Biology subjects

Peterson, E. J.

Publications and source records attributed to Peterson, E. J..

12 recordsLinked to original sources

bioRxiv2025

Glycan Profiling Identifies Chondroitin-4-sulfate as a Biomarker for Platinum Response and Therapeutic Target in Ovarian Cancer

For women with advanced ovarian cancer (OC), remission is typically achieved through surgery and combination chemotherapy, with duration largely dependent on tumor sensitivity to platinum-based drugs. Here, we show that tumor-associated glycosaminoglycans (GAGs) influence platinum drug efficacy in preclinical models of ovarian cancer. Due to the complexity of GAG biosynthesis and the involvement of multiple enzymes, traditional transcriptomic and proteomic approaches cannot accurately estimate their levels or correlation with patient response and survival. To address this, we quantitatively analyzed the full compositional profile of GAGs in OC patient-derived xenograft (PDX) models with known carboplatin sensitivity. Our results revealed a significant correlation between carboplatin resistance and high levels of the predominant GAG sequence, chondroitin-4-sulfate (C4S). Further investigation in cellular models demonstrated that high GAG expression reduces carboplatin uptake, DNA adduct formation, and tumor accumulation, whereas the opposite effect was observed for Triplatin, a GAG-targeting platinum agent. These trends were further validated in vivo, where treatment of OC PDX models with varying C4S levels confirmed that carboplatin efficacy decreases while Triplatin activity increases in tumors with high C4S expression. Based on these findings, we established a C4S cut-off score to predict tumor sensitivity, identifying a threshold above which tumors are likely to be carboplatin-resistant but Triplatin-sensitive. Analysis of patient tissue microarrays estimated that 40-83% of OC tumors, depending on subtype, exhibit high C4S expression. Collectively, these findings highlight the predictive power of C4S as a biomarker for platinum response and support the clinical evaluation of Triplatin as a targeted treatment for patients with carboplatin-resistant tumors expressing high levels of C4S. One Sentence SummaryElevated chondroitin-4-sulfate in ovarian cancer results in resistance to carboplatin and sensitivity to Triplatin.

pharmacology and toxicology↗

bioRxiv2021

What can astrocytes compute?

A foundational result in neural computation was proving the firing-rate model of neurons defines a universal function approximator. In this paper I prove it is possible for astrocytes to compute anything neurons can, by extending this original proof to a model of calcium waves in astrocytes. I confirm this in a series of computer simulations. The major limit for astrocytes, it turns out, is not their ability to learn solutions but the computational complexity of their network. I suggest some initial experiments that might be used to confirm these predictions.

neuroscience↗

bioRxiv2021

Exploitation of sulfated glycosaminoglycan status for precision medicine of platinums in triple-negative breast cancer

Triple negative breast cancer (TNBC) is a subtype of breast cancer lacking targetable biomarkers. TNBC is known to be most aggressive, and when metastatic is often drug resistant and uncurable. Biomarkers predicting response to therapy improve treatment decisions and allow personalized approaches for TNBC patients. This study explores sulfated glycosaminoglycan (sGAG) levels as a predictor of TNBC response to platinum therapy. sGAG levels were quantified in three distinct TNBC tumor models including cell line-derived, patient-derived xenograft (PDX) tumors, and isogenic models deficient in sGAG biosynthesis. The in vivo antitumor efficacy of Triplatin, a sGAG-directed platinum agent, was compared in these models to the clinical platinum agent, carboplatin. We determined that >40% of TNBC PDX tissue microarray samples have high levels of sGAGs. The in vivo accumulation of Triplatin in tumors as well as antitumor efficacy of Triplatin positively correlated with sGAG levels on tumor cells, whereas carboplatin followed the opposite trend. In carboplatin-resistant tumor models expressing high levels of sGAGs, Triplatin decreased primary tumor growth, reduced lung metastases and inhibited metastatic growth in lungs, liver, and ovaries. sGAG levels served as a predictor of Triplatin sensitivity in TNBC. Triplatin may be particularly beneficial in treating patients with chemotherapy-resistant tumors who have evidence of residual disease after standard neoadjuvant chemotherapy. More effective neoadjuvant and adjuvant treatment will likely improve clinical outcome of TNBC. SignificanceTNBC is a heterogenous disease, often defined by the absence of a therapeutic target. Our recent results show sGAGs may provide a viable biomarker for Triplatin in patients with TNBC, producing a significant advantage over carboplatin in this setting. Selective precision medicine agents, such as Triplatin, that are active against chemotherapy-resistant disease and exploit molecular biomarkers like sGAGs may significantly benefit patients in this setting.

pharmacology and toxicology↗

bioRxiv2021

Costs and benefits of using rhythmic rate codes

Neural oscillations are observed ubiquitously in the mammalian nervous system, and the benefits of oscillatory coding have been the topic of frequent analysis. Many prior studies focused on communication between populations which were already oscillating and sought to understand how these rhythms and overall communication interact. We take a contrary view here. In this paper, we focus on measuring the costs of translating from an aperiodic code to a rhythmic one. We study two models. The first is simulated independent populations of neurons subjected to a theta-band (6 Hz) pacemaker using Linear-Nonlinear Poisson (LNP) sampling. The second is a model of beta-gamma oscillations using biophysical neurons with self-organized dynamics. We measure benefits and costs in both models using information theory. In both models oscillations can only benefit communications by increasing spiking by specific amounts, in effect, correcting for "undersampling" of the stimulus. This is mechanistically similar to theories for how deep brain stimulation can enhance cognition and is consistent with older studies of gamma entrainment. Yet this trend was not universal. No one guiding principle of dynamics determines the cost of a translation in the models we studied. In our models to predict the benefits or costs of an oscillatory translation we need to understand the exacting physical details of the intrinsic connections, the population size, and the external drive.

neuroscience↗

bioRxiv2019

A way around the exploration-exploitation dilemma

Balancing exploration with exploitation is seen as a mathematically intractable dilemma that all animals face. In this paper, we provide an alternative view of this classic problem that does not depend on exploring to optimize for reward. We argue that the goal of exploration should be pure curiosity, or learning for learnings sake. Through theory and simulations we prove that explore-exploit problems based on this can be solved by a simple rule that yields optimal solutions: when information is more valuable than rewards, be curious, otherwise seek rewards. We show that this rule performs well and robustly under naturalistic constraints. We suggest three criteria can be used to distinguish our approach from other theories.

animal behavior and cognition↗

bioRxiv2019

Homeostatic mechanisms may shape the type and duration of oscillatory modulation.

Neural oscillations are observed ubiquitously in the mammalian brain, but their stability is known to be rather variable. Some oscillations are tonic and last for seconds or even minutes. Other oscillations appear as unstable bursts. Likewise, some oscillations rely on excitatory AMPAergic synapses, but others are GABAergic and inhibitory. Why this diversity exists is not clear. We hypothesized Ca 2+ -dependent homeostasis could be important in finding an explanation. We tested this hypothesis in a highly simplified model of hippocampal neurons. In this model homeostasis profoundly alters the modulatory effect of neural oscillations. Under homeostasis, tonic AMPAergic oscillations actually decrease excitability and desynchronize firing. Tonic oscillations that are synaptically GABAergic-like those in real hippocampus-dont provoke a homeostatic response, however. If our simple model is correct, homeostasis can explain why the theta rhythm in the hippocampus is synaptically inhibitory: GABA has little to no intrinsic homeostatic response, and so can preserve the pyramidal cells natural dynamic range. Based on these results we can also speculate that homeostasis may explain why AMPAergic oscillations in cortex, and hippocampus, often appear as bursts. Bursts do not interact with the slow homeostatic time constant, and so retain their normal excitatory effect. NO_SCPLOWEWC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWANDC_SCPLOW NO_SCPLOWOTEWORTHYC_SCPLOWThe intricate interplay of neuromodulators, like acetylcholine, with homeostasis is well known. The interplay between oscillatory modulation and homeostasis is not. We studied oscillatory modulation and homeostasis for the first time using a simplified model of hippocampus. We report a paradoxical result: Ca-mediated homeostasis causes AMPAergic oscillations to become effectively inhibitory. This result, along with other new observations, means homeostasis might be just as complex and important for oscillations as it is for other neuromodulators.

neuroscience↗

bioRxiv2018

In model-based fMRI significant is less than specific

By comparing computational model output to BOLD signal changes model-based fMRI has the potential to offer profound insight into what neural computations occur when. If this potential is to be fully realized, statistically significant outcomes must imply specific outcomes. That is, we must have a clear idea of how often a model not present in the BOLD signal but present in the predictor set will reach significance. We ran Monte Carlo simulations of reinforcement learning to examine this kind of specificity, focusing in on two aspects. One, to what degree can we tell related but theoretically distinct predictors apart. About 40% of the time the studied predictors were indistinguishable. Two, how well can we separate out different parameterizations of the same reinforcement learning terms. Nearly all parameter settings were indistinguishable. The lack of specificity between models and between parameters suggests a uncertain relation between significance and specificity. Follow up analyses suggest the temporally slow and prototyped nature of the haemodynamic response (HRF) can substantially increase correlations, ranging from -0.16 to 0.73 with an average of 0.27. Though we focused on a single case study, i.e., reinforcement learning, specificity concerns are potentially present in any design which does not account for the slow prototyped nature of the HRF. We suggest more specific conclusions can be reached by moving from null hypothesis testing approach to a model selection or model comparison framework.

neuroscience↗

bioRxiv2018

The trade-off between neural computation and oscillatory coordination.

Neural oscillations can improve the fidelity of neural coding by grouping action potentials into synchronous windows of activity but this same effect can interfere with coding when action potentials become "over-synchronized". Diseases ranging from Parkinsons to epilepsy suggest such over-synchronization can lead to pathological outcomes, but the precise boundary separating healthy from pathological synchrony remains an open theoretical problem. In this paper, we focus on measuring the costs of translating from an aperiodic code to a rhythmic one and use the errors introduced in this translation to predict the rise of pathological results. We study a simple model of entrainment featuring a pacemaker population coupled to biophysical neurons. This model shows that "error" in individual cells computations can be traded for population-level synchronization of spike-times. But in this model error and synchronization are not traded linearly, but nonlinearly. The bulk of synchronization happens early with relatively low error. To predict this phenomenon we conceive of "voltage budget analysis", where small time windows of membrane voltage in single cells can be partitioned into "oscillatory" and "computational" terms. By comparing these terms we discover a set of inequalities that align with an inflection point in the curve of measured errors. In particular, when the entrainment and computational voltage terms are equal, the error curve plateaus. We show this point serves as a reliable natural boundary to define pathological synchrony in neurons. We also derive optimal algorithms for exchanging computational error with population synchrony. New and Noteworthy. We establish exact conditions for when rhythmic entrainment of precise spike-times in a neural population will improve or harm its ability to communicate.

neuroscience↗

bioRxiv2017

Alpha oscillations control cortical gain by modulating excitatory-inhibitory background activity.

The first recordings of human brain activity in 1929 revealed a striking 8-12 Hz oscillation in the visual cortex. During the intervening 90 years, these alpha oscillations have been linked to numerous physiological and cognitive processes. However, because of the vast and seemingly contradictory cognitive and physiological processes to which it has been related, the physiological function of alpha remains unclear. We identify a novel neural circuit mechanism--the modulation of both excitatory and inhibitory neurons in a balanced configuration--by which alpha can modulate gain. We find that this model naturally unifies the prior, highly diverse reports on alpha dynamics, while making the novel prediction that alpha rhythms have two functional roles: a sustained high-power mode that suppresses scortical gain and a weak, bursting mode that enhances gain.

neuroscience↗

bioRxiv2017

Pegivirus avoids immune recognition but does not attenuate acute-phase disease in a macaque model of HIV infection

Human pegivirus (HPgV) protects HIV+ people from HIV-associated disease, but the mechanism of this protective effect remains poorly understood. We sequentially infected cynomolgus macaques with simian pegivirus (SPgV) and simian immunodeficiency virus (SIV) to model HIV+HPgV co-infection. SPgV had no effect on acute-phase SIV pathogenesis - as measured by SIV viral load, CD4+ T cell destruction, and immune activation - suggesting that HPgVs protective effect is exerted primarily during the chronic phase of HIV infection. We also examined the immune response to SPgV in unprecedented detail, and found that this virus elicits virtually no activation of the immune system despite persistently high titers in the blood over long periods of time. Overall, this study expands our understanding of the pegiviruses - an understudied group of viruses with a high prevalence in the global human population - and suggests that the protective effect observed in HIV+HPgV co-infected people occurs primarily during the chronic phase of HIV infection.\n\nOne Sentence SummaryPegivirus avoids immune recognition but does not attenuate acute-phase disease in a macaque model of HIV infection.\n\nShort TitlePegivirus and AIDS-virus co-infection\n\nAccessible SummaryPeople infected with HIV live longer, healthier lives when they are co-infected with the human pegivirus (HPgV) - an understudied virus with a high prevalence in the global human population. To better understand how HPgV protects people with HIV from HIV-associated disease, we infected macaques with simian versions of these two viruses (SPgV and SIV). We found that SPgV had no impact on the incidence of SIV-associated disease early during the course of SIV infection - a time when SIV and HIV are known to cause irreversible damage to the immune system. Oddly, we found that the immune system did not recognize SPgV; a finding that warrants further investigation. Overall, this study greatly expands on our understanding of the pegiviruses and their interaction with the immune system.

immunology↗

bioRxiv2017

1/f neural noise is a better predictor of schizophrenia than neural oscillations

Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better predicted group status than traditional band-limited oscillatory power in a classification analysis. Aperiodic activity also outperformed the predictions made using participants behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls. Significance statementUnderstanding the neurobiological origins of schizophrenia and identifying reliable and consistent biomarkers are of critical importance to improving treatment of that disease. Numerous studies have reported disruptions to neural oscillations in patients with schizophrenia. This has, in part, led to schizophrenia being characterized as a disease of disrupted neural coordination, reflected by changes in frequency band power. We report however that changes in the aperiodic signal can also predict clinical status. Unlike band-limited power though, aperiodic activity predicts status better than participants own behavioral performance and acts as a consistent predictor across all electrodes. Alterations in the aperiodic signal are consistent with well-established inhibitory neuron dysfunctions associated with schizophrenia, allowing for a direct link between noninvasive EEG and chronic, widespread, neurobiological deficits.

neuroscience↗

bioRxiv2016

Field Potential Reflects the Balance of Synaptic Excitation and Inhibition

Neural circuits sit in a dynamic balance between excitation (E) and inhibition (I). Fluctuations in this E:I balance have been shown to influence neural computation, working memory, and information processing. While more drastic shifts and aberrant E:I patterns are implicated in numerous neurological and psychiatric disorders, current methods for measuring E:I dynamics require invasive procedures that are difficult to perform in behaving animals, and nearly impossible in humans. This has limited the ability to examine the full impact that E:I shifts have in neural computation and disease. In this study, we develop a computational model to show that E:I ratio can be estimated from the power law exponent (slope) of the electrophysiological power spectrum, and validate this relationship using previously published datasets from two species (rat local field potential and macaque electrocorticography). This simple method--one that can be applied retrospectively to existing data--removes a major hurdle in understanding a currently difficult to measure, yet fundamental, aspect of neural computation.

neuroscience↗