Relationship Between Cortical Iron and Diabetes Mellitus in Older Adults With Cognitive Complaints: A Quantitative Susceptibility Map Study

Purpose@#Diabetes mellitus (DM) is implicated in the pathogenesis of iron dysregulation and Alzheimer’s disease. We aimed to evaluate whether the presence of DM and status of cognitive impairment affect cortical iron accumulation in older adults, as quantified by the susceptibility measurements using the deep neural network QSMnet+. @*Materials and Methods@#In this retrospective analysis of a prospective cohort, 50 patients with normal cognition with or without subjective memory impairment (controls), 49 with early mild cognitive impairment (MCI), and 43 with late MCI were evaluated. We employed QSMnet+ to compute a quantitative susceptibility map and FreeSurfer 6.0 to obtain anatomical labels. The effects of MCI and DM on cortical susceptibility and volume were evaluated using a two-way analysis of covariance. @*Results@#Whole-cortex susceptibility differed according to MCI (p < 0.001) but not according to DM (p = 0.554), with higher values in the early and late MCI groups than in the control group. MCI and the DM status showed a significant interaction in the whole cortex (p = 0.023). Among the patients with early MCI, those with DM exhibited higher cortical susceptibility than those without DM, whereas those with late MCI showed no such difference. Cortical susceptibility did not correlate with the cortical volume in patients with DM and inversely correlated with the cortical volume in patients without DM. Only disease status (p = 0.008) and DM (p = 0.023) were independent predictors of whole-cortex susceptibility, after correcting for covariates. @*Conclusion@#Our findings demonstrated that cognitive impairment and DM are linked to alterations in cortical susceptibility in older adults. This observation suggests that cortical iron accumulation results from the combined effects of DM and neurodegenerative processes related to the cognitive status.

Lung Disease Diagnostic Model Through IgG Sensitization to Microbial Extracellular Vesicles

Purpose@#Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease. @*Methods@#We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis. @*Results@#Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81. @*Conclusions@#The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.

Lung Disease Diagnostic Model Through IgG Sensitization to Microbial Extracellular Vesicles

Purpose@#Recently, there has been a rise in the interest to understand the composition of indoor dust due to its association with lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and lung cancer. Furthermore, it has been found that bacterial extracellular vesicles (EVs) within indoor dust particles can induce pulmonary inflammation, suggesting that these might play a role in lung disease. @*Methods@#We performed microbiome analysis of indoor dust EVs isolated from mattresses in apartments and hospitals. We developed diagnostic models based on the bacterial EVs antibodies detected in serum samples via enzyme-linked immunosorbent assay (ELISA) in this analysis. @*Results@#Proteobacteria was the most abundant bacterial EV taxa observed at the phylum level while Pseudomonas, Enterobacteriaceae (f) and Acinetobacter were the most prominent organisms at the genus level, followed by Staphylococcus. Based on the microbiome analysis, serum anti-bacterial EV immunoglobulin G (IgG), IgG1 and IgG4 were analyzed using ELISA with EV antibodies that targeted Staphylococcus aureus, Acinetobacter baumannii, Enterobacter cloacae and Pseudomonas aeruginosa. The levels of anti-bacterial EV antibodies were found to be significantly higher in patients with asthma, COPD and lung cancer compared to the healthy control group. We then developed a diagnostic model through logistic regression of antibodies that showed significant differences between groups with smoking history as a covariate. Four different variable selection methods were compared to construct an optimal diagnostic model with area under the curves ranging from 0.72 to 0.81. @*Conclusions@#The results of this study suggest that ELISA-based analysis of anti-bacterial EV antibodies titers can be used as a diagnostic tool for lung disease. The present findings provide insights into the pathogenesis of lung disease as well as a foundation for developing a novel diagnostic methodology that synergizes microbial EV metagenomics and immune assays.

Microbe-derived extracellular vesicles as a smart drug delivery system

The human microbiome is known to play an essential role in influencing host health. Extracellular vesicles (EVs) have also been reported to act on a variety of signaling pathways, distally transport cellular components such as proteins, lipids, and nucleic acid, and have immunomodulatory effects. Here we shall review the current understanding of the intersectionality of the human microbiome and EVs in the emerging field of microbiota-derived EVs and their pharmacological potential. Microbes secrete several classes of EVs: outer membrane vesicles (OMVs), membrane vesicles (MVs), and apoptotic bodies. EV biogenesis is unique to each cell and regulated by sophisticated signaling pathways. EVs are primarily composed of lipids, proteins, nucleic acids, and recent evidence suggests they may also carry metabolites. These components interact with host cells and control various cellular processes by transferring their constituents. The pharmacological potential of microbiomederived EVs as vaccine candidates, biomarkers, and a smart drug delivery system is a promising area of future research. Therefore, it is necessary to elucidate in detail the mechanisms of microbiome-derived EV action in host health in a multi-disciplinary manner.

