Neonatal apneic phenotype in a murine congenital central hypoventilation syndrome model is induced through non-cell autonomous developmental mechanisms

Metabolic trade-offs in Neonatal sepsis triggered by TLR4 and TLR1/2 ligands result in unique dysfunctions in neural breathing circuits

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.

DNA hypomethylation promotes the expression of CASPASE-4 which exacerbates inflammation and amyloid-β deposition in Alzheimer's disease

Alzheimer's disease (AD) is the sixth leading cause of death in the USA. It is established that neuroinflammation contributes to the synaptic loss, neuronal death, and symptomatic decline of AD patients. Accumulating evidence suggests a critical role for microglia, innate immune phagocytes of the brain. For instance, microglia release pro-inflammatory products such as IL-1β which is highly implicated in AD pathobiology. The mechanisms underlying the transition of microglia to proinflammatory promoters of AD remain largely unknown. To address this gap, we performed reduced representation bisulfite sequencing (RRBS) to profile global DNA methylation changes in human AD brains compared to no disease controls. We identified differential DNA methylation of CASPASE-4 (CASP4), which when expressed promotes the generation of IL-1β and is predominantly expressed in immune cells. DNA upstream of the CASP4 transcription start site was hypomethylated in human AD brains, which was correlated with increased expression of CASP4. Furthermore, microglia from a mouse model of AD (5xFAD) express increased levels of CASP4 compared to wild-type (WT) mice. To study the role of CASP4 in AD, we developed a novel mouse model of AD lacking the mouse ortholog of CASP4 and CASP11, which is encoded by mouse Caspase-4 (5xFAD/Casp4-/-). The expression of CASP11 was associated with increased accumulation of pathologic protein aggregate amyloid-β (Aβ) and increased microglial production of IL-1β in 5xFAD mice. Utilizing RNA-sequencing, we determined that CASP11 promotes unique transcriptomic phenotypes in 5xFAD mouse brains, including alterations of neuroinflammatory and chemokine signaling pathways. Notably, in vitro, CASP11 promoted generation of IL-1β from macrophages in response to cytosolic Aβ through cleavage of downstream effector Gasdermin D (GSDMD). Therefore, here we unravel the role for CASP11 and GSDMD in the generation of IL-1β in response to Aβ and the progression of pathologic inflammation in AD. Overall, our results demonstrate that overexpression of CASP4 due to differential DNA methylation in AD microglia contributes to the progression of AD pathobiology. Thus, we identify CASP4 as a potential target for immunotherapies for the treatment and prevention of AD.

Multigenerational inheritance of breathing deficits following perinatal exposure to titanium dioxide nanoparticles in the offspring of mice

BACKGROUND

The utilization of titanium dioxide nanoparticles (TIO2NPs) has experienced a significant surge in recent decades, and these particles are now commonly found in various everyday consumer products. Due to their small size, TIO2NPs can penetrate biological barriers and elicit adverse interactions with biological tissues. Notably, exposure of pregnant females to TIO2NPs during the perinatal period has been shown to disrupt the growth of offspring. Furthermore, this exposure induces epigenetic modifications in the DNA of newborns, suggesting the possibility of multigenerational effects. Thus, perinatal exposure to TIO2NPs may induce immediate metabolic impairments in neonates, which could be transmitted to subsequent generations in the long term.

RESULTS

In this study, we utilized perinatal exposure of female mice to TIO2NPs through voluntary food intake and observed impaired metabolism in newborn male and female F1 offspring. The exposed newborn mice exhibited reduced body weight gain and a slower breathing rate compared to non-exposed animals. Additionally, a higher proportion of exposed F1 newborns experienced apneas. Similar observations were made when the exposure was limited to the postnatal period, highlighting lactation as a critical period for the adverse effects of TIO2NPs on postnatal metabolism. Importantly, the breathing deficits induced by TIO2NPs were transmitted from F1 females to the subsequent F2 generation. Moreover, re-exposure of adult F1 females to TIO2NPs exacerbated the breathing deficits in newborn F2 males.

CONCLUSIONS

Our findings demonstrate that perinatal exposure to TIO2NPs disrupts postnatal body weight gain and respiration in the offspring, and these deficits are transmissible to future generations.

