Figure - available from: Frontiers in Immunology
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Multivariate analysis of immunologic assessment across samples of spleen, mesenteric lymph node, inguinal lymph node, peritoneal cavity lavage fluid, amniotic fluid, and placenta. (A) Recursive feature reduction used in an ensemble machine-learning strategy to determine the number of top features needed to achieve robust (>90% accuracy) classification. (B) Top 4 immune features that allow for distinction between s. Normalized expression levels are depicted. (C) Principal component analysis based on the top 4 features that allowed for optimal classification. (D) Individual classification algorithms were run with the top 4 features of the ensemble ranking. The receiver operating curve of Ridge regression is shown. The same results were observed using Passive-Aggressive or Logistic regression. Additional receiver operating curves are depicted in Supplementary Figure S2 . AB, treated with antibiotics; MLN, mesenteric lymph nodes.
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Background
Pregnancy is a portentous stage in life, during which countless events are precisely orchestrated to ensure a healthy offspring. Maternal microbial communities are thought to have a profound impact on development. Although antibiotic drugs may interfere in these processes, they constitute the most frequently prescribed medication during...
Citations
... 1. The Recursive Ensemble Feature Selection (REFS),which is an algorithm for identifying biomarkers by determining the features that are most effective in differentiating between groups in datasets achieving the highest accuracy with the fewest number of features [48,[57][58][59][60][61][62]. The ensemble is composed by 8 classifiers from the scikit-learn toolbox [63]: Stochastic Gradient Descent (SGD) on linear models, Support Vector Machine classifier (SVC), Gradient Boosting, Random Forest, Logistic Regression, Passive Aggressive classifier, Ridge Classifier and Bagging. ...
Background
In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research.
Results
Three experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods.
Conclusions
We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.
... 1 The Recursive Ensemble Feature Selection (REFS),which is an algorithm for identifying biomarkers by determining the features that are most effective in differentiating between groups in datasets achieving the highest accuracy with the fewest number of features [48,57,58,59,60,61,62]. To minimize overfitting and bised performance, REFS employs a nested approach within a 10-fold cross-validation scheme, which is a proven solution to yield more accurate and unbiased results, even with a small sample size [42]. ...
Background: In recent years, human microbiome studies have receivedincreasing attention as this field is considered a potential source for clinicalapplications. With the advancements in omics technologies and AI, researchfocused on the discovery for potential biomarkers in the human microbime usingmachine learning tools has produced positive outcomes. Despite the promisingresults, several issues can still be found in these studies such as datasets withsmall number of samples, inconsistent results, lack of uniform processing andmethodologies, and other additional factors lead to lack of reproducibility inbiomedical research. In this work, we propose a methodology that combines theDADA2 pipeline for 16s rRNA sequences processing and the Recursive EnsembleFeature Selection (REFS) in multiple datasets to increase reproducibility andobtain robust and reliable results in biomedical research.
Results: Three experiments were performed analysing microbiome data frompatients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder(ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarkersignature in one dataset and applied to 2 other as further validation. Theeffectiveness of the proposed methodology was compared with other featureselection methods such as K-Best with F-score and random selection as a baseline. The Area Under the Curve (AUC) was employed as a measure of diagnosticaccuracy and used as a metric for comparing the results of the proposedmethodology with other feature selection methods.
Conclusions: We developed a methodology for reproducible biomarker discoveryfor 16s rRNA microbiome sequence analysis, addressing the issues related withdata dimensionality, inconsistent results and validation across independentdatasets. The findings from the three experiments, across 9 different datasets,show that the proposed methodology achieved higher accuracy compared toother feature selection methods. This methodology is a first approach to increasereproducibility, to provide robust and reliable results.
... Increased expression of CD80 on monocytes indicates the activation of the monocytes [24]. In a recent study, Benner et al. also observed the effects of AB on the maternal immune response in pregnant mice [25]. They observed effects in the peripheral immune cells and placenta, but not in the MLNs. ...
The gut microbiota are involved in adaptations of the maternal immune response to pregnancy. We therefore hypothesized that inducing gut dysbiosis during pregnancy alters the maternal immune response. Thus, pregnant mice received antibiotics from day 9 to day 16 to disturb the maternal gut microbiome. Feces were collected before, during and after antibiotic treatment, and microbiota were measured using 16S RNA sequencing. Mice were sacrificed at day 18 of pregnancy and intestinal (Peyer's patches (PP) and mesenteric lymph nodes (MLN)) and peripheral immune responses (blood and spleen) were measured using flow cytometry. Antibiotic treatment decreased fetal and placental weight. The bacterial count and the Shannon index were significantly decreased (Friedman, followed by Dunn's test, p < 0.05) and the bacterial genera abundance was significantly changed (Permanova, p < 0.05) following antibiotics treatment as compared with before treatment. Splenic Th1 cells and activated blood monocytes were increased, while Th2, Th17 and FoxP3/RoRgT double-positive cells in the PP and MLNs were decreased in pregnant antibiotics-treated mice as compared with untreated pregnant mice. In addition, intestinal dendritic cell subsets were affected by antibiotics. Correlation of immune cells with bacterial genera showed various correlations between immune cells in the PP, MLN and peripheral circulation (blood and spleen). We conclude the disturbed gut microbiota after antibiotics treatment disturbed the maternal immune response. This disturbed maternal immune response may affect fetal and placental weight.
