Biology subjects

Hall, I.

Publications and source records attributed to Hall, I..

13 recordsLinked to original sources

bioRxiv2026

A two-scale model of Legionnaires disease to predict incubation periods and risk of symptomatic disease

Legionella pneumophila is an intracellular pathogen that causes Legionnaires disease, a severe pneumonia acquired primarily through contaminated water systems. Public health interventions rely on accurate estimates of the incubation period and dose-response (DR) relationship, yet currently used approaches in the literature underestimate incubation periods by assuming Markovian rupture times for infected macrophages. Here, we develop the first non-Markovian, two-scale within-host framework for Legionnaires disease, coupling stochastic intracellular replication in individual macrophages with extracellular Legionella-macrophage population dynamics. At the cellular level, we model intracellular replication using a stochastic logistic birth-death (SLBD) process, coupled with non-Markovian rupture-time distributions (Erlang and Burr). The Erlang distribution preserves tractability via the method of separation, whereas the Burr distribution captures heavy-tailed rupture times consistent with experimental data. Simulations are implemented using a renewal-based non-Markovian Gillespie algorithm. At the host level, successive infection and rupture events describe population-scale infection dynamics, enabling estimation of DR curves and incubation-period distributions. Across six model variants, DR predictions remain robust, with ID50 estimates narrowly ranging between 8.79 and 8.94 Legionella, consistent with guinea pig challenge data. In contrast, incubation-period estimates show strong dependence on rupture-time assumptions: non-Markovian models predict median incubation periods of 5-6 days, correcting the previous 2-3 day underestimation and aligning with human outbreak data (2-10 days, up to 13 days). Sensitivity analysis identifies rupture size, phagocytosis rates, and threshold effects as key determinants of incubation-period results. By relaxing exponential assumptions, our framework provides biologically realistic within-host dynamics that improve epidemiological predictions. These results refine the quantitative basis for outbreak investigations and environmental risk assessment and are generalizable to other intracellular pathogens such as Coxiella burnetii and Francisella tularensis. Author summaryLegionella pneumophila causes Legionnaires disease, a serious pneumonia often linked to contaminated water systems, but key quantities such as the incubation period remain difficult to estimate accurately. Existing models assume that infected immune cells rupture at random times with no memory, an assumption that simplifies mathematics but does not reflect experimental observations. We developed a model that follows bacterial growth inside individual macrophages and connects these cellular events to infection dynamics within a host. Unlike previous approaches, our model allows rupture times to follow more realistic, non-exponential patterns that better match laboratory data. Using simulations, we show that commonly used assumptions systematically underestimate the incubation period of Legionnaires disease. Our results predict incubation periods of 5-6 days, consistent with human outbreak data, while leaving estimates of infectious dose largely unchanged. This work improves the biological realism of within-host infection models and provides a stronger quantitative foundation for outbreak investigation and environmental risk assessment. The modelling framework can be adapted to study other intracellular pathogens that replicate inside host immune cells.

cell biology↗

bioRxiv2025

A hybrid model of the within-host dynamics post-infection with Legionnaires disease;

Understanding the incubation period of Legionnaires disease is vital for accurate source-term identification. Traditionally, researchers estimate the dose-dependent incubation period from human outbreak data, but this method suffers from the inability to estimate the exposure dose retrospectively for each case. This challenge limit the precision of incubation-period analysis using human case data. Existing within-host models, such as ordinary differential equation (ODE)-based and discrete-event stochastic approaches, estimate the dose-dependent incubation period of Legionnaires disease. However, discrete-event models, while useful, are so computationally costly that the within-host dynamics must be simplified to solely the Legionella and macrophage interactions. This simplification makes the computation feasible, but precludes cytokine interactions and adaptive immune response modelling. In this paper, we develop a new approach to model the within-host dynamics of Legion-naires disease that focuses on reducing computational cost while maintaining accuracy. Specifically, we propose a hybrid framework that integrates and improves upon existing ODE and discrete event within-host models of Legionnaires disease. By integrating the previously developed ODE and discrete-event stochastic models with stochastic differential equation (SDE) models, we create a unified system that adapts dynamically throughout the infection process. We quantify the points at which each model becomes the optimal tool for describing the infection, resulting in a flexible simulation of disease dynamics. Our hybrid model aligns with observed human incubation-period data and is the first framework of its kind in this context. This advancement offers a more robust platform for testing additional biological assumptions and improving our understanding of Legionnaires disease.

