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Joana Alves M, Browe BM, Carolina Rodrigues Dias A, Torres JM, Zaza G, Bangudi S, Blackburn J, Wang W, de Araujo Fernandes-Junior S, Fadda P, Toland A, Baer LA, Stanford KI, Czeisler C, Garcia AJ, Javier Otero J. Metabolic trade-offs in Neonatal sepsis triggered by TLR4 and TLR1/2 ligands result in unique dysfunctions in neural breathing circuits. Brain Behav Immun 2024; 119:333-350. [PMID: 38561095 DOI: 10.1016/j.bbi.2024.03.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 03/05/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024] Open
Abstract
Neonatal sepsis remains one of the leading causes of mortality in newborns. Several brainstem-regulated physiological processes undergo disruption during neonatal sepsis. Mechanistic knowledge gaps exist at the interplay between metabolism and immune activation to brainstem neural circuits and pertinent physiological functions in neonates. To delineate this association, we induced systemic inflammation either by TLR4 (LPS) or TLR1/2 (PAM3CSK4) ligand administration in postnatal day 5 mice (PD5). Our findings show that LPS and PAM3CSK4 evoke substantial changes in respiration and metabolism. Physiological trade-offs led to hypometabolic-hypothermic responses due to LPS, but not PAM3CSK4, whereas to both TLR ligands blunted respiratory chemoreflexes. Neuroinflammatory pathways modulation in brainstem showed more robust effects in LPS than PAM3CSK4. Brainstem neurons, microglia, and astrocyte gene expression analyses showed unique responses to TLR ligands. PAM3CSK4 did not significantly modulate gene expression changes in GLAST-1 positive brainstem astrocytes. PD5 pups receiving PAM3CSK4 failed to maintain a prolonged metabolic state repression, which correlated to enhanced gasping latency and impaired autoresuscitation during anoxic chemoreflex challenges. In contrast, LPS administered pups showed no significant changes in anoxic chemoreflex. Electrophysiological studies from brainstem slices prepared from pups exposed to either TLR4 or PAM3CSK4 showed compromised transmission between preBötzinger complex and Hypoglossal as an exclusive response to the TLR1/2 ligand. Spatial gene expression analysis demonstrated a region-specific modulation of PAM3CSK4 within the raphe nucleus relative to other anatomical sites evaluated. Our findings suggest that metabolic changes due to inflammation might be a crucial tolerance mechanism for neonatal sepsis preserving neural control of breathing.
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Affiliation(s)
- Michele Joana Alves
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Brigitte M Browe
- Institute for Integrative Physiology, Grossman Institute for Neuroscience Quantitative Biology and Human Behavior, The Neuroscience Institute, The University of Chicago, Chicago, IL, United States
| | - Ana Carolina Rodrigues Dias
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Juliet M Torres
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Giuliana Zaza
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Suzy Bangudi
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Jessica Blackburn
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Wesley Wang
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | | | - Paolo Fadda
- Genomics Shared Resource, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| | - Amanda Toland
- Genomics Shared Resource, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States; Department of Cancer Biology and Genetics and Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States
| | - Lisa A Baer
- Department of Cancer Biology and Genetics and Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States
| | - Kristin I Stanford
- Department of Physiology and Cell Biology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Catherine Czeisler
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Alfredo J Garcia
- Institute for Integrative Physiology, Grossman Institute for Neuroscience Quantitative Biology and Human Behavior, The Neuroscience Institute, The University of Chicago, Chicago, IL, United States.
| | - José Javier Otero
- Division of Neuropathology, Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, United States.
