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Howell, A.

Publications and source records attributed to Howell, A..

14 recordsLinked to original sources

Task-evoked flexibility of intrinsic neural timescales is hierarchically organized, behaviorally relevant, and refined across two developmental stages

Cortical regions integrate temporal information over different timescales at rest, ranging from fast in sensorimotor regions to slow in the association cortex. Here, we study how far regions adapt these timescales to meet task demands, as well as how this \emph{timescale flexibility} is organized across the cortical hierarchy, and how it develops in humans. We quantified rest-to-task changes in intrinsic neural timescales, referred to as \emph{timescale deflections}, using fMRI data from four independent datasets totaling 1,712 participants aged 8--64 years. We found that timescales reduced under cognitive load in all four datasets, most strongly in association cortex, which partially flattened the resting-state timescale hierarchy. Individual differences in timescale deflections were organized along the sensorimotor-association (S-A) axis and tracked cognitive performance, both in adults and in youth. Next, in youth, deflections followed a two-stage developmental course: a generalized reduction throughout childhood, followed by a selective expansion in association cortex from mid-adolescence. Furthermore, the timing of these stages aligned with cortical gradients of inhibitory gene expression and intracortical myelin content, both features implicated in the opening and closing of critical periods of neural plasticity. Overall, our results demonstrate that task-evoked timescale flexibility is hierarchically organized, behaviorally and neurobiologically relevant, and refined across adolescence.

neuroscience↗

Cell-type specific impact of opioid use disorder and HIV on the human forebrain and cerebellum

Opioid use disorder (OUD), which frequently co-occurs with HIV infection, causes long-term neurological disease, yet the epigenetic and transcriptomic effects of OUD and HIV on specific cell types and regions of the brain are poorly understood. To assess the cell-type specific impacts of OUD and HIV across the human brain, we measured single cell transcriptomes and epigenomes of 580,353 cells in the prefrontal cortex, amygdala and cerebellum of 44 donors. We cataloged over 750k candidate cis-regulatory elements (cCREs) and identified gene regulatory networks (GRNs) of transcription factor activity across 35 neuronal and non-neuronal cell types. We identified specific neuronal and glial populations whose cCREs were significantly enriched for genetic risk of addiction-related traits. In OUD donors, we found evidence for reduced metabolic function in neurons in the PFC and cerebellum as well as increased gene expression related to voltage-gated calcium channel activity in the cerebellum. Using a cerebellar organoid model, fentanyl treatment reduced metabolic activity while increasing neuronal activity. Across brain regions, HIV activated immune-related pathways in glial populations, while comorbid OUD and HIV exacerbated metabolic changes in cortical glial cells. Cerebellum-specific Bergmann glia, in addition to forebrain microglia and astrocytes, showed expansion of reactive state identity in HIV. These results highlight shared and specific changes to immune, synaptic, and metabolic processes in OUD and HIV across brain regions and reveal that cerebellar cell types are distinctly affected by opioid abuse.

genomics↗

Tracking GAD-specific T-cell expansions in Type 1 diabetes by intradermal GAD-Alum challenge

Identifying and monitoring autoreactive T cells that drive beta cell destruction remains a major obstacle to developing effective immunotherapies for type 1 diabetes (T1D). These cells are extremely rare in peripheral blood and cannot be accessed directly from the pancreas. We used intradermal injection of GAD-Alum to recruit GAD-specific T cells to accessible sites in the skin and skin-draining lymph nodes (LNs), sampled by skin suction blisters and ultrasound-guided LN aspiration. Peripheral blood samples obtained before GAD injection were restimulated with GAD in vitro to detect reactive CD4+ T cells. Re-expression of selected T cell receptors (TCRs) confirmed antigen specificity. Up to 70% of T cells at the skin injection site were clonally-expanded and 4 of 14 (28%) re-expressed TCRs were GAD-reactive. In draining LNs 1 of 14 (4%) clonally-expanded TCRs was GAD-reactive, representing [~]0.08% of all T-cells. GAD-reactive cells across compartments displayed Th1 and Th17-associated transcription signatures. These results demonstrate the intradermal autoantigen challenge, coupled with scRNAseq, enables direct identification and molecular profiling of autoreactive T cells in vivo. This minimally invasive approach provides a powerful platform for tracking antigen-specific specific T cells to monitor disease activity and evaluate immune interventions in T1D.

