Biology subjects

Moore, B. M.

Publications and source records attributed to Moore, B. M..

12 recordsLinked to original sources

bioRxiv2026

Quantifying Evidence for Competing Biomedical Hypotheses using Large Language Models and Bayesian Analysis

Science fundamentally depends on the generation and testing of hypotheses, many of them controversial. An explosion in scientific literature has made evaluating hypotheses even within a domain a problem of scale, and risks slowing an already extensive consensus-building process. While this challenge has prompted interest in automated hypothesis evaluation tools, existing methods have not yet proven effective for comparing hypotheses. Here, we introduce KM-GPT-DCH, an algorithm that combines co-occurrence methods with large language models (LLMs) to develop a transparent and reproducible literature-based algorithm to compare controversial hypotheses using a structured scoring approach with Bayesian methods to estimate confidence. When testing the algorithm on historical controversial hypotheses previously decided, KM-GPT-DCH chooses the correct hypothesis with high confidence several years before the scientific community or public do so. We further apply the algorithm to compare twenty unresolved controversial hypothesis pairs providing guidance for future research. The method can help researchers and the public to evaluate biomedical hypotheses such as "Is it more likely that monoamine deficiency or inflammation causes depression?" It can also be used to assess and visualize historical trends in the scientific literature. A web-based implementation of the algorithm is freely available at https://skim.morgridge.org.

bioinformatics↗

bioRxiv2026

Cannabidiol dose modulates behavioral response to acute and repeated administration of Δ9-Tetrahydrocannabinol by strain and sex

Cannabis contains many bioactive compounds, including {Delta}9-Tetrahydrocannabinol (THC) and cannabidiol (CBD), which influence behavior through complex pharmacological interactions with endogenous targets. This study examines whether CBD influences THC-induced changes in motor activity, hypothermia, and antinociception traits across different THC:CBD ratios, sexes, and genetic backgrounds. Traits were measured in C57BL/6J (B6) and DBA/2J (D2) mice of both sexes following baseline intraperitoneal (i.p.) injection of vehicle (VEH) and two consecutive daily doses of VEH or THC (10 mg/kg) alone or in combination with 0.56, 5, or 10 mg/kg CBD (THC:0.56CBD, THC:5CBD, or THC:10CBD, respectively). Motor activity and hypothermia were quantified daily from 0 to 120 min following injection and antinociception was measured daily at 60 min. We found that CBD alters THC-induced changes in motor activity and hypothermia as a function of day, dose, time, sex, and strain. In D2 females, CBD dose-dependently attenuated the hypolocomotor effects of THC immediately following acute injection and enhanced these effects later at 75 min. Following repeated exposure, CBD dose-dependently enhanced THC-induced hypolocomotion in B6 females at 75 min and in D2 males at 30 min while attenuating THC-induced hypolocomotion in D2 females immediately following injection. In D2 females, CBD dose-dependently attenuated THC-induced hypothermia at 15 min and enhanced hypothermia relative to THC at 30 min in D2 males following acute injection. After repeated exposure, CBD dose-dependently enhanced THC-induced hypothermia in B6 females at 15 min and in D2 males from 30 to 120 mins, while attenuating hypothermia in D2 females at 30 min. No significant effects of CBD on antinociception were observed. Our results indicate that CBD can modulate some THC-induced traits acutely and after repeated exposure. Regulation of THC-induced behavioral responses is dependent on CBD dose, genetic background, and sex. A candidate gene search using brain gene expression in recombinant inbred mice revealed greater genetic variation in ion channel genes relative to key metabolic genes, suggesting an underlying pharmacodynamic mechanism. Future research and validation of molecular mechanisms underlying these differences is expected to enhance our understanding of potential health risks and clinical relevance of cannabis and cannabinoid compounds containing THC and CBD.

animal behavior and cognition↗

bioRxiv2025

SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation

BackgroundGenerating and testing hypotheses is a critical aspect of biomedical science. Typically, researchers generate hypotheses by carefully analyzing available information and making logical connections, which are then tested. The accelerating growth of biomedical literature makes it increasingly difficult to keep pace with connections between biological entities emerging across biomedical research. Recently developed automated means of generating hypotheses can generate many more hypotheses than can be easily tested. One such approach involves literature based discovery (LBD) systems such as Serial KinderMiner (SKiM), which surfaces putative A B C links derived from term co occurrence. However, LBD systems leave three critical gaps: (i) they find statistical associations, not biological relationships; (ii) they can produce false positive leads; and (iii) they do not assess agreement with a hypothesis in question. As a result, LBD search results often require costly manual curation to be of practical utility to the researcher. Large language models (LLMs) have the potential to automate much of this curation step, but standalone LLMs are hampered by hallucinations, lack of transparency in information sources, and the inability to reference data not included in the training corpus. ResultsWe introduce SKiM-GPT, a retrieval-augmented generation (RAG) system that combines SKiMs co-occurrence search and retrieval with frontier LLMs to evaluate user- defined hypotheses. For every chosen A-B-C SKiM hit, SKiM-GPT retrieves appropriate PubMed abstract texts, filters out irrelevant abstracts with a fine-tuned relevance model, and prompts an LLM to evaluate the users hypothesis, given the relevant abstracts. Importantly, the SKiM-GPT system is transparent and human-verifiable: it displays the retrieved abstracts, the hypothesis score, and a justification for the score grounded in the texts and written in natural language. On a benchmark consisting of 14 disease-gene-drug hypotheses, SKiM-GPT achieves strong ordinal agreement with four expert biologists (Cohens {kappa} = 0.84), demonstrating its ability to replicate expert judgment. ConclusionsSKiM-GPT is open-source (https://github.com/stewart-lab/skimgpt) and available through a web interface (https://skim.morgridge.org), enabling both wet-lab and computational researchers to systematically and efficiently evaluate biomedical hypotheses at scale.

