Biology subjects

Hamilton, D. J.

Publications and source records attributed to Hamilton, D. J..

6 recordsLinked to original sources

bioRxiv2026

Estrogen-Deficiency Degrades Left Ventricular Diastolic Function and Energy Metabolism in Hypertensive Female Mice

AimsHeart failure (HF) due to diastolic dysfunction (DD) but normal left ventricular (LV) ejection fraction (EF) is termed HF with preserved EF (HFpEF). Given its high prevalence in post-menopausal women, we hypothesized that 17{beta}-estradiol (E2) function is mechanistically linked to DD HF and investigated E2-deficiency in the etiology of DD in a mouse model. MethodsFemale C57BL/6J mice were divided into one group with hypertension induced by N({omega})-nitro-L-arginine methyl ester (L) and cardiac pressure overload induced by angiotensin II (A), collectively inducing DD, and one group with sham treatment. Groups were subdivided to receive an ovariectomy (OVX) to induce estrogen-deficiency and emulate menopause or sham surgery. During the next 21 days, mice were tested for cardiac function, food intake, response to E2 agonists, gene profiling, cardiomyocyte-contractility and elasticity, and cardiac mitochondrial function. ResultsOVX-associated E2-deficiency exacerbated DD in a time-dependent way without affecting EF or stroke volume, emulating a severe DD phenotype. These changes paralleled those for mitochondrial dysfunction, i.e., upregulation of genes associated with stress, energy metabolism, and fibrosis, as well as functional and structural defects in cardiomyocytes. Treatment of OVX + (L + A) with E2 or a G-protein-coupled estrogen receptor agonist normalized diastolic function, whereas estrogen receptor beta agonists did not. The OVX DD mice exhibited moderately impaired mitochondrial function, which delayed cardiomyocyte relaxation but not contraction, altered cardiac substrate utilization, reduced cardiomyocyte elasticity, increased production of reactive oxygen species, and potentiated extracellular fibrosis. ConclusionsOVX-induced E2-deficiency generates metabolic, structural, and functional changes in cardiomyocytes and the adjacent extracellular matrix, exacerbating the effects of L and A on diastolic function. This robust DD model revealed a role for E2 via ER in diastole-regulation in female mice and raised questions about similar mechanisms operative in postmenopausal women. HIGHLIGHTSO_LIOvariectomy (OVX)-induced estrogen-deficiency exacerbated diastolic dysfunction (DD) induced by hypertension and pressure overload with preserved ejection fraction in female C57BL/6J mice. C_LIO_LIIn post-OVX-treated DD mice, heart weight and fibrosis preceded other metrics of cardiac dysfunction. C_LIO_LIOVX-induced estrogen-deficiency increased the expression of genes associated with stress, energy metabolism, and especially fibrosis. C_LIO_LIMetabolic imaging of the heart by positron emission tomography revealed that DD with preserved ejection fraction was associated with mitochondrial dysfunction presenting as increased accretion of cardiac energy substrates, [18F]deoxyglucose and [11C]palmitate, effects that worsened following OVX. C_LIO_LIOVX increased cardiomyocyte stiffness and fibrosis and reduced cardiomyocyte lengthening and compliance in the context of female DD. C_LIO_LIImproved diastolic function following delivery of GPER agonists or estradiol to OVX DD female mice implicates estrogen receptor in the maintenance of normal cardiac function. C_LI

physiology↗

bioRxiv2025

Utility of alternate stable isotope calibration equations and informative priors when estimating the breeding origin of Arctic-breeding shorebirds