Importance of indoor dust biological ultrafine particles in the pathogenesis of chronic inflammatory lung diseases

The role of infectious agents in the etiology of inflammatory diseases once believed to be non-infectious is increasingly being recognized. Many bacterial components in the indoor dust can evoke inflammatory lung diseases. Bacteria secrete nanometer-sized vesicles into the extracellular milieu, so-called extracellular vesicles (EV). which are pathophysiologically related to inflammatory diseases. Microbiota compositions in the indoor dust revealed the presence of both Gram-negative and Gram-positive bacteria. Escherichia coli is a model organism of Gram-negative Enterobacteriaceae. The repeated inhalation of E. coli-derived EVs caused neutrophilic inflammation and emphysema in a dose-dependent manner. The emphysema induced by E. coli-derived EVs was partially eliminated by the absence of Interferon-gamma or interleukin-17, suggesting that Th1 and/or Th17 cell responses are important in the emphysema development. Meanwhile, the repeated inhalation of Staphylococcus aureus-derived EVs did not induce emphysema, although they induced neutrophilic inflammation in the lung. In terms of microbial EV compositions in the indoor dust, genera Pseudomonas, Acinetobacter, Enterobacter, and Staphylococcus were dominant. As for the clinical significance of sensitization to EVs in the indoor dust, EV sensitization was closely associated with asthma, chronic obstructive pulmonary disorder (COPD), and lung cancer. These data indicate that biological ultrafine particles in the indoor dust, which are mainly composed of microbial EVs, are important in the pathogenesis of chronic lung diseases associated with neutrophilic inflammation. Taken together, microbial EVs in the indoor dust are an important diagnostic and therapeutic target for the control of chronic lung diseases, such as asthma, COPD, and lung cancer.

Rapid Assessment of Microbiota Changes in Individuals with Autism Spectrum Disorder Using Bacteria-derived Membrane Vesicles in Urine

Individuals with autism spectrum disorder (ASD) have altered gut microbiota, which appears to regulate ASD symptoms via gut microbiota-brain interactions. Rapid assessment of gut microbiota profiles in ASD individuals in varying physiological contexts is important to understanding the role of the microbiota in regulating ASD symptoms. Microbiomes secrete extracellular membrane vesicles (EVs) to communicate with host cells and secreted EVs are widely distributed throughout the body including the blood and urine. In the present study, we investigated whether bacteria-derived EVs in urine are useful for the metagenome analysis of microbiota in ASD individuals. To address this, bacterial DNA was isolated from bacteria-derived EVs in the urine of ASD individuals. Subsequent metagenome analysis indicated markedly altered microbiota profiles at the levels of the phylum, class, order, family, and genus in ASD individuals relative to control subjects. Microbiota identified from urine EVs included gut microbiota reported in previous studies and their up- and down-regulation in ASD individuals were partially consistent with microbiota profiles previously assessed from ASD fecal samples. However, overall microbiota profiles identified in the present study represented a distinctive microbiota landscape for ASD. Particularly, the occupancy of g_Pseudomonas, g_Sphingomonas, g_Agrobacterium, g_Achromobacter, and g_Roseateles decreased in ASD, whereas g_Streptococcus, g_Akkermansia, g_Rhodococcus, and g_Halomonas increased. These results demonstrate distinctively altered gut microbiota profiles in ASD, and validate the utilization of urine EVs for the rapid assessment of microbiota in ASD.

Metagenome Analysis of Bodily Microbiota in a Mouse Model of Alzheimer Disease Using Bacteria-derived Membrane Vesicles in Blood

Emerging evidence has suggested that the gut microbiota contribute to brain dysfunction, including pathological symptoms of Alzheimer disease (AD). Microbiota secrete membrane vesicles, also called extracellular vesicles (EVs), which contain bacterial genomic DNA fragments and other molecules and are distributed throughout the host body, including blood. In the present study, we investigated whether bacteria-derived EVs in blood are useful for metagenome analysis in an AD mouse model. Sequence readings of variable regions of 16S rRNA genes prepared from blood EVs in Tg-APP/PS1 mice allowed us to identify over 3,200 operational taxonomic units corresponding to gut microbiota reported in previous studies. Further analysis revealed a distinctive microbiota landscape in Tg-APP/PS1 mice, with a dramatic alteration in specific microbiota at all taxonomy levels examined. Specifically, at the phylum level, the occupancy of p_Firmicutes increased, while the occupancy of p_Proteobacteria and p_Bacteroidetes moderately decreased in Tg-APP/PS1 mice. At the genus level, the occupancy of g_Aerococcus, g_Jeotgalicoccus, g_Blautia, g_Pseudomonas and unclassified members of f_Clostridiale and f_Ruminococcaceae increased, while the occupancy of g_Lactobacillus, unclassified members of f_S24-7, and g_Corynebacterium decreased in Tg-APP/PS1 mice. A number of genus members were detected in Tg-APP/PS1 mice, but not in wild-type mice, while other genus members were detected in wild-type mice, but lost in Tg-APP/PS1 mice. The results of the present study suggest that the bodily microbiota profile is altered in Tg-APP/PS1 mice, and that blood EVs are useful for the metagenome analysis of bodily microbiota in AD.