bioinformatics↗

bioRxiv2023

Modulation of cannabinoid receptor 2 alters neuroinflammation and reduces formation of alpha-synuclein aggregates in a rat model of nigral synucleinopathy

Research into the disequilibrium of microglial phenotypes has become an area of intense focus in neurodegenerative disease as a potential mechanism that contributes to chronic neuroinflammation and neuronal loss in Parkinsons disease (PD). There is growing evidence that neuroinflammation accompanies and may promote progression of alpha-synuclein (Asyn)-induced nigral dopaminergic (DA) degeneration. From a therapeutic perspective, development of immunomodulatory strategies that dampen overproduction of pro-inflammatory cytokines from chronically activated immune cells and induce a pro-phagocytic phenotype is expected to promote Asyn removal and protect vulnerable neurons. Cannabinoid receptor-2 (CB2) is highly expressed on activated microglia and peripheral immune cells, is upregulated in the substantia nigra of individuals with PD and in mouse models of nigral degeneration. Furthermore, modulation of CB2 protects against rotenone-induced nigral degeneration; however, CB2 has not been pharmacologically and selectively targeted in an Asyn model of PD. Here, we report that 7 weeks of peripheral administration of CB2 inverse agonist SMM-189 reduced phosphorylated (pSer129) alpha-synuclein in the substantia nigra compared to vehicle treatment. Additionally, SMM-189 delayed Asyn-induced immune cell infiltration into the brain as determined by flow cytometry, increased CD68 protein expression, and elevated wound-healing-immune-mediator gene expression. Additionally, peripheral immune cells increased wound-healing non-classical monocytes and decreased pro-inflammatory classical monocytes. In vitro analysis of RAW264.7 macrophages treated with lipopolysaccharide (LPS) and SMM-189 revealed increased phagocytosis as measured by the uptake of fluorescence of pHrodo E. coli bioparticles. Together, results suggest that targeting CB2 with SMM-189 skews immune cell function toward a phagocytic phenotype and reduces toxic aggregated species of Asyn. Our novel findings demonstrate that CB2 may be a target to modulate inflammatory and immune responses in proteinopathies.

immunology↗

bioRxiv2021

Genetic modulation of initial sensitivity to Δ9-tetrahydrocannabinol (THC) among the BXD family of mice

Cannabinoid receptor 1 activation by the major psychoactive component in cannabis, {Delta}9-tetrahydrocannabinol (THC), produces motor impairments, hypothermia, and analgesia upon acute exposure. In previous work, we demonstrated significant sex and strain differences in acute responses to THC following administration of a single dose (10 mg/kg, i.p.) in C57BL/6J (B6) and DBA/2J (D2) inbred mice. To determine the extent to which these differences are heritable, we quantified acute responses to a single dose of THC (10 mg/kg, i.p.) in males and females from 20 members of the BXD family of inbred strains derived by crossing and inbreeding B6 and D2 mice. Acute THC responses (initial sensitivity) were quantified as changes from baseline for: 1. spontaneous activity in the open field (mobility), 2. body temperature (hypothermia), and 3. tail withdrawal latency to a thermal stimulus (analgesia/antinociception). Initial sensitivity to the immobilizing, hypothermic, and antinociceptive effects of THC varied substantially across the BXD family. Heritability was highest for mobility and hypothermia traits, indicating that segregating genetic variants modulate initial sensitivity to THC. We identified genomic loci and candidate genes, including Ndufs2, Scp2, Rps6kb1 or P70S6K, Pde4d, and Pten, that may control variation in THC initial sensitivity. We also detected strong correlations between initial responses to THC and legacy phenotypes related to intake or response to other drugs of abuse (cocaine, ethanol, and morphine). Our study demonstrates the feasibility of mapping genes and variants modulating THC responses in the BXDs to systematically define biological processes and liabilities associated with drug use and abuse.

genetics↗

bioRxiv2020

Genome-wide predictions of genetic redundancy in Arabidopsis thaliana

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features has not yet been established. In addition, the relative importance of these characteristics for genetic redundancy remains unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana. Benchmark gene pairs were classified based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including post-translational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used affected model performance significantly. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. Predictions were then tested using phenotype data withheld from model building and validated using well-characterized, redundant and nonredundant gene pairs. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs that are seemingly functionally redundant, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

evolutionary biology↗

bioRxiv2020

Optimizing the use of gene expression data to predict metabolic pathway memberships with unsupervised and supervised machine learning

