the use of data fusion and machine learning techniques to probe high-throughput omics and clinical data with a goal of exploring the etiology of developmental dyslexia. Developmental dyslexia is the leading learning disability in school aged children affecting roughly 5-10% of the US population. The complex biological and neurological phenotype of this life altering disability complicates its diagnosis. Phenome, exome, and metabolome data was collected allowing us to fully explore this system from a behavioral, cellular, and molecular point of view. This study provides a proof of concept showing that data fusion and ensemble learning techniques can outperform traditional machine learning techniques when provided small and complex multi-omics and clinical datasets. Heterogenous stacking classifiers consisting of single-omic experts/models achieved an accuracy of 86%, F1 score of 0.89, and AUC value of 0.83. Ensemble methods also provided a ranked list of important features that suggests exome single nucleotide polymorphisms found in the thalamus and cerebellum could be potential biomarkers for developmental dyslexia and heavily influenced the classification of DD within our machine learning models.

bioinformatics↗