... Experimental work on gut dysbiosis indicates that altered intestinal barrier coupled with dysregulated microbial populations may allow for leaking of antigenic gastrointestinal molecules causing activation of the complement system of immune cells including microglia (Lambert, 2009;Mossad and Erny, 2020). Experimentally induced immune alterations during prenatal life with antibiotics were also shown to alter the microbiota system in mice (Russell et al., 2013;Gonzalez-Perez et al., 2016;Benner et al., 2021). Additionally, studies examining neuronal functioning revealed that mice exposed to maternal immune activation with viral mimicry agents display during adolescence and adulthood SCZand autistic-like behavior including reduced communication and social interactions, together with increased stereotypy, anxiety and sensorimotor deficits (Coiro et al., 2015;Meehan et al., 2017;Pendyala et al., 2017;Hui et al., 2018). ...
Brain aging, which involves a progressive loss of neuronal functions, has been reported to be premature in probands affected by schizophrenia (SCZ). Evidence shows that SCZ and accelerated aging are linked to changes in epigenetic clocks. Recent cross-sectional magnetic resonance imaging analyses have uncovered reduced brain reserves and connectivity in patients with SCZ compared to typically aging individuals. These data may indicate early abnormalities of neuronal function following cyto-architectural alterations in SCZ. The current mechanistic knowledge on brain aging, epigenetic changes, and their neuropsychiatric disease association remains incomplete. With this review, we explore and summarize evidence that the dynamics of gut-resident bacteria can modulate molecular brain function and contribute to age-related neurodegenerative disorders. It is known that environmental factors such as mode of birth, dietary habits, stress, pollution, and infections can modulate the microbiota system to regulate intrinsic neuronal activity and brain reserves through the vagus nerve and enteric nervous system. Microbiota-derived molecules can trigger continuous activation of the microglial sensome, groups of receptors and proteins that permit microglia to remodel the brain neurochemistry based on complex environmental activities. This remodeling causes aberrant brain plasticity as early as fetal developmental stages, and after the onset of first-episode psychosis. In the central nervous system, microglia, the resident immune surveillance cells, are involved in neurogenesis, phagocytosis of synapses and neurological dysfunction. Here, we review recent emerging experimental and clinical evidence regarding the gut-brain microglia axis involvement in SCZ pathology and etiology, the hypothesis of brain reserve and accelerated aging induced by dietary habits, stress, pollution, infections, and other factors. We also include in our review the possibilities and consequences of gut dysbiosis activities on microglial function and dysfunction, together with the effects of antipsychotics on the gut microbiome: therapeutic and adverse effects, role of fecal microbiota transplant and psychobiotics on microglial sensomes, brain reserves and SCZ-derived accelerated aging. We end the review with suggestions that may be applicable to the clinical setting. For example, we propose that psychobiotics might contribute to antipsychotic-induced therapeutic benefits or adverse effects, as well as reduce the aging process through the gut-brain microglia axis. Overall, we hope that this review will help increase the understanding of SCZ pathogenesis as related to chronobiology and the gut microbiome, as well as reveal new concepts that will serve as novel treatment targets for SCZ.
... The possible explanation for CD8 + T-cell disturbance might be the altered activation and expression of the T-cell receptor (TCR) that sustains cytokine production, as indicated by Gonzalez-Perez et al. [121]. Four markers identified by Benner et al. [122] seem to coordinate the robustness of the immune system after antibiotics administration: splenic T helper 17 cells and CD5 + , CD4 + T cells in mesenteric lymph nodes as well as RORγT mRNA in the placenta. ...
Background:
Antenatal depression (AND) and post-partum depression (PPD) are long-term debilitating psychiatric disorders that significantly influence the composition of the gut flora of mothers and infants that starts from the intrauterine life. Not only does bacterial ratio shift impact the immune system, but it also increases the risk of potentially life-threatening disorders.
Material and methods:
Therefore, we conducted a narrative mini-review aiming to gather all evidence published between 2018-2022 regarding microflora changes in all three stages of pregnancy.
Results:
We initially identified 47 potentially eligible studies, from which only 7 strictly report translocations; 3 were conducted on rodent models and 4 on human patients. The remaining studies were divided based on their topic, precisely focused on how probiotics, breastfeeding, diet, antidepressants, exogenous stressors, and plant-derived compounds modulate in a bidirectional way upon behavior and microbiota. Almost imperatively, dysbacteriosis cause cognitive impairments, reflected by abnormal temperament and personality traits that last up until 2 years old. Thankfully, a distinct technique that involves fecal matter transfer between individuals has been perfected over the years and was successfully translated into clinical practice. It proved to be a reliable approach in diminishing functional non- and gastrointestinal deficiencies, but a clear link between depressive women's gastrointestinal/vaginal microbiota and clinical outcomes following reproductive procedures is yet to be established. Another gut-dysbiosis-driving factor is antibiotics, known for their potential to trigger inflammation. Fortunately, the studies conducted on mice that lack microbiota offer, without a shadow of a doubt, insight.
