Biology subjects

Butler, A.

Publications and source records attributed to Butler, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Near real-time data on the human neutralizing antibody landscape to influenza virus in summer of 2026 shows antigenic advance of H3N2 subclade K region D mutants and H1N1 D.3.1.1 Sa mutants

Human seasonal influenza evolves rapidly, necessitating twice yearly decisions about whether to update the strains in the vaccine. To help inform this decision, we have been using high-throughput sequencing-based neutralization assays to make twice yearly measurements of how recent human sera neutralize current human H3N2 and H1N1 strains. Here we provide the third installment in this series of measurements by reporting 52,268 titers representing neutralization of 148 viral strains by 355 human sera collected between April and August of 2026. Our measurements show that new H3N2 subclade K strains with mutations in antigenic region D and new H1N1 subclade D.3.1.1 strains with mutations in antigenic region Sa (such as G155E) have reduced neutralization by human sera, with notable heterogeneity in the impact of some of these mutations across sera from different individuals. This paper is accompanied by an interactive summary (https://jbloomlab.github.io/flu-seqneut-2026/summary.html) that enables detailed exploration of the results, and all titer data are publicly available for further analysis to aid vaccine antigen selection and studies of viral evolution.

microbiology↗

bioRxiv2026

Novel Methylation Markers in a Prostate Cancer Cohort are Associated with Disease Development and Relapse

Prostate cancer remains one of the most common cancers among men globally. While significant strides have been made in diagnosis and treatment, understanding the complex genetic and epigenetic underpinnings of the disease remains crucial for guiding intervention and developing more personalized and effective therapies. The importance of DNA methylation in prostate cancer has been known for some time, but important facets of the modulation of the epigenome during carcinogenesis remain obscure, partly because the bulk of cancer methylation data have been produced using microarray technologies. Here we utilise the TruSeq methyl capture method (EPICseq) to profile the, previously defined, UK Prostate ICGC cohort of well-annotated primary prostate cancers. To this we add methylation sequencing of benign tissue from the same men. These data allow us to identify differentially methylated regions distinguishing cancerous and non-cancerous prostate tissue, while identifying numerous genes whose methylation profiles can perform that task as well as distinguishing between classes of prostate cancer. We describe a describe a methylation-based control mechanism for prostate-cancer-associated SNPs, and show that this seems a likely mechanism of action for a SNP near the MMP7 gene. We describe three novel molecular signatures that arise from different aspects of the biology of prostate cancer revealed by sequencing. Each is shown to be an independent classifier of cancers into groups with different expected times to relapse. These consist of patterns in driver gene methylation, strand-specific methylation, and signal arising in mitochondrial reads. We show that these signatures, combined with existing molecular tools, provide a powerful predictor of time to recurrence. By substantially enhancing understanding of prostate cancer risk, detection, and prognosis, we pave the way for the development of clinical practices that will benefit patients and improve outcomes.

cancer biology↗

bioRxiv2025

Automated genome mining predicts structural diversity and taxonomic distribution of peptide metallophores across bacteria

Microbial competition for trace metals shapes their communities and interactions with humans and plants. Many bacteria scavenge trace metals with metallophores, small molecules that chelate environmental metal ions. Metallophore production may be predicted by genome mining, where genomes are scanned for homologs of known biosynthetic gene clusters (BGCs). However, accurately detecting non-ribosomal peptide (NRP) metallophore biosynthesis requires expert manual inspection, stymieing large-scale investigations. Here, we introduce automated identification of NRP metallophore BGCs through a comprehensive algorithm, implemented in antiSMASH, that detects chelator biosynthesis genes with 97% precision and 78% recall against manual curation. We showcase the utility of the detection algorithm by experimentally characterizing metallophores from several taxa. High-throughput NRP metallophore BGC detection enabled metallophore detection across 69,929 genomes spanning the bacterial kingdom. We predict that 25% of all bacterial non-ribosomal peptide synthetases encode metallophore production and that significant chemical diversity remains undiscovered. A reconstructed evolutionary history of NRP metallophores supports that some chelating groups may predate the Great Oxygenation Event. The inclusion of NRP metallophore detection in antiSMASH will aid non-expert researchers and continue to facilitate large-scale investigations into metallophore biology.

