Biology subjects

Jones, D. K.

Publications and source records attributed to Jones, D. K..

At least 19 recordsLinked to original sources

bioRxiv2026

Human brain cortical oxygen consumption is positively related to soma surface area density.

Evidence from in-silico, ex vivo and invasive animal studies suggested the function of neural tissue to be inextricably linked to its structural and microstructural organization. However, this link has never been assessed non-invasively in humans. Here, we established a simple quantitative framework linking cerebral oxygen metabolism to gray matter microstructural features in healthy humans using advanced non-invasive MRI methods. The cerebral metabolic rate of oxygen consumption (CMRO2) was quantified with calibrated fMRI, whereas microstructural properties were estimated from diffusion MRI data fitted to the Soma and Neurite Density Imaging (SANDI) model, yielding soma radius (R soma) and soma volume fraction (fsoma) metrics, from which we derived maps of soma surface area density (SAD) and soma numerical density (ND) under the assumption of a spherical soma. CMRO2 was positively associated with SAD (r = 0.29, p < 0.01), fsoma (r = 0.28, p < 0.05) and ND (r = 0.25, p < 0.05), and negatively associated with Rsoma (r = -0.23, p < 0.05) across cortical brain regions. These relationships are consistent with predictions from experimental and theoretical studies, providing the first quantitative evidence that cerebral metabolic demand is coupled to microstructure in humans. The positive association of energy consumption with SAD is consistent with the notion that both the number of synapses (energy-demanding structures), and the energy required to maintain a membrane potential, scale with soma surface area. Our findings establish a non-invasive MRI framework for investigating the microstructural determinants of brain energy metabolism in health and potentially in disease.

neuroscience↗

bioRxiv2026

Myelin-weighted Core Network Connectivity Supports Trait-Level Future Thinking

Why are some individuals able to recollect past events and imagine future events more richly than others? Here we examine how structural connectivity within a 'core network' supporting mnemonic cognition, substantially overlapping with the canonical default network, underpins inter-individual variation in autobiographical thought. We analysed state-of-the-art magnetic resonance imaging data from 109 adults collected on a high-gradient 3T Connectom scanner, in relation to their Survey of Autobiographical Memory (SAM) scores - a psychometric measure of self-reported episodic autobiographical, semantic, spatial, and future thinking abilities. We anatomically defined a core network, employed streamline tractography to derive participant-specific structural brain networks, and weighted their connections with the myelin water fraction derived from quantitative multicomponent relaxometry. We investigated associations between graph theoretical metrics of the core network and self-reported mnemonic abilities. We found a negative association between future thinking abilities and characteristic path length, and a positive association between future thinking abilities and mean clustering coefficient. Further analysis showed that the node-specific clustering coefficient of most core network nodes was positively correlated with future thinking scores, with frontotemporal nodes showing the strongest associations. Our findings indicate that both efficient integration and local segregation of the core network are important for trait-level future thinking.

neuroscience↗

bioRxiv2026

Acute Exposure to Perfluorooctanoic Acid (PFOA) During Cardiomyogenesis disrupts Transcriptional and Electrophysiological Profiles in Differentiated Myocytes

The early developmental environment plays a critical role in the etiology of cardiovascular diseases (CVDs), but underlying molecular mechanisms are poorly understood. Exposure to per and polyfluoroalkyl substances (PFAS) are linked to various CVDs, but effects of developmental PFAS exposures on the human heart remain unclear. Using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM), the objective of this study was to investigate the effects of PFAS exposure during cardiac differentiation on gene expression and function of cardiomyocytes. We exposed two hiPSC lines (one male and one female donor) to perfluorooctanoic acid (PFOA), a common and ubiquitous PFAS (0.05, 0.5, 5, 50, 100, 150, 200 M), followed by assessment of cellular number and pluripotency marker expression. PFOA exposure for 72 hours had no significant effects on hiPSC pluripotency, and modest inhibition of proliferation was observed only at the highest concentration. hiPSCs were then differentiated into ventricular cardiomyocytes in the continued presence or absence of PFOA (0, 0.5, 5, 50 M) using an established small molecules protocol. Optical mapping studies using voltage and calcium-sensitive dyes revealed dose and cell line-specific effects of PFOA on cardiomyocyte voltage and calcium dynamics that were still present 10 days after cessation of exposure. Patch clamping studies demonstrated small but significant reductions in repolarizing IKr currents with 5{micro}M PFOA exposure in cardiomyocytes from both donors. Using RNA-seq, we found that exposure to PFOA led to significant changes in transcriptional pathways related to lipids and lipoproteins in the female hiPSC-CM. In the male hiPSC-CM, we observed significant effects on developmental pathways and calcium homeostasis. Thus, we found that environmentally relevant PFOA exposure during cardiomyocyte differentiation affects the electrophysiological properties and transcriptome of hiPSC-CM even after cessation of exposure, with effects that differ by donor cell line. These findings provide direct experimental evidence that transient developmental exposure to PFOA can durably reprogram human cardiomyocyte function, supporting a developmental origin of PFAS-associated cardiovascular risk. Impact StatementThese studies demonstrate that exposure to environmentally relevant levels of PFOA during the differentiation of hiPSCs into cardiomyocytes alters cardiac gene expression and function, with effects that persist beyond cessation of exposure.