immunology↗

Inferring macroscopic intrinsic neural timescales using optimal control theory

The temporal evolution of brain activity relies on complex interactions within and between brain regions that are mediated by neurobiology and connectivity. To understand these interactions, many large-scale efforts have measured structural connectivity, neural activity, gene expression, and cognition across multiple modalities and species. However, data-driven discovery of large-scale activity models remains difficult owing to the lack of flexible quantitative frameworks for estimating the interplay between brain structure and function while preserving biophysical realism. Here, we provide such a framework by integrating network control theory (NCT) with automatic differentiation to estimate model parameters with greater biophysical realism from data. Specifically, we estimate the structural form of regional self-inhibition--a quantity that is experimentally difficult to measure--from MRI data. Next, we demonstrate that the resulting model-based self-inhibition parameters correlate significantly with regions intrinsic neural timescales (INTs), neurobiological measures of gene expression and cell-type densities, as well as behavioral measures of cognition. We demonstrate consistent results across multiple datasets and species. Finally, we demonstrate that our self-inhibition parameters enable the efficient control of brain dynamics from fewer brain regions. Taken together, our results provide a simple and flexible quantitative framework that more accurately captures the interplay between brain structure, function, and dynamics with greater biophysical realism.

neuroscience↗

GI-NemaTracker - A farm system-level mathematical model to predict the consequences of gastrointestinal parasite control strategies in sheep

Gastro-intestinal nematode infections are considered one of the major endemic diseases of sheep on the grounds of animal health and economic burden, both in the British Isles and globally. Parasites are increasingly developing resistance to commonly used anthelmintic treatments meaning that alternative control strategies that reduce or replace the use of anthelmintics are required. We present GI-NemaTracker, a systems-level mathematical model of the full host-parasite-environment system governing gastro-intestinal nematode transmission on a sheep farm. The model is based on a series of time-varying delay-differential equations that explicitly capture environmentally-driven time delays in nematode development. By taking a farm systems-level approach we represent both in-host and environmentally-driven free-living parasite dynamics and their interaction with a population of individually modelled lambs with diverse trait parameters assigned at birth. Thus we capture seasonally varying rates of parasite transmission and consequently variable weight gain of individual lambs throughout the season. The model is parameterised for Teladorsagia circumcincta, although the framework described could be applied to a range of nematode parasite species. We validate the model against experimental and field data and apply it to study the efficacy of four different anthelmintic treatment regimes (neo-suppresive treatment, strategic prophylactic treatment, treatment based on faecal egg counts and a regime which leaves 10% of the animals untreated) on lamb weight gain and pasture contamination. The model predicts that similar body weights at a flock level can be achieved while reducing the number of treatments administered, thus supporting a health plan that reduces anthelmintic treatments. As the model is capable of combining parasitic and free-living stages of the parasite with host performance, it is well suited to predict complex system responses under non-stationary conditions. The implications of the model and its potential as a tool in the development of sustainable control strategies in sheep are discussed.

systems biology↗

ABHD11 inhibition drives sterol metabolism to modulate T cell effector function and alleviate autoimmunity

Chronic inflammation in autoimmunity is driven by T cell hyperactivation. This unregulated response to self is fuelled by heightened metabolic programmes, which offers a promising new direction to uncover novel treatment strategies. /{beta}-hydrolase domain-containing protein 11 (ABHD11) is a mitochondrial hydrolase that maintains the catalytic function of -ketoglutarate dehydrogenase (-KGDH), and its expression in CD4+ T cells has been linked to remission status in rheumatoid arthritis (RA). However, the importance of ABHD11 in regulating T cell metabolism and function - and thus, the downstream implication for autoimmunity - is yet to be explored. Here, we show that pharmacological inhibition of ABHD11 dampens cytokine production by human and mouse T cells. Mechanistically, the anti-inflammatory effects of ABHD11 inhibition are attributed to increased 24,25-epoxycholesterol (24,25-EC) biosynthesis and subsequent liver X receptor (LXR) activation, which arise from a compromised TCA cycle. The impaired cytokine profile established by ABHD11 inhibition is extended to two patient cohorts of autoimmunity. Importantly, using a murine model of accelerated type 1 diabetes (T1D), we show that targeting ABHD11 suppresses cytokine production in antigen-specific T cells and delays the onset of diabetes in vivo. Collectively, our work provides pre-clinical evidence that ABHD11 is an encouraging drug target in T cell-mediated autoimmunity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/643996v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@bb3fcborg.highwire.dtl.DTLVardef@1594fc8org.highwire.dtl.DTLVardef@84e078org.highwire.dtl.DTLVardef@1ad023e_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