biochemistry↗

bioRxiv2025

Multiomic clocks to predict phenotypic age in mice

Biological age refers to a persons overall health in aging, as distinct from their chronological age. Diverse measures of biological age, referred to as "clocks", have been developed in recent years and enable risk assessments, and an estimation of the efficacy of longevity interventions in animals and humans. While most clocks are trained to predict chronological age, clocks have been developed to predict more complex composite biological age outcomes, at least in humans. These composite outcomes can be made up of a combination of phenotypic data, chronological age, and disease or mortality risk. Here, we develop the first such composite biological age measure for mice: the mouse phenotypic age model (Mouse PhenoAge). This outcome is based on frailty measures, complete blood counts, and mortality risk in a longitudinally assessed cohort of male and female C57BL/6 mice. We then develop clocks to predict Mouse PhenoAge, based on multi-omic models using metabolomic and DNA methylation data. Our models accurately predict Mouse PhenoAge, and residuals of the models are associated with remaining lifespan, even for mice of the same chronological age. These methods offer novel ways to accurately predict mortality in laboratory mice thus reducing the need for lengthy and costly survival studies.

systems biology↗

bioRxiv2024

The Splice Index as a prognostic biomarker of strength and function in myotonic dystrophy type 1

Myotonic dystrophy type 1 (DM1) is a slowly progressive, multisystemic disorder caused by a CTG repeat expansion in the DMPK 3UTR that leads to global dysregulation of alternative splicing. Here, we employed a composite RNA splicing biomarker called the Myotonic Dystrophy Splice Index (SI), which incorporates 22 disease-specific splice events that sensitively and robustly assesses transcriptomic dysregulation across the disease spectrum. Targeted RNA sequencing was used to derive the SI in 95 muscle biopsies of the tibialis anterior collected from DM1 individuals with baseline (n = 52) and 3-months (n = 37) outcomes. The SI had significant associations with timepoint matched measures of muscle strength and ambulation, including ankle dorsiflexion strength (ADF) and 10-meter run/fast walk speed (Pearson r = -0.719 and -0.680, respectively). Linear regression modeling showed that the combination of baseline ADF and SI was predictive of strength at 3-months (adjusted R2 = 0.830) in our cohort. These results indicate the SI can reliably capture the association of disease-specific RNA mis-splicing to physical strength and mobility and may be predictive of future function.

molecular biology↗

bioRxiv2024

RNA mis-splicing in children with myotonic dystrophy is associated with physical function

ObjectivesDysregulated RNA alternative splicing is the hallmark of myotonic dystrophy type 1 (DM1). However, the association between RNA mis-splicing and physical function in children with the most severe form of disease, congenital myotonic dystrophy (CDM), is unknown. Methods82 participants (42 DM1 adults & 40 CDM children) with muscle biopsies and measures of myotonia, motor function, and strength were combined from five observational studies. Data were normalized and correlated with an aggregate measure of alternative splicing dysregulation, [MBNL]inferred in skeletal muscle biopsies. Multiple linear regression analysis was performed to predict [MBNL]inferred using clinical outcome measures alone. Similar analyses were performed to predict 12-month physical function using baseline metrics. ResultsMyotonia (measured via vHOT) was significantly correlated with RNA mis-splicing in our cross-sectional population of all DM1 individuals; CDM participants alone displayed no myotonia despite a similar range of RNA mis-splicing. Measures of motor performance and muscle strength were significantly associated with [MBNL]inferred in our cohort of all DM1 individuals and when assessing CDM children independently. Multiple linear regression analyses yielded two models capable of predicting [MBNL]inferred from select clinical outcome assessments alone in all subjects (adjusted R2 = 0.6723) or exclusively in CDM children (adjusted R2 = 0.5875). InterpretationOur findings establish significant correlations between skeletal muscle performance and a composite measure of alternative splicing dysregulation, [MBNL]inferred, in DM1. The strength of these correlations and the development of the predictive models will assist in designing efficacious clinical trials for individuals with DM1, particularly CDM.

genetics↗

bioRxiv2024

SLO2.1/NALCN Functional Complex Activity in Mouse Myometrial Smooth Muscle Cells During Pregnancy.

