Biology subjects

Holt, M. V.

Publications and source records attributed to Holt, M. V..

3 recordsLinked to original sources

bioRxiv2026

H2AX C-Terminal Dipeptide Truncation: A Master Switch of the DNA Damage Response.

Phosphorylation of histone H2AX at serine 139 ({gamma}H2AX) by ATM/ATR kinases is a central marker of the DNA damage response (DDR), widely used to detect DNA double-strand breaks. However, the molecular basis for tissue- and context-specific variation in {gamma}H2AX signaling remains poorly defined. Here we discover a post-translational truncation of H2AX, catalyzed by lysine demethylase 4A (KDM4A), which removes two C-terminal amino acids critical for ATM/ATR-dependent phosphorylation. This truncation renders H2AX refractory to {gamma}H2AX formation, effectively bypassing canonical DDR signaling. Truncated H2AX accumulates in select cell lines, primary cells, solid tumors, and normal tissues. Genetic knockdown or pharmacologic inhibition of KDM4A reduces H2AX truncation, restores {gamma}H2AX induction, and enhances DNA repair capacity. Conversely, KDM4A overexpression promotes H2AX truncation, impairs {gamma}H2AX signaling, and exacerbates DNA damage accumulation. This previously unrecognized regulatory axis implicates KDM4A catalyzed H2AX truncation as a superseding mechanism that represses the canonical DDR and disrupts the correlation between {gamma}H2AX and DNA damage. This dioxygenase-based protease mechanism represents a new class of proteases and is the first example of c-terminal dipeptide protein truncation. This discovery has broad implications in the basic science of genome maintenance, wound healing, cancer, combinatorial therapy, precision medicine, and technologies such as gene editing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/723272v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1b4050forg.highwire.dtl.DTLVardef@1b8333aorg.highwire.dtl.DTLVardef@2b27c5org.highwire.dtl.DTLVardef@13ff699_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2022

Kinase Inhibitor Pulldown Assay (KiP) for Clinical Proteomics

Protein kinases are frequently dysregulated and/or mutated in cancer and represent essential targets for therapy. Accurate quantification is essential, but current approaches are inadequate. For breast cancer treatment for example, the identification and quantification of the protein kinase ERBB2 is critical for therapeutic decisions. While immunohistochemistry (IHC) is the current clinical diagnostic approach, it is only semiquantitative. Mass spectrometry-based proteomics offers more quantitative assays that, unlike IHC, can be used to accurately evaluate hundreds of kinases simultaneously. The enrichment of less abundant kinase targets for quantification, along with depletion of interfering proteins, improves sensitivity and thus promotes more effective downstream analyses. Multiple kinase inhibitors were therefore deployed as a capture matrix for kinase inhibitor pulldown (KiP) assays designed to profile the human protein kinome as broadly as possible. Optimized assays were initially evaluated in 16 patient derived xenograft models (PDX) where KiP identified multiple differentially expressed and biologically relevant kinases. From these analyses, an optimized single-shot parallel reaction monitoring (PRM) method was developed to improve quantitative fidelity. The PRM KiP approach was then reapplied to low quantities of proteins typical of protein yields from core needle biopsies of human cancers. The initial prototype targeting 100 kinases recapitulated intrinsic subtyping of PDX models obtained from comprehensive proteomic and transcriptomic profiling. Luminal and HER2 enriched OCT-frozen patient biopsies subsequently analyzed through KiP-PRM also clustered by subtype. Finally, stable isotope labeled peptide standards were developed to define a prototype clinical method.

biochemistry↗

bioRxiv2018

Automated literature mining and hypothesis generation through a network of Medical Subject Headings

The scientific literature is vast, growing, and increasingly specialized, making it difficult to connect disparate observations across subfields. To address this problem, we sought to develop automated hypothesis generation by networking at scale the MeSH terms curated by the National Library of Medicine. The result is a Mesh Term Objective Reasoning (MeTeOR) approach that tallies associations among genes, drugs and diseases from PubMed and predicts new ones.\n\nComparisons to reference databases and algorithms show MeTeOR tends to be more reliable. We also show that many predictions based on the literature prior to 2014 were published subsequently. In a practical application, we validated experimentally a surprising new association found by MeTeOR between novel Epidermal Growth Factor Receptor (EGFR) associations and CDK2. We conclude that MeTeOR generates useful hypotheses from the literature (http://meteor.lichtargelab.org/).\n\nAUTHOR SUMMARYThe large size and exponential expansion of the scientific literature forms a bottleneck to accessing and understanding published findings. Manual curation and Natural Language Processing (NLP) aim to address this bottleneck by summarizing and disseminating the knowledge within articles as key relationships (e.g. TP53 relates to Cancer). However, these methods compromise on either coverage or accuracy, respectively. To mitigate this compromise, we proposed using manually-assigned keywords (MeSH terms) to extract relationships from the publications and demonstrated a comparable coverage but higher accuracy than current NLP methods. Furthermore, we combined the extracted knowledge with semi-supervised machine learning to create hypotheses to guide future work and discovered a direct interaction between two important cancer genes.

bioinformatics↗