cell biology↗

bioRxiv2025

Long-acting hydrogel-based depot formulations of tirzepatide and semaglutide for the management of type 2 diabetes and weight

Several incretin hormone therapies have been clinically approved and have revolutionized the treatment of diabetes and obesity. Promising therapeutics include semaglutide (Ozempic(R) and Wegovy(R)), an agonist for glucagon-like peptide-1 (GLP-1) receptor, and tirzepatide (Mounjaro(R)), a dual agonist for GLP-1 and glucose-dependent insulinotropic polypeptide (GIP) receptors. These molecules help to regulate blood glucose levels, enhance insulin secretion and sensitivity, and reduce appetite. Currently, these treatments require weekly injections, which can be challenging for patients to adhere to. We recently reported the development of an injectable hydrogel depot technology enabling months-long release of semaglutide (Sema). Here, we further develop this technology for improved prolonged release of both Sema and tirzepatide (TZP). In a rat model of diabetes, we show a single administration of hydrogel-based formulations of either Sema or TZP maintained relevant drug levels for over 6 weeks. In these studies, single administrations of long-acting hydrogel-based therapies of Sema or TZP were similarly effective at regulating blood glucose and weight compared to daily injections of either Sema or TZP in standard aqueous vehicles. This hydrogel depot technology is easy to manufacture, injectable, and exhibits excellent biocompatibility, enabling months-long-acting treatments with the potential to improve management of diabetes and weight.

bioengineering↗

bioRxiv2024

Variant Set Distillation

Allelic heterogeneity - the presence of multiple causal variants at a given locus - has been widely observed across human traits. Combining the association signals across these distinct causal variants at a given locus presents an opportunity for empowering gene discovery. This opportunity is growing with the increasing population diversity and sequencing depth of emerging genomic datasets. However, the rapidly increasing number of null (non-causal) variants within these datasets makes leveraging allelic heterogeneity increasingly difficult for existing testing approaches. We recently-proposed a general theoretical framework for sparse signal problems, Stable Distillation (SD). Here we present a SD-based method vsdistill, which overcomes several major shortcomings in the simple SD procedures we initially proposed and introduces many innovations aimed at maximizing power in the context of genomics. We show via simulations that vsdistill provides a significant power boost over the popular STAAR method. vsdistill is available in our new R package gdistill, with core routines implemented in C. We also show our method scales readily to large datasets by performing an association analysis with height in the UK Biobank.

genomics↗

bioRxiv2024

Clade Distillation for Genome-wide Association Studies

Testing inferred haplotype genealogies for association with phenotypes has been a longstanding goal in human genetics with several underlying challenges. A key advantage of these methods is the potential to detect association signals caused by allelic heterogeneity -- when multiple causal variants modulate a phenotype -- in both coding and noncoding regions. Recent scalable methods for inferring locus-specific genealogical trees along the genome, or representations thereof, have made substantial progress towards this goal; however, the problem of testing these trees for association with phenotypes has remained unsolved due to the growth in the number of clades with increasing sample size. To address this issue, we introduce several practical improvements to the kalis ancestry inference engine, including a general optimal checkpointing algorithm for decoding hidden Markov models, thereby enabling efficient genome-wide analyses. We then propose LOCATER, a powerful new procedure based on the recently proposed Stable Distillation framework, to test local tree representations for trait association. Although LOCATER is demonstrated here in conjunction with kalis, it may be used for testing output from any ancestry inference engine, regardless of whether such engines return discrete tree structures, relatedness matrices, or some combination of the two at each locus. Using simulated quantitative phenotypes, our results indicate that LOCATER achieves substantial power gains over traditional single marker testing and window-based testing in cases of allelic heterogeneity, while also improving causal region localization relative to single marker tests. These findings suggest that genealogy based association testing will be a fruitful approach for gene discovery, especially for signals driven by multiple ultra-rare variants. Author summaryFor a given set of individuals and at particular location in the genome, there is an underlying genealogical tree relating those individuals. Due to recombination, this tree is not static but rather varies along the genome. For decades investigators have sought to learn and use these trees to identify regions of the genome that impact human traits and disease. In other words, to find trait-associated trees where different clusters of relatives have, for example, high blood pressure. However, since these trees can be so enormous, it is difficult computationally to build them from DNA samples and difficult statistically to find trees with disease clusters: since each tree encodes so many possible clusters, it becomes hard to distinguish signal from noise. Here, we develop a new statistical method, LOCATER, to efficiently aggregate signals across disease clusters within each tree and thereby detect trait-associated trees. LOCATER can work with any ancestry inference method. We show LOCATER is better at detecting these trees than existing methods. We also introduce a suite of broadly applicable algorithms that make our ancestry inference software, kalis, and LOCATER computationally efficient. LOCATER is designed to work with any ancestry inference method.