At the end of pregnancy, the uterus transitions from a quiescent to a highly contractile state. This is partly due to depolarization of the resting membrane potential in uterine (myometrial) smooth muscle cells (MSMCs). In human MSMCs, the membrane potential is regulated by a functional complex between the sodium (Na+)-activated potassium (K+) channel SLO2.1 and the Na+ Leak Channel Non-Selective (NALCN). Na+ entering through NALCN activates SLO2.1, leading to K+ efflux, membrane hyperpolarization (cells become more negative inside), and reduced contractility. Decreased SLO2.1/NALCN activity results in reduced K+ efflux, leading to membrane depolarization, Ca2+ influx via voltage-dependent calcium channels, and increased MSMC contractility. However, all of these data are from MSMCs isolated from women at term, so the role of the SLO2.1/NALCN complex early in pregnancy was speculative. To address this question here, we examined the role of the SLO2.1/NALCN complex in regulating mouse MSMC membrane potential across pregnancy. We report that Slo2.1 and Nalcn are more highly expressed in MSMCs from non-pregnant and early pregnant mice than in those from late-pregnant mice. Functional studies revealed that SLO2.1 channels mediate a significant portion of the K+ current in mouse MSMCs, particularly in cells from non-pregnant and early pregnant mice. Activation of SLO2.1 by Na+ influx through NALCN led to membrane hyperpolarization in MSMCs from early pregnancy but not in MSMCs from later pregnancy. Moreover, the NALCN/SLO2.1 complex regulates intracellular Ca2+ responses more in MSMCs from non-pregnant and early pregnancy mice than in MSMCs from late pregnancy. Together, these findings reveal that the SLO2.1/NALCN functional complex is conserved between mouse and humans and functions throughout pregnancy. This work could open avenues for targeted pharmacological interventions for pregnancy-related complications.

physiology↗

bioRxiv2024

Holding glycolysis in check though Alox15 activity is required for macrophage M2 commitment and function in tissue repair and anti-helminth immunity.

Macrophage polarization by type-2 cytokines is central to anti-helminth immunity and tissue repair. While some hallmark changes in macrophages are well-characterized and associated with protection against helminths, it is still unclear how macrophages exert their anti-helminth effects. In this context, we investigated Arachidonate 15-lipoxygenase (Alox15), a lipoxygenase well known for its role in macrophage polarization in the context of metabolic diseases, and a hallmark of type-2 macrophage (M2) human polarization. We show that in the absence of Alox15, M2 cannot trap and kill helminths. Surprisingly, expression of M2 markers was normal despite a loss of function. Instead, we found a concomitant increase in pro-inflammatory responses due to an uncontrolled activation of glycolysis. We further show that activation of Peroxisome proliferator-activated receptor-delta (PPAR-{delta}) by lipids downstream of Docosapentaenoic acid (DPA) can restore normal glycolysis control, highlighting a novel role for lipids in the fine-tuning of the metabolic support required for optimal macrophage polarization.

immunology↗

bioRxiv2023

Measuring technical variability in Illumina DNA methylation microarrays

DNA methylation microarrays have become a widely used tool for investigating epigenetic modifications in various aspects of biomedical research. However, technical variability in methylation data poses challenges for downstream applications such as predictive modeling of health and disease. In this study, we measure the impact of common sources of technical variability in Illumina DNA methylation microarray data, with a specific focus on positional biases inherent within the microarray technology. By utilizing a dataset comprised of multiple, highly similar technical replicates, we identified a chamber number bias, with different chambers of the microarray exhibiting systematic differences in fluorescence intensities and their derived methylation beta values, which are only partially corrected for by existing preprocessing methods, and demonstrate that this positional bias can lead to false positive results during differential methylation testing. Additionally, our investigation identified outliers in low-level fluorescence data which might play a role in contributing to predictive error in computational models of health-relevant traits such as age.

bioinformatics↗

bioRxiv2022

Automated genome mining predicts combinatorial diversity and taxonomic distribution of peptide metallophore structures