Very long-chain ceramides in muscle associate with insulin resistance independent of obesity

Lipids, in particular ceramides and diacylglycerols (DAGs), are implicated in insulin resistance, however their precise roles remain unclear. We leverage natural genetic variation to examine muscle lipids and systemic insulin resistance (IR) in 399 Diversity Outbred Australia mice. Adipose mass was associated with 55% of muscle lipids and IR, with DAGs as the only enriched lipid class. To disentangle adiposity and muscle lipid contributions, we used two approaches: (1) linear modelling of muscle lipids corrected for adipose mass on systemic IR, and (2) stratifying mice into insulin sensitivity quartiles within adiposity bins. Both revealed that very long-chain ceramides, but not DAGs, were linked to IR. Transcriptomic and proteomics further associated these ceramides with cellular and mitochondrial stress. DAGs correlated with leptin expression in muscle, suggesting they originate from muscle-residing adipocytes. We propose that many muscle lipids, including DAGs, associate with IR due to adipose accumulation rather than directly influencing muscle insulin sensitivity. By addressing the relationship between adiposity and metabolic state, we identified very long-chain muscle ceramides as highly associated with IR independently of adiposity.

systems biology↗

GLOWORM-META: Modelling gastrointestinal nematode metapopulation dynamics to inform cattle biosecurity research

Gastrointestinal nematode (GIN) parasite infections in grazing livestock cause significant disease, and are responsible for estimated annual losses of over {euro}1.8 billion in Europe alone. The management of GINs in cattle is threatened by anthelmintic drug resistance (AR). Immediate action is needed to slow the progression of AR in cattle GINs and avoid the increasingly common scenario of multiple drug resistance seen in sheep. Although AR can arise independently on multiple farms, it may also be spread between holdings via purchased cattle. Therefore, effective biosecurity measures on cattle enterprises could help to reduce the risk of establishment of AR populations. A metapopulation model was developed and validated for two GIN species infecting cattle, Ostertagia ostertagi and Cooperia oncophora, incorporating the full parasite life cycle, weather- and immunity-dependent parasite life history traits and multiple pasture sub-populations. This allowed for complex grazing management strategies and weather influences to be simulated. The models successfully replicated the seasonal patterns and intensity of infections reported in multiple published longitudinal datasets. Global sensitivity analysis against four Quantities of Interest (QoIs) related to factors affecting the safety of the resident herd and of the purchased animals was used to quantify the influence of candidate biosecurity measures. The duration of quarantine, the date of purchase (weather/seasonal influences) and the intensity of infection on the day of purchase strongly influenced the QoIs. The outcomes for the UK were not significantly influenced by the geographic location of the purchasing farm, suggesting that the influence of weather patterns on GIN populations outweighs that of regional climate differences, and thus regional variations to GIN biosecurity recommendations are not warranted without alternative evidence to support this. The model presented here is the first full lifecycle GIN metapopulation model for O. ostertagi and C. oncophora, validated against longitudinal field data, and can be broadly used to evaluate the relative efficacy of a range of cattle GIN management strategies, as demonstrated here. These findings offer valuable insights to focus initial biosecurity recommendations for cattle enterprises, and are being used to direct qualitative and quantitative research to refine recommendations. HighlightsO_LIA metapopulation model for cattle GINs was developed C_LIO_LIThe model includes weather-dependence and complex grazing C_LIO_LIThe model replicated seasonal infection patterns from multiple datasets C_LIO_LISensitivity analysis explored influential biosecurity measures C_LIO_LIThe influence of weather outweighed that of geographic location (climate) C_LIO_LIInitial recommendations for biosecurity and further research are made C_LI

ecology↗

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs driving type 1 diabetes progression

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub- types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

genomics↗

Deep proteome profiling of white adipose tissue reveals marked conservation and distinct features between different anatomical depots

