Interaction with intestinal microbes in infancy has a profound impact on health and disease in later life through programming of immune and metabolic pathways. We collected maternal faeces, placenta, amniotic fluid, colostrum, meconium and infant faeces samples from 15 mother-infant pairs in an effort to rigorously investigate prenatal and neonatal microbial transfer and gut colonisation. To ensure sterile sampling, only deliveries at full term by elective caesarean section were studied. Microbiota composition and activity assessment by conventional bacterial culture, 16S rRNA gene pyrosequencing, quantitative PCR, and denaturing gradient gel electrophoresis revealed that the placenta and amniotic fluid harbour a distinct microbiota characterised by low richness, low diversity and the predominance of Proteobacteria. Shared features between the microbiota detected in the placenta and amniotic fluid and in infant meconium suggest microbial transfer at the foeto-maternal interface. At the age of 3–4 days, the infant gut microbiota composition begins to resemble that detected in colostrum. Based on these data, we propose that the stepwise microbial gut colonisation process may be initiated already prenatally by a distinct microbiota in the placenta and amniotic fluid. The link between the mother and the offspring is continued after birth by microbes present in breast milk.

Breast feeding results in long term health benefits in the prevention of communicable and non-communicable diseases at both individual and population levels. Geographical location directly impacts the composition of breast milk including microbiota and lipids. The aim of this study was to investigate the influence of geographical location, i.e., Europe (Spain and Finland), Africa (South Africa), and Asia (China), on breast milk microbiota and lipid composition in samples obtained from healthy mothers after the 1 month of lactation. Altogether, 80 women (20 from each country) participated in the study, with equal number of women who delivered by vaginal or cesarean section from each country. Lipid composition particularly that of polyunsaturated fatty acids differed between the countries, with the highest amount of n-6 PUFA (25.6%) observed in the milk of Chinese women. Milk microbiota composition also differed significantly between the countries (p = 0.002). Among vaginally delivered women, Spanish women had highest amount of Bacteroidetes (mean relative abundance of 3.75) whereas Chinese women had highest amount of Actinobacteria (mean relative abundance 5.7). Women who had had a cesarean section had higher amount of Proteobacteria as observed in the milk of the Spanish and South African women. Interestingly, the Spanish and South African women had significantly higher bacterial genes mapped to lipid, amino acid and carbohydrate metabolism (p < 0.05). Association of the lipid profile with the microbiota revealed that monounsaturated fatty acids (MUFA) were negatively associated with Proteobacteria (r = -0.43, p < 0.05), while Lactobacillus genus was associated with MUFA (r = -0.23, p = 0.04). These findings reveal that the milk microbiota and lipid composition exhibit differences based on geographical locations in addition to the differences observed due to the mode of delivery.

Human milk oligosaccharides (HMOs) are structurally diverse unconjugated glycans with a composition unique to each lactating mother. While HMOs have been shown to have an impact on the development of infant gut microbiota, it is not well known if HMOs also already affect milk microbial composition. To address this question, we analysed eleven colostrum samples for HMO content by high-pressure liquid chromatography and microbiota composition by quantitative PCR. Higher total HMO concentration was associated with higher counts of Bifidobacterium spp. (ρ=0.63, P=0.036). A distinctive effect was seen when comparing different HMO groups: positive correlations were observed between sialylated HMOs and Bifidobacterium breve (ρ=0.84, P=0.001), and non-fucosylated/non-sialylated HMOs and Bifidobacterium longum group (ρ=0.65, P=0.030). In addition to associations between HMOs and bifidobacteria, positive correlations were observed between fucosylated HMOs and Akkermansia muciniphila (ρ=0.70, P=0.017), and between fucosylated/sialylated HMOs and Staphylococcus aureus (ρ=0.75, P=0.007). Our results suggest that the characterised HMOs have an effect on specific microbial groups in human milk. Both oligosaccharides and microbes provide a concise inoculum for the compositional development of the infant gut microbiota.