Microbial competition for trace metals shapes their communities and interactions with humans and plants. Many bacteria scavenge trace metals with metallophores, small molecules that chelate environmental metal ions and transport them back into the cell. Our incomplete knowledge of metallophores diversity stymies our ability to fight infectious diseases and harness beneficial microbiome interactions. The majority of known metallophores are non-ribosomal peptides (NRPs), which feature metal-chelating moieties rarely found in other classes of natural products. NRP metallophore production may be predicted by genome mining, where genomes are scanned for homologs of known biosynthetic gene clusters (BGCs). However, accurately detecting NRP metallophore biosynthesis currently requires expert manual inspection. Here, we introduce automated identification of NRP metallophore BGCs through a comprehensive detection algorithm, newly implemented in antiSMASH. Custom-designed profile hidden Markov models detect genes encoding the biosynthesis of most known NRP metallophore chelating moieties (2,3-dihydroxybenzoate, hydroxamates, salicylate, {beta}-hydroxyamino acids, graminine, Dmaq, and the pyoverdine chromophore), achieving 97% precision and 78% recall against manual curation. We leveraged the algorithm, in combination with transporter gene detection, to detect NRP metallophore BGCs in 15,562 representative bacterial genomes and predict that 25% of all non-ribosomal peptide synthetases encode metallophore production. BiG-SCAPE clustering of 2,562 NRP metallophore BGCs revealed that significant diversity remains unexplored, including new combinations of chelating groups. Additionally, we find that Cyanobacteria are severely understudied and should be the focus of more metallophore isolation efforts. The inclusion of NRP metallophore detection in antiSMASH version 7 will aid non-expert researchers and facilitate large-scale investigations into metallophore biology.

bioinformatics↗

bioRxiv2022

Influenza virus transcription and progeny production are poorly correlated in single cells

The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza-virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny, and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cells contribution to the progeny population.

microbiology↗

bioRxiv2021

An ensemble of toxic channel types underlies the severity of the de novo variant G375R of the human BK channel

The molecular basis of a severe developmental and neurological disorder associated with a de novo G375R variant of the tetrameric BK channel is unknown. Here we address this question by recording from single BK channels expressed for a heterozygous G375R mutation. Five different types of functional BK channels were observed: 3% were WT, 12% were homomeric mutant, and 85% were three different types of hybrid channels. All channel types except WT showed a marked gain-of-function in voltage activation and a smaller loss-of-function in single channel conductance, with both becoming more pronounced as the number of mutant subunits per tetrameric channel increased. The molecular phenotype suggested codominance for the two homomeric channels and partial dominance for the hybrid channels. A model in which BK channels are randomly assembled from mutant and WT subunits, with each subunit contributing increments of activation and conductance, approximated the molecular phenotype of the heterozygous G375R mutation.

neuroscience↗

bioRxiv2021

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.

The problem of selecting targeted gene panels that capture maximum variability encoded in scRNA-sequencing data has become of great practical importance. scRNA-seq datasets are increasingly being used to identify gene panels that can be probed using alternative molecular technologies, such as spatial transcriptomics. In this context, the number of genes that can be probed is an important limiting factor, so choosing the best subset of genes is vital. Existing methods for this task are limited by either a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cell types. We resolve this by introducing an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. We demonstrate, using a variety of metrics and diverse datasets, that our approach outperforms existing strategies, and can not only resolve cell types but also more subtle cell state differences. Our approach is available as an open source, easy-to-use, documented R package (https://github.com/MarioniLab/geneBasisR).

bioinformatics↗

bioRxiv2020

Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce weighted-nearest neighbor analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. AvailabilityInstallation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

genomics↗

bioRxiv2020

Molecular basis for substrate specificity of the Phactr1/PP1 phosphatase holoenzyme