Developmental disorders affecting the respiratory system: CCHS and ROHHAD

Rapid-onset Obesity with Hypothalamic dysfunction, Hypoventilation, and Autonomic Dysregulation (ROHHAD) and Congenital Central Hypoventilation Syndrome (CCHS) are ultra-rare distinct clinical disorders with overlapping symptoms including altered respiratory control and autonomic regulation. Although both disorders have been considered for decades to be on the same spectrum with necessity of artificial ventilation as life-support, recent acquisition of specific knowledge concerning the genetic basis of CCHS coupled with an elusive etiology for ROHHAD have definitely established that the two disorders are different. CCHS is an autosomal dominant neurocristopathy characterized by alveolar hypoventilation resulting in hypoxemia/hypercarbia and features of autonomic nervous system dysregulation (ANSD), with presentation typically in the newborn period. It is caused by paired-like homeobox 2B (PHOX2B) variants, with known genotype-phenotype correlation but pathogenic mechanism(s) are yet unknown. ROHHAD is characterized by rapid weight gain, followed by hypothalamic dysfunction, then hypoventilation followed by ANSD, in seemingly normal children ages 1.5-7 years. Postmortem neuroanatomical studies, thorough clinical characterization, pathophysiological assessment, and extensive genetic inquiry have failed to identify a cause attributable to a traditional genetic basis, somatic mosaicism, epigenetic mechanism, environmental trigger, or other. To find the key to the ROHHAD pathogenesis and to improve its clinical management, in the present chapter, we have carefully compared CCHS and ROHHAD.

Obstructive Apneas in a Mouse Model of Congenital Central Hypoventilation Syndrome

Rationale: Congenital central hypoventilation syndrome (CCHS) is characterized by life-threatening sleep hypoventilation and is caused by PHOX2B gene mutations, most frequently the PHOX2B^27Ala/+ mutation, with patients requiring lifelong ventilatory support. It is unclear whether obstructive apneas are part of the syndrome. Objectives: To determine if Phox2b^27Ala/+ mice, which present the main symptoms of CCHS and die within hours after birth, also express obstructive apneas, and to investigate potential underlying mechanisms. Methods: Apneas were classified as central, obstructive, or mixed by using a novel system combining pneumotachography and laser detection of abdominal movement immediately after birth. Several respiratory nuclei involved in airway patency were examined by immunohistochemistry and electrophysiology in brainstem-spinal cord preparations. Measurements and Main Results: The median (interquartile range) of obstructive apnea frequency was 2.3 (1.5-3.3)/min in Phox2b^27Ala/+ pups versus 0.6 (0.4-1.0)/min in wild types (P < 0.0001). Obstructive apnea duration was 2.7 seconds (2.3-3.9) in Phox2b^27Ala/+ pups versus 1.7 seconds (1.1-1.9) in wild types (P < 0.0001). Central and mixed apneas presented similar significant differences. In Phox2b^27Ala/+ preparations, the hypoglossal nucleus had fewer (P < 0.05) and smaller (P < 0.01) neurons, compared with wild-type preparations. Importantly, coordination of phrenic and hypoglossal motor activities was disrupted, as evidenced by the longer and variable delay of hypoglossal activity with respect to phrenic activity onset (P < 0.001). Conclusions: The Phox2b^27Ala/+ mutation predisposed pups not only to hypoventilation and central apneas, but also to obstructive and mixed apneas, likely because of hypoglossal dysgenesis. These results thus demand attention toward obstructive events in infants with CCHS.

Elevated Expression of MiR-17 in Microglia of Alzheimer's Disease Patients Abrogates Autophagy-Mediated Amyloid-β Degradation

Autophagy is a proposed route of amyloid-β (Aβ) clearance by microglia that is halted in Alzheimer’s Disease (AD), though mechanisms underlying this dysfunction remain elusive. Here, primary microglia from adult AD (5xFAD) mice were utilized to demonstrate that 5xFAD microglia fail to degrade Aβ and express low levels of autophagy cargo receptor NBR1. In 5xFAD mouse brains, we show for the first time that AD microglia express elevated levels of microRNA cluster Mirc1/Mir17-92a, which is known to downregulate autophagy proteins. By in situ hybridization in post-mortem AD human tissue sections, we observed that the Mirc1/Mir17-92a cluster member miR-17 is also elevated in human AD microglia, specifically in the vicinity of Aβ deposits, compared to non-disease controls. We show that NBR1 expression is negatively correlated with expression of miR-17 in human AD microglia via immunohistopathologic staining in human AD brain tissue sections. We demonstrate in healthy microglia that autophagy cargo receptor NBR1 is required for Aβ degradation. Inhibiting elevated miR-17 in 5xFAD mouse microglia improves Aβ degradation, autophagy, and NBR1 puncta formation in vitro and improves NBR1 expression in vivo. These findings offer a mechanism behind dysfunctional autophagy in AD microglia which may be useful for therapeutic interventions aiming to improve autophagy function in AD.