The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Overnight polysomnographic recordings from a public dataset of healthy individuals (Sleep-EDF, n = 153) and from a clinical dataset (n = 891) of patients with

Study Objectives Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and having low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to develop an automatic method for identifying the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter. Methods PPG signals from the diagnostic polysomnographies of susptected OSA patients (n = 894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for three-stage (wake/NREM/REM), four-stage (wake/N1+N2/N3/REM), and five-stage (wake/N1/N2/N3/REM) classification of sleep. Results The three-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen’s κ of 0.65. The four- and five-stage models achieved 68.5% (κ = 0.54), and 64.1% (κ = 0.51) accuracies, respectively. With the five-stage model, the total sleep time was underestimated with a mean bias error (SD) of of 7.5 (55.2) minutes. Conclusion The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitoring of sleep and help assess otherwise overlooked conditions such as REM-related OSA.

Aging is associated with alterations in the intestinal microbiota and with immunosenescence. Probiotics have the potential to modify a selected part of the intestinal microbiota as well as improve immune functions and may, therefore, be particularly beneficial to elderly consumers. In this randomized, controlled cross-over clinical trial, we assessed the effects of a probiotic cheese containing Lactobacillus rhamnosus HN001 and Lactobacillus acidophilus NCFM on the intestinal microbiota and fecal immune markers of 31 elderly volunteers and compared these effects with the administration of the same cheese without probiotics. The probiotic cheese was found to increase the number of L. rhamnosus and L. acidophilus NCFM in the feces, suggesting the survival of the strains during the gastrointestinal transit. Importantly, probiotic cheese administration was associated with a trend towards lower counts of Clostridium difficile in the elderly, as compared with the run-in period with the plain cheese. The effect was statistically significant in the subpopulation of the elderly who harbored C. difficile at the start of the study. The probiotic cheese was not found to significantly alter the levels of the major microbial groups, suggesting that the microbial changes conferred by the probiotic cheese were limited to specific bacterial groups. Despite that the administration of the probiotic cheese to the study population has earlier been shown to significantly improve the innate immunity of the elders, we did not observe measurable changes in the fecal immune IgA concentrations. No increase in fecal calprotectin and β-defensin concentrations suggests that the probiotic treatment did not affect intestinal inflammatory markers. In conclusion, the administration of probiotic cheese containing L. rhamnosus HN001 and L. acidophilus NCFM, was associated with specific changes in the intestinal microbiota, mainly affecting specific subpopulations of intestinal lactobacilli and C. difficile, but did not have significant effects on the major microbial groups or the fecal immune markers.

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof of concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package, diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratoryassembled microbial mixtures as well as human fecal samples.

Gut microbiota participates in diverse metabolic and homeostatic functions related to health and well-being. Its composition varies between individuals, and depends on factors related to host and microbial communities, which need to adapt to utilize various nutrients present in gut environment. We profiled fecal microbiota in 63 healthy adult individuals using metaproteomics, and focused on microbial CAZy (carbohydrate-active) enzymes involved in glycan foraging. We identified two distinct CAZy profiles, one with many Bacteroides-derived CAZy in more than one-third of subjects (n = 25), and it associated with high abundance of Bacteroides in most subjects. In a smaller subset of donors (n = 8) with dietary parameters similar to others, microbiota showed intense expression of Prevotella-derived CAZy including exo-beta-(1,4)-xylanase, xylan-1,4-beta-xylosidase, alpha-larabinofuranosidase and several other CAZy belonging to glycosyl hydrolase families involved in digestion of complex plant-derived polysaccharides. This associated invariably with high abundance of Prevotella in gut microbiota, while in subjects with lower abundance of Prevotella, microbiota showed no Prevotella-derived CAZy. Identification of Bacteroides-and Prevotella-derived cAZy in microbiota proteome and their association with differences in microbiota composition are in evidence of individual variation in metabolic specialization of gut microbes affecting their colonizing competence.