pharmacology and toxicology↗

bioRxiv2025

Cross-modal quality transfer: enhancing MEG spatial resolution using BOLD-fMRI and Explainable machine learning.

Purpose:1Blood-oxygen-level-dependent functional MRI (BOLD-fMRI) and magnetoencephalog-raphy (MEG) offer complementary insights into brain function, with BOLD-fMRI providing high spatial resolution and MEG offering high temporal sensitivity. This study aimed to enhance MEGs spatial resolution through learning inter-modal relationships via data-driven fusion with BOLD-fMRI using explainable machine learning (xML). MethodsMEG and BOLD-fMRI data were collected for sixteen participants watching the same naturalistic visual stimulus. MEG signals were filtered into standard frequency bands and processed to match the haemodynamic response. Data were then decomposed using Tenso-rial Independent Component Analysis (TICA), yielding 250 MEG (25 per frequency band) and 30 BOLD-fMRI components. A set of Extreme Gradient Boosted tree (XGBoost) models were trained to predict MEG component activity from downsampled BOLD-fMRI components. The models with best-performing configuration of hyperparameters at this resolution were then used to generate voxelwise upsampled MEG maps using the natively higher-resolution BOLD-fMRI data. Performance was compared to trilinear interpolation using R{superscript 2}, mean squared error (MSE), and structural similarity index (SSI). Model inter-pretability was enhanced by generating Shapley values (SHAP), describing relationships between input data and model output. ResultsModels trained on 6mm3 data achieved high predictive performance on previously unseen data (R{superscript 2} = 0.80, MSE = .07). Using higher-resolution 2mm3 BOLD-fMRI data, MEG activ-ity was upsampled to 2mm3, resulting in more detailed image, while maintaining reasonable congruence to naive interpolation (R{superscript 2} = .81, MSE = .06). ConclusionsThis work demonstrates that explainable machine learning enables spatial super-resolution of MEG maps via BOLD-fMRI-informed quality transfer, enabling enhancement of spatial resolution, and offering improved localization, as well as means of generating data-driven insights into neurovascular coupling.

neuroscience↗

bioRxiv2025

Microbiome-behavior coupling shapes infant adaptation to early maternal unpredictability

We asked whether infant microbiome composition, together with infants behavioral responses to early maternal unpredictability, can offer novel mechanistic insights into behavioral phenotypic variation in human development. Maternal unpredictability was computed as the entropy rate of transitions among maternal sensory signals during naturalistic mother-infant interaction (n=256 dyads; 2-6 months). Infant visual orienting behavior (VOB) was indexed by infants simultaneous gaze shifts to and from the mother. Higher maternal unpredictability predicted more frequent VOB shifts, indicating a mature-for-age profile, and longitudinally forecast poorer inhibitory control at 19-28 months. A subgroup of infants with high maternal unpredictability and high VOB had inhibitory control outcomes closer to those of infants who experienced low maternal unpredictability, suggesting VOB strategy might buffer against the effects of high maternal unpredictability. In participants with metagenomic data (n=93), VOB was associated with taxonomic and functional gut microbial profiles along a Bifidobacterium breve to Bifidobacterium longum axis. Neuroactive gene-set enrichment analysis linked VOB to increase in tryptophan synthesis and glutamate synthesis genes. In contrast, maternal unpredictability showed depletions of GABA and tryptophan synthesis in the unique-effects model and, when VOB and the maternal unpredictability by VOB term were included, enrichment of acetate synthesis and quinolinic acid degradation. Notably, the maternal unpredictability by VOB interactions for tryptophan and glutamate were negative, indicating that the VOB-pathway coupling attenuates as unpredictability increases. Together, these findings support a framework in which maternal unpredictability is the environmental challenge, infant VOB expresses the behavioral strategy, and microbial metabolism modulates whether that strategy is biochemically feasible. TeaserInfant gut microbiome is linked to infant behavioral responses to maternal unpredictability, which in turn impacts how they learn to control their actions.