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availability of open-source programming languages, and the objectivity permitted in their data analysis. We used classification-based algorithms, including random forest, gradient boosted machines, support vector machines, and neural networks, to test the hypothesis that the animal genotypes could be separated into their genotype based on interpretation of neurophysiological recordings. We then interrogate the models to identify what were the major features utilized by the algorithms to designate genotype classification. By using raw EEG and respiratory plethysmography data, we were able to predict which recordings came from genotype class with accuracies that were significantly improved relative to the no information rate, although EEG analyses showed more overlap between groups than respiratory plethysmography. In comparison, conventional methods where single features between animal classes were analyzed, differences between the genotypes tested using baseline neurophysiology measurements showed no statistical difference. However, ML/AI workflows successfully were capable of providing successful classification, indicating that interactions between features were different in these genotypes. ML/AI workflows provide new methodologies to interrogate neurophysiology data. However, their implementation must be done with care so as to provide high rigor and reproducibility between laboratories. We provide a series of recommendations on how to report the utilization of ML/AI workflows for the neurophysiology community.NEW & NOTEWORTHY ML/AI classification workflows are capable of providing insight into differences between genotypes for neurophysiology research. Analytical techniques utilized in the neurophysiology community can be augmented by implementing ML/AI workflows. Random forest is a robust classification algorithm for respiratory plethysmography data. Utilization of ML/AI workflows in neurophysiology research requires heightened transparency and improved community research standards.

Research Advances on Therapeutic Approaches to Congenital Central Hypoventilation Syndrome (CCHS)

Congenital central hypoventilation syndrome (CCHS) is a genetic disorder of neurodevelopment, with an autosomal dominant transmission, caused by heterozygous mutations in the PHOX2B gene. CCHS is a rare disorder characterized by hypoventilation due to the failure of autonomic control of breathing. Until now no curative treatment has been found. PHOX2B is a transcription factor that plays a crucial role in the development (and maintenance) of the autonomic nervous system, and in particular the neuronal structures involved in respiratory reflexes. The underlying pathogenetic mechanism is still unclear, although studies in vivo and in CCHS patients indicate that some neuronal structures may be damaged. Moreover, in vitro experimental data suggest that transcriptional dysregulation and protein misfolding may be key pathogenic mechanisms. This review summarizes latest researches that improved the comprehension of the molecular pathogenetic mechanisms responsible for CCHS and discusses the search for therapeutic intervention in light of the current knowledge about PHOX2B function.

Revisiting the Neuropathology of Sudden Infant Death Syndrome (SIDS)

Background: Sudden infant death syndrome (SIDS) is one of the leading causes of infant mortality in the United States (US). The extent to which SIDS manifests with an underlying neuropathological mechanism is highly controversial. SIDS correlates with markers of poor prenatal and postnatal care, generally rooted in the lack of access and quality of healthcare endemic to select racial and ethnic groups, and thus can be viewed in the context of health disparities. However, some evidence suggests that at least a subset of SIDS cases may result from a neuropathological mechanism. To explain these issues, a triple-risk hypothesis has been proposed, whereby an underlying biological abnormality in an infant facing an extrinsic risk during a critical developmental period SIDS is hypothesized to occur. Each SIDS decedent is thus thought to have a unique combination of these risk factors leading to their death. This article reviews the neuropathological literature of SIDS and uses machine learning tools to identify distinct subtypes of SIDS decedents based on epidemiological data.

Methods: We analyzed US Period Linked Birth/Infant Mortality Files from 1990 to 2017 (excluding 1992–1994). Using t-SNE, an unsupervised machine learning dimensionality reduction algorithm, we identified clusters of SIDS decedents. Following identification of these groups, we identified changes in the rates of SIDS at the state level and across three countries.

Results: Through t-SNE and distance based statistical analysis, we identified three groups of SIDS decedents, each with a unique peak age of death. Within the US, SIDS is geographically heterogeneous. Following this, we found low birth weight and normal birth weight SIDS rates have not been equally impacted by implementation of clinical guidelines. We show that across countries with different levels of cultural heterogeneity, reduction in SIDS rates has also been distinct between decedents with low vs. normal birth weight.

Conclusions: Different epidemiological and extrinsic risk factors exist based on the three unique SIDS groups we identified with t-SNE and distance based statistical measurements. Clinical guidelines have not equally impacted the groups, and normal birth weight infants comprise more of the cases of SIDS even though low birth weight infants have a higher SIDS rate.

Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution

Respiratory parameters change during post-natal development, but the nature of their changes have not been well-described. The advent of commercially available plethysmographic instruments provided improved repeatability of measurements and standardization of measured breathing in mice across laboratories. These technologies thus allowed for exploration of more precise respiratory pattern changes during the post-natal developmental epoch. Current methods to analyze respiratory behavior utilize plethysmography to acquire standing values of frequency, volume and flow at specific time points in murine maturation. These metrics have historically been independently analyzed as a function of time with no further analysis examining the interplay these variables have with each other and in the context of postnatal maturation or during blood gas homeostasis. We posit that machine learning workflows can provide deeper physiological understanding into the postnatal development of respiration. In this manuscript, we delineate a machine learning workflow based on the R-statistical programming language to examine how variation and relationships of frequency (f) and tidal volume (TV) change with respect to inspiratory and expiratory parameters. Our analytical workflows could successfully predict age and found that the variation and relationships between respiratory metrics are dynamically shifting with age and during hypercapnic breathing. Thus, our work demonstrates the utility of high dimensional analyses to provide reliable class label predictions using non-invasive respiratory metrics. These approaches may be useful in large-scale phenotyping across development and in disease.