Metrics of high cofluctuation and entropy to describe control of cardiac function in the stellate ganglion

  1. Nil Z Gurel  Is a corresponding author
  2. Koustubh B Sudarshan
  3. Joseph Hadaya
  4. Alex Karavos
  5. Taro Temma
  6. Yuichi Hori
  7. J Andrew Armour
  8. Guy Kember
  9. Olujimi A Ajijola
  1. University of California, Los Angeles, United States
  2. Dalhousie University, Canada

Abstract

Stellate ganglia within the intrathoracic cardiac control system receive and integrate central, peripheral, and cardiopulmonary information to produce postganglionic cardiac sympathetic inputs. Pathological anatomical and structural remodeling occurs within the neurons of the stellate ganglion (SG) in the setting of heart failure. A large proportion of SG neurons function as interneurons whose networking capabilities are largely unknown. Current therapies are limited to targeting sympathetic activity at the cardiac level or surgical interventions such as stellectomy, to treat heart failure. Future therapies that target the stellate ganglion will require understanding of their networking capabilities to modify any pathological remodeling. We observe SG networking by examining cofluctuation and specificity of SG networked activity to cardiac cycle phases. We investigate network processing of cardiopulmonary transduction by SG neuronal populations in porcine with chronic pacing-induced heart failure and control subjects during extended in-vivo extracellular microelectrode recordings. We find that information processing and cardiac control in chronic heart failure by the SG, relative to controls, exhibits: i) more frequent, short-lived, high magnitude cofluctuations, ii) greater variation in neural specificity to cardiac cycles, and iii) neural network activity and cardiac control linkage that depends on disease state and cofluctuation magnitude.

Data availability

Data is available in the Dryad repositoryCode AvailabilitySupporting Apache License codes are at GitHub (https://github.com/Koustubh2111/Cofluctuation-and-Entropy-Code-Data).

The following data sets were generated

Article and author information

Author details

  1. Nil Z Gurel

    UCLA Cardiac Arrhythmia Center, University of California, Los Angeles, Los Angeles, United States
    For correspondence
    gurelnil@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3702-0449
  2. Koustubh B Sudarshan

    Department of Engineering Mathematics and Internetworking, Dalhousie University, Halifax, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Joseph Hadaya

    UCLA Cardiac Arrhythmia Center, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Alex Karavos

    Department of Engineering Mathematics and Internetworking, Dalhousie University, Halifax, Canada
    Competing interests
    The authors declare that no competing interests exist.
  5. Taro Temma

    UCLA Cardiac Arrhythmia Center, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Yuichi Hori

    UCLA Cardiac Arrhythmia Center, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. J Andrew Armour

    UCLA Cardiac Arrhythmia Center, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Guy Kember

    Department of Engineering Mathematics and Internetworking, Dalhousie University, Halifax, Canada
    Competing interests
    The authors declare that no competing interests exist.
  9. Olujimi A Ajijola

    UCLA Cardiac Arrhythmia Center, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6197-7593

Funding

National Institutes of Health (DP2 OD024323-01)

  • Olujimi A Ajijola

NHLBI Division of Intramural Research (R01 HL159001)

  • Olujimi A Ajijola

National Science Foundation (ASEE 2127509)

  • Nil Z Gurel

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: The study was performed under a protocol approved by the University of California Los Angeles (UCLA) Animal Research Committee (ARC), in compliance with the UCLA Institutional Animal Care and Use Committee (IACUC) guidelines and the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals (Protocol #: ARC 2015-022). For SG neural data collection, the animals were sedated with tiletamine and zolazepam (Telazol, 4-8mg/kg) intramuscularly, intubated, and maintained under general anesthesia with inhaled isoflurane (2%). Continuous intravenous saline (8 − 10𝑚𝑙∕𝑘𝑔∕h) was infused throughout the protocol and animals were temperature maintained using heated water blankets (37𝑜𝐶 − 38𝑜𝐶).At the end of the protocol, animals were euthanized under deep sedation of isoflurane and cardiac fibrillation was induced.

Copyright

© 2022, Gurel et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 556
    views
  • 69
    downloads
  • 2
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Nil Z Gurel
  2. Koustubh B Sudarshan
  3. Joseph Hadaya
  4. Alex Karavos
  5. Taro Temma
  6. Yuichi Hori
  7. J Andrew Armour
  8. Guy Kember
  9. Olujimi A Ajijola
(2022)
Metrics of high cofluctuation and entropy to describe control of cardiac function in the stellate ganglion
eLife 11:e78520.
https://doi.org/10.7554/eLife.78520

Share this article

https://doi.org/10.7554/eLife.78520

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.