Biology subjects

Green, H.

Publications and source records attributed to Green, H..

10 recordsLinked to original sources

bioRxiv2026

Detecting and Subtyping Ketoacidosis from Metabolomic Patterns in Forensic Casework

Subtyping of ketoacidosis, a metabolic state characterized by blood acidification due to various causes, remains challenging in forensic casework. Postmortem omics samples paired with machine learning offers an independent tool to address this challenge. However, such data, especially related to real forensic cases, are rare. In Sweden, high-resolution mass spectrometry data routinely collected in forensic toxicology, can be leveraged for metabolomic analysis. Here, we integrate postmortem metabolomics and machine learning models to detect and subtype ketoacidosis-related deaths using real forensic cases in Sweden. From femoral blood samples of 109 alcoholic ketoacidosis cases, 220 diabetic ketoacidosis cases, 140 hypothermia cases, and 1,229 controls (hanging cases), we developed and tested three machine learning models, which achieved over 90% accuracy in ketoacidosis detection and over 80% in subtyping. Validation with independent cohorts (21 starvation cases, 29 alcoholic controls, and 40 diabetic controls) confirmed robustness with over 80% of starvation cases classified as ketoacidosis-related. Feature clustering highlighted metabolites such as cortisol to be important for subtyping. In summary, our findings demonstrate that combining machine learning with postmortem metabolomics enables accurate detection and subtyping of ketoacidosis-related deaths, which is useful for forensic casework.

systems biology↗

bioRxiv2025

A broad set of synthetic cannabinoid receptor agonists inhibit the cardiac voltage-gated potassium channel hERG

Synthetic cannabinoid receptor agonists (SCRAs) is a large group of structurally diverse designer drugs (analogues of controlled substances) associated with intense and sometimes fatal intoxication. Cardiac symptoms including tachycardia and arrhythmia are common consequences of SCRA consumption. However, little is known about the mechanisms through which SCRAs may perturb cardiac rhythm. Here we used electrophysiological techniques to screen 36 SCRAs on two ion channels responsible for cardiomyocyte repolarization, hERG (also called KV11.1) and KV7.1/KCNE1. We report that the majority of tested SCRAs inhibited hERG, primarily by reducing channel conductance, and some also inhibited KV7.1/KCNE1. In silico data suggests that SCRAs use a known drug binding site in the pore of the hERG channel, shared by established hERG blockers like astemizole, where a planar SCRA molecule lays perpendicular to the ion conducting pathway. Experimental and in silico data identify SCRA structural features associated with prominent inhibitory effects on hERG, with chemical moieties allowing bond formation and/or the ability to fit into the vestibule being important. Structure-activity-relationships for SCRA effects on hERG, KV7.1/KCNE1 and the cannabinoid receptor 1 (CB1) varied, demonstrating the importance of assessing SCRA effects on multiple potential targets. In conclusion, we found SCRAs to be inhibitors of cardiac voltage-gated potassium channels important for cardiomyocyte repolarization. These results offer mechanistic insight into potentially detrimental SCRA effects on the heart and highlight the urgency of more extensive investigation of SCRAs on cardiac function.

pharmacology and toxicology↗

bioRxiv2025

Unmasking Pathogen Traits for Chronic Colonization in Neurogenic Bladder Patients

Individuals with neurogenic bladder are particularly susceptible to both chronic bacterial colonization of the bladder and urinary tract infections (UTIs). Neurogenic bladder can arise from a variety of diseases such as diabetes, spinal cord injuries, and spina bifida. To study the ecological and evolutionary dynamics of the microbiome in neurogenic bladder, we developed a longitudinal cohort of 77 children and young adults with spina bifida from two medical centers. We used enhanced urine culture, 16S rRNA sequencing, and whole genome sequencing to characterize the microbial composition of urine and fecal samples. In addition to prospective sample collection, we retrieved prior bacterial isolates from enrolled patients from Vanderbilts clinical microbial biobank, MicroVU. This allowed us to compare bacterial isolates from the same patients over a period of five years. Urine samples were characterized by high abundance of urinary pathogens, such as E. coli and Klebsiella. From longitudinal isolates from individual patients, we identified two common patterns of urinary tract colonization. We observed either the rapid cycling of strains and/or species, often following antibiotic treatment, or we observed the persistence of a single strain across timepoints. Neither persistence of a strain nor colonization with a new strain or species was associated with increased antibiotic resistance. Rather, in paired longitudinally collected strains from the same patients, mutations were identified in genes that code for cell envelope components associated with immune or phage evasion. Experimental testing revealed that O-antigen/LPS biosynthesis mutations confer protection from the immune system while altering susceptibility to phage predation, reflecting a fitness trade-off. We argue that this unparalleled cohort offers the opportunity to identify mechanisms of bacterial adaptation to the urinary tract that can be exploited in future therapeutic approaches.

microbiology↗

bioRxiv2025

The Human Metabolome and Machine Learning Improves Predictions of the Post-Mortem Interval

An accurate prediction of the time since death, known as the post-mortem interval (PMI), remains a critical research question in forensic and police investigations. Current methods, such as rectal temperature or vitreous potassium levels, only provide reliable PMI estimations up to 48-72 hours. In this study, we utilized metabolomic data from femoral whole blood samples of 4,876 individuals with known PMIs ranging from 1 to 67 days. We developed a neural network model that predicted PMI with a mean/median absolute error of 1.45/1.03 days in unseen test cases, outperforming six other machine learning architectures. To further highlight the biological signal, we performed pseudo time-series clustering of metabolic features used by the model, revealing 158 decreasing, 254 increasing, and 398 features with more complex patterns over the pseudo-time scale. Our findings also indicate that metabolomic data from approximately 256 individuals is sufficient to train a machine learning model for PMI prediction, making this approach widely applicable for researchers and forensic institutes worldwide.

systems biology↗

bioRxiv2024

Microbiology and Anthropology: a multidisciplinary approach for estimating time since death during Winter in rural New South Wales, Australia.

