The effect of early probiotic exposure on the preterm infant gut microbiome development

Premature birth, especially if born before week 32 of gestation, is associated with increased risk of neonatal morbidity and mortality. Prophylactic use of probiotics has been suggested to protect preterm infants via supporting a healthy gut microbiota (GM) development, but the suggested strains and doses vary between studies. In this study, we profiled the GM of 5, 10 and 30-day fecal samples from two cohorts of preterm neonates (born <30 weeks of gestation) recruited in the same neonatal intensive care unit. One cohort (n = 165) was recruited from September 2006 to January 2009 before probiotics were introduced in the clinic. The second cohort (n = 87) was recruited from May 2010 to October 2011 after introducing Lacticaseibacillus rhamnosus GG and Bifidobacterium animalis ssp. lactis BB-12 supplementation policy. Through V3-V4 region 16S rRNA gene amplicon sequencing, a distinct increase of L. rhamnosus and B. animalis was found in the fecal samples of neonates supplemented with probiotics. During the first 30 days of life, the preterm GM went through similarly patterned progression of bacterial populations. Staphylococcus and Weissella dominated in early samples, but was gradually overtaken by Veillonella, Enterococcus and Enterobacteriaceae. Probiotic supplementation was associated with pronounced reduction of Weissella, Veillonella spp. and the opportunistic pathogen Klebsiella. Potential nosocomial pathogens Citrobacter and Chryseobacterium species also gradually phased out. In conclusion, probiotic supplementation to preterm neonates affected gut colonization by certain bacteria, but did not change the overall longitudinal bacterial progression in the neonatal period. Abbreviations: GM: Gut microbiota; ASV: Amplicon sequence variant; NEC: Necrotizing enterocolitis; DOL: Days of life; NICU: Neonatal intensive care unit; ESPGHAN: European Society for Pediatric Gastroenterology, Hepatology and Nutrition; Db-RDA: Distance-based redundancy analysis; PERMANOVA: Permutational multivariate analysis of variance; ANCOM: Analysis of compositions of microbiomes; LGG: Lacticaseibacillus (former Lactobacillus) rhamnosus GG; BB-12: Bifidobacterium animalis ssp. lactis BB-12; DGGE: Denaturing Gradient Gel Electrophoresi

Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

BACKGROUND: There is an immense scientific interest in the human microbiome and its effects on human physiology, health, and disease. A common approach for examining bacterial communities is high-throughput sequencing of 16S rRNA gene hypervariable regions, aggregating sequence-similar amplicons into operational taxonomic units (OTUs). Strategies for detecting differential relative abundance of OTUs between sample conditions include classical statistical approaches as well as a plethora of newer methods, many borrowing from the related field of RNA-seq analysis. This effort is complicated by unique data characteristics, including sparsity, sequencing depth variation, and nonconformity of read counts to theoretical distributions, which is often exacerbated by exploratory and/or unbalanced study designs. Here, we assess the robustness of available methods for (1) inference in differential relative abundance analysis and (2) beta-diversity-based sample separation, using a rigorous benchmarking framework based on large clinical 16S microbiome datasets from different sources. RESULTS: Running more than 380,000 full differential relative abundance tests on real datasets with permuted case/control assignments and in silico-spiked OTUs, we identify large differences in method performance on a range of parameters, including false positive rates, sensitivity to sparsity and case/control balances, and spike-in retrieval rate. In large datasets, methods with the highest false positive rates also tend to have the best detection power. For beta-diversity-based sample separation, we show that library size normalization has very little effect and that the distance metric is the most important factor in terms of separation power. CONCLUSIONS: Our results, generalizable to datasets from different sequencing platforms, demonstrate how the choice of method considerably affects analysis outcome. Here, we give recommendations for tools that exhibit low false positive rates, have good retrieval power across effect sizes and case/control proportions, and have low sparsity bias. Result output from some commonly used methods should be interpreted with caution. We provide an easily extensible framework for benchmarking of new methods and future microbiome datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-016-0208-8) contains supplementary material, which is available to authorized users

The developing hypopharyngeal microbiota in early life

BACKGROUND: The airways of healthy humans harbor a distinct microbial community. Perturbations in the microbial community have been associated with disease, yet little is known about the formation and development of a healthy airway microbiota in early life. Our goal was to understand the establishment of the airway microbiota within the first 3 months of life. We investigated the hypopharyngeal microbiota in the unselected COPSAC(2010) cohort of 700 infants, using 16S rRNA gene sequencing of hypopharyngeal aspirates from 1 week, 1 month, and 3 months of age. RESULTS: Our analysis shows that majority of the hypopharyngeal microbiota of healthy infants belong to each individual’s core microbiota and we demonstrate five distinct community pneumotypes. Four of these pneumotypes are dominated by the genera Staphylococcus, Streptococcus, Moraxella, and Corynebacterium, respectively. Furthermore, we show temporal pneumotype changes suggesting a rapid development towards maturation of the hypopharyngeal microbiota and a significant effect from older siblings. Despite an overall common trajectory towards maturation, individual infants’ microbiota are more similar to their own, than to others, over time. CONCLUSIONS: Our findings demonstrate a consolidation of the population of indigenous bacteria in healthy airways and indicate distinct trajectories in the early development of the hypopharyngeal microbiota. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-016-0215-9) contains supplementary material, which is available to authorized users

Maturation of the gut microbiome and risk of asthma in childhood

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

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Exploration of Shared Genetic Architecture Between Subcortical Brain Volumes and Anorexia Nervosa

In MRI scans of patients with anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknown whether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlying AN. Here, we used a battery of methods (LD score regression, genetic risk scores, sign test, SNP effect concordance analysis, and Mendelian randomization) to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes (ENIGMA consortium, n = 13,170) and genetic risk for AN (PGC-ED consortium, n = 14,477). Genetic correlations ranged from − 0.10 to 0.23 (all p > 0.05). There were some signs of an inverse concordance between greater thalamus volume and risk for AN (permuted p = 0.009, 95% CI: [0.005, 0.017]). A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune system relevant genes, in particular DRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level (that is, common variant genetic architecture), but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain- and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN