Biology subjects

Peacock, J.

Publications and source records attributed to Peacock, J..

4 recordsLinked to original sources

bioRxiv2026

Persistent impairment of spatial hearing and neural binaural interaction after temporary noise-induced hearing loss

Many people have trouble understanding speech in noisy environments despite normal audiometric thresholds, a condition referred to as "hidden hearing loss." A leading hypothesis attributes this deficit to inner hair cell synaptic pathology (cochlear synaptopathy), which can persist after recovery from noise-induced temporary elevation of audiometric thresholds. This pathology impairs the temporally precise sound encoding necessary for spatial hearing, but direct evidence in primates, human or nonhuman, is lacking. Here, we show that a single noise exposure, producing only a temporary threshold elevation, induced long-lasting alterations in synaptic morphology, without synapse loss, resulting in persistent spatial hearing deficits for up to 11 months post-exposure in nonhuman primates of both sexes. These perceptual deficits were accompanied by a reduction in a neurophysiological measure of binaural processing - the binaural interaction component (BIC) of the auditory brainstem response (ABR). Both behavioral and neural deficits persisted despite full recovery of audiometric thresholds. Together, these findings provide the first evidence in primates that noise exposure disrupts spatial hearing and binaural neural circuit function without loss of hair cells or synapses. Because spatial hearing tests and the ABR/BIC are clinically accessible, this work also establishes translational biomarkers for early neural dysfunction underlying hidden hearing loss.

neuroscience↗

bioRxiv2021

Clinically Validated Model Predicts the Effect of Intratumoral Heterogeneity on Overall Survival for Non-Small Cell Lung Cancer (NSCLC) Patients

PurposeWe demonstrate the importance of considering intratumoral heterogeneity and the development of resistance during fractionated radiotherapy when the same dose of radiation is delivered for all fractions (Fractional Equivalent Dosing FED). Materials and MethodsA mathematical model was developed with the following parameters: a starting population of 1011 non-small cell lung cancer (NSCLC) tumor cells, 48-hour doubling time, and cell death per the linear-quadratic (LQ) model with and {beta} values derived from RSI/{beta}, in a previously described gene expression based model that estimates and {beta}. To incorporate both inter- and intratumor radiation sensitivity, RSI/{beta} output for each patient sample is assumed to represent an average value in a gamma distribution with the bounds set to -50% and +50% of RSI/b. Therefore, we assume that within a given tumor there are subpopulations that have varying radiation sensitivity parameters that are distinct from other tumor samples with a different mean RSI/{beta}. A simulation cohort (SC) comprised of 100 lung cancer patients with available RSI/{beta} (patient specific and {beta} values) was used to investigate 60Gy in 30 fractions with fractionally equivalent dosing (FED). A separate validation cohort (VC) of 57 lung cancer patients treated with radiation with available local control (LC), overall survival (OS), and tumor gene expression was used to clinically validate the model. Cox regression was used to test for significance to predict clinical outcomes as a continuous variable in multivariate analysis (MVA). Finally, the VC was used to compare FED schedules with various altered fractionation schema utilizing a Kruskal-Wallis test. This was examined using the end points of end of treatment log cell count (LCC) and by a parameter described as mean log kill efficiency (LKE) defined as: O_FD O_INLINEFIG[Formula 1]C_INLINEFIGM_FD(1)C_FD ResultsCox regression analysis on LCC for the VC demonstrates that, after incorporation of intratumoral heterogeneity, LCC has a linear correlation with local control (p = 0.002) and overall survival (p =< 0.001). Other suggested treatment schedules labeled as High Intensity Treatment (HIT) with a total 60Gy delivered over 6 weeks have a lower mean LCC and an increased LKE compared to standard of care 60Gy delivered in FED in the VC. ConclusionWe find that LCC is a clinically relevant metric that is correlated with local control and overall survival in NSCLC. We conclude that 60Gy delivered over 6 weeks with altered HIT fractionation leads to an enhancement in tumor control compared to FED when intratumoral heterogeneity is considered.

cancer biology↗

bioRxiv2017

Cancer associated fibroblasts and alectinib switch the evolutionary games that non-small cell lung cancer plays

Heterogeneity in strategies for survival and proliferation among the cells which constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumours strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-cultures of non-small cell lung cancer cells which are sensitive and resistant to the anaplastic lymphoma kinase inhibitor Alectinib. The games are not only quantitatively different between different environments, but targeted therapy and cancer associated fibroblasts qualitatively switch the type of game being played by the in-vitro population from Leader to Deadlock. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.

cancer biology↗

bioRxiv2017

Using competition assays to quantitatively model cooperative binding by transcription factors and other ligands

BACKGROUNDThe affinities of DNA binding proteins for target sites can be used to model the regulation of gene expression. These proteins can bind to DNA cooperatively, strongly impacting their affinity and specificity. However, current methods for measuring cooperativity do not provide the means to accurately predict binding behavior over a wide range of concentrations.\n\nMETHODSWe use standard computational and mathematical methods, and develop novel methods as described in Results.\n\nRESULTSWe explore some complexities of cooperative binding, and develop an improved method for relating in vitro measurements to in vivo function, based on ternary complex formation. We derive expressions for the equilibria among the various complexes, and explore the limitations of binding experiments that model the system using a single parameter. We describe how to use single-ligand binding and ternary complex formation in tandem to determine parameters that have thermodynamic relevance. We develop an improved method for finding both single-ligand dissociation constants and concentrations simultaneously. We show how the cooperativity factor can be found when only one of the single-protein dissociation constants can be measured.\n\nCONCLUSIONSThe methods that we develop constitute an optimized approach to accurately model cooperative binding.\n\nGENERAL SIGNIFICANCEThe expressions and methods we develop for modeling and analyzing DNA binding and cooperativity are applicable to most cases where multiple ligands bind to distinct sites on a common substrate. The parameters determined using these methods can be fed into models of higher-order cooperativity to increase their predictive power.\n\nHIGHLIGHTSO_LIHill plots remain prominent in biology, but can mask cooperativity\nC_LIO_LIEffective modeling of binding by two ligands requires the use of 3 parameters\nC_LIO_LIWe develop novel ways to find these parameters for two cooperating ligands\nC_LIO_LIWe show how they can be used to enhance the power of established methods\nC_LIO_LIWe describe how this framework can be extended to multiple cooperating ligands\nC_LI

biochemistry↗