Suchergebnisse

Abstract
In today's socio-cultural context, the period of youth is less experienced as a predictable path towards the assumption of the adult status and increasingly characterised by the difficulty of defining one's identity, prefiguring one's future paths and choices to make. To these problems, one must add the so-called collateral damages produced by the Web, which condition, or can condition, the configuration of the personalities of young people, the shape and quality of their relationships, the sense of events, their experiences and how values and faith are conceived.

We assembled the mitogenome of Apis mellifera siciliana, which was previously identified as African by the tRNA-leu-cox2 intergenic region. The mitogenome is 16,590 bp long. The gene content and organization are identical to other A. mellifera mitogenomes, containing 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Phylogenetic analysis showed a close mitochondrial relationship between A. m. siciliana and other African subspecies such as Apis mellifera sahariensis, Apis mellifera intermissa, and Apis mellifera ruttneri. ; This work was supported by MEDIBEES - Monitoring the Mediterranean Honey Bee Subspecies and their Resilience to Climate Change for the Improvement of Sustainable Agro-Ecosystems; BEEHAPPY ([POCI-01-0145- FEDER-029871]; FCT and COMPETE/QREN/EU). MEDIBEES is part of the PRIMA program supported by the European Union. Fundac¸~ao para a Ciência e a Tecnologia (FCT) provided financial support by national funds (FCT/MCTES) to CIMO [UIDB/00690/2020]. Dora Henriques is supported by the project BEEHAPPY ([POCI-01-0145-FEDER-029871]; FCT and COMPETE/QREN/EU). ; info:eu-repo/semantics/publishedVersion

Breastfeeding is critical for adequate neonatal microbial and immune system development affecting neonate health outcomes in the short and long term. There is a great interest in ascertaining which are the maternal factors contributing to the milk microbiota and the potential relevance for the developing infant. Thus, our study aimed to characterize the effect of mixed and exclusive breastfeeding practices on the milk microbiota and to determine the impact of pre-pregnancy body mass index (BMI) and weight gain over pregnancy on its composition. Breast milk samples from 136 healthy women were collected within the first month post-partum and milk microbiota profiling was analyzed by 16S rRNA gene sequencing. Information on breastfeeding habits and maternal-infant clinical data were recorded. Breastfeeding practices (exclusive vs. mixed), maternal pre-gestational BMI, and weight gain over pregnancy contributed to the milk microbiota variation. Pre-gestational normal-weight women with exclusive breastfeeding habits harbored a significantly higher abundance of Bifidobacterium genus, and also, higher alpha-diversity compared to the rest of the women. Our results confirm the importance of controlling weight during pregnancy and breastfeeding practices in terms of milk microbiota. Further studies to clarify the potential impact of these maternal factors on milk and infant development and health will be necessary. ; This work was supported by the European Research Council under the European Union's Horizon 2020 research and innovation program (ERC starting grant, no. 639226). E.C.-M. and M.S.-R. are supported by a Predoctoral Fellowship from Generalitat Valenciana—European Social Fund (GRISOLÍA2019 and ASCII2016, respectively). ; Peer reviewed

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F-ST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% +/- 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees. ; European CommissionEuropean CommissionEuropean Commission Joint Research Centre [2013.1.3-02, 613960]; Basque GovernmentBasque Government [IT1233-19] ; The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7.MP was supported by a Basque Government grant (IT1233-19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript. ; WOS:000614429400001 ; 2-s2.0-85100413967 ; PubMed: 33535965

Nutrition during pregnancy plays an important role in maternal–neonatal health. However, the impact of specific dietary components during pregnancy on maternal gut microbiota and the potential effects on neonatal microbiota and infant health outcomes in the short term are still limited. A total of 86 mother–neonate pairs were enrolled in this study. Gut microbiota profiling on maternal–neonatal stool samples at birth was carried out by 16S rRNA gene sequencing using Illumina. Maternal dietary information and maternal–neonatal clinical and anthropometric data were recorded during the first 18 months. Longitudinal Body Mass Index (BMI) and Weight-For-Length (WFL) z-score trajectories using the World Health Organization (WHO) curves were obtained. The maternal microbiota was grouped into two distinct microbial clusters characterized by Prevotella (Cluster I) and by the Ruminococcus genus (Cluster II). Higher intakes of total dietary fiber, omega-3 fatty acids, and polyphenols were observed in Cluster II compared to Cluster I. Higher intakes of plant-derived components were associated with a higher presence of the Christensellaceae family, Dehalobacterium and Eubacterium, and lower amounts of the Dialister and Campylobacter species. Maternal microbial clusters were also linked to neonatal microbiota and infant growth in a birth-dependent manner. C-section neonates from Cluster I showed the highest BMI z-score at age 18 months, along with a higher risk of overweight. Longitudinal BMI and WL z-score trajectories from birth to 18 months were shaped by maternal microbial cluster, diet, and birth mode. Diet was an important perinatal factor in early life that may impact maternal microbiota; in particular, fiber, lipids and proteins, and exert a significant effect on the neonatal microbiome and contribute to infant development during the first months of life. ; This research has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC starting grant, n° 639226). M. Selma-Royo is supported by a Pre-doctoral Fellowship from Generalitat Valenciana (GVA)-European Social Fund (ASCII2016) ; Peer reviewed

