Biology subjects

Warner, J. L.

Publications and source records attributed to Warner, J. L..

5 recordsLinked to original sources

bioRxiv2026

Regulation of the histone H3K36 methyltransferase Set2 by the histone chaperone Spt6

Histone H3 lysine 36 methylation is a conserved histone modification that is critical for maintaining eukaryotic transcriptional fidelity and genomic stability. In Saccharomyces cerevisiae, this modification is catalyzed by Set2, an ortholog of the mammalian H3K36 methyltransferase SETD2. Previous genetic, biochemical, and structural studies showed that Set2 activity is repressed by a Set2 autoinhibitory domain (AID) and that activation requires the direct binding of the histone chaperone Spt6. To study the role of Spt6 and Set2 autoinhibition in vivo, we have isolated and analyzed multiple classes of Spt6 and Set2 mutants. Our results suggest an autoinhibited form of Set2 in which the catalytic domain is bound by the AID. In strong agreement with our genetic results, biophysical experiments demonstrate that the catalytic domain and AID physically interact, and that the autoinhibition mutants disrupt this interaction. Finally, RNA-seq and ChIP-seq studies show the importance of the Set2-Spt6 interaction for transcription and H3K36 methylation genome-wide. Taken together, our results support a model in which Set2 exists in an inactive, autoinhibited state in vivo through direct catalytic domain-AID interactions, with binding by Spt6 required to release this autoinhibited state.

molecular biology↗

bioRxiv2026

A Multimodal Neural Network Model for Early Recurrence Prediction in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is the most common primary lung cancer worldwide. Despite advancements in early detection and treatment, up to 39% of patients develop recurrent tumors following complete resection. Currently, no widely available models exist for reliably predicting early recurrence of LUAD, which is a significant prognostic factor of post-recurrence survival. Models leveraging deep learning (DL) techniques have demonstrated notable utility in cancer recurrence prediction, particularly when used in combination with both clinical and genomic data. We developed a DL-based model, Predicting Lung Adenocarcinoma recurrence via Selective Multimodal Attention (PLASMA), to predict early recurrence using clinical, mRNA expression, and mutation data from patients with primary stage I-III LUAD. Trained on The Cancer Genome Atlas (TCGA) dataset, PLASMA outperformed traditional machine learning models in predicting early recurrence in both the TCGA test set and an external validation set (TRACERx Lung), achieving area under the receiver operating characteristic curve (AUROC) scores of 85.0% and 76.5%, respectively. Our results support the potential of multimodal DL for early LUAD recurrence prediction and risk stratification.

bioinformatics↗

bioRxiv2024

The histone chaperone Spt6 controls chromatin structure through its conserved N-terminal domain

The disassembly and reassembly of nucleosomes by histone chaperones is an essential activity during eukaryotic transcription elongation. This highly conserved process maintains chromatin integrity by transiently removing nucleosomes as barriers and then restoring them in the wake of transcription. While transcription elongation requires multiple histone chaperones, there is little understanding of how most of them function and why so many are required. Here, we show that the histone chaperone Spt6 acts through its acidic, intrinsically disordered N-terminal domain (NTD) to bind histones and control chromatin structure. The Spt6 NTD is essential for viability and its histone binding activity is conserved between yeast and humans. The essential nature of the Spt6 NTD can be bypassed by changes in another histone chaperone, FACT, revealing a close functional connection between the two. Our results have led to a mechanistic model for dynamic cooperation between multiple histone chaperones during transcription elongation.

molecular biology↗

bioRxiv2024

LUNAR: A Deep Learning Model to Predict Glioma Recurrence Using Integrated Genomic and Clinical Data

Gliomas account for approximately 25.5% of all primary brain and central nervous system tumors, with a striking 80.8% of these being malignant. The prognosis varies significantly; low-grade gliomas (LGGs) can exhibit 5-year survival rates of up to 80%, while higher-grade gliomas (HGGs) often see rates below 5%. Recurrence is a common challenge, occurring in 52%-62% of LGGs and 90% of HGGs, complicating clinical management and treatment planning. Currently, no widely available models exist for predicting glioma recurrence, which is critical for optimizing patient outcomes. Machine learning (ML) and deep learning (DL) techniques have shown promise in predicting recurrence for various cancers, most using Electronic Health Records (EHR). This study introduces gLioma recUrreNce Attention-based classifieR (LUNAR), a DL-based model to predict early versus late glioma recurrence by integrating clinical, genomic, and mRNA expression data from patients with primary grade II-IV gliomas. The data was obtained from The Cancer Genome Atlas and the Glioma Longitudinal Analysis Consortium (GLASS).

bioinformatics↗

bioRxiv2018

Using Topic Modeling via Non-negative Matrix Factorization to Identify Relationships between Genetic Variants and Disease Phenotypes: A Case Study of Lipoprotein(a) (LPA)

Genome-wide and phenome-wide association studies are commonly used to identify important relationships between genetic variants and phenotypes. Most of these studies have treated diseases as independent variables and suffered from heavy multiple adjustment burdens due to the large number of genetic variants and disease phenotypes. In this study, we propose using topic modeling via non-negative matrix factorization (NMF) for identifying associations between disease phenotypes and genetic variants. Topic modeling is an unsupervised machine learning approach that can be used to learn the semantic patterns from electronic health record data. We chose rs10455872 in LPA as the predictor since it has been shown to be associated with increased risk of hyperlipidemia and cardiovascular diseases (CVD). Using data of 12,759 individuals from the biobank at Vanderbilt University Medical Center, we trained a topic model using NMF from 1,853 distinct phecodes extracted from the cohorts electronic health records and generated six topics. We quantified their associations with rs10455872 in LPA. Topics indicating CVD had positive correlations with rs10455872 (P < 0.001), replicating a previous finding. We also identified a negative correlation between LPA and a topic representing lung cancer (P < 0.001). Our results demonstrate the applicability of topic modeling in exploring the relationship between the genome and clinical diseases.\n\nAuthor summaryIdentifying the clinical associations of genetic variants remains crucial in understanding how the human genome modulates disease risk. Traditional phenome-wide association studies consider each disease phenotype as an independent variable, however, diseases often present as complex clusters of comorbid conditions. In this study, we propose using topic modeling to model electronic health record data as a mixture of topics (e.g., disease clusters or relevant comorbidities) and testing associations between topics and genetic variants. Our results demonstrated the feasibility of using topic modeling to replicate and discover novel associations between the human genome and clinical diseases.

genetics↗