genomics↗

bioRxiv2024

Selective deuteration of an RNA:RNA complex for structural analysis using small-angle scattering

The structures of RNA:RNA complexes regulate many biological processes. Despite their importance, protein-free RNA:RNA complexes represent a tiny fraction of experimentally-determined structures. Here, we describe a joint small-angle X-ray and neutron scattering (SAXS/SANS) approach to structurally interrogate conformational changes in a model RNA:RNA complex. Using SAXS, we measured the solution structures of the individual RNAs in their free state and of the overall RNA:RNA complex. With SANS, we demonstrate, as a proof-of-principle, that isotope labeling and contrast matching (CM) can be combined to probe the bound state structure of an RNA within a selectively deuterated RNA:RNA complex. Furthermore, we show that experimental scattering data can validate and improve predicted AlphaFold 3 RNA:RNA complex structures to reflect its solution structure. Our work demonstrates that in silico modeling, SAXS, and CM-SANS can be used in concert to directly analyze conformational changes within RNAs when in complex, enhancing our understanding of RNA structure in functional assemblies.

biophysics↗

bioRxiv2024

Functional Validation of SAM Riboswitch Element A from Listeria monocytogenes

SreA is one of seven candidate S-adenosyl methionine (SAM) class I riboswitches identified in Listeria monocytogenes, a saprophyte and opportunistic foodborne pathogen. SAM is essential to all domains of life, serving as a ubiquitous methyl donor and mediator of trans-sulfuration. SreA precedes genes encoding a methionine ATP-binding cassette (ABC) transporter, which imports methionine, a sulfur containing amino acid and substrate for sulfur metabolism. SreA is presumed to regulate transcription of its downstream genes in a SAM-dependent manner. The proposed role of SreA in controlling the transcription of genes encoding an ABC transporter complex may have important implications for how the bacteria senses and responds to the availability of the metabolite SAM in the diverse environments in which L. monocytogenes persists. Here we validate SreA as a functional SAM-I riboswitch through ligand binding studies, structure characterization, and transcription termination assays. We determined that SreA has both a similar structure and SAM binding properties to other well characterized SAM-I riboswitches. Interestingly, SreA regulates transcription at a distinctly lower (nM) ligand concentration than other SAM riboswitches but does not substantially terminate transcription, even in the presence of mM SAM. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/596223v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@16b3af4org.highwire.dtl.DTLVardef@eb6aecorg.highwire.dtl.DTLVardef@1b8e7ecorg.highwire.dtl.DTLVardef@823cf7_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2022

Analysis and comprehensive lineage identification for SARS-CoV-2 genomes through scalable learning methods

Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and globally caused multiple waves of infection. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern (VoC). They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta, but not Omicron. Phylogenetic methods provide the gold standard for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges together with the increasing volumes of genomic data available provide a motivation to develop complementary methods that can incorporate all of the genetic data available, without down-sampling, to extract meaningful information rapidly and with minimal curation. Here, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies, and while not serving as a substitute for current phylogenetic analyses the proposed methods can be used as a complementary approach to identify and confirm new emerging variants.

