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Asthma is believed to arise through early life aberrant immune development in response to environmental exposures that may influence the airway microbiota. Here, we examine the airway microbiota during the first three months of life by 16S rRNA gene amplicon sequencing in the population-based Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC 2010 ) cohort consisting of 700 children monitored for the development of asthma since birth. Microbial diversity and the relative abundances of Veillonella and Prevotella in the airways at age one month are associated with asthma by age 6 years, both individually and with additional taxa in a multivariable model. Higher relative abundance of these bacteria is furthermore associated with an airway immune profile dominated by reduced TNF-α and IL-1β and increased CCL2 and CCL17, which itself is an independent predictor for asthma. These findings suggest a mechanism of microbiota-immune interactions in early infancy that predisposes to childhood asthma. Here, Thorsen et al. examine the microbiota during the first three months of life in a cohort of 700 children and find that microbial diversity and the relative abundances of Veillonella and Prevotella in the airways at one month of age are associated with topical immune mediators and asthma by age 6 years.

The composition of the human gut microbiome matures within the first years of life. It has been hypothesized that microbial compositions in this period can cause immune dysregulations and potentially cause asthma. Here we show, by associating gut microbial composition from 16S rRNA gene amplicon sequencing during the first year of life with subsequent risk of asthma in 690 participants, that 1-year-old children with an immature microbial composition have an increased risk of asthma at age 5 years. This association is only apparent among children born to asthmatic mothers, suggesting that lacking microbial stimulation during the first year of life can trigger their inherited asthma risk. Conversely, adequate maturation of the gut microbiome in this period may protect these pre-disposed children. Colonization of commensal bacteria is thought to impact immune development, especially in the earliest years of life. Here, the authors show, by analyzing the development of the gut microbiome of 690 children, that microbial composition at the age of 1 year is associated with asthma diagnosed in the first 5 years of life.

Supplementation with fish oil–derived fatty acids during pregnancy reduced the incidence of cases of persistent wheeze or asthma in offspring. The incidence of asthma and wheezing disorders has more than doubled in westernized countries in recent decades. 1 These conditions often originate in early childhood 2 and currently affect one in five young children. 3 Concomitantly, the increased use of vegetable oils in cooking and of grain in the feeding of livestock has resulted in an increase in the intake of n−6 polyunsaturated fatty acids and a decrease in the intake of n−3 polyunsaturated fatty acids, especially the long-chain polyunsaturated fatty acids (LCPUFAs) — eicosapentaenoic acid (20:5n–3, EPA) and docosahexaenoic acid (22:6n–3, DHA) — found in cold-water fish. 4 Observational studies have suggested an . . .

Background Analysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. Results We introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups. Conclusions Our experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

Abstract The gut microbiome is shaped through infancy and impacts the maturation of the immune system, thus protecting against chronic disease later in life. Phages, or viruses that infect bacteria, modulate bacterial growth by lysis and lysogeny, with the latter being especially prominent in the infant gut. Viral metagenomes (viromes) are difficult to analyse because they span uncharted viral diversity, lacking marker genes and standardized detection methods. Here we systematically resolved the viral diversity in faecal viromes from 647 1-year-olds belonging to Copenhagen Prospective Studies on Asthma in Childhood 2010, an unselected Danish cohort of healthy mother–child pairs. By assembly and curation we uncovered 10,000 viral species from 248 virus family-level clades (VFCs). Most (232 VFCs) were previously unknown, belonging to the Caudoviricetes viral class. Hosts were determined for 79% of phage using clustered regularly interspaced short palindromic repeat spacers within bacterial metagenomes from the same children. Typical Bacteroides -infecting crAssphages were outnumbered by undescribed phage families infecting Clostridiales and Bifidobacterium . Phage lifestyles were conserved at the viral family level, with 33 virulent and 118 temperate phage families. Virulent phages were more abundant, while temperate ones were more prevalent and diverse. Together, the viral families found in this study expand existing phage taxonomy and provide a resource aiding future infant gut virome research.

The human gut microbiome is highly personal. However, the contribution of gut physiology and environment to variations in the gut microbiome remains understudied. Here we performed an observational trial using multi-omics to profile microbiome composition and metabolism in 61 healthy adults for 9 consecutive days. We assessed day-to-day changes in gut environmental factors and measured whole-gut and segmental intestinal transit time and pH using a wireless motility capsule in a subset of 50 individuals. We observed substantial daily fluctuations, with intra-individual variations in gut microbiome and metabolism associated with changes in stool moisture and faecal pH, and inter-individual variations accounted for by whole-gut and segmental transit times and pH. Metabolites derived from microbial carbohydrate fermentation correlated negatively with the gut passage time and pH, while proteolytic metabolites and breath methane showed a positive correlation. Finally, we identified associations between segmental transit time/pH and coffee-, diet-, host- and microbial-derived metabolites. Our work suggests that gut physiology and environment are key to understanding the individuality of the human gut microbial composition and metabolism. An observational longitudinal clinical trial, incorporating a SmartPill and metabolomics, reveals the role of host factors in shaping the gut microbiome in healthy human adults.