Conclusions:
It can be concluded that the microbiota is a powerful organ, and its optimum functionality is crucial, likely being the missing puzzle piece in the etiopathogenesis of psychiatric disorders.
... Together these studies demonstrated the influence that the early microbiome and ABX have on offspring brain and, subsequently, behavior. Conversely, in MIA offspring, ABX administered over time to eradicate the gut microbiota dampened the degree of the maternal immune response during pregnancy, and subsequent effects on behavior and immune responses in the offspring were blunted [42][43][44][45][46]. ...
This study investigated the effect of antibiotics administered to pregnant dams on offspring gut microbiome composition and metabolic capabilities, and how these changes in the microbiota may influence their immune responses in both the periphery and the brain. We orally administered a broad-spectrum antibiotic (ABX) cocktail consisting of vancomycin 0.5 mg/mL, ampicillin 1 mg/mL, and neomycin 1 mg/mL to pregnant dams during late gestation through birth. Bacterial DNA was extracted from offspring fecal samples, and 16S ribosomal RNA gene was sequenced by Illumina, followed by analysis of gut microbiota composition and PICRUSt prediction. Serum and brain tissue cytokine levels were analyzed by Luminex. Our results indicate that the ABX-cocktail led to significant diversity and taxonomic changes to the offspring’s gut microbiome. In addition, the predicted KEGG and MetaCyc pathways were significantly altered in the offspring. Finally, there were decreased innate inflammatory cytokines and chemokines and interleukin (IL)-17 seen in the brains of ABX-cocktail offspring in response to lipopolysaccharide (LPS) immune challenge. Our results suggest that maternal ABX can produce long-lasting effects on the gut microbiome and neuroimmune responses of offspring. These findings support the role of the early microbiome in the development of offspring gastrointestinal and immune systems.
Antimicrobialpolicyinpregnancyisanimportantareaofconcerninthefieldofobstetricsandgynecology. Theuseofantibioticsandother antimicrobial agentsduringpregnancycanhavesignificant effectson boththemotherandthedevelopingfetus.Theobjectiveofantimicrobial institutingpolicyinpregnancy istopreventandtreat infectionswhileminimizingtheriskofadverseoutcomes.Theguidelinearebased onacareful evaluationof thebenefitsandrisksassociatedwiththeuseofantimicrobial agentsduring pregnancy, aswell as thepotential impact on thedevelopment of antimicrobial resistance.This study providesabriefoverviewof thecurrentantimicrobialpolicyinpregnancy,highlightingtheimportance ofappropriateantibioticselection,dosing,anddurationoftherapy.Italsodiscussestheroleofhealthcare providersinimplementingandmonitoringantimicrobialpolicyinpregnancy,aswellastheimportanceof patienteducationandinformedchoicesforrationaluseofantimicrobial.
Uncovering mechanisms underlying fetal programming during pregnancy is of critical importance. Atypical neurodevelopment during the pre- and immediate postnatal period has been associated with long-term adverse health outcomes, including mood disorders and aberrant cognitive ability in offspring. Maternal factors that have been implicated in anomalous offspring development include maternal inflammation and tress, anxiety, and depression.
One potential mechanism through which these factors perturb normal offspring postnatal development is through microbiome disruption. The mother is a primary source of early postnatal microbiome seeding for the offspring, and the transference of a healthy microbiome is key in normal neurodevelopment. Since psychological stress, mood disorders, and inflammation have all been implicated in altering maternal microbiome community structure, passing on aberrant microbial communities to the offspring that may then affect developmental outcomes. Therefore, we examined how maternal stress, anxiety and depression assessed with standardized instruments, and maternal inflammatory cytokine levels in the pre- and postnatal period are associated with the offspring microbiome within the first 13 months of life, utilizing full length 16S sequencing on infant stool samples, that allowed for species-level resolution.
Results revealed that infants of mothers who reported higher anxiety and perceived stress had reduced alpha diversity. Additionally, the relative taxonomic quantitative abundances of Bifidobacterium dentium and other species that have been associated with either modulation of the gut-brain axis, or other beneficial health outcomes, were reduced in the offspring of mothers with higher anxiety, perceived stress, and depression. We also found associations between bifidobacteria and prenatal maternal pro-inflammatory cytokines IL-6, IL-8, and IL-10. In summary, specific microbial taxa involved in maintaining proper brain and immune function are lower in offspring born to mothers with anxiety, depression, or stress, providing strong evidence for a mechanism by which maternal factors may affect offspring health through microbiota dysregulation.