Plant metabolites produced via diverse pathways are important for plant survival, human nutrition and medicine. However, the pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilizing >600 expression values and similarity data combinations from tomato, three strategies for predicting membership in 85 pathways were explored: naive prediction (identifying pathways with the most similarly expressed genes), unsupervised and supervised learning. Optimal predictions for different pathways require distinct data combinations that, in some cases, are indicative of biological processes relevant to pathway functions. Naive prediction produced higher error rates compared with machine learning methods. In 52 pathways, unsupervised learning performed better than a supervised approach, which may be due to the limited availability of training data. Furthermore, using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on gene expression levels. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximize the accuracy of metabolic pathway membership assignment. We anticipate that the prediction framework outlined here can be applied to other species and also be used to improve plant pathway annotation.

plant biology↗

bioRxiv2020

Modeling gene regulation in response to wounding: temporal variations, hormonal variations, and specialized metabolism pathways induced by wounding

Plants respond to wounding stress by changing gene expression patterns and inducing jasmonic acid (JA), as well as other plant hormones. This includes activating some specialized metabolism pathways, including the glucosinolate pathways, in the case of Arabidopsis thaliana. We model how these responses are regulated by using machine learning to incorporate putative cis-regulatory elements (pCREs), known transcription factor binding sites from literature, in-vitro DNA affinity purification sequencing (DAP-seq) and DNase I hypersensitive sites to predict gene expression for genes clustered by their wound response using machine learning. We found temporal patterns where regulatory sites and regions of open chromatin differed between clusters of genes up-regulated at early and late wounding time points as well as clusters where JA response was induced relative to clusters where JA response was not induced. Overall, we identified pCREs that improved model predictions of expression clusters over known binding sites. We discovered 4,255 pCREs related to wound response at different time points and 2,569 pCREs related to differences between JA-induced and non-JA induced wound response. In addition, pCREs found to be important at different wounding time points were mapped to the promoters of genes in a glucosinolate biosynthesis pathway indicating regulation of this pathway under wounding stress. Finally, we experimentally validated a predicted cis-regulatory element, CCGCGT, showing that knock-out via CRISPR-Cas9 reduces gene expression in response to wounding.

plant biology↗

bioRxiv2020

Within and cross species predictions of plant specialized metabolism genes using transfer learning

Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure=0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure=0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.

plant biology↗

bioRxiv2019

Factors influencing read coverage variation in a genome assembled using short reads

Availability of genome sequences has led to significant advance in biology. With few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues. In tomato, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. We established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have lower simple sequence repeat but higher tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially mis-assembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a machine learning model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to misassembly when using short reads.

genomics↗

bioRxiv2018

Robust predictions of specialized metabolism genes through machine learning

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, co-expressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a well performing prediction model was established with a true positive rate of 0.87 and a true negative rate of 0.71. In addition, 86% of known SM genes not used to create the machine learning model were predicted as SM genes, further demonstrating its accuracy. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways. Application of the prediction model led to the identification of 1,217 A. thaliana genes with previously unknown functions, providing a global, high-confidence estimate of SM gene content in a plant genome.\n\nSignificanceSpecialized metabolites are critical for plant-environment interactions, e.g., attracting pollinators or defending against herbivores, and are important sources of plant-based pharmaceuticals. However, it is unclear what proportion of enzyme-encoding genes play roles in specialized metabolism (SM) as opposed to general metabolism (GM) in any plant species. This is because of the diversity of specialized metabolites and the considerable number of incompletely characterized pathways responsible for their production. In addition, SM gene ancestors frequently played roles in GM. We evaluate features distinguishing SM and GM genes and build a computational model that accurately predicts SM genes. Our predictions provide candidates for experimental studies, and our modeling approach can be applied to other species that produce medicinally or industrially useful compounds.

plant biology↗

bioRxiv2018

Factors influencing gene family size variation among related species in a plant family

Gene duplication and loss contribute to gene content differences as well as phenotypic divergence across species. However, the extent to which gene content varies among closely related plant species and the factors responsible for such variation remain unclear. Here, we used the Solanaceae family as a model to investigate differences in gene family size and the likely factors contributing to these differences. We found that genes in highly variable families have high turnover rate and tend to be involved in processes that have diverged between Solanaceae species, whereas genes in low-variability families tend to have housekeeping roles. In addition, genes in high-and low-variability gene families tend to be duplicated by tandem and whole genome duplication, respectively. This finding together with the observation that genes duplicated by different mechanisms experience different selection pressures suggests that duplication mechanism impacts gene family turnover. We explored using pseudogene number as a proxy for gene loss but discovered that a substantial number of pseudogenes are actually products of pseudogene duplication, contrary to the expectation that most plant pseudogenes are remnants of once-functional duplicates. Our findings reveal complex relationships between variation in gene family size, gene functions, duplication mechanism, and evolutionary rate. The patterns of lineage-specific gene family expansion within the Solanaceae provide the foundation for a better understanding of the genetic basis underlying phenotypic diversity in this economically important family.

evolutionary biology↗