Background and Objectives: Breast milk contains several bioactive factors including oligosaccharides (HMO) and microbes that shape the infant gut microbiota. HMO profile is determined by secretor status, however their influence on milk microbiota is still uncovered. This study is aimed to determine the impact of the FUT2 genotype on the milk microbiota during the first month of lactation and the association with HMO. Methods: Milk microbiota from 25 healthy lactating women was determined by quantitative PCR and 16S gene pyrosequencing. Secretor genotype was obtained by PCR-RFLPs and by HMO identification and quantification. Results: The most abundant bacteria were Staphylococcus and Streptococcus, followed by enterobacteriaceae-related bacteria. The predominant HMO in secretor milk samples were 2FL and LNFP I whereas non-secretor milk was characterized by LNFP II and LNDFH II. Differences in microbiota composition and quantity were found depending on secretor/non-secretor status. Lactobacillus spp, Enterococcus spp., and Streptococcus spp. were lower in non-secretor than in secretor samples. Bifidobacterium genus and species were less prevalent in non-secretor samples. Despite no differences on diversity and richness, non-secretor samples had lower Actinobacteria and higher relative abundance of Enterobacteriaceae, Lactobacillaceae and Staphylococcaceae. Conclusions: Maternal secretor status is associated with the human milk microbiota composition and is maintained during the first 4 weeks. Specific associations between milk microbiota, HMO and secretor status were observed although the potential biological impact on the neonate remains elusive. Future studies are needed to reveal the early nutrition influence on the reduction of risk of disease. ; European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (ERC Starting Grant, 639226). ; Peer reviewed

This work was funded by the EU Joint Programming Initiative—A Healthy Diet for a Healthy Life (Project EarlyMicroHealth) and the Project AGL2017-83653R funded by the Spanish "Ministerio de Ciencia, Innovación y Universidades (MCIU), Agencia Estatal de Investigación (AEI) and FEDER" and by the European Research Council under the European Union's Horizon 2020 research and innovation program (ERC starting grant, no. 639226). Silvia Arboleya is the recipient of a Juan de la Cierva Postdoctoral Contract from the Spanish Ministry of Science and Innovation (Ref. IJCI-2017-32156) funded by the Spanish Ministry of Science, Innovation and Universities.

The early life gut microbiota has been reported to be involved in neonatal weight gain and later infant growth. Therefore, this early microbiota may constitute a target for the promotion of healthy neonatal growth and development with potential consequences for later life. Unfortunately, we are still far from understanding the association between neonatal microbiota and weight gain and growth. In this context, we evaluated the relationship between early microbiota and weight in a cohort of full-term infants. The absolute levels of specific fecal microorganisms were determined in 88 vaginally delivered and 36 C-section-delivered full-term newborns at 1 month of age and their growth up to 12 months of age. We observed statistically significant associations between the levels of some early life gut microbes and infant weight gain during the first year of life. Classifying the infants into tertiles according to their Staphylococcus levels at 1 month of age allowed us to observe a significantly lower weight at 12 months of life in the C-section-delivered infants from the highest tertile. Univariate and multivariate models pointed out associations between the levels of some fecal microorganisms at 1 month of age and weight gain at 6 and 12 months. Interestingly, these associations were different in vaginally and C-section-delivered babies. A significant direct association between Staphylococcus and weight gain at 1 month of life was observed in vaginally delivered babies, whereas in C-section-delivered infants, lower Bacteroides levels at 1 month were associated with higher later weight gain (at 6 and 12 months). Our results indicate an association between the gut microbiota and weight gain in early life and highlight potential microbial predictors for later weight gain. ; This work was funded by the EU Joint Programming Initiative—A Healthy Diet for a Healthy Life (JPI HDHL. http://www.healthydietforhealthylife.eu/; accessed on 14 July 2021. Project EarlyMicroHealth) and the Project AGL2017-83653R funded by the Spanish "Ministerio de Ciencia, Innovación y Universidades (MCIU), Agencia Estatal de Investigación (AEI) and FEDER" and by the European Research Council under the European Union's Horizon 2020 research and innovation program (ERC starting grant, no. 639226). Silvia Arboleya is the recipient of a Juan de la Cierva Postdoctoral Contract from the Spanish Ministry of Science and Innovation (Ref. IJCI-2017-32156) funded by the Spanish Ministry of Science, Innovation and Universities. ; Peer reviewed

With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. Results: Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions: The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees. ; The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3–02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7. MP was supported by a Basque Government grant (IT1233–19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript. ; info:eu-repo/semantics/publishedVersion