Evgeni Levin

Development of Upper Respiratory Tract Microbiota in Infancy is Affected by Mode of Delivery.

Birth by Caesarian section is associated with short- and long-term respiratory morbidity. We hypothesized that mode of delivery affects the development of the respiratory microbiota, thereby altering its capacity to provide colonization resistance and consecutive pathobiont overgrowth and infections. Therefore, we longitudinally studied the impact of mode of delivery on the nasopharyngeal microbiota development from birth until six months of age in a healthy, unselected birth cohort of 102 children (n = 761 samples). Here, we show that the respiratory microbiota develops within one day from a variable mixed bacterial community towards a Streptococcus viridans-predominated profile, regardless of mode of delivery. Within the first week, rapid niche differentiation had occurred; initially with in most infants Staphylococcus aureus predominance, followed by differentiation towards Corynebacterium pseudodiphteriticum/propinquum, Dolosigranulum pigrum, Moraxella catarrhalis/nonliquefaciens, Streptococcus pneumoniae, and/or Haemophilus influenzae dominated communities. Infants born by Caesarian section showed a delay in overall development of respiratory microbiota profiles with specifically reduced colonization with health-associated commensals like Corynebacterium and Dolosigranulum, thereby possibly influencing respiratory health later in life.

On the ecosystemic network of saliva in healthy young adults

A dysbiotic state is believed to be a key factor in the onset of oral disease. Although oral diseases have been studied for decades, our understanding of oral health, the boundaries of a healthy oral ecosystem and ecological shift toward dysbiosis is still limited. Here, we present the ecobiological heterogeneity of the salivary ecosystem and relations between the salivary microbiome, salivary metabolome and host-related biochemical salivary parameters in 268 healthy adults after overnight fasting. Gender-specific differences in the microbiome and metabolome were observed and were associated with salivary pH and dietary protein intake. Our analysis grouped the individuals into five microbiome and four metabolome-based clusters that significantly related to biochemical parameters of saliva. Low salivary pH and high lysozyme activity were associated with high proportions of streptococcal phylotypes and increased membrane-lipid degradation products. Samples with high salivary pH displayed increased chitinase activity, higher abundance of Veillonella and Prevotella species and higher levels of amino acid fermentation products, suggesting proteolytic adaptation. An over-specialization toward either a proteolytic or a saccharolytic ecotype may indicate a shift toward a dysbiotic state. Their prognostic value and the degree to which these ecotypes are related to increased disease risk remains to be determined.

Distinct fecal and oral microbiota composition in human type 1 diabetes, an observational study

Objective Environmental factors driving the development of type 1 diabetes (T1D) are still largely unknown. Both animal and human studies have shown an association between altered fecal microbiota composition, impaired production of short-chain fatty acids (SCFA) and T1D onset. However, observational evidence on SCFA and fecal and oral microbiota in adults with longstanding T1D vs healthy controls (HC) is lacking. Research design and methods We included 53 T1D patients without complications or medication and 50 HC matched for age, sex and BMI. Oral and fecal microbiota, fecal and plasma SCFA levels, markers of intestinal inflammation (fecal IgA and calprotectin) and markers of low-grade systemic inflammation were measured. Results Oral microbiota were markedly different in T1D (eg abundance of Streptococci) compared to HC. Fecal analysis showed decreased butyrate producing species in T1D and less butyryl-CoA transferase genes. Also, plasma levels of acetate and propionate were lower in T1D, with similar fecal SCFA. Finally, fecal strains Christensenella and Subdoligranulum correlated with glycemic control, inflammatory parameters and SCFA. Conclusions We conclude that T1D patients harbor a different amount of intestinal SCFA (butyrate) producers and different plasma acetate and propionate levels. Future research should disentangle cause and effect and whether supplementation of SCFA-producing bacteria or SCFA alone can have disease-modifying effects in T1D.

Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography

Trillions of microorganisms inhabit the human gut and are regarded as potential key factors for health1,2. Characteristics such as diet, lifestyle, or genetics can shape the composition of the gut microbiota2–6 and are usually shared by individuals from comparable ethnic origin. So far, most studies assessing how ethnicity relates to the intestinal microbiota compared small groups living at separate geographical locations7–10. Using fecal 16S ribosomal RNA gene sequencing in 2,084 participants of the Healthy Life in an Urban Setting (HELIUS) study11,12, we show that individuals living in the same city tend to share similar gut microbiota characteristics with others of their ethnic background. Ethnicity contributed to explain the interindividual dissimilarities in gut microbiota composition, with three main poles primarily characterized by operational taxonomic units (OTUs) classified as Prevotella (Moroccans, Turks, Ghanaians), Bacteroides (African Surinamese, South-Asian Surinamese), and Clostridiales (Dutch). The Dutch exhibited the greatest gut microbiota α-diversity and the South-Asian Surinamese the smallest, with corresponding enrichment or depletion in numerous OTUs. Ethnic differences in α-diversity and interindividual dissimilarities were independent of metabolic health and only partly explained by ethnic-related characteristics including sociodemographic, lifestyle, or diet factors. Hence, the ethnic origin of individuals may be an important factor to consider in microbiome research and its potential future applications in ethnic-diverse societies.Stool microbiota composition correlates with the ethnic backgrounds of people living in the same city, suggesting that geographical location and ethnicity have distinct effects on microbiota.

Feature Selection via Co-regularized Sparse-Group Lasso

We propose the co-regularized sparse-group lasso algorithm: a technique that allows the incorporation of auxiliary information into the learning task in terms of “groups” and “distances” among the predictors. The proposed algorithm is particularly suitable for a wide range of biological applications where good predictive performance is required and, in addition to that, it is also important to retrieve all relevant predictors so as to deepen the understanding of the underlying biological process. Our cost function requires related groups of predictors to provide similar contributions to the final response, and thus, guides the feature selection process using auxiliary information. We evaluate the proposed method on a synthetic dataset and examine various settings where its application is beneficial in comparison to the standard lasso, elastic net, group lasso and sparse-group lasso techniques. Last but not least, we make a python implementation of our algorithm available for download and free to use (Available at www.learning-machines.com).

Effect of Vegan Fecal Microbiota Transplantation on Carnitine‐ and Choline‐Derived Trimethylamine‐N‐Oxide Production and Vascular Inflammation in Patients With Metabolic Syndrome

Background Intestinal microbiota have been found to be linked to cardiovascular disease via conversion of the dietary compounds choline and carnitine to the atherogenic metabolite TMAO (trimethylamine‐N‐oxide). Specifically, a vegan diet was associated with decreased plasma TMAO levels and nearly absent TMAO production on carnitine challenge. Methods and Results We performed a double‐blind randomized controlled pilot study in which 20 male metabolic syndrome patients were randomized to single lean vegan‐donor or autologous fecal microbiota transplantation. At baseline and 2 weeks thereafter, we determined the ability to produce TMAO from d6‐choline and d3‐carnitine (eg, labeled and unlabeled TMAO in plasma and 24‐hour urine after oral ingestion of 250 mg of both isotope‐labeled precursor nutrients), and fecal samples were collected for analysis of microbiota composition. 18F‐fluorodeoxyglucose positron emission tomography/computed tomography scans of the abdominal aorta, as well as ex vivo peripheral blood mononuclear cell cytokine production assays, were performed. At baseline, fecal microbiota composition differed significantly between vegans and metabolic syndrome patients. With vegan‐donor fecal microbiota transplantation, intestinal microbiota composition in metabolic syndrome patients, as monitored by global fecal microbial community structure, changed toward a vegan profile in some of the patients; however, no functional effects from vegan‐donor fecal microbiota transplantation were seen on TMAO production, abdominal aortic 18F‐fluorodeoxyglucose uptake, or ex vivo cytokine production from peripheral blood mononuclear cells. Conclusions Single lean vegan‐donor fecal microbiota transplantation in metabolic syndrome patients resulted in detectable changes in intestinal microbiota composition but failed to elicit changes in TMAO production capacity or parameters related to vascular inflammation. Clinical Trial Registration URL: http://www.trialregister.nl. Unique identifier: NTR 4338.

