Movement ecology and sex are linked to barn owl microbial community composition.

The behavioural ecology of host species is likely to affect their microbial communities, because host sex, diet, physiology, and movement behaviour could all potentially influence their microbiota. We studied a wild population of barn owls (Tyto alba) and collected data on their microbiota, movement, diet, size, coloration, and reproduction. The composition of bacterial species differed by the sex of the host and female owls had more diverse bacterial communities than their male counterparts. The abundance of two families of bacteria, Actinomycetaceae and Lactobacillaceae, also varied between the sexes, potentially as a result of sex differences in hormones and immunological function, as has previously been found with Lactobacillaceae in the microbiota of mice. Male and female owls did not differ in the prey they brought to the nest, which suggests that dietary differences are unlikely to underlie the differences in their microbiota. The movement behaviour of the owls was associated with the host microbiota in both males and females because owls that moved further from their nest each day had more diverse bacterial communities than owls that stayed closer to their nests. This novel result suggests that the movement ecology of hosts can impact their microbiota, potentially on the basis of their differential encounters with new bacterial species as the hosts move and forage across the landscape. Overall, we found that many aspects of the microbial community are correlated with the behavioural ecology of the host and that data on the microbiota can aid in generating new hypotheses about host behaviour

Estimating nest-switching in free-ranging wild birds: an assessment of the most common methodologies, illustrated in the White Stork (Ciconia ciconia)

Reliable estimates of nest-switching are required to study avian mating systems and manage wild populations, yet different estimation methods have rarely been integrated or assessed. Through a literature review and case study, we reveal that three common methods for assessing nest-switching blend different components, producing a wide range of estimates. Careful component definition and reporting are essential to properly estimate this behaviour

Preterm infant meconium microbiota transplant induces growth failure, inflammatory activation, and metabolic disturbances in germ-free mice

Preterm birth may result in adverse health outcomes. Very preterm infants typically exhibit postnatal growth restriction, metabolic disturbances, and exaggerated inflammatory responses. We investigated the differences in the meconium microbiota composition between very preterm (37 weeks) human neonates by 16S rRNA gene sequencing. Human meconium microbiota transplants to germ-free mice were conducted to investigate whether the meconium microbiota is causally related to the preterm infant phenotype in an experimental model. Our results indicate that very preterm birth is associated with a distinct meconium microbiota composition. Fecal microbiota transplant of very preterm infant meconium results in impaired growth, altered intestinal immune function, and metabolic parameters as compared to term infant meconium transplants in germ-free mice. This finding suggests that measures aiming to minimize the long-term adverse consequences of very preterm birth should be commenced during pregnancy or directly after birth.</p

Positive effects of diet-induced microbiome modification on GDM in mice following human faecal transfer

This study was funded by the Israeli Ministry of Innovation, Science & Technology (grant number 3-15521). OK and MCC acknowledge the support by Biostime Institute Nutrition & Care (BINC) research grant. OK is supported by the European Research Council Consolidator grant (grant agreement no. 101001355).Peer reviewe

Children with idiopathic short stature have significantly different gut microbiota than their normal height siblings: a case-control study

ObjectivesTo investigate the role of gut microbiota (GM) in pathogenesis of idiopathic short stature (ISS) by comparing GM of ISS children to their normal-height siblings.MethodsThis case-control study, conducted at the Schneider Children’s Medical Center’s Institute for Endocrinology and Diabetes between 4/2018-11/2020, involved 30 pairs of healthy pre-pubertal siblings aged 3-10 years, each comprising one sibling with ISS and one with normal height. Outcome measures from fecal analysis of both siblings included GM composition analyzed by 16S rRNA sequencing, fecal metabolomics, and monitoring the growth of germ-free (GF) mice after fecal transplantation.ResultsFecal analysis of ISS children identified higher predicted levels of genes encoding enzymes for pyrimidine, purine, flavin, coenzyme B, and thiamine biosynthesis, lower levels of several amino acids, and a significantly higher prevalence of the phylum Euryarchaeota compared to their normal-height siblings (p&lt;0.001). ISS children with higher levels of Methanobrevibacter, the dominant species in the archaeal gut community, were significantly shorter in stature than those with lower levels (p=0.022). Mice receiving fecal transplants from ISS children did not experience stunted growth, probably due to the eradication of Methanobrevibacter caused by exposure to oxygen during fecal collection.DiscussionOur findings suggest that different characteristics in the GM may explain variations in linear growth. The varying levels of Methanobrevibacter demonstrated within the ISS group reflect the multifactorial nature of ISS and the potential ability of the GM to partially explain growth variations. The targeting of specific microbiota could provide personalized therapies to improve growth in children with ISS