Accurate determination of the post-mortem interval (PMI) is critical in forensic casework. Most studies conclude that PMI determinations are dependent on local climatic and geographical factors. Despite this, there is little data in an Australian environment outside of entomological studies. In the absence of insect data on or around the remains, alternative methods are required. Anthropological methods of observing and scoring the extent of decomposition at the time of discovery provide only broad estimates of PMI. Microbial succession is responsible for these observable soft tissue changes, particularly in early and late decomposition. The aim of this study was to combine anthropological and microbiological methods to provide data for determining PMI in a temperate Australian climate. Microbial DNA was isolated from skin and cavity swabs and used to perform 16S rRNA metagenomic analysis of pooled DNA samples to allow the choice of target taxa for qPCR. qPCR indicated significant changes in microbial communities with a dominant population of Gammaproteobacteria at early time points giving way to Firmicutes and Bacteroidetes near the end of the experiment. Expressing qPCR data as a ratio of Lactobacillales/Enterobacteriaceae provided data that could be useful in determining early and late decomposition. The genus Psychrobacter was identified as a good indicator of late decomposition in winter conditions. qPCR analysis, with further refinements, could be part of an accurate quantitative method of determining PMI.

microbiology↗

bioRxiv2024

Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime, we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily active mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.

cancer biology↗

bioRxiv2023

Interferon response and epigenetic modulation by SMARCA4 mutations drive ovarian tumor immunogenicity

Cell-intrinsic mechanisms of immunogenicity in ovarian cancer (OC) are not well understood. The presence of damaging mutations in the SWI/SNF chromatin remodeling complex, such as the SMARCA4 (BRG1) catalytic subunit, has been associated with improved response to ICB, however the mechanism by which this occurs is unclear. The aim of this current study was to examine the alterations in tumor cell-intrinsic and extrinsic immune signaling caused by SMARCA4 loss. Using OC models with loss-of-function mutations in SMARCA4, we found that SMARCA4 loss resulted in increased cancer cell-intrinsic immunogenicity, characterized by upregulation of long-terminal RNA repeats such as endogenous retroviruses, increased expression of interferon-stimulated genes, and upregulation of antigen presentation machinery. Notably, this response was dependent on IRF3 signaling, but was independent of the type I interferon receptor. Mice inoculated with cancer cells bearing SMARCA4 loss demonstrated increased activation of cytotoxic T cells and NK cells in the tumor microenvironment as well as increased infiltration with activated dendritic cells. These results were recapitulated when animals bearing SMARCA4-proficient tumors were treated with a BRG1 inhibitor, suggesting that modulation of chromatin remodeling through targeting SMARCA4 may serve as a strategy to reverse immune evasion in OC.

cancer biology↗

bioRxiv2021

DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization

1Lead optimization, a critical step in early-stage drug discovery, involves making chemical modifications to a small-molecule ligand to improve its drug-like properties (e.g., binding affinity). We recently developed DeepFrag, a deep-learning model capable of recommending such modifications. Though a powerful hypothesis-generating tool, DeepFrag is currently implemented in Python and so requires a certain degree of computational expertise. To encourage broader adoption, we have created the DeepFrag browser app, which provides a user-friendly graphical user interface that runs the DeepFrag model in users web browsers. The browser app does not require users to upload their molecular structures to a third-party server, nor does it require the separate installation of any third-party software. We are hopeful that the app will be a useful tool for both researchers and students. It can be accessed free of charge, without requiring registration, at http://durrantlab.com/deepfrag. The source code is also available at http://git.durrantlab.com/jdurrant/deepfrag-app, released under the terms of the open-source Apache License, Version 2.0.

pharmacology and toxicology↗

bioRxiv2021

DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization

1Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6,500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0. A copy can be obtained free of charge from http://durrantlab.com/deepfragmodel.

pharmacology and toxicology↗

bioRxiv2017

A reassessment of DNA immunoprecipitation-based genomic profiling

DNA immunoprecipitation sequencing (DIP-seq) is a common enrichment method for profiling DNA modifications in mammalian genomes. However, DIP-seq profiles often exhibit significant variation between independent studies of the same genome and from profiles obtained by alternative methods. Here we show that these differences are primarily due to intrinsic affinity of IgG for short unmodified DNA repeats. This pervasive experimental error accounts for 50 - 99% of regions identified as enriched for DNA modifications in DIP-seq data. Correction of this error profoundly alters DNA modification profiles for numerous cell types, including mouse embryonic stem cells, and subsequently reveals novel associations between DNA modifications, chromatin modifications and biological processes. We propose new methodological guidelines that minimize the impact of these errors on future DIP-seq experiments and allow new insights to be made from the wealth of existing DIP-seq data.

genetics↗