neuroscience↗

bioRxiv2025

Sudden Death-Associated KCNH2 Variants Have Opposing Effects on hERG1NP Function

hERG1NP is a non-conducting subdomain of the full-length hERG1 channel that is expressed in the nuclei of developing cardiomyocytes. From the nucleus, hERG1NP modulates gating and expression of the full-length hERG1 channel. hERG1NP variants are linked with sudden death in the young, but the impact of these variants on hERG1NP activity has not been studied. To determine the effect of hERG1NP variants on hERG1NP activity, we measured hERG1NP intracellular localization and modulation of membrane current from the full-length hERG1a channel in HEK293 cells. The wildtype hERG1NP suppressed both hERG1a current (IhERG) and protein. We then screened six hERG1NP variants for changes in either intracellular targeting or modulation of hERG1a current. Two variants, R885C and R1047L, disrupted hERG1NP activity by altering nuclear transport or abolishing both IhERG current suppression and nuclear targeting, respectively. Two additional variants, G1036D and Q1068R, enhanced hERG1NP activity by depolarizing hERG1as voltage dependence of activation via accelerated channel deactivation and recovery from inactivation. Lastly, two variants had no effect: R1035W and R1069S. This work demonstrates that hERG1NP variants associated with sudden death in the young can trigger both loss-of-function and gain-of-function effects on hERG1NP. Additional work is needed to identify specific pathogenic mechanisms of hERG1NP variants.

cell biology↗

bioRxiv2025

Resolving Cellular Morphology in the Human Brain with Multiparametric Diffusion MR Spectroscopy

Diffusion-weighted magnetic resonance spectroscopy (dMRS) noninvasively probes the diffusion of mostly intracellular metabolites and can therefore report on brain microstructure with a cell-type specificity that water-based diffusion MRI cannot achieve. However, the morphological information accessible to conventional dMRS is limited: estimating both cell-body (soma) and neurite dimensions from a single diffusion-encoding scheme is an ill-posed problem, and neither ultra-high b-value nor diffusion-time-dependent measurements alone distinguish soma size from neurite radius. Here we introduce multiparametric dMRS in the human brain, combining diffusion-time- dependent encoding (apparent diffusion coefficient, ADC, and diffusion kurtosis, K, sampled over diffusion times of 6 ms to 250 ms and b-values up to 8 ms {micro}m-2) with double-diffusion-encoded spectroscopy (DDES). Using Monte-Carlo analysis, we show that fitting a two-compartment (soma + neurite) tissue model to diffusion-time data alone is degenerate, admitting two near-indistinguishable solutions. Adding the orthogonal angular information from DDES breaks this degeneracy: jointly fitting both experiments converges to a single, biophysically plausible solution irrespective of initialization, yielding cell-type- specific estimates of intrinsic diffusivity, soma radius, neurite radius, and neurite signal fraction for neuronal (NAA, glutamate) and glial (choline, myo-inositol) metabolites. Metabolite microscopic anisotropy approaches unity, consistent with predominantly intra-neurite diffusion, while simultaneously acquired water data reveal short-range structural disorder and intercompartmental exchange ({approx}15 ms). Multiparametric dMRS thus extends the standard model of metabolite diffusion by a soma compartment and offers a route toward in vivo, cell-type- specific morphometry of neurons and glia in humans--a foundation for biomarkers in conditions where soma and neurite morphology are altered.

neuroscience↗

bioRxiv2025

ZOnally magnified Oblique Multi-slice (ZOOM) for cardiac diffusion weighted imaging in vivo