Understanding migratory connectivity is essential for monitoring and conserving Arctic-breeding shorebirds, particularly given divergent rates of decline across populations. Stable hydrogen isotope analysis of feathers offers a scalable, non-invasive method to assign breeding origin, but current applications are limited by the absence of shorebird-specific calibration equations, which associate feather isotope values with environmental isotope gradients ("isoscapes"), and by longitudinal isoscape bands in the Arctic that result in diffuse origin estimates across broad, heterogeneous regions. We evaluated the utility of alternate calibration equations and the incorporation of occupancy-based informative priors for improving breeding origin assignments. Specifically, we analyzed feather isotope data from hatch-year semipalmated sandpipers (Calidris pusilla) captured at staging sites in the Canadian Maritimes, as well as known-origin Arctic samples, to compare calibration equations from different reference datasets and test priors derived from eBird habitat occupancy predictions. We demonstrate that Arctic samples of known origin largely align with calibration equations based on broad passerine datasets, although incorporating Arctic samples into the calibration may correct for a marginal westward bias in origin estimates. Using occupancy-based priors that reflect habitat preferences increased precision by 18.8%, identifying hotspots within otherwise expansive isotopic bands. We highlight the validity of existing reference datasets with broad geographic coverage and demonstrate the value of informative priors to refine origin estimates. We recommend expanding Arctic sample collections across a wider longitudinal gradient to define a shorebird-specific calibration equation and further exploring potential biases in prior data sources to improve accuracy of Arctic-breeding shorebird origin assignments.

ecology↗

bioRxiv2020

Multiplexed experimental strategies for fragment library screening using SPR biosensors

Surface plasmon resonance biosensor technology (SPR) is ideally suited for fragment-based lead discovery. However, generally suitable experimental procedures or detailed protocols are lacking, especially for structurally or physico-chemically challenging targets or when tool compounds are lacking. Success depends on accounting for the features of both the target and the chemical library, purposely designing screening experiments for identification and validation of hits with desired specificity and mode-of-action, and availability of orthogonal methods capable of confirming fragment hits. By adopting a multiplexed strategy, the range of targets and libraries amenable to an SPR biosensor-based approach for identifying hits is considerably expanded. We here illustrate innovative strategies using five challenging targets and variants thereof. Two libraries of 90 and 1056 fragments were screened using two different flow-based SPR biosensor systems, allowing different experimental approaches. Practical considerations and procedures accounting for the characteristics of the proteins and libraries, and that increase robustness, sensitivity, throughput and versatility are highlighted.

biophysics↗

bioRxiv2019

Molecular Expression Profiles of Morphologically Defined Hippocampal Neuron Types: Empirical Evidence and Relational Inferences

Gene and protein expressions are key determinants of cellular function. Neurons are the building blocks of brain circuits, yet the relationship between their molecular identity and the spatial distribution of their dendritic inputs and axonal outputs remain incompletely understood. The open-source knowledge base Hippocampome.org amasses such transcriptomic data from the scientific literature for morphologically defined neuron types in the rodent hippocampal formation: dentate gyrus (DG), CA3, CA2, CA1, subiculum (SUB), and entorhinal cortex (EC). Positive, negative, or mixed expression reports were initially obtained from published articles directly connecting molecular evidence to neurons with known axonal and dendritic patterns across hippocampal layers. Here, we supplement this information by collating, formalizing, and leveraging relational expression inferences (REIs) that link a gene or protein expression or lack thereof to that of another molecule or to an anatomical location. With these additional interpretations, we freely release online a comprehensive human- and machine-readable molecular profile for the more than 100 neuron types in Hippocampome.org. Analysis of these data ascertains the ability to distinguish unequivocally most neuron types in each of the major subdivisions of the hippocampus based on currently known biochemical markers. Moreover, grouping neuron types by expression similarity reveals eight super-families characterized by a few defining molecules.\n\nSignificance StatementThe molecular composition of cells underlies their structure, activity, and function. Neurons are arguably the most diverse cell types with their characteristic tree-like shapes mediating synaptic communication throughout the brain. Biochemical marker data are available online for hundreds of morphologically identified neuron types in the mammalian hippocampus, including expression of calcium-binding proteins, receptors, and enzymes. Here, we augment this evidence by systematically applying logical rules empirically derived from the published literature (e.g. \"presence of molecule X implies lack of molecule Y\"). The resulting substantially expanded expression profiles provide nearly unique molecular identities for most known hippocampal neuron types while revealing previously unrecognized genetic similarities across anatomical regions and morphological phenotypes.