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Lusk S, Ward CS, Chang A, Twitchell-Heyne A, Fattig S, Allen G, Jankowsky J, Ray R. An automated respiratory data pipeline for waveform characteristic analysis. J Physiol 2023; 601:4767-4806. [PMID: 37786382 PMCID: PMC10841337 DOI: 10.1113/jp284363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 08/11/2023] [Indexed: 10/04/2023] Open
Abstract
Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic and degenerative diseases converging on autonomic control failure. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. We present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds and defining experimental variables. Breathe Easy was validated against manual selection by experts, which represents the current standard in the field. We demonstrate Breathe Easy's utility by examining a 2-year longitudinal study of an Alzheimer's disease mouse model to assess contributions of forebrain pathology in disordered breathing. Whole body plethysmography has become an important experimental outcome measure for a variety of diseases with primary and secondary respiratory indications. Respiratory dysfunction, while not an initial symptom in many of these disorders, often drives disability or death in patient outcomes. Breathe Easy provides an open-source respiratory analysis tool for all respiratory datasets and represents a necessary improvement upon current analytical methods in the field. KEY POINTS: Respiratory dysfunction is a common endpoint for disability and mortality in many disorders throughout life. Whole body plethysmography in rodents represents a high face-value method for measuring respiratory outcomes in rodent models of these diseases and disorders. Analysis of key respiratory variables remains hindered by manual annotation and analysis that leads to low throughput results that often exclude a majority of the recorded data. Here we present a software suite, Breathe Easy, that automates the process of data selection from raw recordings derived from plethysmography experiments and the analysis of these data into operative outcomes and publication-worthy graphs with statistics. We validate Breathe Easy with a terabyte-scale Alzheimer's dataset that examines the effects of forebrain pathology on respiratory function over 2 years of degeneration.
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Affiliation(s)
- Savannah Lusk
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Christopher S. Ward
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andersen Chang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Shaun Fattig
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Genevera Allen
- Departments of Electrical and Computer Engineering, Statistics, and Computer Science, Rice University, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Joanna Jankowsky
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
- Departments of Neurology, Neurosurgery, and Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Russell Ray
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
- McNair Medical Institute, Houston, TX 77030, USA
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Malhotra A, Ayappa I, Ayas N, Collop N, Kirsch D, Mcardle N, Mehra R, Pack AI, Punjabi N, White DP, Gottlieb DJ. Metrics of sleep apnea severity: beyond the apnea-hypopnea index. Sleep 2021; 44:6164937. [PMID: 33693939 DOI: 10.1093/sleep/zsab030] [Citation(s) in RCA: 155] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/31/2021] [Indexed: 12/13/2022] Open
Abstract
Obstructive sleep apnea (OSA) is thought to affect almost 1 billion people worldwide. OSA has well established cardiovascular and neurocognitive sequelae, although the optimal metric to assess its severity and/or potential response to therapy remains unclear. The apnea-hypopnea index (AHI) is well established; thus, we review its history and predictive value in various different clinical contexts. Although the AHI is often criticized for its limitations, it remains the best studied metric of OSA severity, albeit imperfect. We further review the potential value of alternative metrics including hypoxic burden, arousal intensity, odds ratio product, and cardiopulmonary coupling. We conclude with possible future directions to capture clinically meaningful OSA endophenotypes including the use of genetics, blood biomarkers, machine/deep learning and wearable technologies. Further research in OSA should be directed towards providing diagnostic and prognostic information to make the OSA diagnosis more accessible and to improving prognostic information regarding OSA consequences, in order to guide patient care and to help in the design of future clinical trials.
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Affiliation(s)
- Atul Malhotra
- Department of Medicine, University of California San Diego, La Jolla, CA
| | - Indu Ayappa
- Department of Medicine, Mt. Sinai, New York, NY
| | - Najib Ayas
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Nancy Collop
- Department of Medicine, Emory University, Atlanta, GA
| | - Douglas Kirsch
- Department of Medicine, Atrium Health Sleep Medicine, Atrium Health, Charlotte, NC
| | - Nigel Mcardle
- Department of Medicine, The University of Western Australia, Perth, Australia
| | - Reena Mehra
- Department of Medicine, Cleveland Clinic, Cleveland, OH
| | - Allan I Pack
- Department of Medicine, University of Pennsylvania, Philadelphia, PA
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