PurposeCardiac diffusion weighted imaging (cDWI) commonly utilizes 2D-selective RF-pulses (such as ZOOMit) to avoid aliasing artifacts when reducing the field of view (FOV). These RF-pulses, which may require parallel transmit capability, typically take several 10s of milliseconds, prolonging the echo time (TE) of the imaging sequence. Conversely, slice selection using ZOnally-magnified Oblique Multi-slice (ZOOM) (i.e. tilting the excitation relative to the refocusing RF-pulse) is an alternative for reducing FOV while shortening TE. We hypothesized that ZOOM, with appropriate choices for the RF-pulse tilt angle and thickness, can be reliably used as an alternative to ZOOMit for cardiac Diffusion Tensor Imaging (cDTI). MethodsWe scanned phantoms using full FOV and ZOOM with various parameters chosen from a 2-dimensional grid of angles and thicknesses. We identified a subset of 5 ZOOM parameters for in vivo cDTI experiments. We performed cDTI on healthy volunteers on a Siemens Prisma (6 subjects) and a Connectom (6 subjects) MR system using the 5 identified ZOOM settings, as well as ZOOMit (Prisma only) and no ZOOM (Connectom only). We compared mean diffusivity (MD), fractional anisotropy (FA), and secondary eigenvector angle (|E2A|) between scans. ResultsAverage MD, and standard deviation of MD and FA, were significantly reduced by ZOOM compared to no ZOOM on Connectom. We found no significant differences in any averaged diffusion measures between ZOOMit and ZOOM on Prisma. ConclusionCombining ZOOM with cardiac diffusion weighted imaging allows for TE to be considerably shortened (from 79ms to 67ms) while reducing FOV to avoid aliasing artifacts.

biophysics↗

bioRxiv2025

Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging

SummaryRobust estimation with convexity constraints significantly improves signal fitting for in vivo human cardiac diffusion kurtosis imaging. PurposeCardiac diffusion tensor imaging (cDTI) is an emerging technique to investigate the microstructure of heart tissue. At sufficiently high b-values, additional information on microstructure can be observed, but the data require a representation beyond DTI such as diffusion kurtosis imaging (DKI). cDTI is highly prone to image corruption, which researchers usually attempt to handle with shot-rejection. However, this can be handled more generally with robust estimation techniques. Recent work has also demonstrated the need to perform constrained fitting for DKI, as fitted parameters can otherwise violate necessary constraints on the signal behaviour, causing significant errors in estimated measures. MethodsWe developed robust constrained weighted least squares (RCWLS) by combining robust estimation with convexity constraints specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom scanner, we tested various combinations of fitting techniques, with/without robustness and with/without constraints. ResultsRCWLS was the only tested technique that convincingly showed radial kurtosis to be larger than axial kurtosis for all subjects, something that is expected in myocardium due to increased restrictions to diffusion in the plane perpendicular to the primary myocyte direction. RCWLS also showed the best correction of corrupted regions in diffusion parameter maps for individual subjects. ConclusionFitting techniques utilizing both robust estimation and constraints are essential to facilitate applicability of in vivo cardiac DKI.

physiology↗

bioRxiv2025

Cardiac diffusion kurtosis imaging in the human heart in vivo using 300mT/m gradients

PurposeDiffusion tensor imaging (DTI) is commonly used in cardiac diffusion magnetic resonance imaging (dMRI). However, the tissues microstructure (cells, membranes, etc.) restricts the movement of the water molecules, making the spin displacements deviate from Gaussian behaviour. This effect may be observed with diffusion kurtosis imaging (DKI) using sufficiently high b-values (b > 450 s/mm2), which are presently outside the realm of routine cardiac dMRI due to the limited gradient strength of clinical scanners. The Connectom scanner with Gmax = 300 mT/m enables high b-values at echo times (TE) similar to DTI on standard clinical scanners, therefore facilitating cardiac DKI in humans. MethodsCardiac-gated, second-order motion-compensated dMRI was performed with bmax = 1350 s/mm2 in 10 healthy volunteers on a 3T MRI scanner with Gmax = 300 mT/m. The signal was fitted to a cumulant expansion up to and including the kurtosis term and diffusion metrics such as fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) were calculated. ResultsWe demonstrate deviation of the signal from monoexponential decay for b-values > 450 s/mm2 (MK = 0.32 {+/-} 0.03). Radial kurtosis (RK = 0.35 {+/-} 0.04) was observed slightly larger than axial kurtosis (AK = 0.27 {+/-} 0.02), and the difference is statistically significant (RK - AK = 0.08 {+/-} 0.04, p = 2e - 4). ConclusionThis work demonstrates the feasibility of quantifying kurtosis effect in the human heart in vivo (at an echo time shorter than typical TEs reported for cardiac DTI), using high-performance gradient systems (which are 4-8 times stronger than on standard clinical scanners). Our work lays the foundation for exploring new biomarkers in cardiac dMRI beyond DTI.