neuroscience↗

bioRxiv2019

Simple models of quantitative firing phenotypes in hippocampal neurons: comprehensive coverage of intrinsic diversity

Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with/without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In a previous work, Hippocampome.org, a comprehensive knowledgebase of hippocampal neuron types, systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a comprehensive set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. Furthermore, we observed that adding dendritic compartments to point-neuron models, in general, allowed soma to reproduce features of bursting patterns and abrupt non-linearities in some frequency adapting patterns slightly more accurately. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the dynamical interactions of various types at the mesoscopic level.\n\nAuthor SummaryThe neurons in the hippocampus show enormous diversity in their intrinsic activity patterns. A comprehensive characterization of various intrinsic types using a neuronal modeling system is necessary to simulate biologically realistic networks of brain regions. Morphologically detailed neuronal modeling frameworks often limit the scalability of such network simulations due to the specification of hundreds of rules governing each neurons intrinsic dynamics. In this work, we have accomplished a comprehensive mapping of experimentally identified intrinsic dynamics in a simple modeling system with only two governing rules. We have created over a hundred point-neuron models that reflect the intrinsic differences among the hippocampal neuron types both qualitatively and quantitatively. In addition, we compactly extended our point-neurons to include up to four compartments, which will allow anatomically finer-grained connections among the neurons in a network. Our compact model representations, which are freely available in Hippocampome.org, will allow future researchers to investigate dynamical interactions among various intrinsic types and emergent integrative properties using scalable, yet biologically realistic network simulations.

neuroscience↗

bioRxiv2017

Firing pattern classification and phenotyping in a knowledge base of hippocampal neuron types

Systematically organizing the anatomical, molecular, and physiological properties of cortical neurons is important for understanding their computational functions. Hippocampome.org defines 122 neuron types in the rodent hippocampal formation (dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex) based on their somatic, axonal, and dendritic locations, putative excitatory/inhibitory outputs, molecular marker expression, and biophysical properties such as time constant and input resistance. Here we augment the electrophysiological data of this knowledge base by collecting, quantifying, and analyzing the firing responses to depolarizing current injections for every hippocampal neuron type from available published experiments. We designed and implemented objective protocols to classify firing patterns based on both transient and steady-state activity. Specifically, we identified 5 transients (delay, adapting spiking, rapidly adapting spiking, transient stuttering, and transient slow-wave bursting) and 4 steady states (non-adapting spiking, persistent stuttering, persistent slow-wave bursting, and silence). By characterizing the set of all firing responses reported for hippocampal neurons, this automated classification approach revealed 9 unique families of firing pattern phenotypes while distinguishing potential new neuronal subtypes. Several novel statistical associations also emerged between firing responses and other electrophysiological properties, morphological features, and molecular marker expression. The firing pattern parameters, complete experimental conditions (including solution and stimulus details), digitized spike times, exact reference to the original empirical evidence, and analysis scripts are released open-source through Hippocampome.org for all neuron types, greatly enhancing the existing search and browse capabilities. This information, collated online in human-and machine-accessible form, will help design and interpret both experiments and hippocampal model simulations.\n\nSignificance StatementComprehensive characterization of nerve cells is essential for understanding signal processing in biological neuronal networks. Firing patterns are important identification characteristics of neurons and play crucial roles in information coding in neural systems. Building upon the comprehensive knowledge base Hippocampome.org, we developed and implemented automated protocols to classify all known firing responses exhibited by each neuron type of the rodent hippocampus based on analysis of transient and steady-state activity. This approach identified the most distinguishing elements of every firing phenotype and revealed previously unnoticed statistical associations of firing responses with other electrophysiological, morphological, and molecular properties. The resulting data, freely released online, constitute a powerful resource for designing and interpreting experiments as well as developing and testing hippocampal models.

neuroscience↗