The gut microbiome in pregnancy and pregnancy complications

During gestation, the female body undergoes a number of changes; accordingly, her microbiota also undergoes marked changes throughout the duration of her pregnancy. Some shifts in the microbial community are imprinted even before pregnancy and may affect a woman's ability to conceive. Prepregnancy obesity and inflammatory bowel disease are associated with gestational dysbiosis, as are several conditions occurring during pregnancy, including gestational diabetes mellitus and preeclampsia. Here, we review pregnancy and associated complications in the context of the gut microbiota, but dysbiosis in other microbial communities, including those of the vagina, oral cavity, and cervix, is also associated with pregnancy-related conditions. We not only highlight the numerous studies conducted thus far but also discuss some of the shortcomings in the field and provide important directions for future research.European Research Council under the European Union's Horizon 2020 research and innovation programme (ERC consolidator grant, n° 101001355 ), and The Israeli Ministry of Science, Technology and Space (grant 3-15521)Peer reviewe

Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy

ABSTRACTThe human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine-learning-based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing or shotgun metagenomics. However, several properties of microbial sequence-based studies hinder machine learning (ML), including non-uniform representation, a small number of samples compared with the dimension of each sample, and sparsity of the data, with the majority of taxa present in a small subset of samples. We show here using a graph representation that the cladogram structure is as informative as the taxa frequency. We then suggest a novel method to combine information from different taxa and improve data representation for ML using microbial taxonomy. iMic (image microbiome) translates the microbiome to images through an iterative ordering scheme, and applies convolutional neural networks to the resulting image. We show that iMic has a higher precision in static microbiome gene sequence-based ML than state-of-the-art methods. iMic also facilitates the interpretation of the classifiers through an explainable artificial intelligence (AI) algorithm to iMic to detect taxa relevant to each condition. iMic is then extended to dynamic microbiome samples by translating them to movies

Comparison of canine colostrum and milk using a multi-omics approach

Abstract Background A mother’s milk is considered the gold standard of nutrition in neonates and is a source of cytokines, immunoglobulins, growth factors, and other important components, yet little is known about the components of canine milk, specifically colostrum, and the knowledge related to its microbial and metabolic profiles is particularly underwhelming. In this study, we characterized canine colostrum and milk microbiota and metabolome for several breeds of dogs and examined profile shifts as milk matures in the first 8 days post-whelping. Results Through untargeted metabolomics, we identified 63 named metabolites that were significantly differentially abundant between days 1 and 8 of lactation. Surprisingly, the microbial compositions of the colostrum and milk, characterized using 16S rRNA gene sequencing, were largely similar, with only two differentiating genera. The shifts observed, mainly increases in several sugars and amino sugars over time and shifts in amino acid metabolites, align with shifts observed in human milk samples and track with puppy development. Conclusion Like human milk, canine milk composition is dynamic, and shifts are well correlated with developing puppies’ needs. Such a study of the metabolic profile of canine milk, and its relation to the microbial community, provides insights into the changing needs of the neonate, as well as the ideal nutrition profile for optimal functionality. This information will add to the existing knowledge base of canine milk composition with the prospect of creating a quality, tailored milk substitute or supplement for puppies

Image and graph convolution networks improve microbiome-based machine learning accuracy

The human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine learning based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing. However, several properties of microbial 16S rRNA gene sequencing hinder machine learning, including non-uniform representation, a small number of samples compared with the dimension of each sample, and sparsity of the data, with the majority of bacteria present in a small subset of samples. We suggest two novel methods to combine information from different bacteria and improve data representation for machine learning using bacterial taxonomy. iMic and gMic translate the microbiome to images and graphs respectively, and convolutional neural networks are then applied to the graph or image. We show that both algorithms improve performance of static 16S rRNA gene sequence-based machine learning compared to the best state-of-the-art methods. Furthermore, these methods ease the interpretation of the classifiers. iMic is then extended to dynamic microbiome samples, and an iMic explainable AI algorithm is proposed to detect bacteria relevant to each condition.Comment: 19 pages of manuscript, 3 figures, and 4 pages of Supp. Ma

Preterm infant meconium microbiota transplant induces growth failure, inflammatory activation, and metabolic disturbances in germ-free mice

Preterm birth may result in adverse health outcomes. Very preterm infants typically exhibit postnatal growth restriction, metabolic disturbances, and exaggerated inflammatory responses. We investigated the differences in the meconium microbiota composition between very preterm (37 weeks) human neonates by 16S rRNA gene sequencing. Human meconium microbiota transplants to germ-free mice were conducted to investigate whether the meconium microbiota is causally related to the preterm infant phenotype in an experimental model. Our results indicate that very preterm birth is associated with a distinct meconium microbiota composition. Fecal microbiota transplant of very preterm infant meconium results in impaired growth, altered intestinal immune function, and metabolic parameters as compared to term infant meconium transplants in germ-free mice. This finding suggests that measures aiming to minimize the long-term adverse consequences of very preterm birth should be commenced during pregnancy or directly after birth.Peer reviewe