biophysics↗

bioRxiv2024

Deep-learning and analytical models give distinct results for the brain structure-function relationship in health and in psychosis

Understanding the intricate relationship between brain structure and function is a cornerstone challenge in neuroscience, critical for deciphering the mechanisms that underlie healthy and pathological brain function. In this work, we present a comprehensive framework for mapping structural connectivity measured via diffusion-MRI to resting-state functional connectivity measured via magnetoencephalography, utilizing a deep-learning model based on a Graph Multi-Head Attention AutoEncoder. We compare the results to those from an analytical model that utilizes shortest-path-length and search-information communication mechanisms. The deep-learning model outperformed the analytical model in predicting functional connectivity in healthy participants at the individual level, achieving mean correlation coefficients higher than 0.8 in the alpha and beta frequency bands. Our results imply that human brain structural connectivity and electrophysiological functional connectivity are tightly coupled. The two models suggested distinct structure-function coupling in people with psychosis compared to healthy participants (p < 2 x 10-4 for the deep-learning model, p < 3 x 10-3 in the delta band for the analytical model). Importantly, the alterations in the structure-function relationship were much more pronounced than any structure-specific or function-specific alterations observed in the psychosis participants. The findings demonstrate that analytical algorithms effectively model communication between brain areas in psychosis patients within the delta and theta bands, whereas more sophisticated models are necessary to capture the dynamics in the alpha and beta band.

neuroscience↗

bioRxiv2024

The developing hippocampus: Microstructural evolution through childhood and adolescence

The hippocampus is a structure in the medial temporal lobe which serves multiple cognitive functions. While important, the development of the hippocampus in the formative period of childhood and adolescence has not been extensively investigated, with most contemporary research focusing on macrostructural measures of volume. Thus, there has been little research on the development of the micron-scale structures (i.e., microstructure) of the hippocampus, which engender its cognitive functions. The current study examined age-related changes of hippocampal microstructure using diffusion MRI data acquired with an ultra-strong gradient (300 mT/m) MRI scanner in a sample of children and adolescents (N=88; 8-19 years). Surface-based hippocampal modelling was combined with established microstructural approaches, such as Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion Density Imaging (NODDI), and a more advanced gray matter diffusion model Soma And Neurite Density Imaging (SANDI). No significant changes in macrostructural measures (volume, gyrification, and thickness) were found between 8-19 years, while significant changes in microstructure measures related to neurites (from NODDI and SANDI), soma (from SANDI), and mean diffusivity (from DTI) were found. In particular, there was a significant increase across age in neurite MR signal fraction and a significant decrease in extracellular MR signal fraction and mean diffusivity across the hippocampal subfields and long-axis. A significant negative correlation between age and MR apparent soma radius was found in the subiculum and CA1 throughout the anterior and body of the hippocampus. Further surface-based analyses uncovered variability in age-related microstructural changes between the subfields and long-axis, which may reflect ostensible developmental differences along these two axes. Finally, correlation of hippocampal surfaces representing age-related changes of microstructure with maps derived from histology allowed for postulation of the potential underlying microstructure that diffusion changes across age may be capturing. Overall, distinct neurite and soma developmental profiles in the human hippocampus during late childhood and adolescence are reported for the first time.