Differential metabolic effects of oral butyrate treatment in lean versus metabolic syndrome subjects

Background: Gut microbiota‐derived short‐chain fatty acids (SCFAs) have been associated with beneficial metabolic effects. However, the direct effect of oral butyrate on metabolic parameters in humans has never been studied. In this first in men pilot study, we thus treated both lean and metabolic syndrome male subjects with oral sodium butyrate and investigated the effect on metabolism. Methods: Healthy lean males (n = 9) and metabolic syndrome males (n = 10) were treated with oral 4 g of sodium butyrate daily for 4 weeks. Before and after treatment, insulin sensitivity was determined by a two‐step hyperinsulinemic euglycemic clamp using [6,6‐2H2]‐glucose. Brown adipose tissue (BAT) uptake of glucose was visualized using 18F‐FDG PET‐CT. Fecal SCFA and bile acid concentrations as well as microbiota composition were determined before and after treatment. Results: Oral butyrate had no effect on plasma and fecal butyrate levels after treatment, but did alter other SCFAs in both plasma and feces. Moreover, only in healthy lean subjects a significant improvement was observed in both peripheral (median Rd: from 71 to 82 &mgr;mol/kg min, p < 0.05) and hepatic insulin sensitivity (EGP suppression from 75 to 82% p < 0.05). Although BAT activity was significantly higher at baseline in lean (SUVmax: 12.4 ± 1.8) compared with metabolic syndrome subjects (SUVmax: 0.3 ± 0.8, p < 0.01), no significant effect following butyrate treatment on BAT was observed in either group (SUVmax lean to 13.3 ± 2.4 versus metabolic syndrome subjects to 1.2 ± 4.1). Conclusions: Oral butyrate treatment beneficially affects glucose metabolism in lean but not metabolic syndrome subjects, presumably due to an altered SCFA handling in insulin‐resistant subjects. Although preliminary, these first in men findings argue against oral butyrate supplementation as treatment for glucose regulation in human subjects with type 2 diabetes mellitus.

Effect of experimental gingivitis induction and erythritol on the salivary metabolome and functional biochemistry of systemically healthy young adults

IntroductionUnderstanding the changes occurring in the oral ecosystem during development of gingivitis could help improve prevention and treatment strategies for oral health. Erythritol is a non-caloric polyol proposed to have beneficial effects on oral health.ObjectivesTo examine the effect of experimental gingivitis and the effect of erythritol on the salivary metabolome and salivary functional biochemistry.MethodsIn a two-week experimental gingivitis challenge intervention study, non-targeted, mass spectrometry-based metabolomic profiling was performed on saliva samples from 61 healthy adults, collected at five time-points. The effect of erythritol was studied in a randomized, controlled trial setting. Fourteen salivary biochemistry variables were measured with antibody- or enzymatic activity-based assays.ResultsBacterial amino acid catabolites (cadaverine, N-acetylcadaverine, and α-hydroxyisovalerate) and end-products of bacterial alkali-producing pathways (N-α-acetylornithine and γ-aminobutyrate) increased significantly during the experimental gingivitis. Significant changes were found in a set of 13 salivary metabolite ratios composed of host cell membrane lipids involved in cell signaling, host responses to bacteria, and defense against free radicals. An increase in mevalonate was also observed. There were no significant effects of erythritol. No significant changes were found in functional salivary biochemistry.ConclusionsThe findings underline a dynamic interaction between the host and the oral microbial biofilm during an experimental induction of gingivitis.

Unsupervised Multi-View Feature Selection for Tumor Subtype Identification

Existing unsupervised feature selection algorithms are designed to extract the most relevant subset of features which can facilitate clustering and interpretation of the obtained results. However, these techniques are not directly applicable in many real-world situations in which one has access to datasets consisting of multiple views or dissimilar representations. By leveraging information from these different views one can obtain more robust and accurate results compared with single-view methods. In this paper Unsupervised Multi-View Feature Selection algorithm is used to simultaneously extract a relevant subset of features and to perform clustering that is consistent across different views. In the empirical evaluation of synthetic multi-view data we demonstrate the advantage of using the proposed algorithm over the use of its single-view analogue. We used the National Cancer Institutes NCI-60 panel dataset, exploiting information from various -omics representations to identify tumor subtypes based on very few requisite, biologically relevant features.