White adipose tissue is deposited mainly as subcutaneous adipose tissue (SAT), often associated with metabolic protection, and abdominal/visceral adipose tissue (VAT), which contributes to metabolic disease. To investigate the molecular underpinnings of these differences, we conducted comprehensive proteomics profiling of whole tissue and isolated adipocytes from these two depots across two diets from C57Bl/6J mice. The adipocyte proteomes from lean mice were highly conserved between depots, with the major depot-specific differences encoded by just 3% of the proteome. Adipocytes from SAT (SAdi) were enriched in pathways related to mitochondrial complex I and beiging, whereas visceral adipocytes (VAdi) were enriched in structural proteins and positive regulators of mTOR presumably to promote nutrient storage and cellular expansion. This indicates that SAdi are geared toward higher catabolic activity, while VAdi are more suited for lipid storage. By comparing adipocytes from mice fed chow or Western diet (WD), we define a core adaptive proteomics signature consisting of increased extracellular matrix proteins and decreased fatty acid metabolism and mitochondrial Coenzyme Q biosynthesis. Relative to SAdi, VAdi displayed greater changes with WD including a pronounced decrease in mitochondrial proteins concomitant with upregulation of apoptotic signaling and decreased mitophagy, indicating pervasive mitochondrial stress. Furthermore, WD caused reduction in lipid handling and glucose uptake pathways particularly in VAdi, consistent with adipocyte de-differentiation. By overlaying the proteomics changes with diet in whole adipose tissue and isolated adipocytes, we uncovered concordance between adipocytes and tissue only in the VAT, indicating a unique tissue-specific adaptation to sustained WD in SAT. Finally, an in-depth comparison of isolated adipocytes and 3T3-L1 proteomes revealed a high degree of overlap, supporting the utility of the 3T3-L1 adipocyte model. These deep proteomes provide an invaluable resource highlighting differences between white adipose depots that may fine-tune their unique functions and adaptation to an obesogenic environment.

molecular biology↗

Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

Genome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n=1,194 and whole blood, n=31,684) and glioma subtype (all glioma (7,400 cases, 8,257 controls) glioblastoma (GBM, 3,112 cases) and non-GBM gliomas (2,411 cases)). We also leveraged tissuespecific eQTLs collected from 13 brain tissues (n=114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

genetics↗

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations

Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS solutions is stability of feature patterns driving an association. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues in a systematic manner, we developed a generative modeling framework to simulate synthetic datasets, parameterized by dimensionality, variance structure, and association strength. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the latent pattern underlying the discovered association. We confirmed these trends across two neuroimaging modalities, functional and diffusion MRI, and in independent datasets, Human Connectome Project (n{approx}1000) and UK Biobank (n{approx}20000) and found that only the latter comprised sufficient samples for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses for future studies.

neuroscience↗

Finding the neural correlates of collaboration using a three-person fMRI hyperscanning paradigm

Humans have an extraordinary ability to interact and cooperate with others, which plays a pivotal role in societies at large. Despite its potential social and evolutionary significance, research on finding the neural correlates of collaboration has been limited partly due to restrictions on simultaneous neuroimaging of more than one participant (a.k.a. hyperscanning). A series of works now exists that used dyadic fMRI hyperscanning to examine the interaction between two participants. However, to our knowledge, no study to date has aimed at revealing the neural correlates of social interactions using a 3-person (or triadic) fMRI hyperscanning paradigm. Here, for the first time, we simultaneously measured the blood-oxygenation-level-dependent (BOLD) signal of triads (m=12 triads; n=36 participants), while they engaged in a joint drawing task based on the social game of Pictionary(R). General linear model (GLM) analysis revealed increased activation in the brain regions previously linked with the theory of mind (ToM) during the collaborative phase compared to the independent phase of the task. Furthermore, using intersubject brain synchronization (IBS) analysis, we revealed increased synchrony of the right temporo-parietal junction (R TPJ) during the collaborative phase. The increased synchrony in the R TPJ was observed to be positively associated with the overall team performance on the task. In sum, our novel paradigm revealed a vital role of the R TPJ among other ToM regions during a triadic collaborative drawing task.

neuroscience↗

The human gut virome database

The gut microbiome profoundly impacts human health and disease, but viruses that infect these microbes are likely also important. Problematically, viral sequences are often missed due to insufficient reference viral genomes. Here we (i) built a human gut virome database, GVD, from 648 viral particle metagenomes or microbial metagenomes from 572 individuals previously searched for viruses, (ii) assessed its effectiveness, and (iii) conducted meta-analyses. GVD contains 13,203 unique viral populations (approximately species-level taxa) organized into 702 novel genera, which roughly doubles known phage genera and improves viral detection rates over NCBI viral RefSeq nearly 60-fold. Applying GVD, we assessed and rejected the idea of a core gut virome in healthy individuals, and found through meta-analyses that technical artifacts are more impactful than any treatment effect across the entire meta-study dataset. Together, this foundational resource and these findings will help human microbiome researchers better identify viral roles in health and disease.

bioinformatics↗