ABSTRACTPPP-family phosphatases such as PP1 have little intrinsic specificity. Cofactors can target PP1 to substrates or subcellular locations, but it remains unclear how they might confer sequence-specificity on PP1. The cytoskeletal regulator Phactr1 is a neuronally-enriched PP1 cofactor that is controlled by G-actin. Structural analysis showed that Phactr1 binding remodels PP1’s hydrophobic groove, creating a new composite surface adjacent to the catalytic site. Using phosphoproteomics, we identified numerous fibroblast and neuronal Phactr1/PP1 substrates, which include cytoskeletal components and regulators. We determined high-resolution structures of Phactr1/PP1 bound to the dephosphorylated forms of its substrates IRSp53 and spectrin αII. Inversion of the phosphate in these holoenzyme-product complexes supports the proposed PPP-family catalytic mechanism. Substrate sequences C-terminal to the dephosphorylation site make intimate contacts with the composite Phactr1/PP1 surface, which are required for efficient dephosphorylation. Sequence specificity explains why Phactr1/PP1 exhibits orders-of-magnitude enhanced reactivity towards its substrates, compared to apo-PP1 or other PP1 holoenzymes.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗

bioRxiv2020

Coupling of Ca2+ and voltage activation in BK channels through the αB helix/voltage sensor interface

Large conductance Ca2+ and voltage activated K+ (BK) channels control membrane excitability in many cell types. BK channels are tetrameric. Each subunit is comprised of a voltage sensor domain (VSD), a central pore gate domain, and a large cytoplasmic domain (CTD) that contains the Ca2+ sensors. While it is known that BK channels are activated by voltage and Ca2+, and that voltage and Ca2+ activations interact, less is known about the mechanisms involved. We now explore mechanism by examining the gating contribution of an interface formed between the VSDs and the B helices located at the top of the CTDs. Proline mutations in the B helix greatly decreased voltage activation while having negligible effects on gating currents. Analysis with the HCA model indicated a decreased coupling between voltage sensors and pore gate. Proline mutations decreased Ca2+ activation for both Ca2+ bowl and RCK1 Ca2+ sites, suggesting that both high affinity Ca2+ sites transduce their effect, at least in part, through the B helix. Mg2+ activation was also decreased. The crystal structure of the CTD with proline mutation L390P showed a flattening of the first helical turn in the B helix compared to WT, without other notable differences in the CTD, indicating structural change from the mutation was confined to the B helix. These findings indicate that an intact B helix/VSD interface is required for effective coupling of Ca2+ binding and voltage depolarization to pore opening, and that shared Ca2+ and voltage transduction pathways involving the B helix may be involved. SignificanceLarge conductance BK (Slo1) K+ channels are activated by voltage, Ca2+, and Mg2+ to modulate membrane excitability in neurons, muscle, and other cells. BK channels are of modular design, with pore-gate and voltage sensors as transmembrane domains and a large cytoplasmic domain CTD containing the Ca2+ sensors. Previous observations suggest that voltage and Ca2+ sensors interact, but less is known about this interaction and its involvement in the gating process. We show that a previously identified structural interface between the CTD and voltage sensors is required for effective activation by both voltage and Ca2+, suggesting that these processes may share common allosteric activation pathways. Such knowledge should help explain disease processes associated with BK channel dysfunction.

neuroscience↗

bioRxiv2019

Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR/Cas9 screening

Many gene fusions have been reported in tumours and for most their role remains unknown. As fusions can be used clinically for diagnostic and prognostic purposes, and are targets for treatment, it is crucial to assess their functional implications in cancer. To investigate the role of fusions in tumor cell fitness, we developed a systematic analysis utilising RNA-sequencing data from 1,011 human cancer cell lines to functionally link 8,354 gene fusion events with genomic data, sensitivity to >350 anti-cancer drugs and CRISPR-Cas9 loss-of-fitness information. Established clinically-relevant fusions were readily identified. Overall, functional fusions were rare, including those involving cancer driver genes, suggesting that many fusions are dispensable for tumor cell fitness. Novel therapeutically actionable fusions involving RAF1, BRD4 and ROS1 were verified in new histologies. In addition, recurrent YAP1-MAML2 fusions were identified as activators of Hippo-pathway signaling in multiple cancer types, supporting therapeutic targeting of Hippo signalling. Our approach discriminates functional fusions, identifying new drivers of carcinogenesis and fusions that could have important clinical implications. SignificanceWe identify fusions as new potential candidates for drug repurposing and drivers of carcinogenesis. These results support histology agnostic marker-driven precision cancer medicine. Most fusions are not functional with implications for interpreting cancer fusions reported from clinical sequencing studies.

cancer biology↗

bioRxiv2018

Comprehensive integration of single cell data

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to \"anchor\" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets.\n\nAvailability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

genomics↗