neuroscience↗

bioRxiv2024

MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression

Neuroanatomical changes to the cortex during adolescence have been well documented using MRI, revealing ongoing cortical thinning and volume loss with age. However, the underlying cellular mechanisms remain elusive with conventional neuroimaging. Recent advances in MRI hardware and new biophysical models of tissue informed by diffusion MRI data hold promise for identifying the cellular changes driving these morphological observations. This study used ultra-strong gradient MRI to obtain high-resolution, in vivo estimates of cortical neurite and soma microstructure in sample of typically developing children and adolescents. Cortical neurite signal fraction, attributed to neuronal and glial processes, increased with age (mean R2fneurite=.53, p<3.3e-11, 11.91% increase over age), while apparent soma radius decreased (mean R2Rsoma=.48, p<4.4e-10, 1% decrease over age) across domain-specific networks. To complement these findings, developmental patterns of cortical gene expression in two independent post-mortem databases were analysed. This revealed increased expression of genes expressed in oligodendrocytes, and excitatory neurons, alongside a relative decrease in expression of genes expressed in astrocyte, microglia and endothelial cell-types. Age-related genes were significantly enriched in cortical oligodendrocytes, oligodendrocyte progenitors and Layer 5-6 neurons (pFDR<.001) and prominently expressed in adolescence and young adulthood. The spatial and temporal alignment of oligodendrocyte cell-type gene expression with neurite and soma microstructural changes suggest that ongoing cortical myelination processes contribute to adolescent cortical development. These findings highlight the role of intra-cortical myelination in cortical maturation during adolescence and into adulthood.

neuroscience↗

bioRxiv2024

finsyncR, an R package to synchronize 27 years of fish and invertebrate data across the United States

The United States government collects some of the most comprehensive data on freshwater systems in existence, but barriers exist for new users to apply these data without institutional guidance. finsyncR (fish and invertebrate synchronizer in R) is an R package that streamlines the process of acquiring, processing, and integrating fish and macroinvertebrate datasets collected in streams and rivers, increasing access to cleaned data and ensuring straightforward application of the data. The data sources are the US Environmental Protection Agencys National River and Streams Assessment and US Geological Surveys BioData. The resulting datasets span 27 years (1993 to 2019) and include 8,115 sites, 15,169 sampling events, 963 macroinvertebrate genera, and 687 fish species. We document common challenges to working with these data including understanding sampling designs, harmonizing taxonomy, calculating densities and standardized abundances, accounting for differences in sampling effort, and considering improvements in taxonomic identifications. We anticipate this package will spur research exploring changes in fish and macroinvertebrate communities across space and time and the causes and consequences of those changes.

ecology↗

bioRxiv2023

Developmental differences in canonical cortical networks: insights from microstructure-informed tractography

There is a growing interest in incorporating white matter fibre-specific microstructural properties into structural connectomes to obtain a more quantitative assessment of brain connectivity. In a developmental sample aged 8-18 years, we studied age-related patterns of microstructure-informed network properties locally and globally. First, we computed the diffusion-weighted signal fraction associated with each tractography-reconstructed streamline. Then, we generated microstructure-informed connectomes from diffusion MRI data using the convex optimization modelling for microstructure-informed tractography (COMMIT) approach. Finally, we estimated network characteristics in eight functionally defined networks (visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, default mode and subcortical networks). Our findings reveal that throughout child and adolescent development, global efficiency increases in the visual, somatomotor, and default mode networks, and mean strength increases in the somatomotor and visual networks. Nodes belonging to the dorsal and ventral visual pathways demonstrate the largest age-dependence in local efficiency, supporting previous evidence of protracted maturation of dorsal and ventral visual pathways. Our results provide compelling evidence that there is a prolonged development of visual association cortices.

neuroscience↗

bioRxiv2023

Differences in white matter detected by ex vivo 9.4T MRI are associated with axonal changes in the R6/1 model of Huntington's Disease

White matter (WM) volume loss has been reported in people with Huntingtons disease (HD), but the cellular basis of this deficit remains to be elucidated. To address this, we assessed ex vivo WM microstructure in the transgenic R6/1 mouse model of HD with magnetic resonance imaging (MRI) and studied the neurobiological basis of the MRI brain signals with histological and electron microscopy analyses in a separate cohort of age- and sex-matched mice. Differences in the macromolecular proton fraction (MPF) from quantitative magnetization transfer (qMT) as a proxy myelin measure, and the intra-axonal signal fraction (FR) from the composite hindered and restricted model of diffusion (CHARMED) as a proxy marker of axon density, were assessed alongside diffusion tensor imaging (DTI) parameters. A tractometry approach was employed to inspect region-specific differences across the corpus callosum (CC). Furthermore, voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to explore brain-wise WM macro- and microstructure abnormalities. To gain insight into disease-associated impairments in attentional and visuospatial processing, a third cohort of age-matched mice was assessed with the 5-choice serial reaction time task (5-CSRTT). We report cognitive impairments in R6/1 mice and, by evaluating MRI and light and electron microscopy results, we show that this HD mouse model presents disruptions in axonal morphology (i.e. less complex, thinner axons) and organization (i.e. more densely packed axons). Furthermore, we show that, at least early in disease progression, R6/1 mice present a reduction in the expression or content of myelin-associated proteins without significant alterations in the structure of myelin sheaths. Finally, our findings indicate that neuroinflammation-driven glial and axonal swelling might also affect this mouse model early in disease progression. Crucially, we demonstrate the potential of FR, an in vivo estimate of axon density, as a novel MRI biomarker of HD-associated changes in WM microstructure.