The infant urine metabolome provides a body metabolic snapshot, and the sample collection can be done without stressing the fragile infant. 424 infant urine samples from 157 infants were sampled longitudinally at 1-, 2-, and 3 months of age. 49 metabolites were detected using proton nuclear magnetic resonance spectroscopy. Data were analyzed with multi- and univariate statistical methods to detect differences related to infant age-stage, gestational age, mother’s pre-pregnancy BMI, C-section, infant birth weight, and infant sex. Significant differences were identified between age-stage (p bonferoni  < 0.05) in 30% (15/49) of the detected metabolites. Urine creatinine increased significantly from 1 to 3 months. In addition, myo-inositol, taurine, methionine, and glucose seem to have conserved levels within the individual over time. We calculated a urine metabolic maturation age and found that the metabolic age at 3 months is negatively correlated to weight at 1 year. These results demonstrate that the metabolic maturation can be observed in urine metabolome with implications on infant growth and specifically suggesting that the systematic age effect on creatinine promotes caution in using this as normalization of other urine metabolites.

Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training time, lower complexity, and statistical flexibility. In this work, we investigate the utility of three prominent unsupervised embedding techniques (principal component analysis—PCA, uniform manifold approximation and projection—UMAP, and variational autoencoders—VAEs) for solving classification tasks in the domain of toxicology. To this end, we compare these embedding techniques against a set of molecular fingerprint-based models that do not utilize additional pre-preprocessing of features. Inspired by the success of transfer learning in several fields, we further study the performance of embedders when trained on an external dataset of chemical compounds. To gain a better understanding of their characteristics, we evaluate the embedders with different embedding dimensionalities, and with different sizes of the external dataset. Our findings show that the recently popularized UMAP approach can be utilized alongside known techniques such as PCA and VAE as a pre-compression technique in the toxicology domain. Nevertheless, the generative model of VAE shows an advantage in pre-compressing the data with respect to classification accuracy.

Background From early life, children are exposed to a multitude of environmental exposures, which may be of crucial importance for healthy development. Here, the environmental microbiota may be of particular interest as it represents the interface between environmental factors and the child. As infants in modern societies spend a considerable amount of time indoors, we hypothesize that the indoor bed dust microbiota might be an important factor for the child and for the early colonization of the airway microbiome. To explore this hypothesis, we analyzed the influence of environmental exposures on 577 dust samples from the beds of infants together with 542 airway samples from the Copenhagen Prospective Studies on Asthma in Childhood 2010 cohort. Results Both bacterial and fungal community was profiled from the bed dust. Bacterial and fungal diversity in the bed dust was positively correlated with each other. Bacterial bed dust microbiota was influenced by multiple environmental factors, such as type of home (house or apartment), living environment (rural or urban), sex of siblings, and presence of pets (cat and/or dog), whereas fungal bed dust microbiota was majorly influenced by the type of home (house or apartment) and sampling season. We further observed minor correlation between bed dust and airway microbiota compositions among infants. We also analyzed the transfer of microbiota from bed dust to the airway, but we did not find evidence of transfer of individual taxa. Conclusions Current study explores the influence of environmental factors on bed dust microbiota (both bacterial and fungal) and its correlation with airway microbiota (bacterial) in early life using high-throughput sequencing. Our findings demonstrate that bed dust microbiota is influenced by multiple environmental exposures and could represent an interface between environment and child. 9CY7jUm6BHvSS9UiakFtbY Video Abstract

In this article we analyse diary reports concerning childhood symptoms of illness, these data are part of a larger study with other types of measurements on childhood asthma. The children are followed for three years and the diaries are updated, by the parents, on a daily basis. Here we focus on the methodological implications of analysing such data. We investigate two ways of representing the data and explore which tools are applicable given both representations. The first representation relies on proper alignment and point by point comparison of the signals. The second approach takes into account combinations of symptoms on a day by day basis and boils down to the analysis of counts. In the present case both methods are well applicable. However, more generally, when symptom episodes are occurring more at random locations in time, a point by point comparison becomes less applicable and shape based approaches will fail to come up with satisfactory results. In such cases, pattern based methods will be of much greater use. The pattern based representation focuses on reoccurring patterns and ignores ordering in time. With this representation we stratify the data on the level of years, so that possibly yearly differences can still be detected.