bioinformatics↗

bioRxiv2022

Solution structure of NPSL2, a regulatory element in the oncomiR-1 RNA

The miR-17~92a polycistron, also known as oncomiR-1, is commonly overexpressed in multiple cancers and has several oncogenic properties. OncomiR-1 encodes six constituent microRNAs (miRs), each enzymatically processed with different efficiencies. However, the structural mechanism that regulates this differential processing remains unclear. Chemical probing of oncomiR-1 revealed that the Drosha cleavage sites of pri-miR-92a are sequestered in a four-way junction. NPSL2, an independent stem loop element, is positioned just upstream of pri-miR-92a and sequesters a crucial part of the sequence that constitutes the basal helix of pri-miR-92a. Disruption of the NPSL2 hairpin structure could promote the formation of a pri-miR-92a structure that is primed for processing by Drosha. Thus, NPSL2 is predicted to function as a structural switch, regulating pri-miR-92a processing. Here, we determined the solution structure of NPSL2 using solution NMR spectroscopy. This is the first high-solution structure of an oncomiR-1 element. NPSL2 adopts a hairpin structure with a large, but highly structured, apical and internal loops. The 10-bp apical loop contains a pH-sensitive A+{middle dot}C mismatch. Additionally, several adenosines within the apical and internal loops have elevated pKa values. The protonation of these adenosines can stabilize the NPSL2 structure through electrostatic interactions. Our study provides fundamental insights into the secondary and tertiary structure of an important RNA hairpin proposed to regulate miR biogenesis.

biophysics↗

bioRxiv2021

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

bioinformatics↗

bioRxiv2020

De novo assembly of 64 haplotype-resolved human genomes of diverse ancestry and integrated analysis of structural variation

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average contig N50: 26 Mbp) integrate all forms of genetic variation across even complex loci such as the major histocompatibility complex. We focus on 107,590 structural variants (SVs), of which 68% are inaccessible by short-read sequencing. We identify new SV hotspots (spanning megabases of gene-rich sequence), characterize 130 of the most active mobile element source elements, and find that 63% of all SVs arise by homology-mediated mechanisms--a twofold increase from previous studies. Our resource now enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1,525 expression quantitative trait loci (SV-eQTLs) as well as SV candidates for adaptive selection within the human population.

genetics↗

bioRxiv2019

Diverse transcriptomic signatures across human tissues identify functional rare genetic variation

Rare genetic variation is abundant in the human genome, yet identifying functional rare variants and their impact on traits remains challenging. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants. Here, we expand detection of genetically driven transcriptome abnormalities by evaluating and integrating gene expression, allele-specific expression, and alternative splicing from multi-tissue RNA-sequencing data. We demonstrate that each signal informs unique classes of rare variants. We further develop Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function. Assessing rare variants prioritized by Watershed in the UK Biobank and Million Veterans Program, we identify large effects across 34 traits, and 33 rare variant-trait combinations with both high Watershed scores and large trait effect sizes. Together, we provide a comprehensive analysis of the transcriptomic impact of rare variation and a framework to prioritize functional rare variants and assess their trait relevance.\n\nOne-sentence summaryIntegrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

genomics↗

bioRxiv2019

Telomere-to-telomere assembly of a complete human X chromosome

After nearly two decades of improvements, the current human reference genome (GRCh38) is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has been finished end to end, and hundreds of unresolved gaps persist 1,2. The remaining gaps include ribosomal rDNA arrays, large near-identical segmental duplications, and satellite DNA arrays. These regions harbor largely unexplored variation of unknown consequence, and their absence from the current reference genome can lead to experimental artifacts and hide true variants when re-sequencing additional human genomes. Here we present a de novo human genome assembly that surpasses the continuity of GRCh38 2, along with the first gapless, telomere-to-telomere assembly of a human chromosome. This was enabled by high-coverage, ultra-long-read nanopore sequencing of the complete hydatidiform mole CHM13 genome, combined with complementary technologies for quality improvement and validation. Focusing our efforts on the human X chromosome 3, we reconstructed the [~]2.8 megabase centromeric satellite DNA array and closed all 29 remaining gaps in the current reference, including new sequence from the human pseudoautosomal regions and cancer-testis ampliconic gene families (CT-X and GAGE). This complete chromosome X, combined with the ultra-long nanopore data, also allowed us to map methylation patterns across complex tandem repeats and satellite arrays for the first time. These results demonstrate that finishing the human genome is now within reach and will enable ongoing efforts to complete the remaining human chromosomes.

bioinformatics↗