neuroscience↗

bioRxiv2022

hERG1 channel subunit composition mediates proton inhibition of IKr in hiPSC CMs

hERG1 conducts cardiac IKr and is critical for repolarization of the human heart. Reduced IKr causes long QT syndrome and increases the risk for cardiac arrhythmia and sudden cardiac death. At least two subunits combine to form functional hERG1 channels, hERG1a and hERG1b. Changes in hERG 1a/1b subunit abundance modulates IKr kinetics, magnitude, and drug sensitivity. Studies from native cardiac tissue have suggested that hERG1 subunit abundance is dynamically regulated, but the impact of altered subunit abundance on IKr and its response to external stressors is not well understood. Here, we used a substrate-driven hiPSC-CM maturation model to investigate how changes in relative hERG 1a/1b subunit abundance impact the response of native IKr to extracellular acidosis, a known component of ischemic heart disease and sudden infant death syndrome. IKr recorded from immature hiPSC-CMs display a two-fold greater inhibition by extracellular acidosis (pH 6.3) compared to matured hiPSC-CMs. qRT-PCR and immunocytochemistry demonstrated that hERG1a subunit mRNA and protein were upregulated, and hERG1b subunit mRNA and protein were downregulated in matured hiPSC-CMs compared to immature hiPSC-CMs. The shift in subunit abundance in matured hiPSC-CMs was accompanied by an increased in IKr density. Silencing the impact of hERG1b on native IKr kinetics by overexpressing a polypeptide identical to the hERG1a PAS domain reduced the magnitude of IKr proton inhibition in immature hiPSC-CMs to levels comparable to those observed in matured hiPSC-CMs. These data demonstrate that hERG1 subunit abundance is dynamically regulated and that hERG1 subunit abundance determines IKr sensitivity to protons in hiPSC-CMs.

physiology↗

bioRxiv2022

KCNH2 encodes a nuclear-targeted polypeptide that mediates hERG1 channel gating and expression

KCNH2 encodes hERG1, the voltage-gated potassium channel that conducts the rapid delayed rectifier potassium current (IKr) in human cardiac tissue. hERG1 is one of the first channels expressed during early cardiac development, and its dysfunction is associated with intrauterine fetal death, sudden infant death syndrome, cardiac arrhythmia, and sudden cardiac death. Here, we identified a novel hERG1 polypeptide (hERG1NP) that is targeted to the nuclei of immature cardiac cells, including hiPSC-CMs and neonatal rat cardiomyocytes. The nuclear hERG1NP immunofluorescent signal is diminished in matured hiPSC-CMs and absent from adult rat cardiomyocytes. Antibodies targeting distinct hERG1 channel epitopes demonstrated that the hERG1NP signal maps to the hERG1 distal C-terminal domain. KCNH2 deletion using CRISPR simultaneously abolished IKr and the hERG1NP signal in hiPSC-CMs. We then identified a putative nuclear localization sequence (NLS) within the distal hERG1 C-terminus, 883-RQRKRKLSFR-892. Interestingly, the distal C-terminal domain was targeted almost exclusively to the nuclei when overexpressed HEK293 cells. Conversely, deleting the NLS from the distal peptide abolished nuclear targeting. Similarly, blocking or {beta}1 karyopherin activity diminished nuclear targeting. Finally, overexpressing the putative hERG1NP peptide in the nuclei of HEK cells significantly reduced hERG1a current density, compared to cells expressing the NLS-deficient hERG1NP or GFP. These data identify a developmentally regulated polypeptide encoded by KCNH2, hERG1NP, whose presence in the nucleus indirectly modulates hERG1 current magnitude and kinetics.

cell biology↗