Higher frequency of vertebrate‐infecting viruses in the gut of infants born to mothers with type 1 diabetes
Background Microbial exposures in utero and early life shape the infant microbiome, which can profoundly impact on health. Compared to the bacterial microbiome, very little is known about the virome. We set out to characterize longitudinal changes in the gut virome of healthy infants born to mothers...
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Veröffentlicht in: | Pediatric diabetes 2020-03, Vol.21 (2), p.271-279 |
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Sprache: | eng |
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Zusammenfassung: | Background
Microbial exposures in utero and early life shape the infant microbiome, which can profoundly impact on health. Compared to the bacterial microbiome, very little is known about the virome. We set out to characterize longitudinal changes in the gut virome of healthy infants born to mothers with or without type 1 diabetes using comprehensive virome capture sequencing.
Methods
Healthy infants were selected from Environmental Determinants of Islet Autoimmunity (ENDIA), a prospective cohort of Australian children with a first‐degree relative with type 1 diabetes, followed from pregnancy. Fecal specimens were collected three‐monthly in the first year of life.
Results
Among 25 infants (44% born to mothers with type 1 diabetes) at least one virus was detected in 65% (65/100) of samples and 96% (24/25) of infants during the first year of life. In total, 26 genera of viruses were identified and >150 viruses were differentially abundant between the gut of infants with a mother with type 1 diabetes vs without. Positivity for any virus was associated with maternal type 1 diabetes and older infant age. Enterovirus was associated with older infant age and maternal smoking.
Conclusions
We demonstrate a distinct gut virome profile in infants of mothers with type 1 diabetes, which may influence health outcomes later in life. Higher prevalence and greater number of viruses observed compared to previous studies suggests significant underrepresentation in existing virome datasets, arising most likely from less sensitive techniques used in data acquisition. |
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ISSN: | 1399-543X 1399-5448 |
DOI: | 10.1111/pedi.12952 |